{"id": "typesafe-ai-daggerverse", "name": "TypeSafe Daggerverse", "author": "TypeSafe AI", "date": "2026-09-20", "category": "official", "url": "https://github.com/typesafe-ai/daggerverse", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 21, "likes": "", "detail": "", "arch": "Official collection of reusable Dagger modules for TypeSafe AI and System One workflows.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "TypeSafe's shared Dagger modules, installable into any Dagger pipeline: uv, which audits uv-locked Python deps, builds minimal containers and exports relocatable virtualenvs; github, which polls commit statuses and check runs until a final verdict; twingate, a CONNECT proxy as a Dagger service in userspace; pinact, GitHub Actions pinned to full commit SHAs; zizmor, static analysis of GitHub Actions for injection, credential leaks and excessive permissions, offline by default; deptry, Python dependency lint fanned out across every uv project.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "typesafe-sdk-js", "name": "JavaScript / TypeScript SDK", "author": "TypeSafe AI", "date": "2026-09-19", "category": "official", "url": "https://github.com/typesafe-ai/typesafe-sdk-js", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 237, "likes": "", "detail": "", "arch": "Official TypeScript/JavaScript client with inferred answer types. npm install @typesafe-ai/sdk. Official TypeScript and JavaScript SDK with inferred answer types. Official client. npm install @typesafe-ai/sdk. Docs: JavaScript SDK.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Official JavaScript and TypeScript SDK for TypeSafe AI. npm package @typesafe-ai/sdk, Node.js 20 or newer.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "typesafe-ai-skills", "name": "Official agent skills repository", "author": "TypeSafe AI", "date": "2026-09-19", "category": "official", "url": "https://github.com/typesafe-ai/skills", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2144, "likes": "", "detail": "arch: npx skills add typesafe-ai/skills", "arch": "Official agent skill for Claude Code, Codex, and compatible agents: primitives, patterns, and how to structure evaluations. Official tooling: installable agent skills package that teaches agents the Jev workflow. System One adapter for Python Official agent skills for building and evaluating System One workflows. Official skill: primitives", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Official agent skills repository for building with TypeSafe: typed decisions and probabilities from System One models. Ships a SKILL.md and installs as a Claude Code plugin.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "typesafe-sdk-python", "name": "TypeSafe Python SDK", "author": "TypeSafe AI", "date": "2026-09-19", "category": "official", "url": "https://github.com/typesafe-ai/typesafe-sdk-python", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 227, "likes": "", "detail": "", "arch": "Official sync and async Python client. pip install typesafe-sdk. Official JavaScript SDK Official synchronous and asynchronous Python SDK. Official client. pip install typesafe-sdk. Docs: Python SDK.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Official Python SDK for TypeSafe AI. Installed with uv add typesafe-sdk.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-1.13-jaggedness", "name": "Jev 1.13 'jaggedness' docs", "author": "TypeSafe AI", "date": "2026-09-17", "category": "official", "url": "https://docs.typesafe.ai/model-jaggedness/jev-1.13", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: official docs for jev-1.13; arch: version string jev-1.13-20260917", "arch": "Failure-mode catalog for jev-1.13: 'fast, calibrated, and good at common-sense judgment but it is not perfect'", "approach": "", "training": "", "data": "", "result": "Documented failure modes with mitigations: literal reading → write the exact condition/criteria per option; math & numbers → keep the arithmetic in code; date/time comparison → extract components, compare in code; indirection → reduce hops, point to the relevant state; large state full of irrelevant detail → filter first, send only what the question needs", "notes": "Also: do NOT use score outputs to reconstruct exact magnitudes between two levels — score levels are weak in numerical calibration; use expectations only for threshold checks. Official acknowledgment of the model's 'jagged' edges.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev", "name": "Jev", "author": "TypeSafe AI", "date": "2026-09-15", "category": "official", "url": "https://typesafe.ai/blog/introducing-system-one-models-and-jev", "base_model": "undisclosed", "params": "undisclosed", "metric": "61.7–76.0%", "license": "Closed", "paper": "", "stars": "", "likes": "", "detail": "author: Diogo Almeida, ex-OpenAI RLHF lead; base: closed weights · ArcherHume; metric: evals; training: Reinforcement Learning for Calibrated Decisions; license: proprietary; result: \"too cheap to meter\" · mathematically impossible by construction; notes: waitlist dropped, $5 credit; name: System One model", "arch": "Shared-state encoding → parallel isolated question branches → probability readouts. Cardinality up to 255; 2-stage scoring for high-cardinality choice. Context budget: 32k tokens for state plus the longest question, 64k per request.", "approach": "", "training": "RLCD; probabilities trained against outcomes", "data": "Undisclosed; TypeSafe says workflows in evals were not deliberately chosen and not in training distribution", "result": "70–500 ms end-to-end; $0.042/MTok input, output tokens free; no type errors; claimed 193.6× faster / 444.6× cheaper vs frontier LLMs on their workflows; evals dashboard 61.7–76.0% accuracy, 0.3–0.5s/case", "notes": "Released 2026-09-15 in early access; current version Jev 1.13. Named after William Stanley Jevons; 'System One' from Kahneman's Thinking Fast and Slow. Primitives: Choice, Score, Noul. HN launch thread: 1,850 pts / 485 comments; CEO confirmed the zero-shot-classifier + encoder-with-heads reading. Co-founders Erik Gafni and Sasha Sheng; $40M seed led by DCVC; fully open to all users from 09-20. Available via Vercel AI SDK and opencode/zen gateways. Third-party zero-shot on the 400-case LocalLLaMA/typed-decisions set, 2026-09-24 card: accuracy 0.727, Brier 0.148, KL 1.442 from gold, p50 710 ms, $0.042 per 1M input tokens, ranked second behind meraGPT Decider 1.", "domain": "general", "score": "61.7–76.0%", "latency": "70–500 ms", "benchmark": "", "cost": "$0.042/MTok input, output free", "speedup": "193.6× faster"}
{"id": "system-one-adapter", "name": "System One Adapter", "author": "TypeSafe AI", "date": "2026-09-15", "category": "official", "url": "https://github.com/typesafe-ai/system-one-adapter-python", "base_model": "Any OpenAI or Anthropic model", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 298, "likes": "", "detail": "size: wrapperkill files teaching Claude Code/Codex to design System One workflows", "arch": "Serves the same typed Choice/Score/Noul interface from OpenAI/Anthropic models — the official baseline for every comparison", "approach": "", "training": "None", "data": "None", "result": "Official baseline for A/B-ing Jev against LLMs on cost/speed/intelligence", "notes": "Official drop-in that exposes the System One typed-decision contract from a regular LLM, so users can A/B 'Jev-shaped' calls against frontier models. Related: typesafe-ai/skills.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "typesafe-evals", "name": "TypeSafe evals dashboard", "author": "TypeSafe AI", "date": "2026-09-15", "category": "official", "url": "https://evals.typesafe.ai/", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "61.7–76.0%", "license": "Closed", "paper": "", "stars": "", "likes": "", "detail": "data: agent observability, invoice processing, security incidents, customer service; notes: Agentpedia, warmersun", "arch": "TypeSafe's own public evaluation dashboard: the same real-world tasks run as a structured workflow versus a standalone prompt, plotted on a cost-time-accuracy frontier across TypeSafe, OpenAI, Anthropic and Fireworks models; categories: security, incidents, agent trace, observability, invoice processing and customer service; the headline claim: structure is always better, and nothing sits both cheaper and more accurate than the workflow frontier", "approach": "", "training": "", "data": "Four internal workflows", "result": "Workflow beats prompt on every model pair measured: haiku 4.5 53.6% vs 18.1% at $0.0195 vs $0.0363 per case; opus 5 73.1% vs 64.8% at $0.1761 vs $0.3417; sonnet 5 67.8% vs 60.4%; sol 74.1% vs 63.4%; DS v4 flash 64.4% vs 59.3%; DS v4 pro 65.5% vs 59.7%; luna 66.8% vs 51.9%; terra 67.9% vs 61.6%; Jev itself plots at 67.8% for $0.0004 per case in 0.4 s, the fastest point on the frontier; Jev 61.7–76.0% accuracy, 0.3–0.5 s per case, against frontier-model baselines", "notes": "TypeSafe's own public evaluation dashboard with examples, disagreements, full queries and per-workflow numbers. Third parties re-scored these workflows at ~67.8% mean agreement vs ~73–74% for the best frontier LLM. A second chart, accuracy versus time, makes the same claim on the speed axis: nothing is both faster and more accurate.", "domain": "general", "score": "61.7–76.0%", "latency": "300–500 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "unpublished-27b-wave", "name": "27B Jev-derivatives without cards, 09-24/25", "author": "Hugging Face community", "date": "2026-09-26", "category": "model", "url": "https://huggingface.co/models?search=jev+27b", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped row: 27B-parameter Jev-style models published 09-24/25 whose cards were not yet published at sweep time; full member list in the notes field", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; 27B Jev-derivatives without cards at 09-26 sweep: autotrust/JEV-27B, 09-25, jev-typed-decisions pipeline, text-classification, the 27B member of the autotrust org that released the Jev student JEV, row autotrust-jev; dowands/NeoJev-Qwen3.8-27B-L56 and dowands/NeoJev-Qwen3.8-27B-L56-MLX, 09-24/25, Qwen3.8-27B-based with L56 LoRA layers. Both returned 404 on the model page at sweep time; listed for provenance, to be promoted to full rows when cards appear.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hf-models-tail-wave", "name": "HF model long tail, 09-24/25", "author": "Hugging Face community", "date": "2026-09-26", "category": "model", "url": "https://huggingface.co/models?search=typed-decisions", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped row: small typed-decision HF models created 09-24/25, individually too thin for a full entry; full member list in the notes field", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; small typed-decision models 09-24/25: ryandcunha/mmbu-jev-qwen3-0.6b-freetext and mmbu-jev-qwen3-0.6b-closed, 09-25, Jev-style models made with any2jev from Qwen3-0.6B, LoRA r16 with the vocabulary head removed, 10.6M trainable, a pointer head of dimension 256, temperature 1.073 fitted on validation, trained in 7 minutes on one consumer GPU, served at POST /v1/systemone, TypeSafe SDK compatible; oaklight/krino-qwen3-reranker-0.6b-heads, 09-24, 402K trainable decision heads of rank 64 on a frozen 596M Qwen3-Reranker-0.6B, 58.1% aggregate accuracy across 19 benchmarks, choice 52.7, noul 71.3, score 46.7, best on mnli 87.4 and agnews 86.8, MIT; jaswanthsanjay88/rev-vision, 09-24, non-autoregressive multimodal decision engine derived from SmolVLM-256M, one forward prefill under 40 ms reading calibrated probabilities off the option-terminator hidden states, sibling rev-decision; jaichang/korean-toxicity-pii-guard-qwen3, 09-24, frozen Qwen3-Embedding-0.6B plus a calibrated two-layer probe, under 50 ms Korean profanity detection and PII plus cloud API-key masking across Korean, English, Japanese and Chinese; canbingol/jev-mmlu-classifier, 09-25, auto-generated card, no content at sweep time. Excluded at sweep time: careseposito79/judge-qwen2.5-14b-instruct-l32 returned 404, moganai/lavoir-tr had an empty card.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kev-community-wave", "name": "Kev community builds, 09-24/25", "author": "community", "date": "2026-09-26", "category": "model", "url": "https://huggingface.co/models?search=kev", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped row: community Kev and Kev-adjacent builds across Hugging Face and GitHub created 09-24/25, none with a card carrying substantive numbers; full member list in the notes field", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; Kev community builds 09-24/25: espetro/kev-0.8b-demo-gguf, kev-4b-demo-gguf and kev-9b-demo-gguf, GGUF demo builds; Rckflr/kev-gemma-270m-experimental and Rckflr/kev-gemma-270m-rule-classifier-es-v8-experimental, 09-24; Carchofa/kev-1.5b-decision, 09-24; codesoda/kev-heads, 09-25; ollaya-dev/kev, ollaya-dev/von and ollaya-dev/qwen3guard, HF models of the ollaya runtime org, 09-25; Monstrofil/kev-ner, 09-25, reading not writing, one forward pass answers all entity types and fields; imyzt/kev-docker and HappyMonkeyAI/kev-docker, 09-25, docker serving of released Kev checkpoints, the latter for Windows; RoderickQiu/qualm, 09-25, the first screen-time app built on Kev and Jev; pxp888/autobot, 09-25, Kev-powered LLM routing; airpocket-soundman/kev-tetris, 09-25; payalkanyan/jev-kev, 09-25; IluvatarLabs/kev, 09-25, GitHub, convert LLMs into Jev-like decision models with runtime questions, calibrated probabilities and shared-state inference, DecisionModel.from_pretrained then evaluate with state, questions and form=packed. The Kev base row is jaredpalmer/kev.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hf-laya-wave-3", "name": "Laya fine-tune wave, 09-25/26", "author": "Hugging Face community", "date": "2026-09-26", "category": "model", "url": "https://huggingface.co/models?search=laya", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped row: Laya derivative models on Hugging Face created 09-25/26, each too thin for a full entry; full member list in the notes field", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; Laya derivatives created 09-25/26: RoeiG/laya-hebrew, 09-25, Hebrew Laya; Hagwell/laya, 09-25; faall7479/laya-idjvsuen-v1, 09-25; dimitrisdais/laya-banking77-typed-decisions, 09-25; Modusnsus/laya-typed-decisions-multilingual, 09-25; sallout/laya-multitask-intents, 09-25; hackoffice/laya-multilingual, 09-25; leehack/laya-command-head, 09-25; aaronalexS/daylens-laya-onnx, 09-25, ONNX build for a day-lens app; archevel/laya-web, 09-25, browser WebGPU; Emerald7664/laya-multilingual-onnx, 09-25, ONNX multilingual; sky9262/laya-jd-resume-ranking-enja, 09-25. Builds with cards and results got their own rows: zheqiushui/laya-omni, row laya-omni; thaitea/laya-vision-microduck-kick, row laya-vision-microduck; VishalMysore/laya-moe-web, row laya-moe-web.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-vision-microduck", "name": "Laya Vision microduck find-and-kick", "author": "thaitea", "date": "2026-09-26", "category": "model", "url": "https://huggingface.co/thaitea/laya-vision-microduck-kick", "base_model": "laya-vision", "params": "201M", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Laya Vision, 201M, fine-tuned to drive a simulated Microduck robot from its own camera: each step it sees one 256x256 head-camera frame and answers one choice question over FORWARD, LEFT, RIGHT and KICK; it plays in both of quackd's simulators, microduck:mujoco with MuJoCo physics and the upstream Microduck walking on its upstream trained policy, and microduck:sim2d, a flat cartoon", "approach": "", "training": "", "data": "", "result": "Experimental research: it has only seen those two simulators as rendered by quackd 0.14.0, not a real camera and not the browser simulator at quackd.org, which draws its scene with a different renderer; the question must be worded exactly as the one it was trained on; data and evaluation in r33drichards/quackd, training in r33drichards/laya-vision", "notes": "Independent Laya fork, not affiliated with Convai Innovations; base row thaitea-laya-vision-201m; demo space thaitea/laya-vision-microduck-kick-demo.", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-omni", "name": "laya-omni", "author": "zheqiushui", "date": "2026-09-26", "category": "model", "url": "https://huggingface.co/zheqiushui/laya-omni", "base_model": "Laya multilingual + SigLIP2 + Qwen3-ASR", "params": "~7M fusion + frozen encoders", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Laya with optional image and audio inputs: a ~7M-parameter, 28 MB trained fusion on top of frozen Laya multilingual, mmBERT-base ~310M, a SigLIP 2 base/16-256 image encoder ~93M, and a Qwen3-ASR-0.6B audio encoder, 186M; without an image or audio clip the output is bit-identical to Laya; detail=True switches from 64 to 256 image tokens for text, documents and spatial questions; code and fine-tuning tool at github.com/ZHEQIUSHUI/laya-omni", "approach": "", "training": "", "data": "", "result": "One question with an image or a 10 s clip takes 30-120 ms on an RTX 4090, encoders included; on test questions never trained, against Laya asked the same question without the media: photo questions on VQAv2/GQA 0.45/0.55 to 0.78/0.78, reading text in images on TextVQA/OCR-VQA 0.36/0.44 to 0.82/0.89, charts and documents on ChartQA/DocVQA 0.36/0.40 to 0.82/0.72", "notes": "Multimodal Laya, formal-v2 checkpoint.", "domain": "multimodal", "score": "0.78/0.78 VQAv2/GQA vs 0.45/0.55 text-only", "latency": "30-120 ms per question with media", "benchmark": "", "cost": "", "speedup": ""}
{"id": "code-oracle", "name": "Code Oracle", "author": "wahyuzero", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/wxsys/code-oracle-laya-421m", "base_model": "Laya", "params": "421M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "Sub-50 ms neurosymbolic verification for AI coding agents: the structural layer validates AST topology with Tarjan strongly-connected-components, then a non-autoregressive Laya 421M fine-tune makes the semantic call; zero output tokens on success, risk-calibrated typed decisions instead of generative diff review", "approach": "fine-tune", "training": "Fine-tune of Laya ModernBERT 421M on top of ModernBERT-base 164M plus a decision head", "data": "2,400 balanced multilingual code-mutation samples in Python, TypeScript, Go and Rust with 50/50 PASS/REJECT parity, compact Micro-DSL under 400 tokens", "result": "No accuracy published on the card; used as the in-memory neural decision head paired with the deterministic symbolic gates inside code-oracle", "notes": "Code: https://github.com/wahyuzero/code-oracle", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "flymy-decision-4b", "name": "Decision 4B v1.2, FlyMyJev-4B", "author": "flymy-ai", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/flymy-ai/decision-4b-v1.2", "base_model": "Qwen3.5-4B", "params": "4B + 14.4M LoRA", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Open typed-decision preview model: Qwen3.5-4B at a pinned revision plus LoRA rank 16 alpha 32 dropout 0.05 on the attention projections of both layer kinds, 14.4M trainable; the chat template with thinking disabled carries one JSON user message with evidence, criterion and lettered options in the SemIf-style format; the readout takes the option letters' logits at the last prompt token, an fp32 projection of its hidden state, softmax at a temperature of 1.45 fitted on its own data; the output is a distribution over exactly the declared options", "approach": "", "training": "", "data": "", "result": "LoRA folded into the base at load and one CUDA graph per padded input length: p50 19.3 ms / p95 22.7 ms per short decision on one RTX 4090 versus 59 ms eager, matching the eager path on 120/120 checked items; up to 16,384-token inputs; the card explicitly makes no JevBench rank claim", "notes": "v1.1 and v1.2 both published 09-25; independent project, not a TypeSafe release and not a reconstruction of Jev's closed implementation.", "domain": "general", "score": "p50 19.3 ms / p95 22.7 ms, RTX 4090", "latency": "p50 19.3 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "edge-jev-classifiers", "name": "Edge-Computing-JEV service classifiers", "author": "OniReimu", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/OniReimu/Edge-Computing-JEV-classifiers", "base_model": "distilbert-base-uncased", "params": "66.5M DistilBERT per classifier", "metric": "Service top-1 on the EdgeIntent v1 test split: 25% and 50% churn, frozen versus retrained, seen versus unseen", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Four DistilBERT service classifiers released as the reference interpreters for RQ4, the dynamic service catalog, of Replacing Large Language Models with Jev Decision Models for Low-Latency Edge Service Orchestration: each maps a natural-language edge-service request to one service of a fixed catalog or to unsupported, showing what a trained classifier needs when the catalog changes, in contrast to decision models and LLMs that receive the catalog with each request", "approach": "fine-tune", "training": "EdgeIntent v1 development split plus catalog descriptions only, no test case used; fixed hyperparameters with no search: max length 128, lr 5e-5, batch 16, 10 epochs, weight decay 0.01, seed 20260924, Apple M4 Max MPS", "data": "clf_all: 255 classes, 254 services plus unsupported, 554 examples, RQ4 catalog-size conditions K 4–254; clf_frozen: 65 classes of catalog v0, 322 examples; two retrained variants with 339 and 278 examples, 76 and 92 of them new, for 25% and 50% catalog churn", "result": "Service top-1 on the EdgeIntent v1 test split: 25% churn frozen 0.653 seen / 0.000 unseen, retrained 0.708 / 0.147; 50% churn frozen 0.522 / 0.000, retrained 0.441 / 0.142; unseen services stay near zero in every condition", "notes": "Companion model release of arXiv:2609.22753, Delong Li, Xu Wang, Haochen Gong, Rui Lang and Guangsheng Yu of the University of Technology Sydney; code and benchmark run records at OniReimu/Edge-Computing-JEV and the OniReimu/Edge-Computing-JEV dataset; each classifier folder records its label space, example counts and wall time in training.json; scripts/eb_rq4_train.py rebuilds all four", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hf-long-tail-wave", "name": "HF long tail, Laya and Jev thin artifacts, 09-19–09-25 sweeps, grouped", "author": "sweeps", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped catalog of Hugging Face Laya and Jev artifacts individually too thin for a full entry across the 09-19–09-25 sweeps: quantized and runtime ports of Laya typed-decisions, MXFP8, MXFP4, ONNX int8 and fp16, GGUF, MNN, llamafile, llama.cpp, Core ML and MLX; multilingual, Turkish, Thai and Persian variants; browser and CLI apps; intent and triage fine-tunes; plus 58 model cards created 09-19/24 without substantive published results. Full member lists in the notes field below; members promoted to full rows omitted.", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; 09-23/24 Laya typed-decisions wave: quantized and runtime ports, sahilchachra/Laya-TypedDecisions-MXFP8 and MXFP4, strivedi183/laya-typed-decisions-onnx-fp16, yehor-oleksiuk/laya-typed-decisions-onnx, ti3x-m/laya-typed-decisions-onnx, m1rhan/laya-typed-decisions-ONNX; multilingual, SGK86/laya-multilingual-typed-decisions; clones and apps, huanggengwei, rayss868123, agk4444, Ankit1106 laya-typed-decisions forks, uzuzw/laya-cli-gate, Layasaran/research_model_v1; on-device, FluidInference/system-one-gemma-coreml, a source-only Core ML conversion toolkit for the system-one-gemma scorer on gemma-3-270m, blocked with no weights at sweep time; 09-25 additions, fr0stbit3/laya-typed-decisions-gguf, llama.cpp f16 plus quants, mys/laya-typed-decisions-GGUF, a ggmlc build, p-rk/decision-engine-laya-onnx, 09-22, no model card; 09-22/23 quant and runtime ports, wigcheng5566/laya-neutron-gguf, zerodegress/laya-gguf, fibonacciai/laya-gguf-persian-multilingual-decision, sahilchachra/laya-english-mxfp8 and -mxfp4, yehor-oleksiuk/laya-english-onnx, sharjeelbaig/laya-multilingual-onnx-int8, yunfengwang/laya-mnn-int8 and -fp16, fluidinference/laya-english-coreml, chaoliangunsw/macjev-322m-4k-laya-gguf and -mlx; fine-tunes and apps, wilburdev/pigai-laya-intent, leehack/laya-tetris-head, talalml123/laya-multimodal-best-all-tasks, abedinia/laya-web-agent, mazenddr/laya-crisis-triage, tsukihime-exe/tsukiai-laya, quantum08/laya-browser-mind2web, thaitea/laya-vision-smolvlm2-256m-split1024, vishalmysore/layaforwebtrained, madhavbiplov/laya-snake-mlx\n09-24/25 one-offs: hanish-builds/laya-browser-v1, a browser-decision Laya checkpoint with no card; assafvayner/laya-dup, a multilingual Laya duplicate-detection fine-tune; chaoliangUNSW/Jev-Style-0.8B-Decision-v3-MLX, MLX 8bit and bf16 builds of the Jev-Style v3 model; Falconsai/laya-v796, a Laya fine-tune with attested lineage\n09-19/24 model cards, 58 cards without substantive published results, mirrors, thin fine-tunes or empty cards: 58 model cards created 09-19/24 without substantive published results, mirrors, thin fine-tunes, empty cards: wwydmanski/bielik-minitron-jev-v0.1 (v0.2 followed), 09-23; Ruiruiz30/Jev-Omni-MLX-4bit, 09-23; chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-MLX-bf16, 09-23; chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF, 09-23; featherless-ai/laya-plus-classifier, 09-23; sahilchachra/Laya-TypedDecisions-MXFP4, 09-23; sahilchachra/Laya-Multilingual-MXFP8, 09-23; sahilchachra/Laya-Multilingual-MXFP4, 09-23; mpuig/system-one-qwen3-0.6b, 09-22; onnx-community/system-one-qwen3.5-4b-scorer-ONNX, 09-20; shreyanbr/system-one-distilled, 09-19; shreyanbr/system-one-zeroshot, 09-19; pngwn/system-one-qwen3.5-4b-scorer-v2b, 09-17; pngwn/system-one-qwen3.5-4b-scorer-v2-2ep, 09-17; pngwn/system-one-qwen3.5-4b-scorer-smoke, 09-16; AF-Enterprises-LLC/one-person-business-system, 07-04; onewanto/LFM2-350M-with-system-prompt, 08-29; OneFineStarstuff/Unified_AGI_System, 11-13; OneFineStarstuff/AGI_System_Enhancement_Ideas, 11-12; NewSonnet/cua-s1-forge-rlcd-v3, 09-22; BlackwoodAI/blackwood-rlcd, 09-18; TaylorAI/Llama-3B-RLCD-SFT_Llama-3B-Flash_936_full_model, 08-30; s1lv3rj1nx/openjev-general-lora, 09-21; ngila-ai/openjev, 09-22, empty card; ZefanCai/Open-Jev-27B-v1.1, 09-22; gionebeats/Open-Jev-2B, 09-22; s1lv3rj1nx/openjev-general-lora-bigmenu, 09-22; cainai/OpenJev-Qwen3.5-0.8b, 09-22; apus-ailab/APUS-OpenJev-v1-4B, 09-22, standalone 4B checkpoint of the APUS-OpenJev-v1 family; apus-ailab/APUS-OpenJev-v1-4B-GGUF, 09-23; apus-ailab/APUS-OpenJev-v1-4B-MLX-4bit, 09-23; apus-ailab/APUS-OpenJev-v1-4B-MLX-8bit, 09-23; junetask/Open-Jev-9B, 09-23; openjev/openjev-GGUF, 09-23, GGUF quantization of openjev/openjev for 24 GB cards, Macs and CPUs; m3kro/openjev-NVFP4-W4A4, 09-23; killkli/open-jev-laya-multilingual-onnx, 09-23, ONNX export; canbingol/magibuembed-typed-decisions-turkish-mmlu-10k, 09-24; huanggengwei/laya-typed-decisions, 09-23; rayss868123/laya-typed-decisions, 09-23; mlboydaisuke/system-one-qwen3.5-4b-scorer-CoreAI, 09-23, Core AI export of the pngwn scorer family; Ankit1106/laya-typed-decisions, 09-22; IJyad/jeb-typed-decisions, 09-22; akshatbindal/laya-typed-decisions, 09-22; liskasYR/laya-typed-decisions, 09-22; Lucyfer1718/tinyjev-v1, 09-22; agk4444/laya-typed-decisions, 09-21; abidlabs/jev-typed-decisions-causal-0.6b-smoke, 09-21; Cruzex/laya-typed-decisions-smoketest, 09-21; alfred361/laya-multilingual-typed-decisions, 09-21; binoykunhi/laya-typed-decisions-endpoint, 09-21; alfred361/laya-typed-decisions-web-q8, 09-21; lostargon/Tiny-Jev, 09-21; aac6fef/laya-typed-decisions-coreml, 09-20; aac6fef/laya-typed-decisions-mlx, 09-19; gopalanj/jevons-lfm25-1.2b-systemone, 09-20; Lucyfer1718/tinyjev-v0, 09-20; Lucyfer1718/tinyjev-v0-lora, 09-20; JonesLin/next-jev-stage2-last-block, 09-25, mid-training checkpoint step 3100/9181 from a Qwen3.5-0.8B last-block CoT decoder, explicitly not a final or validated model; JonusNattapong/jev-my-bro-th960, 09-22 and -th1200, 09-23, Thai governance and safety risk-classification fine-tunes of laya-multilingual, no model card; ldov/openjevv, 09-21, mirror of AlexWortega/openjev; saivamshiatukuri/decision-model-checkpoints, 09-19, undocumented frozen-Qwen3.5-4B readout-head checkpoints, config.json plus heads.pt per arm across banking77 and vitaminc variants, no card\nLadorure/Laya_llamafile_0.3.20, 09-24, single-file Q8_0 llamafile binary with the weights embedded, no card; Members promoted to full rows are omitted from the lists above; official-model mirrors are cataloged in the laya row notes.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-lct", "name": "Jev-LCT", "author": "gitchw / CaoHaoWei", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/CaoHaoWei/Jev-LCT-Qwen2.5-1.5B", "base_model": "Qwen2.5-0.5B, Qwen2.5-1.5B, Qwen3-8B", "params": "0.5B, 1.5B, 8B", "metric": "300-question cross-domain eval: overall accuracy, ARC scientific reasoning, error self-detection, 1.5B Adaptive LCT versus Laya versus OpenJev", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Jev-LCT, Looped Calibration Transformer, open-source System-One decision engine: loops only the last k layers of a small model with scale-preserving RMS injection for 100% forward-representation stability, extracts calibrated confidence for free from hidden-state convergence, decision entropy and candidate-margin trajectories, and early-exits on a confidence-margin gate plus a fixed-point criterion; decisions, not strings; native FastAPI server and typed Python SDK speaking the TypeSafe Jev protocol, Choice, Noul and Score; PyTorch 2.9+, 52 tests, bilingual EN and ZH docs; 50.8–89.2 ms per decision on RTX 3090 Ti", "approach": "modified", "training": "", "data": "", "result": "On a 300-question cross-domain eval, the 1.5B Adaptive LCT reaches 70.3% overall accuracy versus 51.3% for Laya and 59.3% for OpenJev, with ARC scientific reasoning at 71.7% versus 30.0% and 45.0%; error self-detection AUROC 0.9043 in-domain and 0.9524 out-of-domain after post-hoc isotonic calibration; ECE-15 compressed from 0.1646–0.1259", "notes": "Code: https://github.com/gitchw/LCT. Checkpoints: CaoHaoWei/Jev-LCT-Qwen2.5-0.5B, CaoHaoWei/Jev-LCT-Qwen3-8B, and CaoHaoWei/Jev-LCT-Adapters, the shared looped layers and calibration mappers, about 2.0 GB.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hoi4-jevai", "name": "JevAI HOI4", "author": "zwh20081", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/zwh20081/hoi4-jevai", "base_model": "DeBERTa-v3-large", "params": "open-jev-deberta-v3-large", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "The model behind JevAI, a Hearts of Iron IV mod on the Steam Workshop in which a trained model chooses the strategic posture of AI countries: defensive buildup, industrial expansion, armored offensive, air power, naval power or total war economy; open-jev-deberta-v3-large, a DeBERTa-v3-large with an option-scoring head, fine-tuned on data from 33 observer games of HOI4 1.19; the input is the country's month as text, factories, divisions, manpower, stability, war support, strength ratios, wars, allies, neighbours and the posture in force, followed by up to three questions with their options, packed as CLS STATE ... Q ... OPT ... SEP in at most 512 tokens with the state cut to 256", "approach": "", "training": "", "data": "", "result": "For each posture the runner asks three questions about the next six months, the power trend, whether no territory is lost and whether territory is gained; it ships a PyTorch fp32 bundle at 1.7 GB plus OpenVINO IR in FP16 at a fixed 1x512 shape, 1.5 GB for the Intel NPU and 0.9 GB for CPU", "notes": "Code in the companion GitHub repository.", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevembed-kalm", "name": "JevEmbed KaLM-Embedding V2.5", "author": "HIT-TMG", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/HIT-TMG/JevEmbed-KaLM-Embedding-V2.5", "base_model": "KaLM-Embedding-V2.5", "params": "896-dim embeddings", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Embedding-based Jev-style decision maker: KaLM-Embedding v2.5 fine-tuned with LoRA on all 1,601,157 training questions in JevEmbed-Data, then merged for standalone use; it produces 896-dimensional embeddings and JevEmbed applies the prompts and scoring in jevembed.yaml to make Choice, Score and Noul decisions; the setup uses a 1,024-token limit with truncation, Choice/Score temperature 0.1 and Noul slope 10", "approach": "", "training": "", "data": "", "result": "On the 66,482-question test split, the LoRA fine-tune lifts overall hard-label accuracy from 32.33% to 76.03%, +43.70 pp; Choice 28.61% to 71.05%, Score level 27.45% to 66.17%, Noul binary 40.52% to 90.61%, Score MAE 0.9990 to 0.4943; Noul MAE includes 22,363 hard and 1,641 soft targets", "notes": "Needs transformers 4.51.0 and sentence-transformers 5.3.0 with trust_remote_code for KaLM.", "domain": "general", "score": "76.03% overall hard-label accuracy", "latency": "", "benchmark": "JevEmbed-Data test split", "cost": "", "speedup": ""}
{"id": "jevify-readout", "name": "Jevify readout models", "author": "Praveenrajus", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/Praveenrajus/jevify-gemma-4-e4b-readout", "base_model": "Gemma 4 E4B, Qwen3.5-2B, Qwen3.5-4B, Qwen3.5-9B, Qwen3-VL-2B", "params": "2B to 9B plus gemma-4-E4B", "metric": "Accuracy, ECE, held-out accuracy and TVD to human labels on jev-bench, with and without the coherence penalty", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "System One decision models trained readout-style: instead of fine-tuning the model to emit answers, it is trained on its own decision readout, the distribution over the allowed answers read at the answer position, one forward pass, no decoding, scored with the primitive's proper scoring rule; options shuffled per family; a Tier 0 recipe of per-primitive temperature, Noul bias and option-order permutations fitted on validation; rank-16 LoRA builds merged into the weights", "approach": "fine-tune", "training": "", "data": "", "result": "Readout family numbers on jev-bench, released 2026-09-25: qwen3.5-2b full readout 0.703 accuracy, 0.056 ECE, 0.746 held-out, 0.315 TVD to human labels; with the coherence penalty 0.704, 0.056, 0.742 and sure loss 0.028; qwen3.5-2b-base rank-16 LoRA with coherence 0.702, 0.051, 0.742 and sure loss 0.034; gemma-4-e4b-it rank-16 LoRA 0.737 accuracy, 0.057 ECE, 0.792 held-out, the best in the family, matching Jev 1.13.0 accuracy of 0.733 with far lower ECE against 0.113 while Jev keeps a 0.835 held-out; untuned Tier 0 baselines for the same models range from 0.533–0.658 accuracy. The coherence penalty cuts sure loss by an order of magnitude with no accuracy cost.", "notes": "Family on HF: readout, readout-coh, base-readout and base-readout-coh series across Qwen3.5 2B, 4B and 9B plus gemma-4-E4B, alongside the earlier full fine-tunes jevify-qwen3.5-4b, jevify-qwen3.5-2b, jevify-qwen3-vl-2b and the t2 / t2-lowlr continued runs. Six jev-bench sources held out from training. Bench: Praveenrajus/jev-bench dataset, viewer space Praveenrajus/jev-bench.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevk5-9b", "name": "JevK5-9B", "author": "alibiserikbay", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/alibiserikbay/JevK5-9B", "base_model": "Qwen3.5-9B", "params": "9B", "metric": "Mean, teacher-question, hard-set and Decision Index proxy accuracy", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "9B sibling of JevK5, an independent Apache-2.0 open-weight alternative to Jev: reads a state plus a noul, choice or score question and returns a probability for every option in one forward pass with zero generated tokens; same v0.3 training data and recipe on Qwen3.5-9B with a rank-16 attention LoRA merged; SemIf-style readout, softmax over the answer letters' next-token logits with a fitted calibration temperature, multi-pass knockout for option sets over 16; served by the JevK5 runtime, TypeSafe-style /v1/systemone endpoint, self-hosted", "approach": "fine-tune", "training": "Qwen3.5-9B with a rank-16 attention LoRA; same 47,460 rows as JevK5 v0.3 4B, of which 17,408 teacher questions: 3,270 from Qwen3.6-27B and 14,138 from GPT-6 Luna; three candidate checkpoints at 1, 1.5 and 2 epochs, 1.5 adopted", "data": "47,460 rows shared with JevK5 v0.3 4B; see the JevK5 row for the full data table with licenses", "result": "v0.3.3 at 1.5 epochs, adopted release: mean 0.834, teacher 0.848, hard set 0.859, index proxy 0.794; hard tier after the v0.3 to v0.3.3 revision: probability 0.70 to 0.90, long policies 0.58 to 0.74, ambiguous 0.71 to 0.86, multi-hop 0.83 to 0.78, dates and numbers still 0.47; hard-tier ECE 0.071; CLINC150 out-of-scope recall 0.59 with precision 0.84", "notes": "FP8 build: liodon-ai/JevK5-FP8, compressed-tensors, vLLM. Runtime: https://github.com/allebee/jevk5. Family rows: JevK5 and JevK5-2B. Not yet run on the Jev Decision Index or submitted to JevBench; for reference JevK5 v0.2 4B scored 36.31, 15th of 49, on the Decision Index with 4th-best calibration at ECE 0.031, and ranked 2nd of 76 on JevBench v1.4. Runs with more than 16 options use the knockout readout in groups of up to 16; temperature 1.05 fitted on MASSIVE.", "domain": "general", "score": "0.834 mean", "latency": "p50 16 ms and p95 18 ms on easy and standard items, p50 42 ms and p95 223 ms on hard items, H100", "benchmark": "362 held-out teacher questions plus the hard set and the Decision Index proxy", "cost": "", "speedup": ""}
{"id": "alibiserikbay-jevk5-lite", "name": "JevK5-Lite", "author": "alibiserikbay", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/alibiserikbay/JevK5-Lite", "base_model": "DeBERTa-v3-large", "params": "437M", "metric": "Accuracy and top-label calibration error on six single-label sets, vs GLiNER2.5-Decide", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Preview, experimental JevK5-family CPU classifier: reads a text and any number of label sets, heads, in one encoder pass and returns calibrated probabilities", "approach": "fine-tune", "training": "DeBERTa-v3-large, one epoch over a mix of public and synthetic rows; label augmentation varies candidate subsets, option order, naming styles and task names so the model learns what labels mean rather than a fixed class list", "data": "189,026 public train-split rows: BANKING77, CLINC150, MASSIVE, HWU64, SNIPS, four Bitext assistant datasets, DBpedia-14, Civil Comments, arXiv abstracts and Schema-Guided Dialogue; plus 20,822 synthetic operational documents, each repeated three times in the mix, written by GPT-6 Luna with 1-4 label heads per document and kept only when two independent solves agreed", "result": "Significant wins on AG News and Yahoo Answers; within noise on Enron spam and tweet_eval emotion and a tie on sentiment; loses Financial PhraseBank and GoEmotions; lower top-label calibration error than GLiNER2.5-Decide on all six single-label sets", "notes": "Smallest member of the JevK5 family. Overconfident on the fast-decisions dev set, ECE 0.255; the temperature was fitted on held-out synthetic data from the same generator as the training mix. See the JevK5, JevK5-2B and JevK5-9B rows for the larger siblings and published results. The full JevK5 model is row jevk5.", "domain": "general", "score": "", "latency": "54-92 ms per item, bf16, 16 CPU threads", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-email-resume-classifier", "name": "Laya email resume classifier", "author": "pashwinkrish22", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/pashwinkrish22/laya-email-resume-classifier", "base_model": "Laya", "params": "421M / 322M Laya base", "metric": "Held-out accuracy on a private job-application email split", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Laya fine-tuned on a private anonymised dataset of job-application emails to classify each email into one of four categories, freshers, experienced, referrals, others; standard Laya checkpoint: predict takes a state with body and resume plus a caller-defined choice question over the four categories", "approach": "fine-tune", "training": "fine-tuned on a private anonymised job-application email dataset", "data": "Private anonymised job-application emails", "result": "No public evaluation: the card explicitly notes the model is not evaluated against the public typed-decisions benchmark, and the held-out validation accuracy section of the card is left empty at sweep time", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-experts", "name": "Laya Experts", "author": "goku-san", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/goku-san/laya-experts", "base_model": "Laya", "params": "421M / 322M Laya base", "metric": "PII entity F1 and span accuracy on OpenPII held-out documents; Banking77 accuracy and macro-F1", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Fine-tuned single-job flavours of Laya: each Expert is trained for one job. Shipped flavours: PII detection and redaction, trained on ai4privacy OpenPII 1.5M, and Banking77 intent classification; calibrated probabilities, no text generation", "approach": "fine-tune", "training": "fine-tuned on OpenPII 1.5M and Banking77", "data": "ai4privacy/pii-masking-openpii-1.5m, legacy-datasets/banking77", "result": "PII Expert on OpenPII English held-out documents: entity micro-F1 0.967, macro-F1 0.961, span accuracy at 22 ways 0.939; Banking77 Expert on the test split: accuracy 0.917, macro-F1 0.916", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-browser-v32b", "name": "laya-browser-v32b", "author": "ichenney", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/ichenney/laya-browser-v32b", "base_model": "laya-browser", "params": "322M", "metric": "Decision latency p50 on RTX 3080 against the hosted Jev API; score-question accuracy; noul probe accuracy", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: yes/no; notes: JevBench multi_hop 0.22", "arch": "System-1 decision head for browser agents: a 322M-parameter browser-agent decision model fine-tuned from cklxx/laya-browser, which itself fine-tuned convaiinnovations/laya for browser decisions; produces noul done-judgment items alongside choice and score", "approach": "fine-tune", "training": "Counterfactual ranking + 8k noul items, head learning rate 1e-4, bf16 autocast", "data": "", "result": "Decision latency p50 27 ms on RTX 3080 vs ~854 ms network for the hosted Jev API - 31× faster at $0 cost, fully private and offline, and it beats Jev on score questions; generating noul items raised probe accuracy from 37% to 100% and done_judgment to 0.769", "notes": "v32b-b15 checkpoint; known limits per the card: complex multi-hop reasoning and safety-adjacent judgments at this size.", "domain": "general", "score": "", "latency": "27 ms p50 on RTX 3080", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-idjvsuen-v3", "name": "laya-idjvsuen-v3", "author": "faall7479", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/faall7479/laya-idjvsuen-v3", "base_model": "laya-idjvsuen-v1", "params": "322M", "metric": "Calibrated typed decisions on Indonesian, Javanese, Sundanese and English code-switching; 12-category ticket-domain intent", "license": "CC-BY-SA-4.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Domain fine-tune of laya-idjvsuen-v1, which is itself a multilingual fine-tune of laya-multilingual: non-autoregressive decision model, mmBERT-base, 322M parameters, for calibrated typed decisions on Indonesian, Javanese, Sundanese, English and code-switched input, extended with an internal ticket-domain 12-category intent task; the model never generates text, it answers caller-defined choice, score and noul questions with calibrated probabilities in a single forward pass", "approach": "fine-tune", "training": "fine-tuned from v1 with the ticket-domain intent task added", "data": "MASSIVE intent 60-class, NusaX sentiment, internal ticket-domain 12 categories", "result": "v1 covers general multilingual intent and sentiment; v3 adds the ticket-domain 12 categories and is recommended for ticket routing", "notes": "Bilingual card, Indonesian and English Card caveat inherited from the v1 lineage: Javanese and Sundanese intent was trained and evaluated on NLLB machine-translated text.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-mlx-zh", "name": "laya-mlx-zh", "author": "ZLHAOOO", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/ZLHAOOO/laya-mlx-zh", "base_model": "Laya multilingual", "params": "421M", "metric": "Message routing accuracy, urgency interrupt and 4-level priority scoring against a near-random 0.25 baseline", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "result: 4 classes", "arch": "Chinese fine-tuned Laya decision-model weights, native Apple Silicon MLX: millisecond-level Chinese decisions with the dataset, training recipe, eval set and MLX weights fully open source; typed choice / score / noul questions", "approach": "fine-tune", "training": "", "data": "", "result": "Message routing accuracy 0.854 against a near-random ~0.25 baseline; urgency interrupt 0.902; 4-level priority scoring 0.878", "notes": "Bilingual Chinese/English README; 614 MB MLX weights; card caveat: soft-label calibration is not ECE-audited, so the confidence suits ranking rather than direct use as a probability.", "domain": "general", "score": "", "latency": "~27 ms per question on M1 with the model resident, ~210 ms for a 12-question batch", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-vi-vanty120", "name": "laya-vi", "author": "vanty120", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/vanty120/laya-vi", "base_model": "Laya multilingual", "params": "322M", "metric": "Accuracy on Vietnamese business decisions, in-domain and held-out", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Vietnamese fine-tune of Laya starting from the laya-multilingual checkpoint, trained on Vietnamese business decisions; standard Laya checkpoint format, so the Laya runtime loads it with no extra code; like Laya it never generates text, it returns typed answers with calibrated probabilities in a single forward pass", "approach": "fine-tune", "training": "fine-tuned on Vietnamese business decisions", "data": "Vietnamese business decisions, in-domain and held-out domains", "result": "On Vietnamese decisions from the trained domains, accuracy rises from 0.45–0.89; on domains held out from training it also improves, and its probabilities are much better calibrated than the base checkpoint", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-moe-web", "name": "layaMOE for the browser", "author": "VishalMysore", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/VishalMysore/laya-moe-web", "base_model": "laya-typed-decisions", "params": "ModernBERT-large encoder + expert heads", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "ONNX Runtime Web files for layaMOE: the shared encoder of convaiinnovations/laya-typed-decisions, ModernBERT-large in int8 at ~400 MB, plus one small two-layer decision head per expert; general is the original head, the other expert heads were fine-tuned on synthetic domain data; a manifest.json carries the SHA-256 file list, the router question and the per-head calibration temperatures; weights are weight-only int8 and the files are split into parts", "approach": "", "training": "", "data": "", "result": "", "notes": "Unofficial derivative of the ConvAI model, Apache-2.0 with a NOTICE; MoE expert routing in the browser.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "interfazeai-lev", "name": "lev", "author": "InterfazeAI", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/interfaze-ai/lev", "base_model": "Qwen3.5-4B", "params": "4B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "lev, open System One decision model and the harness that measures it: give it a state, text, ticket, email or JSON, and a set of typed questions, yes/no, choice, score, and it answers all of them in one forward pass, reading each answer from logits it already computed; calibrated probabilities over exactly the options supplied", "approach": "fine-tune", "training": "LoRA on one H100", "data": "", "result": "68.9% across all 13 S1Bench subsets; level with reflex-4b on the six subsets the public S1Bench board completed, behind only Jev and three open models of 26B to 35B; label-token readout serving up to the tokenizer limit lifted MASSIVE 60-intent accuracy from 0.231–0.746; one batched forward instead of prefill-and-fork cut H100 compute from 169 ms to 69 ms, 59% less, flat from one question to eight", "notes": "Code: https://github.com/InterfazeAI/lev. Mean ECE 0.115 for lev versus 0.091 for Jev; lev is better calibrated on 5 of 13 subsets. Fine-tuning cost the minimal-edit pairs, paws minus 12.4 and vitaminc minus 13.3 versus the untuned backbone, which scores 0.826 there. lev.load and lev serve read a release manifest for the base model, prompt format, calibration and head, nothing to configure.", "domain": "general", "score": "68.9% on S1Bench, all 13 subsets", "latency": "69 ms engine compute on one H100", "benchmark": "S1Bench", "cost": "", "speedup": ""}
{"id": "mica", "name": "Mica v0.1-4B", "author": "sky7350", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/sky7350/Mica-v0.1-4B", "base_model": "Qwen3.5-4B", "params": "4B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Small decision model: give it a state, a question and the allowed answers and it returns a probability for each answer, yes/no, a choice among 2-255 options, or a score with 2-10 levels; it reads the input once and generates no text, so a decision costs one prefill; it speaks the TypeSafe /v1/systemone format so clients written for Jev work unchanged; trained on English and Korean; Qwen3.5-4B at a pinned revision with a merged rank-16 LoRA on all 32 layers; BF16 safetensors plus GGUF in BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M and Q4_0; inputs longer than 8,192 tokens are rejected with HTTP 400 rather than truncated", "approach": "", "training": "", "data": "", "result": "The unchanged JevBench typesafe adapter runs against the local server; Docker builds for CUDA 86, 89 and 90 around a llama.cpp b11010 runtime; code at github.com/akivet/Mica-v0.1-4B", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench, typesafe adapter", "cost": "", "speedup": ""}
{"id": "millennium-jev-0-5b", "name": "Millennium-Jev-0.5B", "author": "sunziqin", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/sunziqin/millennium-jev-0.5b", "base_model": "Qwen2.5-0.5B-Instruct", "params": "0.5B", "metric": "Single-forward latency on mixed Choice, Score and Noul queries, RTX 5070 Ti", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Provably safe non-autoregressive System 1 decision model, flagship of the Millennium open-source ecosystem, formerly code-named Aegis-S1: vectorized option span mean-pooling, native C++ SDPA bidirectional attention, split-conformal prediction at alpha 0.05 with exchangeable 95% marginal coverage, and a production Tri-Gate protocol that permits autonomous action only when the candidate set collapses to one, top-1 confidence exceeds 60% and anomaly risk stays below 0.70; zero-shot classification, English and Chinese", "approach": "modified", "training": "Built on Qwen2.5-0.5B-Instruct; official benchmark on a disjoint held-out set of 8,657 rows under strict state-disjoint conditions, zero template or state overlap between train, calibration and test splits", "data": "", "result": "Single-forward evaluation of mixed Choice, Score and Noul queries in 37.5 ms on an RTX 5070 Ti; sub-50 ms deterministic decisions", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mini-jev-phase1b", "name": "Mini-Jev shell-safety phase1b", "author": "nicolasembleton", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/nicolasembleton/mini-jev-minicpm5-2b-shell-safety-phase1b-merged", "base_model": "MiniCPM5-2B", "params": "2B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "In-browser decision head for shell-safety judgment: full merged weights of MiniCPM5-2B plus the Phase-1b shell-safety LoRA, classify as allow, warn or block; browser ONNX build at q4 via Transformers.js; research and demo use, not a production security boundary on its own", "approach": "fine-tune", "training": "", "data": "Remapped kontext-security/ShellRisk-Bench plus hand rows and a small Phase-1b boundary set; eval is a frozen 105-item smoke panel with planted gold", "result": "No numbers on this card: the training mix is remapped kontext-security/ShellRisk-Bench plus hand rows and a small phase-1b boundary set; evaluation is a frozen 105-item smoke panel with planted gold, and the counts and smoke metrics live on the ONNX card", "notes": "ONNX: nicolasembleton/mini-jev-minicpm5-2b-shell-safety-phase1b-onnx. Demo: mini-jev.pastel-labs.com", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nano-jev", "name": "Nano-Jev v0.1", "author": "sdmlai", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/models/sdmlai/nano-jev", "base_model": "ms-marco-MiniLM-L6-v2", "params": "22.7M", "metric": "about 1 ms per decision on GPU", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Typed decision model for RAG pipelines: a MiniLM-L6 cross-encoder with a single-logit head that scores each question-option-context triple in one forward pass and returns a calibrated probability per option, no text generation; the three decisions are relevance as a three-way score over irrelevant, partially relevant and directly answers, passage-sufficient as yes or no, and groundedness of a claim against context as yes or no", "approach": "fine-tune", "training": "2 epochs, batch 16, AdamW lr 3e-5, weight decay 0.01, 6% warmup with linear decay, bf16; 14.4 min on one RTX 3060 12 GB", "data": "49,760 examples: HotpotQA distractor 6,000 questions, SQuAD 2.0 6,000, MultiNLI 8,000, seed 0, max input 512 tokens", "result": "Per-decision temperature calibration from calibration.json: relevance 1.445, sufficient 1.378, grounded 1.234; because options are scored independently, option sets never seen in training can be passed, but their probabilities are weak in v0.1", "notes": "v0.1 research preview, released 2026-09-25 under the v0.1 tag; explicitly independent, not affiliated with TypeSafe AI or the NanoJev GitHub project; code at shubham10divakar/nano-jev; also loads with plain transformers AutoModelForSequenceClassification plus the calibration.json", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zircon", "name": "Zircon v2", "author": "FahrenheitResearch", "date": "2026-09-25", "category": "model", "url": "https://huggingface.co/FahrenheitResearch/Zircon-0.6B-v2-mlx", "base_model": "MLX 8-bit 0.6B", "params": "0.6B", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Zircon v2, a 0.6B decision model from Fahrenheit Research that runs fully on-device on Apple silicon via MLX 8-bit at ~634 MB: give it a situation and a set of options and it returns a calibrated probability for each option in under 100 ms; decision types are pick-one, yes-or-no, and score-on-a-scale", "approach": "", "training": "", "data": "", "result": "Median 83 ms per case of 5 decisions and 30-68 ms for a single decision on a MacBook Pro with an M5, the median over 400 cases; internal September 2026 benchmark, 400 cases: 77% accuracy and Brier score 0.049; by workflow, invoice processing 82%, customer service 79%, security incidents 75%, agent-trace observability 70%; by decision type, yes/no 85%, a 1-5 scale 73% exact and 98% within one point, pick-one from a list 73%; capabilities: select the correct math answer 87%, detect an error in a worked solution 83%, match a reply to its request 89% and a request to its reply 96%", "notes": "All figures are the vendor's own internal measurements.", "domain": "general", "score": "77% accuracy, Brier 0.049 internal", "latency": "median 83 ms per 5-decision case, M5", "benchmark": "Fahrenheit Research internal testing, Sept 2026", "cost": "", "speedup": ""}
{"id": "beenara", "name": "BeeNara", "author": "Kwokou", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/Kwokou/BeeNara", "base_model": "", "params": "332 MB ONNX", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Document-file decider: it reads a document together with your own list of folder names and returns one of them, or none fits; its confidence is calibrated and when it is not sure enough it tells you to ask the user instead of guessing; it is the category decider of PollySort, a local, bilingual German-English document archivist; folder names are plain text, optionally with a short description, and new names need no retraining; trained only on synthetic documents, no user data, runs fully offline, a 332 MB ONNX file at ~0.2-0.3 s per document on a laptop CPU with no GPU and no PyTorch; split-conformal prediction decides when to sort automatically", "approach": "", "training": "", "data": "", "result": "It recognizes 96.8% of documents whose folder is missing from the list, where Qwen3.5-4B managed 1% and Qwen3.5-9B 44%; on its benchmark it sorts 64% of documents on its own and 99.6% of those are correct, the rest go to the user; explicitly not for decisions with legal, medical or financial consequences", "notes": "", "domain": "general", "score": "64% auto-sorted, 99.6% of those correct", "latency": "", "benchmark": "PollySort benchmark", "cost": "", "speedup": ""}
{"id": "wwydmanski-bielik-minitron-jev", "name": "Bielik-Minitron-7B any2jev", "author": "wwydmanski", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/wwydmanski/bielik-minitron-jev-v0.3", "base_model": "Bielik-Minitron-7B-v3.0-Instruct + LoRA r=64 + pointer head", "params": "7B", "metric": "Held-out metrics in committed eval.json, no table in the card", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Any2Jev recipe applied to Bielik-Minitron-7B-v3.0-Instruct: LoRA rank 64 plus a 256-dimensional pointer head, served through a /v1/systemone endpoint", "approach": "head", "training": "v0.3: 1 epoch, lr 3e-05, batch 1 x 4, 181 min on one consumer GPU; v0.2 was 3 epochs, lr 5e-05, batch 2 x 2, 103 min", "data": "data/train_mix3v01.jsonl, a few thousand labelled decisions from a handful of sources", "result": "v0.3 is the served checkpoint; /v1/systemone-compatible", "notes": "Lineage: v0.1 then v0.2 on 09-23, v0.3 checkpoint on 09-24, all from speakleash/Bielik-Minitron-7B-v3.0-Instruct with the any2jev toolkit, which turns an HF causal LM into a Jev-style decision model in roughly 1.7 hours; independent project, not affiliated with TypeSafe AI", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "quazim0t0-byrne-jev", "name": "Byrne-Jev 79M", "author": "Quazim0t0", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/Quazim0t0/Byrne-Jev-79M", "base_model": "SpikeWhale 70.4M causal decoder trunk + 8.5M Laya-style decision head", "params": "79M", "metric": "Brier 0.134; soft accuracy 0.509; temperature 1.0 best-calibrated", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "Sub-80M Jev-style local System-1 model for the same job as Jev and Laya: a state in, typed questions out, a calibrated probability for every option in one forward pass with nothing generated. A 70.4M SpikeWhale causal decoder trunk carries an 8.5M Laya-style decision head - two bidirectional layers with one marker position per option under strictly proper scoring rules - served over Jev's /v1/systemone wire protocol with a single model.pt checkpoint and a DecisionAgent interface", "approach": "head", "training": "Custom, single checkpoint", "data": "", "result": "Best-calibrated at temperature 1.0 with ECE 0.045, the lowest in its table; post-hoc temperature fitting made it worse at 0.100. Brier 0.134 beats Jev's published score and soft accuracy 0.509 beats Laya's fine-tuned checkpoint, while top-1 accuracy sits 10–14 points below Jev and Laya with invoice processing the weakest workflow; total 79M parameters", "notes": "Includes a zero-shot jev-doom integration test on Freedoom", "domain": "general", "score": "ECE 0.045", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tomek7667-cbjev", "name": "cbjev", "author": "tomek7667", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/0010101010-1/cbjev", "base_model": "Laya", "params": "421M", "metric": "0.741 mean accuracy", "license": "GPL-3.0", "paper": "", "stars": 0, "likes": 2, "detail": "", "arch": "all questions of a call share one state encoding, token-for-token and position-for-position what a Laya checkpoint was trained on; Laya encodes the state once per question", "approach": "", "training": "fine-tuned from Laya", "data": "15 English suites; typed-decisions 2,000 decisions; MASSIVE 51 languages", "result": "mean accuracy 0.741 vs Laya 0.710, ahead in 11 of 15 suites; typed-decisions 0.783 vs Laya 0.768 and Jev 0.727 published; mean ECE 0.117 vs 0.125 and 0.246; 0.2% answer flips on option reorder vs Laya 7.8% and Jev 13%; MASSIVE macro 0.436 vs Laya 0.401, ahead in 45 of 51 languages; 3 ms one question and 11.4 ms ten questions over a 500-token doc on RTX 4090 vs Laya 5.4 ms and 75.8 ms", "notes": "self-hosted Jev-compatible successor to Laya; faster than both Laya paths in all 10 latency cases measured Code: https://github.com/tomek7667/cbjev.", "domain": "general", "score": "0.741 mean accuracy, 15 suites", "latency": "3 ms per question on RTX 4090", "benchmark": "MASSIVE", "cost": "", "speedup": "1.5× faster"}
{"id": "harshpreet931-cut-laya-onnx", "name": "cut-laya-onnx", "author": "harshpreet931", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/harshpreet931/cut-laya-onnx", "base_model": "Laya", "params": "421M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "Laya exported to 8-bit ONNX for in-browser execution via ONNX Runtime Web: encoder 493 MB + head 37 MB, MatMulNBits weight-only quantization; powers the cut. text editor", "approach": "", "training": "", "data": "", "result": "Same top label as the full PyTorch Laya on all 57 lines of a small hand-made 7-post set, with probabilities within 0.021, run in Chrome on WebGPU; about 0.8 s for a 9-line post on an Apple M4 Pro", "notes": "4-bit weights were tried and dropped: they changed the top label on 11 of 57 test lines; original checkpoint is 1.7 GB fp32 ONNX; Apache-2.0, like the original.", "domain": "general", "score": "", "latency": "about 0.8 s for a 9-line post, Apple M4 Pro, WebGPU", "benchmark": "", "cost": "", "speedup": ""}
{"id": "libertai-deem", "name": "Deem 9B & 0.8B", "author": "LibertAI Labs", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/LibertAIDAI/deem-9b-v1", "base_model": "Qwen3.5 backbone", "params": "9B / 0.8B", "metric": "9B: JevBench public hard 65.8; 0.8B: policy hold-out 96.3%", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": 0, "detail": "base: Qwen3.5 backbone; metric: Deem 9B JevBench public hard 65.8, past the open frontier of reflex-4B at 63.2, ~100 ms short-form P50; Deem 0.8B policy hold-out 96.3%, 0.9 GB resident, 362 ms on a busy desktop CPU; notes: choice 2–255 options, score as ordinal rubric; weights LibertAIDAI/deem-9b-v1 and deem-0.8-v1; all benchmarks reproducible", "arch": "Typed, calibrated decisions from open weights: choice over 2–255 options and score as an ordinal rubric answered without generation; the 9B targets the open accuracy frontier, the 0.8B runs CPU-native", "approach": "head", "training": "", "data": "", "result": "Deem 9B scores 65.8 on the JevBench public hard split, past the open frontier of reflex-4B at 63.2, at ~100 ms short-form P50; Deem 0.8B holds 96.3% on its policy hold-out with 0.9 GB resident and 362 ms on a busy desktop CPU", "notes": "Code: https://github.com/Libertai/deem.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ekvachan-decoder", "name": "ekVachan decoder", "author": "abhi6168", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/abhi6168/ekvachan-decoder", "base_model": "Qwen3.5-4B", "params": "4B", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "The decoder arm of ekVachan, an open, self-hostable alternative to Jev: LoRA adapters, rank 16 alpha 32 with 128 target modules, for Qwen3.5-4B; given a state and a set of typed questions, choice, score, noul, it returns calibrated probabilities in one non-autoregressive forward pass, a restricted-logit read over single-token letter codes, not a generated response; two named adapters over the same base are hot-swapped at serving time, a text-only adapter capped at 26 options and a vision-capable stage3 adapter capped at 588 options that also answers text-only requests in the reference server; each subfolder carries a manifest.json with the training config and measured accuracy, Brier and ECE", "approach": "", "training": "", "data": "", "result": "Measured against the actual serving class, third-party benchmarks with zero training exposure: JevBench 70.99% over 231/231 items and jabr-v2 85.49% over 944/944 items; eager and CUDA-graph modes are identical within noise; full source, training pipeline and evidence trail at github.com/asp616848/better-jev-for-all", "notes": "", "domain": "general", "score": "70.99% JevBench / 85.49% jabr-v2", "latency": "", "benchmark": "JevBench 231 items; jabr-v2 944 items", "cost": "", "speedup": ""}
{"id": "embeddinggemma-tetris", "name": "EmbeddingGemma Tetris System-1", "author": "jaichang", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/jaichang/embeddinggemma-tetris-system1", "base_model": "EmbeddingGemma 300M", "params": "300M", "metric": "Inference latency: GPU forward pass and multi-objective candidate reranking", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Real-time System 1 decision engine on google/embeddinggemma-300m: a bidirectional embedding backbone plus a 2-layer MLP readout head that outputs calibrated probabilities over 40 discrete Tetris piece placements, without generating autoregressive text tokens", "approach": "head", "training": "Trained on a custom Tetris dataset", "data": "custom-dellacherie-tetris", "result": "Inference latency ~8 ms for the GPU forward pass and ~70 ms with multi-objective candidate reranking", "notes": "Full research walkthrough: \"Beyond Next-Token Prediction: Building Sub-10ms System 1 Decision Engines with EmbeddingGemma\".", "domain": "general", "score": "", "latency": "~8 ms GPU forward, ~70 ms with reranking", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gut-laya", "name": "GUT, Laya RLCD fine-tune", "author": "qalarc", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/Qalarc/Laya-GUT-Finetune", "base_model": "Laya 421M", "params": "421M", "metric": "hard gate: agreement >= 0.85 AND ECE <= 0.10; 0 of 9 runs passed, best ECE 0.07", "license": "CC-BY-NC-4.0", "paper": "", "stars": 0, "likes": 0, "detail": "training: RLCD, GRPO-like plus soft cross-entropy, on monitor-triage domain; metric: 9 runs, 0 registered under the hard gate, agreement >= 0.85 and ECE <= 0.10 and no label collapse, best ECE 0.07; notes: ~20 ms on GPU; live at gut.qalarc.com; weights Qalarc/Laya-GUT-Finetune; X-Laya-Domain header Guided Unconscious Thinking.", "arch": "Laya 421M fine-tuned with RLCD, GRPO-like objectives plus soft cross-entropy for a monitor-triage decision domain, served live with an X-Laya-Domain header", "approach": "rl", "training": "Nine RLCD runs; a hard registration gate, agreement >= 0.85 with ECE <= 0.10 and no label collapse failed all nine; best run reached ECE 0.07", "data": "Monitor-triage domain data", "result": "Honest negative: 0 of 9 runs passed the hard gate; best ECE 0.07; ~20 ms per decision on GPU; weights at Qalarc/Laya-GUT-Finetune", "notes": "HF weights card: Qalarc/Laya-GUT-Finetune, cc-by-nc-4.0, 09-24. Code: https://github.com/qalarc/gut-finetuned.", "domain": "health", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "abidlabs-jev-causal-06b", "name": "Jev causal typed scorer 0.6B", "author": "abidlabs", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/abidlabs/jev-typed-decisions-causal-0.6b", "base_model": "Qwen3-0.6B-Base", "params": "596M", "metric": "calibration split n=640: acc 0.6234, NLL 1.2755", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "cached causal typed scorer: prefill the decision prompt once, one branch per decision, loss read at a single answer-letter position; LM head frozen, scoring restricted to option-letter tokens", "approach": "fine-tune", "training": "arm-B recipe from the pngwn experiment: LoRA r=16 alpha=32 dropout 0.05 on q/k/v/o/gate/up/down; AdamW lr 1e-4, 3% warmup plus cosine, batch 16, max_len 1024; 5,742 steps = 2 epochs, 37m21s on A100", "data": "pngwn/typed-decisions-v2: 31,109 states, 45,932 decisions", "result": "report arm-B full eval: acc 0.7518, ECE 0.0154 after temperature scaling", "notes": "Independent reproduction of pngwn/typed-decisions-causal-experiment", "domain": "general", "score": "NLL 1.2755", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-japanese-judgment-v2", "name": "Jev Japanese Judgment v2", "author": "fukayatti0", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/fukayatti0/jev-japanese-judgment-v2", "base_model": "LFM2.5-1.2B-JP-202606", "params": "1.2B", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Japanese judgment model in the candidate-scoring style: the LiquidAI LFM2.5-1.2B-JP backbone returns probabilities directly from the context and the candidates, generating nothing; v2 adds yes/no Noul and graded Score heads on top of v1's three tasks; roughly 29.7k added examples: JNLI and JCoLA with yes/no balanced, JSTS 0-5 capped at 1,500 per level, JSICK 1-5 capped at 800, plus up to 3,400 items from each v1 source as a forgetting countermeasure; fine-tuned from v1's LoRA+head for one epoch at learning rate 1e-4; it does not load through standard transformers, code at github.com/fukayatti/jev-japanese-judgment", "approach": "", "training": "", "data": "", "result": "Evaluated only on splits never used in training: the added 1,600 items go JNLI 80.8% to 85.8%, JCoLA 51.7% to 58.0%, JSTS 20.5% to 58.0%, JSICK 28.0% to 70.2%, overall 45.2% to 68.0% with ECE 0.1245 down to 0.0207; the original three tasks move 92.2% to 90.7%, ECE 0.0145 to 0.0258; a merged-LoRA GGUF, Q4_K_M at 698 MB, runs through llama-embedding last-pooling plus a small MLP head from head.npz", "notes": " v1 checkpoint fukayatti0/jev-japanese-judgment: same candidate-scoring readout, final-token hidden state to an MLP scalar, softmax over candidates, held-out 500 accuracy 0.938, ECE 0.0114, Brier 0.0960.", "domain": "general", "score": "68.0% overall on new tasks, ECE 0.0207", "latency": "", "benchmark": "JNLI, JCoLA, JSTS, JSICK held-out splits", "cost": "", "speedup": ""}
{"id": "nebula-legal-lora-08b", "name": "Jev Qwen3.5 legal LoRA, 0.8B + 4B", "author": "Nebulaw1", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/Nebulaw1/jev-qwen3.5-0.8b-legal-lora", "base_model": "Qwen3.5-0.8B-Base", "params": "0.8B", "metric": "teacher agreement 0.7453 / 0.8478 and macro-F1 0.6981 / 0.8142 for 0.8B / 4B, on 322 audit-accepted rows", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Chinese finite-choice legal judgment scorer: original output head scores candidate letters in one forward pass with candidate-set cross-entropy; no legal reasoning generated", "approach": "fine-tune", "training": "NF4 4-bit double quantization, BF16, LoRA r=16 alpha=32, lr 2e-4, 3 epochs, effective batch 16; 1,269 of 1,580 derived samples accepted by audit; teacher is DeepSeek deepseek-flash", "data": "", "result": "case-disjoint 100-group test, 380 generated rows, 19 excluded; neither checkpoint was selected on this test", "notes": "Metrics measure agreement with teacher labels, not independent legal accuracy. The 4B sibling, Nebulaw1/jev-qwen3.5-4b-legal-lora, Qwen3.5-4B-Base: same one-forward candidate-letter scoring; its case-disjoint test was reused from an earlier 4B run, not a never-observed final holdout.", "domain": "legal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "atmaneayoub-jev-ar", "name": "jev-ar", "author": "atmaneayoub", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/atmaneayoub/jev-ar", "base_model": "mmBERT-base", "params": "322M", "metric": "Routing accuracy and macro-F1 on Jev-AR Bench", "license": "CC-BY-NC-4.0", "paper": "", "stars": "", "likes": 1, "detail": "322M parameters, about 10 ms per query. CC BY-NC 4.0 with gated access that is approved automatically; commercial use requires a separate commercial licence. Benchmark dataset atmaneayoub/jev-ar-bench, companion repo atmaneayoubdev/jev-ar.", "arch": "Arabic intent-routing decision model: reads a message and a list of routes supplied at request time and scores every route in one forward pass, returning the best route, a calibrated probability per route, and an escalate flag when it is not confident. Covers MSA, Gulf dialects and code-switched Arabic. Base jhu-clsp/mmBERT-base.", "approach": "", "training": "", "data": "Emirati and Saudi dialects, Arabic and English", "result": "60 routes: accuracy 83.4, macro-F1 86.4, category accuracy 92.4; 20 routes: 90.0; 40 unseen-domain decisions: 95.0, 38 of 40; at a threshold fitted for 95% accuracy on dev, 85.8% of test queries are answered automatically at 89.7% accuracy; 5.0% of answers change when the 60 routes are shuffled vs 4.7% for Jev 1.13", "notes": "zero-shot classification pipeline. Test set frozen before training with its SHA-256 published on the dataset card; written by a different model family than the training data; includes adversarial queries, routes held out from training and requests outside the route list; every system sees the same route lists; per-category results and a snippet to run your own router on the jev-ar-bench card.", "domain": "routing", "score": "83.4%, 60 routes", "latency": "", "benchmark": "Jev-AR Bench, Gulf-Arabic routing test set", "cost": "", "speedup": ""}
{"id": "leobitz-jev-berta-base-zeroshot-classifier", "name": "jev-berta-base-zeroshot-classifier", "author": "leobitz", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/leobitz/jev-berta-base-zeroshot-classifier", "base_model": "DeBERTa-v3-base", "params": "98M", "metric": "Validation accuracy against OpenJev, kev-0.8b and laya on the same set", "license": "", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "JevBerta: zero-shot variable-choice classifier for structured decision tasks; scores a candidate set conditioned on context and query, with a Jev-style interface over state, instructions and criteria", "approach": "", "training": "", "data": "", "result": "0.854 vs OpenJev 0.556, kev-0.8b 0.713 and laya 0.503; 198M parameters", "notes": "Encoder backbone plus set-attention over the candidate set: each candidate pair is encoded, the CLS representations are reassembled and scored by a masked-softmax MLP head, so each option is scored conditioned on the full set of alternatives; predict_jev maps state, instructions and criteria onto the candidate list.", "domain": "general", "score": "0.854", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ajh-code-jev-bonsai-compass", "name": "Jev-Bonsai-Compass", "author": "ajh-code", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/ajh-code/Jev-Bonsai-Compass", "base_model": "Ternary Bonsai 2 27B GGUF + OpenJev-derived decision adapter", "params": "27B ternary", "metric": "611/720, 84.9% on 720 text questions; mean 177 ms", "license": "CC-BY-NC-4.0", "paper": "", "stars": "", "likes": 0, "detail": "metric: 611 of 720, 84.9%, on 720 text questions at 7,844 MiB on a single RTX 5080, mean 177 ms; notes: vs full OpenJev FP8 reference 615 of 720, 85.4%, at 30,974 MiB on two GPUs; native server + Python API; cc-by-nc-4.0", "arch": "Ternary Bonsai 2 27B GGUF plus an OpenJev-derived decision adapter, with a native server and Python API: one GPU, ~7.8 GB", "approach": "fine-tune", "training": "", "data": "", "result": "611 of 720, 84.9%, on 720 text questions with a mean 177 ms per decision on a single RTX 5080, against 615 of 720, 85.4%, for the full OpenJev FP8 reference at 30,974 MiB on two GPUs", "notes": "The 720 questions are 120 each from AG News, Emotion, BoolQ, SNLI, ARC-Challenge and a 20-intent subset of Banking77; per task: science 117/120, banking 113/120, SNLI 102/120, Emotion 65/120; 327/328 on reused authored numeric regressions with compact calculator assistance, a system result rather than raw arithmetic; differs from full OpenJev by four answers, paired bootstrap interval -2.50 to +1.25 percentage points, which does not establish formal parity; cc-by-nc-4.0.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-ru-4b-lora-v2", "name": "Jev-RU-4B LoRA v2", "author": "nouton", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/nouton/jev-ru-4b-lora-v2", "base_model": "Qwen3.5-4B", "params": "4B", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Typed decision model for Russian, Ukrainian and Belarusian in the Jev style: a LoRA adapter for Qwen3.5-4B distilled from a Qwen3.8-27B FP8 teacher and trained on public ru/uk/be classification data plus synthetic rule-application tasks; question types are boolean with the native yes/no word forms, choice over 2-24 labeled options, and ordered score over 2-10 levels; the answer is one option letter whose token probability is the confidence, so there is no free-form text to parse", "approach": "", "training": "", "data": "", "result": "On 11,071 held-out examples, the test splits of every training source plus translated uk/be test sets plus Belebele ru/uk which was never used in training: the student scores 0.860 accuracy against 0.784 for the zero-shot base and 0.826 for the zero-shot teacher, with the same prompt", "notes": "", "domain": "general", "score": "0.860 vs base 0.784 / teacher 0.826", "latency": "", "benchmark": "11,071-example ru/uk/be held-out set", "cost": "", "speedup": ""}
{"id": "jev-spatial", "name": "Jev-Spatial", "author": "Fr0zencr4nE", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/Fr0zencr4nE/jev-spatial", "base_model": "Molmo2-ER", "params": "4.85B", "metric": "about as good as the AR baseline overall; within a few points on classification; lower error on metric estimation", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "System One spatial-intelligence model: every spatial task becomes a choice among a fixed set of options, so there is nothing to parse and no malformed output. English + Simplified Chinese card", "approach": "fine-tune", "training": "LoRA on the language model plus the unified head; vision encoder and projector frozen; 8× A800; released with the LoRA merged into the backbone", "data": "~72K QA pairs: SAT ~25K, VST-P ~22K, RefSpatial ~25K", "result": "About as good as the AR baseline overall, within a few points on classification and lower error on metric estimation. On 8 questions over one image: ~4.2× faster than answering one question at a time with AR generation; hits the pointing target ~2.7× more often than the AR baseline; same speed as a shared-image AR baseline", "notes": "Independent research project, not affiliated with or endorsed by TypeSafe, not trained on Jev outputs. Code at github.com/Fr0zenCrane/jev-spatial", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": "4.2× faster"}
{"id": "jevstyle-v3", "name": "Jev-Style-0.8B-Decision-v3", "author": "chaoliangUNSW", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/chaoliangUNSW/Jev-Style-0.8B-Decision-v3", "base_model": "Qwen3.5-0.8B", "params": "0.8B", "metric": "79.2% vs Laya typed 76.6% vs Jev 72.7% on 2,000 typed decisions", "license": "Apache-2.0", "paper": "", "stars": 5, "likes": 1, "detail": "", "arch": "Jev-Style v3: sub-billion multilingual Jev-style decision model, a 0.8B full fine-tune of Qwen3.5-0.8B - one state in, one pass out, a calibrated probability for every option of every question; 21 languages, 25,600-token input window versus Laya's 1,024, no letter cap on options, 0.53 GB 4-bit weights, GGUF and MLX ports included, Apache-2.0; successor to the 2B v1/v2 on jevstyle.com. Series lineage: v1 2B, Qwen3.5-2B-Base with LoRA r16; v2 2B, continued training, LoRA r32, 33.6M params, +4,380 rows; v3 0.8B, this model, full fine-tune, 752M text params", "approach": "logits", "training": "", "data": "Train split of LocalLLaMA/typed-decisions; MacJev-0.8B-Decision-Data released alongside", "result": "Pre-registered 25,600-token context claim passed; 77 options scored in one pass; evaluated on MASSIVE intent in 51 languages; Q4_K_M 0.53 GB build matches FP32 top-1 on 240/240 parity rows. On 2,000 typed decisions: 79.2% versus 76.6% for Laya typed and 72.7% for Jev zero-shot, Brier 0.046 versus 0.061 for Laya and 0.148 for Jev", "notes": "Website: jevstyle.com. Evaluated across 51 languages. The MacJev-0.8B round-2 training data is published: chaoliangUNSW/MacJev-0.8B-Decision-Data, 09-24 — 100K<n<1M rows, en/zh/multilingual, inputs up to 25,600 tokens, plus a MiniCPM5 fallback-arm pool and the one-H100 training-run evaluation results. Ports on HF: Jev-Style-0.8B-Decision-v3-GGUF and Jev-Style-0.8B-Decision-v3-MLX, both 09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "guoan-jev08", "name": "jev08", "author": "guoan1", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/guoan1/jev-qwen35-08b", "base_model": "Qwen3.5-0.8B-Base + LoRA r=8", "params": "0.8B", "metric": "Held-out hard-accuracy and ECE per split, published in the card", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": 1, "detail": "author: community; base: frozen; arch: template byte-identical to Open-Jev · hidden→1 · yes-direction; data: CC0; license: adapter · base weights not redistributed; result: never used for training or calibration", "arch": "Prefill-only decision model: each candidate rendered as an independent Yes/No judgment prompt, scalar head Linear initialised as W_lm['Yes']−W_lm['No'], readout at last non-pad token, max_length 768, no silent truncation; choice/score/noul in one batched forward", "approach": "head", "training": "LoRA lr 5e-5 / head lr 1e-4; soft-label CE + 0.1·Brier; 19,779 steps, ~4.6 h; AdamW, accumulation 4, seed 42", "data": "ZefanCai/Open-Jev release-v2-redistributable; temperature T=1.9190 fitted NLL-minimizing on 512 calibration rows only, frozen for eval", "result": "Held-out hard-acc/ECE per split published in card", "notes": "Explicitly 'not affiliated with Alibaba/Qwen or the Open-Jev authors'. Reuses the Open-Jev template.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-vision", "name": "JEVision", "author": "divyanshx11", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/divyanshx11/JEVision", "base_model": "Qwen3.5-0.8B-Base", "params": "0.8B", "metric": "77K-token image plus text proof point, structured Choice output", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "arch: KEV-style", "arch": "Experimental multimodal extension of the Jev decision workflow: image and text go through the same typed request/response path on the /v1/systemone route, publishing the visual adapter and pointer head that extend the public text checkpoint; the tested image path returns a structured Choice result, not free-form chat; Apache-2.0", "approach": "fine-tune", "training": "LoRA adapter on the 0.8B base", "data": "Banking77, BoolQ, AG News, MultiNLI, SST5, Yelp, TREC, DBpedia14, Amazon Reviews Multi, IMDB, Beans", "result": "Image-plus-text requests passed at 76,998 tokens in the POC", "notes": "Card tags: multimodal, vision, decision-model, long-context, typesafe. 1 like at sweep time", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevlet", "name": "Jevlet, NAME0x0", "author": "NAME0x0", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/NAME0x0/Jevlet", "base_model": "bge-small-en-v1.5", "params": "33.5M", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Small System-One decision model built to drive an always-on Windows command palette: base encoder bge-small-en-v1.5 with 33,508,992 parameters; 50 skills, each with typed argument questions answered from live options; one packed forward pass answers which action the command asks for, choice over the 50 skill names, whether it is risky to run without asking, a Noul question, which app, window, file or reminder it targets, choice over the live environment, and which span of the command is the text to use, choice over spans copied verbatim; block_bidir topology restarts positions after the shared state so questions do not see each other; a pointer head scores each option at its boundary token and per-kind temperatures calibrate choices and yes/no answers separately; the app runs an action on one keypress only when P(safe) >= 0.9 and every answer's confidence >= 0.75, otherwise it asks", "approach": "", "training": "", "data": "", "result": "Held-out human commands never used for training or span-rule tuning, TOPv2, MASSIVE and CLINC150 test splits mapped to the 50 skills, 23,279 commands: skill 98.0% accuracy, ECE 0.010 and 0.001 calibrated; risk Noul 99.8%, ECE 0.030; arguments 97.1%, ECE 0.012 and 0.004 calibrated", "notes": "Research replication of the Jev idea, not affiliated with TypeSafe; code, data pipeline and training notebook at github.com/NAME0x0/Jevlet.", "domain": "general", "score": "skill 98.0% / risk 99.8% / arguments 97.1%", "latency": "", "benchmark": "TOPv2 + MASSIVE + CLINC150 test splits", "cost": "", "speedup": ""}
{"id": "adambloebaum-laya-blackjack", "name": "Laya Blackjack", "author": "adambloebaum", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/adambloebaum/laya-blackjack", "base_model": "Laya full-model adaptation", "params": "varies", "metric": "legal-action preferences, next-hit bust probability", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 2, "detail": "arch: full-model adaptation for public-information blackjack research; legal-action preferences, next-hit bust probability and related decisions; Apache-2.0", "arch": "Full-model Laya adaptation for public-information blackjack research: legal-action preferences, next-hit bust probability and related decision heads", "approach": "fine-tune", "training": "", "data": "", "result": "Matched 95.95% of reference actions on a held-out 16,384-state test; a three-million-round benchmark found a continuous-play advantage over a basic-strategy heuristic, differences on fresh shoes were inconclusive, and average returns remained negative", "notes": "Model version 1.0.0; project and dashboard at github.com/adambloebaum/laya-blackjack; developed through broad simulation training, targeted practice on depleted shoes and improved reference targets", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "andyinqtr-laya-decision-plugin", "name": "Laya Decision-Plugin r15", "author": "AndyInQtr", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/AndyInQtr/laya-decision-plugin", "base_model": "Laya multilingual", "params": "322M", "metric": "tool/skill routing + allow/ask/block + act/escalate", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: typed decision classifier for coding agents - which tool, which skill, allow/ask/block, language, reply-or-act, with calibrated confidence and act/escalate readout; notes: zero-token, non-autoregressive, Core AI / Apple Silicon on-device; Apache-2.0; r15 series", "arch": "Combined Core AI decision model for coding agents, r15: answers which tool, which skill, allow/ask/block, language and reply-or-act with calibrated confidence plus an act/escalate readout, zero autoregressive tokens, on-device on Apple Silicon", "approach": "", "training": "", "data": "", "result": "Apache-2.0 on-device coding-agent decision plugin", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vtava-laya-memory-v23", "name": "Laya Integrated Memory V2.3", "author": "vtava", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/vtava/Laya-Integrated-Memory-V23-Decision", "base_model": "Laya with full-attention layers [0,3,...,27] replaced by TinyCeNN Integrated Memory V2.3", "params": "322M base", "metric": "53.12% teacher-student agreement; 0.96× speedup", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "arch: standalone export replacing full-attention layers [0,3,...,27] with TinyCeNN Integrated Memory V2.3; metric: 53.12% teacher-student agreement, 0.96× speed; notes: related card Laya-MemoryFusion-V3, a TinyCeNN research artifact from 09-23", "arch": "Memory-architecture swap for Laya: full-attention layers 0, 3, ..., 27 replaced by TinyCeNN Integrated Memory V2.3 units, exported standalone", "approach": "modified", "training": "", "data": "", "result": "53.12% teacher-student agreement with a 0.96x speedup - memory swap is not yet a win; a related Laya-MemoryFusion-V3 card, a TinyCeNN research stub with base model and dataset not recorded, was released 09-23", "notes": "The complete exported model, not an adapter; inference needs no laya package, load via standalone_decision.load_standalone and call runtime.decide; validation and fast-evaluation metadata in standalone_config.json Sister export: Laya-Integrated-Memory-V22 (prior version).", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sothiem-laya-kvp10k", "name": "Laya KVP-10K", "author": "sothiem", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/sothiem/laya-kvp10k-noul", "base_model": "Laya", "params": "421M", "metric": "key/value-match noul detection on IBM KVP-10K", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "Base convaiinnovations/laya at commit aa8c91c, 421M ModernBERT-large English checkpoint. Load with laya.Agent, tested with laya v0.3.11. Explicitly not a generative LLM: no chat templates, no generate call, and no causal-LM losses.", "arch": "Laya fine-tuned as a key/value match detector: one typed noul question per key-value pair, with options in fixed false-true order so the true-option probability is the decision. Fine-tuned on IBM KVP-10K via the pre-parsed alessandrorusso21/KVP10k mirror with the official RLCD recipe, REINFORCE with strictly proper scoring rules plus a GRPO-style group-mean baseline. Context budget 512, head budget 192, temperature 1.286.", "approach": "fine-tune", "training": "", "data": "", "result": "Apache-2.0 noul decision model for key/value matching", "notes": "", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nafiullah-laya-bn-ecom", "name": "Laya multilingual BN e-com voice", "author": "nafiullah", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/nafiullah/laya-multilingual-bn-ecom-voice", "base_model": "Laya multilingual", "params": "322M", "metric": "order_confirm 0.9946 generated test, 0.6957 hand-crafted frozen; BanglaEComIntent 0.8552", "license": "CC-BY-NC-SA-4.0", "paper": "", "stars": "", "likes": 0, "detail": "metric: order_confirm 0.9946 on generated test vs 0.6957 on hand-crafted frozen set; BanglaEComIntent intent 0.8552; notes: Bangla e-commerce voice decisions; cc-by-nc-sa; dataset nafiullah/bangla-ecom-voice-decisions, 09-23; approach: sft", "arch": "Laya multilingual fine-tuned for Bangla e-commerce voice decisions, order confirmation and intent routing", "approach": "fine-tune", "training": "", "data": "Generated and hand-crafted Bangla e-commerce voice sets; nafiullah/bangla-ecom-voice-decisions released", "result": "On nafiullah/bangla-ecom-voice-decisions: order_confirm 0.9946 accuracy on the generated test, 0.6957 on the hand-written frozen test, intent 0.8552 on the BanglaEComIntent test, escalate 0.9505", "notes": "Fine-tune of the 322M mmBERT laya-multilingual encoder; three typed decisions in one forward pass with no generation; MLX", "domain": "voice", "score": "", "latency": "", "benchmark": "Bangla E-commerce Voice Agent Decisions", "cost": "", "speedup": ""}
{"id": "androidli-laya-onnx-int4", "name": "Laya multilingual ONNX int4", "author": "androidli", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/androidli/laya-multilingual-onnx-int4", "base_model": "ModernBERT-base 22L x 768 + decision head", "params": "322M", "metric": "blk32 199MB", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "notes: first 4-bit ONNX of Laya multilingual, 4-bit even with the 256k vocab; 322M base; blk32 build 199MB", "arch": "First int4 ONNX export of Laya multilingual: ModernBERT-base, 22 layers, 768 dim plus decision head, 4-bit even with the 256k vocabulary; blk32 build at 199 MB", "approach": "", "training": "", "data": "", "result": "199 MB blk32 int4 build of the 322M multilingual model", "notes": "First public 4-bit ONNX build of Laya: existing public quantizations are int8 ONNX, GGUF and CoreML/MLX, and nobody had quantized the 256k vocabulary table; inference-only weights from ONNX Runtime weight-only quantizers", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "techtheist-laya-onnx", "name": "Laya ONNX int4/int8, dynamic length", "author": "techtheist", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/techtheist/laya-onnx", "base_model": "laya + laya-multilingual, quantized", "params": "322M", "metric": "int4 / int8 ONNX, dynamic sequence length", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 3, "detail": "notes: unofficial ONNX exports of Laya and Laya multilingual with dynamic sequence length, int4 and int8, NLI-style typed decisions; made for Engram Alpha, which uses them as an optional contradiction judge; Apache-2.0", "arch": "Unofficial int4/int8 ONNX exports of Laya and Laya multilingual with dynamic sequence length, used by Engram Alpha as an optional contradiction judge", "approach": "", "training": "", "data": "", "result": "Apache-2.0 ONNX builds for contradiction judging", "notes": "Made for Engram Alpha, which uses them as an optional contradiction judge; English int4 at 262 MB and int8 at 554 MB, plus a multilingual int8; int4 is MatMulNBits block 32 symmetric on every MatMul and the embedding Gather; weights unchanged from the upstream checkpoints apart from quantization", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "canbingol-laya-tr-mmlu-10k", "name": "Laya Turkish MMLU typed decisions, 10k", "author": "canbingol", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/canbingol/laya-typed-decisions-turkish-mmlu-10k", "base_model": "Laya", "params": "varies", "metric": "0.365 acc on LocalLLaMA/typed-decisions, model-index", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "notes: fine-tune on Turkish MMLU cast as typed decisions, 10k; model-index reports 0.365 accuracy + Brier on the LocalLLaMA/typed-decisions benchmark; dataset canbingol/mmlu_typed_decision, 09-23", "arch": "Laya fine-tuned on 10k Turkish MMLU questions reformulated as typed decisions", "approach": "fine-tune", "training": "", "data": "", "result": "The card reports accuracy 0.365 and Brier 0.383 from the base checkpoint's numbers on LocalLLaMA/typed-decisions, a 400-case test set, explicitly noting they reflect that benchmark, not this fine-tune's Turkish MMLU performance", "notes": "10k-example dataset canbingol/mmlu_typed_decision: Turkish MMLU converted into state, question, gold triples, choice-type questions with per-option criteria", "domain": "education", "score": "", "latency": "", "benchmark": "LocalLLaMA/typed-decisions", "cost": "", "speedup": ""}
{"id": "thaitea-laya-vision-201m", "name": "Laya Vision 201M", "author": "thaitea", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/thaitea/laya-vision-201m", "base_model": "SmolVLM-256M-Instruct trimmed to 20/30 layers", "params": "201M", "metric": "typed decisions on images", "license": "CC-BY-NC-SA-4.0", "paper": "", "stars": "", "likes": 0, "detail": "training: 2 hours on QA + game frames; notes: found by an autoresearch loop; sister 237M variant; cc-by-nc-sa-4.0; approach: sft", "arch": "Vision decision model: SmolVLM-256M-Instruct trimmed to 20 of 30 layers, 201M parameters answering typed decisions directly about images; a sister 237M trim was named in the card but is not published, 404 as of 2026-09-25", "approach": "fine-tune", "training": "Two hours of fine-tuning on QA and game frames; the project was discovered by an autoresearch loop", "data": "", "result": "201M-parameter image decision model; a 237M sister trim also released", "notes": "", "domain": "multimodal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wuyuanpei-laya-browser", "name": "laya-browser, wuyuanpei", "author": "wuyuanpei", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/wuyuanpei/laya-browser", "base_model": "Laya", "params": "varies", "metric": "6 tasks, 12 decisions, median 22 ms, demo", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: Laya fine-tuned as a browser-agent decision head, drop-in replacement for Jev; notes: Mind2Web; 6 tasks, 12 decisions, median 22 ms per the demo GIF; Apache-2.0", "arch": "Laya fine-tuned as the decision head of a browser agent: drop-in replacement for Jev on web tasks, Mind2Web, median 22 ms per decision in the demo", "approach": "fine-tune", "training": "", "data": "", "result": "v10s drives headless Chromium through the jev-ultrafast tasks with one encoder pass per step: 6 tasks, 12 decisions, median 22 ms per decision; out of the box Laya was near chance at browser decisions, top-1 0.10 among ~45 candidates", "notes": "Drop-in replacement for TypeSafe Jev as the browser-agent decision head", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "adkid-laya-cn-a", "name": "laya-cn-a", "author": "Adkid", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/Adkid/laya-cn-a", "base_model": "Laya multilingual", "params": "421M", "metric": "Binary intent and choice accuracy on reused Chinese test subsets, selected V3 checkpoint versus stock Laya, fixed-seed replications", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "Internal experiment V3 seed42, released as Release A, the first public Chinese adaptation; explicitly not an official Laya release. Apache-2.0. Outputs are not calibrated for arbitrary domains and the frozen action head is not a trustworthy autonomous-action or abstention signal.", "arch": "Community Chinese intent adaptation of Laya multilingual: full FP32 post-trained weights loaded via a supplied PyTorch decision-model wrapper, one predict call returning choice, binary noul and ordered score probabilities. Model files are SHA-verified on load; context/head budgets 1024/512 with long-state truncation reported; at most 16 questions per call.", "approach": "fine-tune", "training": "", "data": "", "result": "On the author's reused Chinese test subsets, binary intent 232/258 and choice 188/222 for the selected V3 checkpoint versus 177/258 and 96/222 for the stock historical Laya; fixed-seed replications scored 231/258 and 229/258, three-seed means 89.41% and 84.23%. Subsets are reused across seeds, not independent.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pieroot-laya-priority-coreml", "name": "laya-priority-coreml", "author": "pieroot", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/pieroot/laya-priority-coreml", "base_model": "Laya", "params": "421M", "metric": "zero-shot fixture accuracy; logit and argmax agreement vs source PyTorch", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Core ML conversion of the English Laya checkpoint, rev 55cf4c4: masked-choice graph generalized from binary to 2–4 options; to-do prioritization and time-slot selection; returns pre-temperature logits", "approach": "", "training": "", "data": "", "result": "Zero-shot on the original 48-item balanced priority fixture: 17/24, 70.8%, English and 15/24, 62.5%, machine-translated Korean, both below the 80% accuracy and 70% recall gate; the 2-4 option scheduling variant is parity-checked only, not accuracy-gated. Core ML versus source PyTorch: 0.0 max-absolute logit error on the 49-case suite, argmax agreement 49/49 in the first run and 48/49 on the repeat with one near-zero-margin case", "notes": "Shipped at the consuming app's explicit request with a rule-based fallback for degraded cases; exports only the masked-choice graph, raw pre-temperature logits over up to 4 options, unused slots forced to -10000; max_len 512, head 192; weights by ConvAI Innovations under Apache-2.0, graph export by this repo", "domain": "general", "score": "70.8% EN, 62.5% KR zero-shot, below the 80% gate", "latency": "", "benchmark": "48-item balanced EN/KR priority fixture; 49-case Core ML parity suite", "cost": "", "speedup": ""}
{"id": "16sulphur-laya-prompt-guard", "name": "laya-prompt-guard", "author": "16sulphur", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/16sulphur/laya-prompt-guard", "base_model": "Laya", "params": "421M", "metric": "base to fine-tuned held-out accuracy", "license": "", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "Laya fine-tuned for the gutcheck prompt-guard pack: screens text sent to LLMs for prompt-injection and jailbreak attempts; fixed 20% held-out slice, pack test sets unused", "approach": "fine-tune", "training": "", "data": "", "result": "Held-out accuracy after the RLCD fine-tune with gutcheck 0.0.1: injection 0.468 to 0.954 on 109 held-out rows, jailbreak 0.809 to 0.994 on 320; a fixed 20% holdout slice was never trained on and the pack's own test sets were left unused", "notes": "Trained on the deepset/prompt-injections and jackhhao/jailbreak-classification splits, both Apache-2.0; reliable only on the two prompt-guard questions worded as in the pack", "domain": "general", "score": "injection 0.468 to 0.954, jailbreak 0.809 to 0.994", "latency": "", "benchmark": "gutcheck prompt-guard held-out slices, injection 109 and jailbreak 320 rows", "cost": "", "speedup": ""}
{"id": "rehman-ali-laya-tetris", "name": "laya-tetris", "author": "rehman-ali", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/rehman-ali/laya-tetris", "base_model": "Laya multilingual", "params": "322M", "metric": "held-out board accuracy; average lines per game; teacher agreement", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Laya multilingual, mmBERT-base 322M, fine-tune that plays Tetris: two choice questions per piece, rotation 4-way and column 10-way; no search or lookahead; 6,000 held-out boards", "approach": "fine-tune", "training": "", "data": "", "result": "No search, no lookahead: each piece costs two typed choice questions, a 4-option turn and a 10-option column. On 6,000 held-out boards turn accuracy moves 33.3% to 84.5% and column 10.7% to 82.6%. Over 5 games of 8,000 pieces with the safety mask off and disjoint seeds it averages 3,196.2 lines, never tops out, and agrees with the El-Tetris teacher on 79.5% of placements, where the untuned base tops out after 25 pieces", "notes": "Teacher is a script scoring all about 34 placements with Dellacherie's six board features at the published El-Tetris weights, target the softmax of score over 3; the loss is the expected log score, the log term of Laya's own RLCD reward; fine-tune of laya-multilingual, mmBERT-base 322M; code at github.com/RehmanaliMomin/TetrisGame_Laya", "domain": "general", "score": "3,196.2 avg lines, 79.5% teacher agreement", "latency": "36.9 ms p50, 43.4 ms p95 per piece", "benchmark": "6,000 held-out boards; 5 games of 8,000 pieces, seeds disjoint from training", "cost": "", "speedup": ""}
{"id": "aimultiple-laya-typed-decisions-browser-ft", "name": "laya-typed-decisions-browser-ft", "author": "AIMultiple", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/AIMultiple/laya-typed-decisions-browser-ft", "base_model": "Laya", "params": "421M", "metric": "Browser tasks completed out of 50, held-out readable tasks out of 20, held-out single decisions out of 88, stock Laya versus two fine-tunes", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "Both fine-tunes are published so the result can be checked; the second fine-tune is at the repository root, the first in first-fine-tune. Stock Laya ran on the same day and hardware as each fine-tune. Tested with laya 0.3.5; load via laya.load with subfolder for the first fine-tune. Apache-2.0.", "arch": "Two fine-tunes of Convai Innovations' Laya typed-decisions on browser-agent decisions for the AIMultiple decision-models benchmark, trained and scored on requests from the browser-use jev-ultrafast runtime: page text, a numbered list of visible controls, an operation question and target questions.", "approach": "", "training": "", "data": "", "result": "Browser tasks completed: 0/50 stock Laya, 1/50 first fine-tune, 3/50 second; held-out tasks Laya can read, 0/20 for both; held-out single decisions 17/88 stock, 66/88 first, 25/88 second; called done on a page that still needed an action, 53, 2 and 5. Neither change exceeds what chance explains, exact McNemar p = 0.25 and p = 1.0.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "malkuth", "name": "Malkuth: small multilingual decision models, Korean focus", "author": "newfull5", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/dhtocks/malkuth-4b", "base_model": "Kev LoRA on frozen Qwen3.5-Base", "params": "4B, 2B; 33.8M trainable, 124 MB adapter for the 4B", "metric": "0.724 held-out accuracy over 29 suites", "license": "", "paper": "", "stars": 1, "likes": 1, "detail": "", "arch": "Malkuth-4B and Malkuth-2B are small multilingual decision models for classification with a focus on Korean that return probabilities over a fixed set of choices instead of generating text; each request carries its own criteria and can hold several independent questions over the same input. The 4B is a LoRA adapter trained on Kev over a frozen Qwen3.5-4B-Base, served through Kev and usable through the TypeSafe System One API. Supports choice with up to 255 options, noul, and score; questions are evaluated independently so they cannot see each other's answers; probabilities are temperature-calibrated on a held-out validation split", "approach": "", "training": "LoRA on Kev", "data": "", "result": "29 suites and about 129,000 questions, every model answering the same items with Jev measured through its API: Jev API 0.754 held-out accuracy versus Malkuth-4B 0.724, Malkuth-2B 0.703 and Kev-9B 0.700; on all 29 suites Malkuth-4B leads at 0.794 versus Jev 0.790; held-out Brier 0.385 versus Jev 0.355", "notes": "Weights at dhtocks/malkuth-4b and dhtocks/malkuth-2b Code: https://github.com/newfull5/malkuth.", "domain": "general", "score": "0.724 held-out acc; 0.794 all-suites, leading Jev 0.790", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openjev-minicpm5-2b-lora-phase1", "name": "OpenJev MiniCPM5-2B LoRA Phase 1", "author": "nicolasembleton", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/nicolasembleton/openjev-minicpm5-2b-lora-phase1", "base_model": "MiniCPM5-2B", "params": "2B", "metric": "Overall accuracy on the frozen n=900 test, zero-shot versus LoRA; Noul recall; OOD n=300 accuracy", "license": "Apache-2.0", "paper": "", "stars": 2, "likes": 0, "detail": "arch: r=16, alpha=32, q/k/v/o/gate/up/down_proj; result: +23.6 pp · +61.8 pp · +23.7 pp", "arch": "PEFT LoRA adapter on MiniCPM5-2B for closed-set first-token scoring on OpenJev Choice, Noul, and Score fields — one forward pass, candidate-token logits, no free-form generation; causal LM loss only on the gold index-surrogate token after the Answer marker; trained on a stratified 6,000-item subset of ZefanCai/Open-Jev-v1.1 on a Modal A10G in 59.5 minutes.", "approach": "logits", "training": "", "data": "", "result": "Frozen panel, never in train: test n=900 overall accuracy 0.467 zero-shot to 0.702 LoRA; Noul recall 0.118–0.735; OOD n=300 accuracy 0.473–0.710.", "notes": "Hugging Face new-model sweep 2026-09-24. The org added phase-1-onnx and phase-1-merged exports, plus the phase-1b shell-safety variant openjev-minicpm5-2b-shell-safety-phase1b, ONNX + merged, all 09-24.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "plumbline", "name": "Plumbline-0.6B", "author": "dsup2026", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/dsup2026/plumbline-0.6b", "base_model": "Qwen3-Reranker-0.6B", "params": "0.6B", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Rule-violation scorer loosely inspired by Jev, running on a CPU: give it any text and any rule in plain English and it returns a score for the proposition that this text breaks the rule; a 0.6B LoRA adapter over Qwen3-Reranker-0.6B; positioned as a ranker for a review queue, not an auto-decider", "approach": "", "training": "", "data": "", "result": "Every training label was machine-verifiable, RLVR without the RL, and the training set was orthogonal to the evaluation sets by kind: no semantic rule in the training data; on 160 Claude-authored conduct rules, 1,920 items, the two-call path scores AUC 0.968, 95% CI 0.955-0.979, F1 at the optimal threshold 0.969, versus one call 0.771, the base reranker 0.718 and Qwen3-Reranker-4B 0.732; on the LegalBench contract clauses, 14 rules and 1,927 expert-labeled items, one and two calls both AUC 0.871, F1 0.863, gap 0.043 versus 4B's 0.935; 82% of conditional-compliant texts comply vacuously because the condition never fires", "notes": "The two-call difference is almost entirely conditional rules: 0.139 in one call versus 0.926 in two.", "domain": "general", "score": "AUC 0.968 two-call on conduct rules", "latency": "", "benchmark": "Claude-authored conduct rules; LegalBench contract_nli", "cost": "", "speedup": ""}
{"id": "joyfox-qwen35-08b-jev", "name": "Qwen3.5-0.8B-JEV", "author": "joyfox", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/joyfox/Qwen3.5-0.8B-JEV", "base_model": "Qwen3.5-0.8B", "params": "0.8B", "metric": "argmax agreement: choice 85.08% / noul 95.84% / score 90.06%; Brier 0.0598 / 0.0125 / 0.0319; mean KL 0.13922 / 0.01681 / 0.05855", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Compact decision model derived from Qwen3.5-0.8B: accepts a state plus one or more dynamic questions and returns a probability distribution over each question candidates. Must be loaded with the JEV decision-model wrapper; standard causal-LM, vLLM, Ollama and GGUF loaders do not expose the decision head. Query/key decision head with dimension-128 projections; rows execution mode; max 1,024-token input; BF16", "approach": "head", "training": "", "data": "Eval table over ~53K typed questions: 20,726 choice, 20,912 noul, 11,026 score", "result": "Eval table over ~53K typed questions: argmax agreement 85.08% on choice, 95.84% on noul, 90.06% on score, with the lowest Brier on noul 0.0125 and the highest on choice 0.0598", "notes": "Inference code and examples at github.com/joyfoxai/jev-inference; en/zh/multilingual", "domain": "general", "score": "choice 85.08 / noul 95.84 / score 90.06 argmax agreement", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reflex-s1", "name": "Reflex-S1", "author": "gowtham-source", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/Gowtham25/reflex-s1", "base_model": "", "params": "23.2M–82.8M", "metric": "BFCL tool selection 95.67%", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": 1, "detail": "author: Gowtham; base: HF: Gowtham25/reflex-s1; arch: MoR, up to 5 recursion steps; notes: 09-24", "arch": "Mixture of Recursions + sparse Top-2/4 MoE expert selection; trained to output a typed decision in one forward pass with an explicit abstention option", "approach": "from-scratch", "training": "Trained from scratch; 'open alternative to Jev'", "data": "", "result": "p50 6.6 ms warm / 11.6 ms cold on L40S; ECE-15 < 0.060; out-of-distribution abstention", "notes": "Newest entrant: smallest published 'System One' family, explicitly pitched as a Jev alternative with calibrated probabilities and a refusal mode. Code: https://github.com/gowtham-source/reflex-s1.", "domain": "general", "score": "95.67% BFCL", "latency": "p50 6.6 ms", "benchmark": "BFCL", "cost": "", "speedup": "2× faster"}
{"id": "rev-qwen3-family", "name": "Rev-style prefill-only decision models on Qwen3", "author": "jaswanthsanjay88", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/jaswanthsanjay88/rev-4b", "base_model": "Qwen3-4B-Base + LoRA for rev-4b; family: rev-0.5b, rev-vision", "params": "0.5B / 4B / vision", "metric": "", "license": "", "paper": "", "stars": "", "likes": 1, "detail": "arch: prefill-only, LoRA on Qwen3 bases; result: card-level, no table", "arch": "Rev-style prefill-only decision model in the Jev shape: LoRA adapters on Qwen3 bases; rev-4b card: 'A Rev-style prefill-only decision model based on Qwen/Qwen3-4B-Base'; tags include decision-model, jev, typesafe, lora, prefill-only", "approach": "fine-tune", "training": "", "data": "", "result": "", "notes": "Three checkpoints rev-0.5b, rev-4b and rev-vision created 09-24, cards title-level with no evaluation table; distinct from the sister row rev-decision, same author, ModernBERT-class zero-shot pipeline Sister LoRA: jaswanthsanjay88/jev-0.5b (0.5B).", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": "", "notes_": "Card re-checked 2026-09-26: still no evaluation table; the 4B card ships adapter_model.safetensors plus head.pt pointer-readout weights and a /v1/systemone endpoint"}
{"id": "shgao-rsi-jev", "name": "RSI-Jev v1.0, 2B", "author": "shgao", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/shgao/rsi-jev-v1.0-qwen3.5-2b", "base_model": "Qwen3.5-0.8B / Qwen3.5-2B-Base + tower fine-tune + option scoring head", "params": "0.8B / 2B", "metric": "typed-decisions 0.6525, reversed-order 0.6525, order-invariant; MMLU-Pro 1k 0.355, 2B seed 17", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "RSI-Jev: typed decision models, not prompted LLMs - the whole tower is fine-tuned together with a cross-attention option-scoring head that looks from the decision position at every option span at once, so a score depends on the options it competes with, not only on itself; the objective is soft cross-entropy against a teacher full distribution, so probabilities carry information rather than being a softmax over a hard argmax; one forward pass returns a calibrated probability for every choice, noul and score option, with no generated text; tower fine-tuning on the Qwen3.5 0.8B and 2B bases, speaking the Jev HTTP API directly", "approach": "head", "training": "Trained on distributions, not labels, on a separate synthetic corpus; the benchmark train split is never touched; three seeds were run, seed 17 pre-registered as primary", "data": "Separate synthetic decision corpus; typed-decisions and MMLU-Pro 1k used for reporting", "result": "Seed-17 2B reads 0.6525 on typed-decisions and the same 0.6525 with options reversed, an explicit order-invariance check, and 0.355 on MMLU-Pro 1k. Reference points: 0.428 untrained base, 0.5185 majority baseline, 0.704 dataset ceiling, Jev 0.727. Where the teacher is confident it reads 0.784 at gold max-probability 0.67 and above and 0.886 at 0.9 and above. All 2,000 confidence bins land at or above their stated value and zero decisions are confidently wrong; acting on the 40% it is surest of is right 80.1% of the time, 88.0% at the top 20%, 65.1% overall against 51.9% majority", "notes": "Project repo github.com/Shanghua-Gao/RSI-Jev with the version record, full recipe in meta.json and the serve README; released 2026-09-24; kernel stack fla-0.5.2 with torch 2.7.1+cu128. Three-seed mean 0.6622, sd 0.011, on the 400-document, 2,000-decision LocalLLaMA/typed-decisions test; trained on 6,977 documents from n4ze3m/typed-decisions-synth with soft cross-entropy, 1,500 steps, batch 16, about 13 min per seed on an H100; the option scorer runs in fp32 after bf16 autocast produced logits up to 12,096; score-mode Brier 0.111 vs 0.335 untrained", "domain": "general", "score": "", "latency": "about 10 ms per decision after the document read on a DGX Spark; 32 decisions on one 404-token document take 418 ms together vs 2,774 ms apart; both sizes resident in 5.1 GiB", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shgao-rsi-jev-v1-0-qwen3-5-0-8b", "name": "rsi-jev-v1.0-qwen3.5-0.8b", "author": "shgao", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/shgao/rsi-jev-v1.0-qwen3.5-0.8b", "base_model": "Qwen3.5-0.8B-Base", "params": "0.8B", "metric": "", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Qwen3.5-0.8B-Base with fine-tuned tower and trained option-scoring head; answers choice, noul and score questions about a document in one forward pass; Jev HTTP API; seed 17, typed-decisions 0.6175, reversed order 0.621", "approach": "head", "training": "", "data": "", "result": "0.8B seed-17 reads 0.6175, 0.621 reversed, three-seed mean 0.6140 with sd 0.007; MMLU-Pro 1k 0.269 vs 0.186 untrained; about 10 ms per decision on a DGX Spark and roughly 1.2x faster than the 2B, which the routing sweep says is not enough to build a cascade; 1.45 GiB bf16 resident", "notes": "CPU-only answers are identical to the GPU within 0.003 probability; the 5-question demo runs in 8.1 s on CPU against 142 ms on the Spark", "domain": "general", "score": "", "latency": "", "benchmark": "LocalLLaMA/typed-decisions, 400 documents, 2,000 decisions, pooled top-1", "cost": "", "speedup": ""}
{"id": "jev-dllm", "name": "Shared Yes/No decision models on a diffusion language model", "author": "zhouzihao11", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/SEU-ZZH/Shared-YesNo-Qwen3-0.6B-S0", "base_model": "dllm-hub Qwen3-0.6B masked diffusion base", "params": "0.6B", "metric": "Six-task accuracy and ECE", "license": "Apache-2.0", "paper": "", "stars": 2, "likes": 0, "detail": "approach: Masked diffusion with shared Yes/No readout", "arch": "Jev-style decisions on a masked diffusion language model: a mask is placed next to every candidate and shared Yes/No weights read structured probabilities in one forward pass, supporting dynamic choice, binary judgment, and ordinal ratings without running the model once per candidate; inference computes only the needed Yes/No projections, so 77 candidates still cost one forward pass; training uses cross-entropy plus a weight-0.25 normalized RPS term that exploits the rating order", "approach": "diffusion", "training": "Full-parameter, three epochs on 40,000 decisions for S1, checkpoint at step 1,300 selected by dev mean KL", "data": "S0 and S1 decision datasets with six evaluation suites", "result": "S1 six-task average accuracy 60.54% versus 60.48 for S0; S1 leads the base MDLM on Banking77 at 60.49 versus 24.09, typed-decisions at 52.25 versus 41.35, and SST5 at 42.58 versus 36.52, while Laya leads AG News at 92.87; batch-32 inference on an RTX A6000 drops per-decision forward time from 25.44–6.61 ms, a 3.85× throughput gain to 151 decisions per second", "notes": "S0 and S1 checkpoints are on Hugging Face under SEU-ZZH, Apache-2.0, not LoRA and not quantized; the Chinese research blog walks through the full recipe and calibration tables Code: https://github.com/zhouzihao11/jev-dllm.", "domain": "science", "score": "", "latency": "", "benchmark": "Banking77", "cost": "", "speedup": ""}
{"id": "tamev", "name": "TAMEV family", "author": "Tamkimd", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/Tamkimd/tamev-large-qwen3.5-4b", "base_model": "Qwen3.5-4B", "params": "4.4B active", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Family of five System One decision models, billed as an open-source, strictly typed, calibrated drop-in for Jev, Kev, Laya and SemIf; the Large tier is incontext_causal on a Qwen3.5-4B backbone with 4,440M active parameters, 16.9 GB in FP32 and 4.2 GB as an INT8 artifact, calibrated temperature 2.406; the readout is claimed to have exact mathematical permutation invariance, zero drift under option reordering", "approach": "", "training": "", "data": "", "result": "The card's own measurements on a held-out multi-domain decision suite: 92.00% top-1 accuracy, 98.00% top-3, Brier score 0.2018, ECE 0.1121; median latency 574.18 ms, 1.2 req/s, on commodity hardware", "notes": "Five sizes published 09-24; the SOTA-for-large-class claim is the card's own and the benchmark suite is not named or linked.", "domain": "general", "score": "92.00% top-1 claimed", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vtx-jev-1", "name": "VTX-JEV-1", "author": "VTXAI", "date": "2026-09-24", "category": "model", "url": "https://huggingface.co/models/VTXAI/VTX-JEV-1", "base_model": "vtx-embed-7M dequantized embedding table", "params": "12.66M", "metric": "73.1% LF4 vs 72.6% FP32", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Smallest known full-corpus Jev-style model: 12.66M-parameter non-autoregressive System One over a dequantized 7M embedding table; up to 255 Choice options, 2–10 Score levels, binary Noul; LF4 block-wise quantization in an 8.0 MB file; typed response helpers response.choices, response.scores and response.nouls; CPU and CUDA, no API key or remote service", "approach": "fine-tune", "training": "2 epochs on the full 655,806-row SargeDev/jev-distill-corpus-v3", "data": "SargeDev/jev-distill-corpus-v3", "result": "73.1% LF4 accuracy vs 72.6% FP32 on 3,000 held-out JEV cases; 2.86× faster evaluation than FP32 on a T4; 8.0 MB LF4 file, 6.33× smaller than the FP32 file", "notes": "Native blockwise LF4 storage in the released weights; Apache-2.0", "domain": "general", "score": "73.1%", "latency": "", "benchmark": "", "cost": "", "speedup": "2.86× faster"}
{"id": "certo", "name": "certo-decision-model", "author": "altslate", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/altslate/certo-decision-model", "base_model": "ModernBERT; v2.x on ModernBERT-base ~150M", "params": "421M", "metric": "JevBench public, v2.1: easy 0.583 / original 0.389 / hard 0.297; v2.2 Ettin-1B one-epoch: 0.312 / 0.319 / 0.297, ECE 0.33 / 0.33 / 0.42", "license": "MIT", "paper": "", "stars": "", "likes": 1, "detail": "arch: certo; v2.1 base ModernBERT-base ~150M; v2.2 base Ettin-encoder-1B, one-epoch undertrained, for continuation of training", "arch": "Decision-making model with calibration. v2.1, the small member: a generic non-generative decision model on ModernBERT-base, about 150M, scoring supplied runtime options against a state and returning a calibrated probability distribution in one forward pass, no text generation, trained 3 epochs on the altslate/certo-decisions-v2 corpus of 971k rows, temperature-calibrated. v2.2, published 09-25: the same recipe on the jhu-clsp/ettin-encoder-1B backbone, a ~1B ModernBERT-architecture encoder, but run only one epoch and published as an undertrained checkpoint for continuation of training, not for use, with temperature T=1.33", "approach": "head", "training": "Fine-tuning", "data": "", "result": "JevBench public tasks via the official certo_local adapter, calibrated: v2.1 accuracy easy 0.583 / original 0.389 / hard 0.297, by type choice 0.31, yes-no 0.50, score 0.50, best of their small encoders on raw accuracy, about $0.001 per 1k decisions; v2.2 after one epoch scores 0.312 easy / 0.319 original / 0.297 hard with ECE 0.33/0.33/0.42, worse than the 150M v2.1, final training loss 0.458 versus the base's 0.34 after three epochs, so the result is dominated by under-training rather than model size", "notes": "Small independent ModernBERT decision model. v2.x: altslate/certo-decision-v2.1 and -v2.2, MIT, library 'certo'; code github.com/AltSlate-Labs/certo; v1 remains at altslate/certo-decision-model; training corpus altslate/certo-decisions-v2; sibling altslate/certo-r1-qwen3-4b, 09-25.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "clm", "name": "Contrastive Language Models", "author": "Jacky Kwok; Hangoo Kang; Tarun Suresh; Jon Saad-Falcon; Marco Pavone; Christopher Ré; Azalia Mirhoseini", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/Contrastive-LM/CLM-v0.1-8B", "base_model": "Qwen3-8B", "params": "8B", "metric": "DeepSWE 81.6%, Terminal-Bench 2.1 87.6%; 13× faster with ~1k cached candidates", "license": "Apache-2.0", "paper": "", "stars": 859, "likes": 201, "detail": "approach: state, questions; approach: Requests written for the TypeSafe API replay unchanged as client.system_one; an in-process engine ranks free-form candidate sets without a server", "arch": "Contrastive System One model: state and action encoders, a frozen LLM backbone plus a 20M-parameter projection head learn bidirectional InfoNCE, candidate actions scored by cosine similarity to the state embedding. The CLM-8B release serving the model behind a TypeSafe-compatible API: vLLM serves Qwen3-8B as a pooling encoder with prefix caching, clm-serve hosts the 75MB reference head, and one primitive - scoring a candidate against a state - underlies typed noul, choice, and score questions, best-of-N ranking, tool routing, and retrieval shortlisting; ships a web playground, the T-Rex benchmark, and fine-tuning scripts", "approach": "head", "training": "", "data": "", "result": "Zero-shot on par with Jev on T-Rex, BFCL v4 tool calling, WikiRacing, and Super Mario at up to 9× lower latency; as a verifier over 38 held-out DeepSWE tasks and 30 held-out Terminal-Bench 2.1 tasks the fine-tuned CLM reaches 81.6% and 87.6% while Jev is below pass@1, at 4.1–5.7× lower latency on H100; with ~1k candidates the cached state and action embeddings make CLM 13× faster than Jev", "notes": "Python 3.10+, Linux, NVIDIA GPU; install pulls PyTorch and vLLM Folds the two HF artifacts: CLM-v0.1-8B is the 20M-parameter projection head learned with bidirectional InfoNCE on ~60M Nemotron Q&A pairs, ~30M synthetic hard negatives and ~1M agentic trajectories, over a frozen Qwen3-8B encoder, downloaded by clm-serve on first run, and with 40% Q&A replay in post-training the held-out Nemotron hard-negative top-1 stays at 68.5%, versus 52.1% pre-train only and 56.2% on agentic data alone; deepswe-clm-heads-8k ships the fine-tuned head, the held-out task list and precomputed embeddings over 8k sampled candidate solutions, reproducing the 81.6% held-out-38 verifier rate, with companion datasets deepswe-clm-embeddings-8k and deepswe-clm-train-embeddings-8k. Stanford project; posted on Notion 2026-09-23, hit HN front page 2026-09-24 with 128 points; multimodal CLM-35B in training; Discord community Code: https://github.com/Contrastive-LM/CLM.", "domain": "general", "score": "81.6%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decision-2b-preview", "name": "Decision 2B, FlyMyJev preview", "author": "flymy-ai", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/flymy-ai/decision-2b-preview", "base_model": "MiniCPM5-2B", "params": "2.28B total, 26.2M trained", "metric": "accuracy on the 231-decision public JevBench subset", "license": "Research preview; code licensing unresolved per the repo", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Frozen checkpoint minicpm5_reduced_v16_4k_v59 bundling a LoRA adapter, structural-token rows, a pointer head and executable inference code; outputs are the trained pointer head's softmax with a frozen temperature fitted on the checkpoint's own transfer replay and known-chance development examples, with no public benchmark labels used; all outputs are native decision probabilities, with no generated answer tokens, token logprobs, external model calls, option-order ensembles or benchmark-ID lookup; supplied for independent benchmark evaluation and explicitly not an official JevBench result or a validated production release", "approach": "fine-tune", "training": "LoRA on MiniCPM5-2B plus a separate pointer-head readout; temperature fit on the checkpoint's own replay", "data": "FlyMyJev training mixture; weight availability, code licensing and training-source conditions are separate facts flagged for review in the repo", "result": "Public JevBench subset of 231 decisions: 174 correct overall, 75.32%; Easy 48 of 48, Standard 60 of 72 at 83.33%, Hard 66 of 111 at 59.46%; measured on one RTX 4090 with Torch 2.8.0, transformers 4.57.6 and peft 0.15.2; the 09-24 JevBench v1.4.1 capture listed Decision 2B at 56.4 with an estimated $0.018 per 1k decisions, before the official table was re-scored to its current 77-system form", "notes": "Carried its own numerical-compatibility review: one shared normalization scale must satisfy every stored six-decimal probability interval to within two float32 epsilons; the full 534-decision suite includes held-out tasks the authors did not have", "domain": "general", "score": "75.32%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "decision-fast-preview", "name": "Decision Fast 0.6B", "author": "flymy-ai", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/flymy-ai/decision-fast-preview", "base_model": "Qwen3-0.6B-Base + LoRA r=64 alpha=128 + 3.0M pointer decision head", "params": "0.6B; 606.7M total, 10.6M trained", "metric": "64.07% on the 231-decision public JevBench subset", "license": "undisclosed", "paper": "", "stars": "", "likes": 0, "detail": "base: Qwen3-0.6B-Base + LoRA + pointer head; arch: native pointer probabilities, frozen temperature; data: provenance-review.json; result: 231/231 parseable; license: code licensing and training-source conditions unresolved", "arch": "FlyMyJev evaluation preview, frozen checkpoint qwen3_06b_headfirst_ep2a_v53: Qwen3-0.6B-Base + LoRA, r64, alpha 128, plus a trained 3.0M-parameter pointer decision head; the download contains the LoRA adapter, structural-token rows, pointer head and executable inference code, the pretrained base is fetched separately at the exact revision in model.json; all outputs are native decision probabilities from the pointer head's softmax with a frozen temperature fitted on the checkpoint's own transfer replay and known-chance development examples — no generated answer tokens, no option-order ensemble, no benchmark-ID lookup", "approach": "fine-tune", "training": "Frozen checkpoint supplied for independent benchmark evaluation; explicitly not an official JevBench result or a validated production release", "data": "", "result": "Public JevBench subset on one RTX 4090, Torch 2.8.0+cu128 / transformers 4.57.6 / peft 0.15.2: 148/231 = 64.07%, 231/231 parseable; Easy 48 of 48, Standard 56 of 72, 77.78%, Hard 44 of 111, 39.64%; the full 534-decision suite includes held-out tasks the authors did not have; no official rank or aggregate score claimed", "notes": "Sister model to flymy-ai/decision-2b-preview, FlyMyJev; the earlier 62.77% public result used length-normalized option-token likelihood on 77 items — a different configuration, not this submission; the initial absolute 5.1e-7 raw-probability gate omitted float32 softmax-sum roundoff, corrected before the Fast run; EVALUATION-PERMISSION.md and provenance-review.json must be read before other uses; 'Fast is 0.6B in the small-model track, not a literal 1B checkpoint'", "domain": "general", "score": "public JevBench 64.07%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "eikos", "name": "Eikos", "author": "caiovicentino", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/caiovicentino1/Eikos-4B", "base_model": "", "params": "4B / 27B", "metric": "JevBench accuracy original and hard plus ECE, DecisionBench OOD, general battery", "license": "MIT", "paper": "", "stars": 17, "likes": 11, "detail": "base: Eikos-4B and Eikos-27B; arch: one forward pass, calibrated probability per option; data: global finance, trading, trade finance rulebooks; license: MIT; result: bf16, FP8 and INT4 GPU builds plus MLX for Apple Silicon", "arch": "Open, calibrated, single-pass typed-decision models for finance and trading.", "approach": "", "training": "", "data": "", "result": "4B final: JevBench 91.7 original and 72.1 hard vs the Jev reference at 98.6 and 73.0, hard ECE 0.049; DecisionBench OOD 77.1 medium and 66.9 hard vs Jev 89.1 and 69.3; nine-task general battery 76.0 vs 84.1; held-out Spanish never seen in training 90.9; decision hidden in 64k tokens of text 74.2; invariance under option reversal and paraphrase 89.9. Eikos-27B sibling: JevBench 100.0 and 82.9, OOD 88.4 and 78.5, general battery 82.5", "notes": "Open typed-decision model family, MIT: Eikos-4B and Eikos-27B, each in bf16, FP8 and INT4 GPU builds plus MLX builds for Apple Silicon. One forward pass returns a calibrated probability for every option, so a caller acts on confident decisions and escalates the rest. Focus: global finance, trading and trade finance — applying stated rules, policies and rulebooks to a case. The repo ships everything used to build the models. Code: https://github.com/caiovicentino/eikos. 09-24: an Eikos-27B-MLX-4bit quantization build joined the family; arena code at row caiovicentino-eikos-arena.", "domain": "finance", "score": "91.7 / 72.1 JevBench, 4B", "latency": "", "benchmark": "JevBench public, DecisionBench OOD, 9-task general battery, CUAD and FinDVer finance battery", "cost": "", "speedup": ""}
{"id": "fern", "name": "fern", "author": "reoring", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/reoring/fern", "base_model": "Qwen3.5-4B", "params": "4B", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "4B decision model with a Jev-compatible API: send a state and up to 50 questions of the choice, score or noul kind and it returns probability distributions from a single forward pass, no text generation, ~30 ms per request on one GPU; distilled from DeepSeek-V4-Flash, a 2-bit GGUF teacher on one 96 GB GPU, into Qwen3.5-4B by matching the teacher's next-token distribution over the answer options; the checkpoint is a plain Qwen3_5ForCausalLM and the serving API reads the logits of the option codes, A-Z, 0-9, yes, no, at the answer position; code and serving at github.com/reoring/fern", "approach": "", "training": "", "data": "", "result": "Demo: 300 multilingual support tickets, each routed to a team with severity, urgency and sentiment, 1,200 decisions in 9 s on one GPU", "notes": "", "domain": "general", "score": "1,200 decisions in 9 s", "latency": "~30 ms per request, one GPU", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dkntzmn-gan8", "name": "GAN8", "author": "DKNTZMN", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/DKNTZMN/gan8-vs-jev", "base_model": "8 abstract frames + 2 Jev-style single-pass heads, linear 2.6KB or MLP 9.6KB", "params": "<10KB heads", "metric": "mechanism-experiment weights", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "arch: 8 abstract frames + 2 single-pass heads, a linear 2.6 KB and an MLP 9.6 KB; notes: mechanism-experiment weights on a controlled judgment bank; Apache-2.0; Space + table dataset", "arch": "Mechanism experiment: eight abstract frames combined with two Jev-style single-pass heads, a 2.6 KB linear head and a 9.6 KB MLP head trained on a controlled judgment bank", "approach": "head", "training": "", "data": "", "result": "Published with a demo space and a table-format dataset for replication", "notes": "Not the hosted jev-latest and not affiliated with TypeSafe AI: 8-frame GAN8, gan8.pt 3.5 KB, plus Jev linear 2.6 KB and MLP noul heads trained on a controlled judgment bank; companion Space DKNTZMN/gan8-vs-jev and tables dataset DKNTZMN/gan8-mvp-scale", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gliner2-5-decide", "name": "GLiNER2.5-Decide", "author": "fastino", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/fastino/GLiNER2.5-Decide", "base_model": "gliner2-large-v1", "params": "340M", "metric": "Fast Decisions avg 60.2% vs JevK5 57.6% and Laya Router 46.6%", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 106, "detail": "", "arch": "340M English classification model in the GLiNER2.5 family for schema-defined decision-making: pass any label set at call time, intent, routing, sentiment, priority, policy or multi-label tags, and get valid answers, probabilities, confidence scores and constraint-feasibility metadata in a single forward pass, no prompt template and no generated tokens; one call can score several heads at once, single-label tasks return one string, multi-label tasks return every label above the threshold, and it also extracts spans and relations and enforces rules across related outputs", "approach": "fine-tune", "training": "", "data": "Fast Decisions benchmark, unseen and internally generated: 5,100 test examples across 17 domains, 300 held-out examples per domain, same text and candidate labels for every model; micro-task accuracy, a decision counts only if the predicted label set exactly matches", "result": "Average 60.2%, ahead of GLiNER2.5-Decide-1B 59.6%, JevK5 57.6%, GLiNER2.5-multi-Decide 56.7%, SemIf on Qwen3.5-4B 56.4%, GLiFormer large-v1 49.0% and Laya Router 46.6%; domains span customer operations, domain routing, and general content understanding", "notes": "Released by Fastino, announced 2026-09-24; runs locally on CPUs and deploys in air-gapped environments; full or LoRA fine-tuning supported; deliberately not a general-purpose model: it does not reason, explain or answer open questions; siblings: GLiNER2.5-Decide-1B, GLiNER2.5-multi-Decide 287M for multilingual input, community ONNX port by onnx-community and CoreML port by FluidInference, and abdelstark/exitreceipt-gliner2.5-decide-lora; GLiNER2 architecture paper arXiv:2507.18546", "domain": "general", "score": "", "latency": "38.3 ms p50 on a V100; 167.3 ms p50 on a 48-vCPU Xeon 8581C", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-playground-rlcd", "name": "Jev Playground, soyrsoyr", "author": "soyrsoyr", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/soyrsoyr/jev-playground-rlcd", "base_model": "DeBERTa-v3-large + NLI entailment head; served at /v1/systemone", "params": "400M", "metric": "Per-primitive accuracy and ECE: noul, choice, score; published OOD degradation on ad-hoc P1 ticket routing", "license": "MIT", "paper": "", "stars": "", "likes": 0, "detail": "author: soyr-redhat; base: zeroshot · repo: soyr-redhat/jev-playground; arch: soft cross-entropy / Brier; data: 5,000 states / 40K pairs; name: RLCD, DeBERTa", "arch": "NLI-style decision model: state+question → calibrated option probabilities; proper scoring rules as the RLCD surrogate on synthetic typed questions", "approach": "head", "training": "40K synthetic pairs over 5,000 synthetic states; ~19 min on an RX 7900 XT", "data": "Synthetic typed decision corpus", "result": "Accuracy: noul 0.731→0.999, choice 0.510→0.998, score 0.898→0.978; ECE 0.0139. Published OOD degradation: ad-hoc P1 ticket routing noul drops to ~0.13 vs 0.90 in-distribution", "notes": "Published OOD report; companion dataset soyrsoyr/jev-playground-rlcd-v0.", "domain": "search", "score": "ECE 0.0139", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "autotrust-jev", "name": "Jev student JEV", "author": "autotrust", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/autotrust/JEV", "base_model": "Qwen3.5-9B LoRA", "params": "9B", "metric": "KL 0.0210, noul AUROC 0.9958 on test_set_30k", "license": "", "paper": "", "stars": "", "likes": 1, "detail": "training: LoRA on Qwen3.5-9B, student of TypeSafe Jev 1.13 on SargeDev/jev-distill-corpus-v3; metric: test_set_30k KL 0.0210, noul AUROC 0.9958, Brier 0.0015, score MAE 0.103, ECE 0.0007", "arch": "Distillation student of TypeSafe Jev 1.13: Qwen3.5-9B with LoRA trained on the SargeDev/jev-distill-corpus-v3 corpus", "approach": "distill", "training": "", "data": "SargeDev/jev-distill-corpus-v3; evaluation on a 30k test set", "result": "KL 0.0210, noul AUROC 0.9958, Brier 0.0015, score MAE 0.103 and ECE 0.0007 on the 30k test set", "notes": " 09-25: autotrust/JEV-27B, a 27B member of the org, card unpublished at sweep time, row unpublished-27b-wave.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gemma-270m-decision", "name": "jev-gemma-3-270m decision adapter", "author": "DawoodKMasood", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/DawoodKMasood/jev-gemma-3-270m", "base_model": "Gemma 3 270M", "params": "270M", "metric": "", "license": "Open", "paper": "", "stars": "", "likes": 1, "detail": "training: LoRA", "arch": "LoRA adapter fine-tuned from Gemma 3 270M IT for context-grounded, schema-constrained decisions: give it a context, a field name, a description of the decision and the allowed enum or boolean values, it scores the allowed choices and returns the selected value with probabilities normalized across the supplied choices; a task-specific decision component, not a general-purpose assistant, arithmetic model or substitute for review by a person, and it generates no explanations or open-ended responses", "approach": "logits", "training": "Fine-tune for Jev-style schema decisions", "data": "", "result": "The repo contains adapter weights, tokenizer, training metadata and evaluation results but not the Gemma base weights; decision outputs on a 2,048-token hard limit; no explanations by design", "notes": "Small open Jev-adapter checkpoint; base model requires accepting the Gemma terms on Hugging Face.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevstyle-qwen35-2b-decision", "name": "Jev-Style-Qwen3.5-2B-Decision", "author": "chaoliangUNSW", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2", "base_model": "Qwen3.5-2B-Base", "params": "2B", "metric": "81.20% macro accuracy; v1 76.68%; English Laya 75.09%", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "base: Qwen3.5-2B-Base; arch: single-prefill option scoring, temperature in RMSNorm; result: v2 81.20% macro on 3,277-decision panel; notes: v1 09-21, v2 09-23, jevstyle.com; approach: sft", "arch": "Jev-style decision model on Qwen3.5-2B-Base: a state, a question and a list of options in, one selected option out with calibrated probabilities from a single prefill, no text generation; v1 uses LoRA rank 16 on all linear layers with a log-score loss built on a custom chunk-parallel differentiable Gated DeltaNet forward that matches the per-token training path to 1e-6 and is 6.5× faster per step; a temperature fitted on 4,366 held-out examples is folded into the final RMSNorm weight, so every logit is calibrated with nothing applied at inference; v2 adds rank-32 LoRA with per-task-group calibration, trained in 36.9 minutes on one H100 80GB; released as HF BF16, MLX bf16 and GGUF Q4_K_M to Q8_0, with the calibration in the final normalization tensor for the GGUF build", "approach": "fine-tune", "training": "", "data": "11 real-label task groups, 3,277 decisions, 3,100 held for calibration", "result": "v2 scores 81.20% macro accuracy, 79.78% Macro-F1, 0.5154 NLL and 0.2787 Brier on a fixed English reference panel of 11 real-label task groups and 3,277 decisions, vs 76.68% for v1 and 75.09% for English Laya on the same panel, with 18.4% lower NLL, 20.0% lower Brier and 26.4% lower task-macro ECE against Laya and ahead in 9 of 12 task groups; v1 scored 82.3% on 1,500 held-out examples across 5 decision tasks with ECE 0.017, vs 65.9% and 0.065 for zero-shot Qwen3.5-2B-Base, including MNLI 52.3% to 86.7% and SST-5 32.0% to 61.7%; option-order flip rate halved relative to Laya with 80.00% accuracy after permutation; 77 ms per decision on M1 Max; Q8_0 makes the same decision as bf16 on 99.4% of examples and Q4_K_M keeps 82.4%", "notes": "Website jevstyle.com; v1 released 2026-09-21, v2 2026-09-23; calibration transfers to unseen task types with ECE 0.075 on emotion classification and RTE; a Core AI port, mlboydaisuke/Qwen3.5-2B-Decision-CoreAI, quantizes the MLX-bf16 source for Apple Core AI; the 0.8B sibling Jev-Style-0.8B-Decision-v3 is a separate row", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevflash-doom", "name": "JevFlash Doom Basic", "author": "super321", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/super321/jevflash-doom-basic-0.6b", "base_model": "Qwen3-0.6B-Base", "params": "0.6B", "metric": "Closed-loop episode success against a uniform-random baseline, with the full failure history of ten training attempts", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "From-scratch replica of the NanoJev non-generative decision-model architecture for ViZDoom basic: from a text game state of health, ammo, position and the visible target box it scores the fixed candidate actions left, right, shoot and noop in one non-autoregressive forward pass, no chain-of-thought and no token generation at inference", "approach": "fine-tune", "training": "Behavior cloning on about 2,300 decision examples collected via epsilon-greedy exploration, 10% of executed actions random, against a simple proportional aim-and-shoot heuristic - the same technique the original NanoJev used", "data": "", "result": "65% closed-loop episode success against 45% for the uniform-random baseline, the first of ten training attempts to beat random; the repo documents the full failure history: degenerate collapses, a checkpoint that was a memorized shortcut, and a bad-random-seed failure mode", "notes": "Code, data and full write-up at github.com/themaker00001/JevFlash", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alibiserikbay-jevk5-2b", "name": "JevK5-2B", "author": "alibiserikbay", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/alibiserikbay/JevK5-2B", "base_model": "Qwen3.5-2B", "params": "2B", "metric": "Accuracy on 362 held-out teacher questions from three unseen business domains", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "2B version of JevK5, an Apache-2.0 open alternative to Jev: reads a document plus yes/no, choice or score questions and returns a probability for every option in one forward pass; LoRA on Qwen3.5-2B, same data and recipe as the 4B", "approach": "fine-tune", "training": "Qwen3.5-2B with a rank-16 LoRA on attention and linear-attention projections merged into the weights; same data and recipe as JevK5 v0.2 4B; readout is a softmax over the answer letters' next-token logits divided by one calibration temperature, T = 1.42", "data": "", "result": "0.751 overall vs JevK5 4B at 0.804; per-domain: extraction 0.91, routing 0.97, trade-offs 0.88, judging 0.70, multi-step 0.67, ambiguous 0.59, dates and numbers 0.42; tiers: easy 1.000, standard 0.806, hard 0.604; hard-tier ECE 0.071, TVD on the 10 probability items 0.241", "notes": "3.5 GB of bf16 weights, about 3.8 GB peak on a 4,000-token document; 2.0 GB GGUF Q8_0 in JevK5-GGUF. Keeps most of JevK5's accuracy at half the memory; weaker on arithmetic, multi-step reasoning and general knowledge. Not affiliated with TypeSafe AI.", "domain": "general", "score": "0.751", "latency": "about 9 ms per decision on an H100", "benchmark": "362 held-out teacher questions: residential leases, public-sector permits, manufacturing QC", "cost": "", "speedup": ""}
{"id": "asjson-jevson-4b", "name": "jevson-4b-01", "author": "asjson", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/asjson/jevson-4b-01", "base_model": "Qwen3-4B", "params": "4B", "metric": "No generated text to parse, no retries, no invalid JSON; adapter shipped by asjson.dev", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "base: LoRA + small readout heads + fitted calibration temperatures; arch: two modes: mode 1, asjson, JSON Schema plus document in, schema-valid JSON record with a calibrated probability on every field in one parallel pass, enums and booleans as choices, presence decisions, span-cast string extraction, explicit absent; mode 2, Jev-style typed yes/no/choice/score with probabilities", "arch": "First public adapter behind asjson: send a JSON Schema and a document, get back a schema-valid JSON record with a calibrated probability on every field from one parallel pass; no generated text to parse, no retries, no invalid JSON; the same adapter answers Jev-style typed questions; a LoRA over Qwen3-4B plus small readout heads and fitted calibration temperatures", "approach": "head", "training": "LoRA + readout heads + temperature fitting", "data": "", "result": "asjson.dev ships this adapter; no generated text to parse, no retries, no invalid JSON", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ksanjiv05-laya-executorch", "name": "Laya ExecuTorch .pte", "author": "ksanjiv05", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/ksanjiv05/laya-for-rn-executorch", "base_model": "Laya multilingual", "params": "322M", "metric": "XNNPACK int8 603MB; Core ML 845MB, ANE, verified on iPhone 15", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "notes: .pte builds for on-device / React Native: XNNPACK int8 603 MB, Core ML 845 MB on the ANE, verified on iPhone 15, plus Vulkan", "arch": "Laya converted to ExecuTorch .pte for on-device and React Native targets: XNNPACK int8 at 603 MB, Core ML with ANE at 845 MB, verified on iPhone 15, and Vulkan builds", "approach": "", "training": "", "data": "", "result": "Working on-device decision model on iPhone 15 via Core ML ANE", "notes": "XNNPACK and Core ML backends, e.g. via react-native-executorch; laya_xnnpack_int8wo.pte is 603 MB int8 weight-only, recommended for mobile", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "owaisali-laya-snake", "name": "Laya Snake", "author": "OwaisAli10", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/OwaisAli10/laya-snake", "base_model": "laya-multilingual, mmBERT-base 322M", "params": "322M", "metric": "1 choice per tick, 4 directions", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: one choice per snake tick, up, down, left or right from a text description of the board; Apache-2.0", "arch": "Laya multilingual fine-tuned as a Snake agent: one direction choice per tick, read from a text description of the board - no vision, no search", "approach": "fine-tune", "training": "", "data": "", "result": "A BFS teacher labeled 114,476 train and 5,524 validation states from teacher games on 10-20 wide boards, with 12% random safe moves; expected log score loss, full fine-tune, 2 epochs on Colab", "notes": "One choice question per tick, up, down, left or right, over a text description of the board; the state text must come from snake/encode.py in the repo, the model only knows that format; code, game UI and training pipeline at github.com/Okbatti/SnakeGame_Laya", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ahmetsemih-laya-tr-mmlu", "name": "Laya Turkish MMLU", "author": "AhmetSemih", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/AhmetSemih/laya-turkish-mmlu", "base_model": "mmBERT-base multilingual checkpoint", "params": "322M", "metric": "6,200 Turkish MMLU questions", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "training: RLCD fine-tune on 6,200 Turkish MMLU questions from the mmBERT-base multilingual checkpoint; notes: dataset AhmetSemih/turkish-mmlu-laya, 09-23", "arch": "The multilingual Laya checkpoint, mmBERT-base, fine-tuned with RLCD on the 6,200 questions of AhmetSemih/turkish-mmlu-laya; used through the laya Agent interface", "approach": "rl", "training": "RLCD on 6,200 Turkish MMLU questions starting from the mmBERT-base multilingual checkpoint", "data": "", "result": "Turkish-language Laya fine-tune with dataset published", "notes": "", "domain": "education", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jojoarumugam-laya-agentguard", "name": "laya-agentguard", "author": "Jojoarumugam", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/Jojoarumugam/laya-agentguard", "base_model": "laya, laya-forge", "params": "322M", "metric": "two pre-action checks: destructive, injection", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "arch: fine-tune of Laya for two pre-action agent checks - destructive, would this tool call destroy data or change production irreversibly, and injection, is this third-party content trying to hijack the agent; notes: laya-forge; Apache-2.0", "arch": "Laya fine-tune via laya-forge for two checks an AI agent runs before acting: destructive-impact and prompt-injection detection", "approach": "fine-tune", "training": "", "data": "", "result": "Failed its own quality gate: the recipe asked for 0.85 accuracy and 0.10 ECE on the hand-written holdout and the model misses both on both questions; Laya as shipped scores 0.647 accuracy, 95% CI 0.51-0.76, and 0.134 ECE on injection at n=51; recommended as a baseline or in shadow mode", "notes": "Trained, calibrated and evaluated with laya-forge; the checkpoint carries the fitted allow, escalate and block thresholds", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-bio", "name": "Laya-Bio, 12 checkpoints", "author": "dnagpt", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/dnagpt/laya-bio-models", "base_model": "Laya typed-decisions", "params": "421M", "metric": "12 final checkpoints", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "author: Liang Wang, HUST; base: pinned upstream revision included in artifacts/; arch: DNA, protein; data: benchmark data, predictions, manuscript · historical corpora + BPE training samples; license: upstream component terms; result: SHA-256, calibrated temperatures, tensor metadata in checkpoint_index.json; notes: manifest, hashes, seed-tagged checkpoints; name: DNA/protein candidate scoring; paper: Manuscript in dataset repo, laya-bio-models artifacts", "arch": "12 final checkpoints — raw / full_bpe / b1 / text_only, each at seeds 20260922/23/24 — for candidate scoring and reliability on short biological sequences", "approach": "head", "training": "Per-run training on biological sequence representations; 'weights were not cast, quantized or retrained for this release'", "data": "dnagpt/laya-bio + laya-bio-historical-corpora", "result": "Every released checkpoint matches the frozen test manifest", "notes": "Laya applied far outside its training domain — biology. Strict reproducibility discipline. Code: https://github.com/maris205/laya_bio. Training corpus published as dataset dnagpt/laya-bio (100K-1M text).", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "millennium-jev-15b", "name": "Millennium-Jev-1.5B", "author": "sunziqin", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/sunziqin/millennium-jev-1.5b", "base_model": "Qwen2.5-1.5B-Instruct", "params": "1.54B + 77.08M decision params", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Flagship of the Millennium Open-Source Ecosystem, code-named Aegis-S1: non-autoregressive System 1 decision model on Qwen2.5-1.5B-Instruct with 77.08M trainable decision parameters, a 36.93M LoRA adapter plus a 40.15M cross-attention decision head; vectorized option-span mean-pooling over the options, native C++ SDPA bidirectional attention, split-conformal prediction at alpha 0.05; several heterogeneous typed questions, Choice Score and Noul, answered in one prompt and one forward pass", "approach": "", "training": "", "data": "", "result": "74.54% autonomous decision pass rate on the state-disjoint test set, +2.40 pp over the 0.5B baseline; selective risk on autonomous action 5.52%, an 11% relative error reduction vs the 0.5B's 6.22%; split-conformal marginal coverage >=94.09% against the 95% nominal, average prediction set size 1.366; sub-50 ms per decision; N=8,657 test, 10,075 calibration samples", "notes": "The 1.5B sibling of sunziqin/millennium-jev-0-5b; checkpoint SHA-256 pinned to prevent silent fallback to legacy uncalibrated weights.", "domain": "general", "score": "74.54% autonomous pass rate", "latency": "", "benchmark": "state-disjoint held-out set", "cost": "", "speedup": ""}
{"id": "mojev", "name": "MoJev", "author": "MoLeMo-Lab", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/MoLeMo-Lab/mojev", "base_model": "Qwen3.5-0.8B", "params": "0.85B", "metric": "0.9323 accuracy on MoJev-Mix test", "license": "MIT", "paper": "", "stars": 29, "likes": 4, "detail": "author: Fudan / di-zhang-fdu; base: MoLeMo-Lab/mojev; arch: TypeSafeClient with base_url swap; data: HF dataset, public; result: di-zhang-fdu/mojev; paper: Preprint PDF in repo, paper/mojev-preprint.pdf; metric: typed decision scoring", "arch": "Text or image state + runtime-defined schema → probability distribution over permitted values in one forward pass; wire-compatible with the official TypeSafe Python SDK system_one API", "approach": "undisclosed", "training": "Trained checkpoint + published mojev-mix training/eval mixture", "data": "MoLeMo-Lab/mojev-mix", "result": "Metrics, controls and serving benchmarks in README; HF Space for text+image+custom candidates on ZeroGPU", "notes": "Project page molemo-lab.github.io/mojev; ROADMAP for next experiments. Direct SDK wire-compatibility. Code: https://github.com/MoLeMo-Lab/mojev HF card model-index: 0.9323 accuracy on MoJev-Mix test, MIT license, base Qwen/Qwen3.5-0.8B.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nagisanzenin-nagi", "name": "Nagi", "author": "nagisanzenin", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/nagisanzeninz/nagi-smol-v0", "base_model": "BIG Qwen3.5-4B; SMOL ModernBERT-large M2 0.5B; HUGE Gemma4 12B; ENORMOUS Qwen3.8-27B", "params": "0.5B-27B, 4 tiers", "metric": "77.92%", "license": "", "paper": "", "stars": 5, "likes": 0, "detail": "Typed decisions in one forward pass: maps a state and a closed option set to a typed decision and a probability distribution. Four model lines: SMOL 0.5B, BIG 4B, HUGE 12B, ENORMOUS 27B; each tier is a separate model, not a rename. HUGE and ENORMOUS ship adapters; the SDK loads the pinned base separately and keeps LoRA unmerged, matching the benchmark. ENORMOUS is a research release: it failed its preregistered rule-reading gate: ENORMOUS-HUGE +2.9 pp on the sealed final set, required at least +5 pp with the lower bound above 0, counterfactual rule pairs -0.3 pp, and is published by owner decision, with no claim that it generalizes better to unseen rules. ENORMOUS needs an 80 GB GPU in BF16. Inputs longer than 768 tokens are accepted up to 4,096 but fall outside the trained range.", "arch": "Typed decisions in one forward pass: Nagi takes a state and a closed set of options and returns a choice with a probability distribution; four open model lines, SMOL 0.5B, BIG 4B, HUGE 12B and ENORMOUS 27B; ships an Arena Live page with three realtime games, a 240 Hz helicopter, four-player Tron and a Tetris battle, 10 rounds each with the same seeds for every model", "approach": "fine-tune", "training": "", "data": "", "result": "full-suite accuracy 38.85% smol 480M, 76.14% big 4B, 77.92% huge 12B; four-system benchmark: Jev 1.13 83.92%, Laya 57.25%, OpenJev SemIf 74.06%, Nagi-HUGE 77.92%; P50 latency 91 ms on H100 for the 12B vs Laya 13 ms and SemIf 51 ms", "notes": "Code: https://github.com/nagisanzenin/nagi. Tier weights: nagi-smol-v0, nagi-big-v0 (superseded by big-v3), nagi-big-v3, nagi-t4-m2p-v0, Nagi-HUGE, Nagi-ENORMOUS (all nagisanzeninz).", "domain": "general", "score": "77.92% full suite", "latency": "91 ms P50 H100", "benchmark": "", "cost": "", "speedup": ""}
{"id": "neohorse-jev-4b", "name": "NeoHorse-Jev-4B", "author": "TokenRhythm", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/TokenRhythm/NeoHorse-Jev-4B", "base_model": "NeoHorse-1-4B, upstream Qwen3.5-4B", "params": "4B", "metric": "6-group aggregate 77.70", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 9, "detail": "result: Open-Jev-9B 75.67, Kev-4B 74.25, Laya English 58.24", "arch": "Prefill-only 4B structured decision model: a separate pointer decision head on the multimodal backbone; Choice, Noul, and Score answered in one forward pass over the application-defined candidates, no autoregressive text; text or one image with text; runs on vLLM 0.28.0, SGLang 0.5.17, or the native neohorse_decision runtime with a /v1/systemone endpoint; backbone weights about 9.08 GB", "approach": "head", "training": "", "data": "", "result": "6-group text aggregate 77.70, highest among the four open-weight decision models with complete results; 3-benchmark mean accuracy 83.26% on Nimble, VitaminC, MASSIVE, 11.50 points above the NeoHorse-1-4B baseline; JevBench 75.73 family-macro, 75.32% per-example accuracy, 100% valid output format; Kev 81.92, best in the table; Image-NLI 60.65% on 8,000 examples; six decision demos: Tetris, Snake, robot manipulation, Mahjong, four-player bomb arena, autonomous driving", "notes": "Card reports no NLL, Brier, or ECE calibration; multiple questions in one request do not imply a single shared forward pass; no latency, GPU memory, or cost comparison under a common timing protocol. GGUF build: TokenRhythm/NeoHorse-Jev-4B-GGUF, 09-24.", "domain": "general", "score": "accuracy 83.26%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "joneslin-next-jev", "name": "next JEV, stage 2 last-loop", "author": "JonesLin", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/JonesLin/next-jev-stage2-last-loop", "base_model": "Qwen3.5-0.8B", "params": "0.8B", "metric": "step 1900/5921, mid-training not final", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "training: stage 1 CoT + stage 2 3-way NLI + CoT, last-loop supervision; 378,903 training rows; notes: workspace 128 tokens x 3 loops; 2× H100 NVL; base Qwen3.5-0.8B; datasets: next-jev-stage1-multi-model-cot, next-jev-stage2-merged-verified-20260923, next-jev-phase1-accepted, next-jev-choice-cot-200k, next-jev-tetris-decisions-10k; approach: sft", "arch": "Iterative-decision architecture: a 128-token workspace recycled across three reasoning loops, trained stage 1 with chain-of-thought and stage 2 with three-way NLI plus last-loop chain-of-thought supervision", "approach": "fine-tune", "training": "378,903 rows across two stages on 2× H100 NVL; the published card is the step-1900/5921 mid-training checkpoint, explicitly not final", "data": "", "result": "Mid-training checkpoint released with all five accompanying datasets: stage1 CoT, stage2 merged-verified, phase1 accepted, choice-cot 200k, tetris decisions 10", "notes": "Stage-1 checkpoint: JonesLin/next-jev-stage1-last-block; demo dataset: JonesLin/next-jev-tetris-demo. On 2026-09-25 two new last-block CoT decoder variants were released, JonesLin/next-jev-stage1-last-block and JonesLin/next-jev-stage2-last-block: the decoder is a new cross-attention block over the 128 workspace tokens plus an exact copy of the Qwen3.5-0.8B final block and final norm with tied embeddings, one pass, decoder learning rate at 2× the rest; stage 1 was stopped on purpose at step 2693 of 6347, last checkpoint 2600, trained on 406,167 multi-response CoT prompts from the multi-model-cot-2730 dataset; stage 2 runs from that init at step 3100 of 9181 on 587,548 train rows, the 387,548-row choice set plus 200,000 choice questions from next-jev-choice-cot-200k, 52,479 of them with verified teacher CoT from claude-haiku-4-5 and grok-4.7, global batch 64 on two H100 NVL, both mid-training and not validated. Stage 1 of the same pipeline is row next-jev-stage1-cot.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tinnel-omnjev", "name": "OmniJev omni-modal Jev", "author": "Zevo, CAS Institute of Automation, Zhongguancun Academy", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/tinnel123/OmniJev-4B", "base_model": "Qwen3.5-0.8B, Qwen3.5-2B, Qwen3.5-4B", "params": "0.8B, 2B, 4B", "metric": "OmniJev-4B held-out: LIBERO-10 0.807, Mind2Web 0.733, Charades-STA 0.859, Catch 0.870, HaGRID 0.987", "license": "Apache-2.0", "paper": "", "stars": 55, "likes": 4, "detail": "base: Qwen3.5 0.8B, 2B and 4B, one code base and one API; arch: one forward pass, zero generated tokens, choice, noul and score answered with calibrated probabilities plus an explicit none-of-the-above; data: about 270,000 decision records and 1.3 million typed questions across web and phone operation, robot episodes, video events, real-time games, gestures, hazards and sounds; license: Apache-2.0; result: OmniJev-4B at 68.7 overall on the shapsider 923-item held-out image suite; notes: omnijev.net online demo, weights at tinnel123 on HF; calibration: ECE 0.007 to 0.079 across the held-out sets; result: POPE 0.902, RoboArena real-robot 0.628, AndroidControl 0.734, Snake 0.833", "arch": "One forward pass over images and video, zero generated tokens, calibrated probabilities per option", "approach": "fine-tune", "training": "Supervised end to end under proper scoring rules, final calibration on held-out data", "data": "About 270,000 decision records and 1.3 million typed questions: web and phone operation, robot episodes, video events, games, gestures, hazards, sounds", "result": "0.8B, 2B and 4B omni-modal decision models for phone and computer operation, real-time games, robot control and camera-feed monitoring", "notes": "Omni-modal Jev from the Beijing Zhongguancun Academy, the Institute of Automation of the Chinese Academy of Sciences and Zevo. A 4B model that sees images and video and makes decisions about them: operate phones and computers, play games in real time, control robots, monitor camera feeds. Trained end to end on about 270,000 decision records and 1.3 million typed questions across web and phone operation, robot episodes in simulation and the real world, video events, real-time games, gestures, hazards and sounds. One code base, one API, all Apache-2.0. Website and online demo at omnijev.net. In the shapsider OmniJev 923-item held-out image benchmark, OmniJev-4B scores 68.7 overall against 76.5 for the 9B v4 and 72.3 for NeoHorse-Jev-4B. Code: https://github.com/tinnel123666888/OmniJev. The org also ships the OmniJev-0.8B, OmniJev-2B and OmniJev-SFT-0.8B LoRA checkpoints.", "domain": "multimodal", "score": "LIBERO-10 0.807, Mind2Web 0.733", "latency": "294 ms per question, 36.3 ms per question packed, one A800", "benchmark": "22 held-out benchmarks: LIBERO-10, Mind2Web, Charades-STA, Catch, HaGRID, POPE, RoboArena, AndroidControl and more", "cost": "", "speedup": ""}
{"id": "timosarkar-oryn", "name": "oryn", "author": "timosarkar", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/timosarkar/oryn", "base_model": "bert-mini", "params": "about 21M", "metric": "accuracy, Brier, NLL, ECE per question type", "license": "BSD-3-Clause", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "Compact dynamic classifier on boltuix/bert-mini: the classification question and candidate answers are supplied at inference time, one MASK marker per candidate, a learned linear option scorer and a softmax over the supplied options, so one model handles binary decisions, multiclass classification and ordinal scoring. Inspired by the publicly described Laya architecture.", "approach": "head", "training": "", "data": "phishing email, ag_news, boolq, sst5", "result": "On 1,000 held-out examples, 71.7% overall accuracy, Brier 0.3468, NLL 0.6228, ECE 0.0381 and 129.9 examples per second batched; by type, binary noul 85.62% on 591, choice 79.59% on 196, ordinal score 25.82% on 213, ordinal sentiment the main weakness", "notes": "About 21 MB trained weights; BERT Mini encoder plus two transformer encoder layers, one MASK marker per candidate option read through a shared linear option scorer; options arrive at request time so one model serves binary, multiclass and ordinal tasks; trained on phishing emails, BoolQ, AG News and SST-5; inspired by Laya; 14 downloads", "domain": "general", "score": "71.7% accuracy, ECE 0.0381", "latency": "about 3 ms warmed-up single request, local test", "benchmark": "1,000-example held-out sample of the multitask validation set", "cost": "", "speedup": ""}
{"id": "xuhaodev-football-jev", "name": "Qwen3-1.7B-Jev v2", "author": "xuhaodev", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/xuhaodev/Qwen3-1.7B-Jev", "base_model": "Qwen3-1.7B + LoRA + scalar head + per-primitive calibration", "params": "1.7B", "metric": "Metrics in evaluation.json, with provenance.json and manifest.json carrying hashes", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": 0, "detail": "base: FEATURE_EXTRACTION; training: renamed 'football-decision-v2' checkpoint · weights unchangedtandard text-classification pipeline does not implement the typed-decision runtime; name: Chinese football domain", "arch": "Choice/Score/Noul from user-defined state+questions without autoregressive generation; independent per-primitive temperature calibration", "approach": "head", "training": "LoRA on football-domain decisions", "data": "Chinese football-domain labelled decisions", "result": "Metrics in evaluation.json; provenance.json + manifest.json with hashes", "notes": "Domain-specific Jev-style clone in Chinese; standalone loader required.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lokinfey-onnx-jev", "name": "Qwen3.5-0.8B Typed Decisions", "author": "lokinfey", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/lokinfey/Qwen3_5_0.8B_jev", "base_model": "Qwen3.5-0.8B + LoRA", "params": "0.8B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": 0, "detail": "author: Microsoft Olive pipeline; base: merged into export; arch: CUDA FP16, onnxruntime-genai 0.14.0, Olive export; data: 7,414 cases / 25,859 questions, 149 domains, soft labels; result: weights export · eval in upstream repo; name: ONNX Runtime GenAI", "arch": "State + typed noul/choice/score question → predicts option letter at first generated position; normalize only over valid option-letter logits", "approach": "logits", "training": "LoRA fine-tune via Microsoft Olive, adapter merged", "data": "n4ze3m/typed-decisions-synth", "result": "The card explicitly publishes no benchmark result: standard full-sequence causal SFT on the gold option letter rather than the Hmm candidate-only objective, so calibration and results are not assumed to match n4ze3m/Qwen3.5-4B-Hmm; applications read the first-token letter logits and normalize only over the valid options", "notes": "Exported through Microsoft Olive at revision 2fbeaf4, onnxruntime-genai-cuda 0.14.0 and onnxruntime-gpu 1.26.0 for CUDA 12; LoRA rank 32 alpha 64 on q, k, v, o, gate, up and down projections, 768-token sequences, 1 epoch, learning rate 1e-4, BF16 training, FP16 export; trained on n4ze3m/typed-decisions-synth preserving its case-level train and validation split", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tiny-jev-17b", "name": "Tiny-Jev-1.7B", "author": "lostargon", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/lostargon/Tiny-Jev-1.7B", "base_model": "Qwen3-1.7B", "params": "1.7B", "metric": "zero-shot accuracy 66.5–92.7 across 2–151 options; ECE 0.013–0.057; acc@0.9 96.3–99.1", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": 0, "detail": "", "arch": "Same interface as Tiny-Jev: state + question + fixed options → probability distribution over options in one forward pass; 'the smart if in a pipeline' — routing, triage, filtering before an expensive context window, guard-railing another model's output. One added marker token plus a 1-dimensional decision head; the language-model head is not used; bfloat16", "approach": "head", "training": "LoRA r=32 on all linear layers plus the head; soft cross-entropy; one epoch; post-hoc temperature calibration. Larger sibling of Tiny-Jev 0.6B", "data": "", "result": "Zero-shot: SST-2 92.7, AG News 90.6, Emotion 82.0, BANKING77 83.5, CLINC150+OOS 66.5 accuracy; ECE 0.013–0.057; at the 0.9 confidence threshold accuracy 96.3–99.1 with coverage 29–82%", "notes": "Explicit disclaimer: 'not built, endorsed or supported by [TypeSafe]… shares no code or weights; Jev in the name refers only to the interface style'. 0.6B sibling: lostargon/Tiny-Jev.", "domain": "general", "score": "SST-2 92.7 / AG News 90.6 / Emotion 82.0 / BANKING77 83.5 / CLINC150+OOS 66.5", "latency": "", "benchmark": "SST-2 / AG News / Emotion / BANKING77 / CLINC150+OOS", "cost": "", "speedup": ""}
{"id": "visual-jev-4b", "name": "Visual Jev", "author": "guanxuyu", "date": "2026-09-23", "category": "model", "url": "https://huggingface.co/models/guanxuyu/visual-jev-4b-answer-sft", "base_model": "Qwen3-VL-4B-Instruct", "params": "4B", "metric": "Macro accuracy, GQA, SNLI-VE, TextVQA, TallyQA before and after tuning", "license": "Apache-2.0", "paper": "https://arxiv.org/abs/2609.25845", "stars": "", "likes": 2, "detail": "author: Guan Xu; base: LoRA r16 · 33M trainable params; arch: Visual Jev; training: open weights for the public paper; data: GQA, SNLI-VE, TextVQA, TallyQA held out; name: answer-supervised open system", "arch": "Open system of arXiv 2609.25845: typed decision head over the visual backbone; answer-supervised SFT; code at guanxuyu-sv/Visual-Jev + project page visualjev.github.io", "approach": "head", "training": "Answer-supervised SFT on the Visual Jev benchmark", "data": "Visual Jev benchmark", "result": "Macro accuracy 0.706 → 0.761; GQA 0.879 → 0.916; SNLI-VE 0.629 → 0.808; held-out TextVQA 0.974 → 0.975; TallyQA 0.340 → 0.345. Key negative result: the typed decision head adds +0.000 macro — 'adapt the backbone, and keep whatever readout the backbone already has'", "notes": "First open checkpoint for the Visual Jev paper — the paper's official open system. Null head-gain: the typed head adds no accuracy over the backbone.", "domain": "general", "score": "accuracy 0.706 → 0.761", "latency": "", "benchmark": "TextVQA", "cost": "", "speedup": ""}
{"id": "apus-openjev-v1", "name": "APUS-OpenJev-v1", "author": "apus-ailab", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/apus-ailab/APUS-OpenJev-v1", "base_model": "Qwen3.5-4B, Qwen3.5-9B, Qwen3.5-35B-A3B", "params": "4B, 9B, 35B-A3B", "metric": "Frozen80 88.75%", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 25, "detail": "base: Qwen3.5 4B, 9B and 35B-A3B MoE; arch: one forward scores the candidates supplied with each request and returns their distribution; effort=low runs 16 layers, effort=high 32 layers; data: APUS-OpenJev-Eval-Frozen80; license: Apache-2.0; result: 4B checkpoint-5949 merged BF16, downloadable without a separate LoRA; notes: en-zh, family collection plus technical report on HF; result: Frozen80 88.75%, 1,000-question panel 82.20% vs Jev 77.00% and Laya 50.50%, 9B service median 25.58 ms; data: 5 task families x 16 questions, Frozen80", "arch": "A Qwen3.5-based decision model family released 2026-09-22 by apus-ailab: 4B dense as merged BF16 checkpoint-5949, 9B dense and 35B-A3B MoE; the included native runtime supports effort=low at 16 layers and effort=high at 32 layers, and application code assembles decisions into structured workflow outputs; Apache-2.0, English and Chinese, with a family collection, technical report and the APUS-OpenJev-Eval-Frozen80 dataset on HF, and community GGUF and MLX ports within hours", "approach": "", "training": "", "data": "", "result": "4B, 9B and 35B-A3B checkpoints for browser action selection, workflow routing and natural-language principle judgments; reuses Qwen language representations and vocabulary projection Frozen80: 88.75%, 85.0% vs Jev API 82.5%; 1,000-question panel: 82.20% / 81.10% / 80.50% vs Jev 77.00% and Laya 50.50%; 9B service median 25.58 ms", "notes": "Qwen3.5-based decision model family released 2026-09-22 by apus-ailab. Three checkpoints: 4B dense as merged BF16 checkpoint-5949, 9B dense, and 35B-A3B MoE. The included native runtime supports effort=low at 16 layers and effort=high at 32 layers; application code assembles decisions into structured workflow outputs. Apache-2.0, English and Chinese. Family collection, technical report and the APUS-OpenJev-Eval-Frozen80 dataset live on HF; community GGUF and MLX ports followed within hours. Claims to lead both Jev and Laya on its own panels. The 9B and 35B-A3B shipped as standalone checkpoints on 09-22; GGUF and MLX-8bit / MLX-4bit builds for the 9B and 35B-A3B followed on 09-23. Ports: mlboydaisuke/APUS-Decision-v1-4B-CoreAI, Apple Core AI with English and Chinese, and prithivMLmods/APUS-OpenJev-v1-4B-GGUF and -9B-GGUF.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vigneshlabs-ballot-jev", "name": "ballot-jev-0.5b", "author": "vigneshlabs", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/vigneshlabs/ballot-jev-0.5b", "base_model": "Qwen2.5-0.5B", "params": "0.5B", "metric": "0/32 order-flips", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Order-invariant by construction: state+question encoded once into a KV cache; every option decoded from its own private clone of that cache — no option is an input to another option's computation, so permuting options permutes the output distribution and nothing else", "approach": "logits", "training": "Trained on typed-decision data", "data": "", "result": "0 of 32 order-flips on the card's test set vs kev 28.1% and laya 25.0%; CPU-runnable; zero-shot classification pipeline", "notes": "Card names the 'quiet bug' of order-dependent option decoding in decision models.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bosun-v3-1", "name": "Bosun v3.1", "author": "Hanno-Labs", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/Hanno-Labs/bosun-v3.1-1.7b", "base_model": "Qwen3-0.6B + Qwen3-1.7B pinned base, rank-16 LoRA adapter, 256 learned decision tokens", "params": "0.6B / 1.7B", "metric": "87.29% on DecisionBench standard applied suite, 1.7B", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 3, "detail": "base: Qwen3-0.6B / 1.7B + LoRA r16 + 256 decision tokens; arch: runtime answer-space readout, up to 255 candidates; training: 80k + 50k decision examples; result: 87.29% / 83.20% standard applied suite; notes: top two on initial leaderboard", "arch": "Qwen3-based decision model: state, instructions, decision type and candidate descriptions form a structured prompt, then candidate descriptions are scored through 256 learned decision tokens, up to 255 candidates per question with one token reserved for the null slot; readout extended from two fixed tokens in earlier Bosun to the runtime answer space; an extension of the Qwen3-Reranker probability idea, not a fine-tune of its reranker checkpoint; the package ships the adapter, new token embeddings, tokenizer, model code and pinned base model details; GGUF weights for the 0.6B at F16, Q8_0 and Q4_K_M", "approach": "fine-tune", "training": "Targets a distribution over valid answers: 80,000 base decision examples plus 50,000 from additional decision task families; the training data is not included in the release", "data": "", "result": "DecisionBench standard applied suite, 22,700 rows: 1.7B 87.29% accuracy, ECE 0.0397, NLL 0.4328, 100% coverage; 0.6B 83.20%, 0.0588, 0.6210; top two on the initial DecisionBench leaderboard; Jev leads on the separate reasoning track", "notes": "Apache-2.0, Copyright 2026 Clause Logic Inc.; revision aaa9dd06... for the 0.6B; served behind the Jev-compatible API via Hanno-Labs/jev-compatible-server; released with the DecisionBench blog post, HF blog Hanno-Labs/decisionbench-bosun-v3-1 GGUF repos on the org: bosun-v3.1-0.6b-gguf, 116 downloads, and bosun-v3.1-1.7b-gguf, 101 downloads, both 09-22.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "certus-jevlike", "name": "certus-jev-like-v0007", "author": "ait-hf", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/ait-hf/certus-jev-like-v0007", "base_model": "Qwen2.5-1.5B-Instruct + small domain LoRA adapters", "params": "1.5B", "metric": "0.796 vs Jev 0.727, Laya 0.766", "license": "Open", "paper": "", "stars": "", "likes": 1, "detail": "base: trunk; arch: base speed; training: sentiment, topic, NLI, boolean QA, intents, commonsense/science QA, logic, word …; data: trunk · hand-written held-out eval sets never trained on; license: HF; result: claims to beat both", "arch": "Trunk adapter merged into base; domain adapters kept separate — 'a new domain = one small LoRA adapter trained on top'", "approach": "fine-tune", "training": "NLL + Brier loss, yes/no balancing, KL anchor to the base model; 61 public datasets for the trunk", "data": "61 public datasets; per-domain training sets", "result": "Laya-benchmark 1,200-case format: 0.796 vs Jev 0.727, Laya 0.766; per-domain val acc table published", "notes": "Claims to beat Jev's published number on the Laya benchmark format.", "domain": "general", "score": "0.796", "latency": "", "benchmark": "Laya-bench 1,200-case format", "cost": "", "speedup": ""}
{"id": "decision-1-0", "name": "Decision 1.0 family", "author": "llm-semantic-router", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/llm-semantic-router/decision-1.0-nox", "base_model": "Kai-0.6B: fine-tuned from Vela-1.0-Encoder-307M; Lex-0.6B: fine-tuned from Kai; Nox-4B: Qwen3.5-4B; Sol-2B: Qwen3.5-2B", "params": "0.6B–4B", "metric": "Weighted accuracy across 5 axes, 3,766 decisions, 54 tasks, per family card", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 10, "detail": "base: jhu-clsp/mmBERT-base lineage; result: panel ranks — Nox 11th of 31, Sol 15th, Lex 27th, Kai 28th; notes: Qwen bases; name: Kai / Lex / Nox / Sol", "arch": "The Open Decision Foundation Models: give the model a state, questions and possible answers, it returns typed decisions and probabilities with labels defined at runtime; Choice routes among 2–255 actions and returns the selected ID plus a distribution, Noul checks a condition against supplied evidence and returns P, Score applies 2–10 ordered rubric descriptions and returns the expected index plus a distribution; Nox-4B and Sol-2B are Qwen fine-tunes, Kai-0.6B and Lex-0.6B are encoder-based, and the Nox family table also lists Lux-9B, Kev-9B, Kev-4B, Eos-0.8B and Kev-0.8B; HF tags: decision-model, classification, ordinal-regression, multilingual, safetensors", "approach": "fine-tune", "training": "Fine-tunes", "data": "", "result": "Nox-4B card: 73.09% weighted accuracy across 3,766 decisions and 54 tasks, +2.99 points over Kev-4B on the same benchmark; family table, composition, reading, inference, transfer, overall: Lux-9B 77.40, Nox-4B 73.09, Kev-9B 71.89, Kev-4B 70.09, Qwen3.5-9B baseline 69.73, Decider 2B 67.71, Qwen3.5-4B baseline 67.29, Sol-2B 66.32, Eos-0.8B 61.89; Jev Decision Index: Nox 45.31, Sol 40.41, Lex 19.57, Kai 18.37; Jev 59.51", "notes": "Nox, Latin for night; sister entries: huggingface.co/llm-semantic-router/decision-1.0-{sol,lex,kai}; Nox/Sol are the strong members; Kai/Lex are encoder-based; the family table compares against Qwen3.5 baselines and Decider 2B; HF likes: Nox 10, Sol 4. On 09-22 the two remaining family-table members were released as standalone checkpoints: Decision-1.0-Lux-9B, Qwen3.5-9B fine-tune, 77.40 weighted accuracy, +5.51 over Kev-9B and +4.32 over Nox-4B; Decision-1.0-Eos-0.8B, a Qwen3.5-0.8B fine-tune, 61.89 overall, ahead of all four reference models on the same 54-task benchmark. Decision-1.0-Nox-4B, Qwen3.5-4B, 73.09% weighted accuracy (+2.99 over Kev-4B), released as a standalone checkpoint.", "domain": "general", "score": "Nox 45.31, Sol 40.41, Lex 19.57", "latency": "", "benchmark": "Jev Decision Index", "cost": "", "speedup": ""}
{"id": "decision-jef", "name": "Decision-Jef-0.1", "author": "bet0x / BarraHome", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/BarraHome/Decision-Jef-0.1", "base_model": "Decision-Jef-0.1", "params": "307M", "metric": "77.30 global", "license": "MIT", "paper": "", "stars": 1, "likes": 1, "detail": "base: HF: BarraHome/Decision-Jef-0.1 · PyPI: decision-jef; data: 600 choice / 600 noul / 800 score; notes: includes a Jev reference column", "arch": "One forward pass answers several runtime-defined questions; answer space built from the request so unoffered values are unrepresentable — no fixed label-set head", "approach": "undisclosed", "training": "Independent", "data": "Typed-decisions test set: 2,000 decisions", "result": "77.30 global / 73.70 choice / 83.70 noul / 75.40 score; ECE 0.010 raw; 12.0 ms for three decisions; 77.45 with options permuted. Comparison table: Decision-1.0-Lex 78.15, Laya 76.60, Jev 72.x", "notes": "Publishes Jev at ~72 on this panel. Code: https://github.com/bet0x/decision-jef.", "domain": "general", "score": "ECE 0.010", "latency": "12 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "qwen35-4b-hmm", "name": "Hmm", "author": "n4ze3m", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/n4ze3m/Qwen3.5-4B-Hmm", "base_model": "Qwen3.5-4B + LoRA", "params": "4B", "metric": "Q4_K_M quantization, same accuracy as the full model in the author tests", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": 5, "detail": "base: 170 MB adapter; arch: A, B, C… · hf.co/…; data: probability per option; license: model + dataset MIT; result: 2.7 GB · author", "arch": "Reads probability of option letters on the first generated token, normalized over supplied options; small bun server exposes /v1/systemone; Ollama pull works", "approach": "logits", "training": "LoRA on soft-labelled decisions", "data": "n4ze3m/typed-decisions-synth: 7,414 cases / 25,859 questions across 149 domains, soft label for every question", "result": "Q4_K_M 'same accuracy as full model in my tests'", "notes": "Author's caveat: 'fun experiment… nowhere near the quality of Jev from TypeSafe'. Exact trained prompt template published in card.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mrmps-hotdog", "name": "hotdog-27B", "author": "mrmps", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/opensporks/hotdog-27B", "base_model": "Qwen3.8-27B dense", "params": "27B", "metric": "Accuracy, Brier and ECE against Jev 1.13.0 on 1,631 independent test decisions; JevBench native-binary 74 items", "license": "", "paper": "", "stars": 5, "likes": 3, "detail": "result: +0.98 pp, paired 95% CI +0.19 to +1.79 · -2.70 pp, CI -12.20 to +5.63", "arch": "hotdog-27B: open binary classifier, rank-16 LoRA on dense Qwen3.8-27B trained with Tinker; answers a yes/no question by reading the A=false and B=true logits at the same token position without generating reasoning.", "approach": "logits", "training": "", "data": "", "result": "vs Jev 1.13.0, independent test 1,631 decisions: 98.28% vs 97.30% accuracy; Brier 0.0132 vs 0.0269; ECE 0.58% vs 6.40%. Public JevBench native-binary 74 items: 81.08% vs 83.78%; Brier 0.1251 vs 0.1063; ECE 11.95% vs 6.54%. Author's note: beats Jev on the independent mix, does not establish a JevBench win.", "notes": "GitHub new-repo sweep 2026-09-24; benchmark dated 2026-09-22 Code: https://github.com/mrmps/hotdog.", "domain": "general", "score": "98.28%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "ohtaman-gemma4-e2b-choice64", "name": "Jev Gemma 4 E2B choice-64", "author": "ohtaman", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/ohtaman/jev-gemma-4-E2B-it-choice-64", "base_model": "Gemma 4 E2B", "params": "2B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "base: ONNX Community export; arch: Transformers.js 4.3.0; training: no fine-tuning", "arch": "Text-only fixed-label scoring artifact: decoder q4f16 output projection restricted to 64 fixed answer tokens; unused full-vocabulary head bytes removed; scored by the Jev JavaScript scorer on WebGPU", "approach": "logits", "training": "None", "data": "", "result": "", "notes": "Exported via the ONNX Community Transformers.js export, no weights fine-tuned; choice_head_64.json defines the 64 output columns, starting with the digits 0-9; example: a Japanese delivery complaint scored against the candidates 配送, 請求, 技術; output shape is batch, answer_positions, 64", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wilflin-jev-qwen38-flash", "name": "JEV Qwen3.8 Flash Next 180B", "author": "WIlfLin", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/WIlfLin/JEV-Qwen3.8-Flash-Next-Linear-Runtime", "base_model": "Qwen3.8-Flash-Next-mixed-NVFP4-FP8", "params": "180B", "metric": "JevBench 37.7%", "license": "Qwen Community 1.0", "paper": "", "stars": "", "likes": 0, "detail": "base: ~184 GB; arch: no full-vocab GEMM · text and/or images; training: no fine-tuning, RL, temperature fitting or prompt tuning; license: backbone; result: I 46.8 / C 67.7 / S 93.3 / K 43.3 · RunPod GPU; name: Linear/vLLM", "arch": "Unofficial Jev-style closed-choice adaptation: 86×2560 BF16 label head; vLLM plugin removes the full-vocabulary projection and scatters the 86 label logits into token-ID space; vision encoder kept, up to 8 images; 2–86 options", "approach": "head", "training": "None", "data": "", "result": "JevBench v1.4.1 #22: score 37.7; public 75.3%, sealed 29.5%, gap +45.8 pp; p50 0.02 s", "notes": "Family: JEV-Qwen3.8-27B-NVFP4-Runtime, JEV-Qwen3.5-9B-Base-NVFP4, JEV-Qwen3.5-0.8B-Base.", "domain": "general", "score": "JevBench 37.7%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "sarge-jev", "name": "Jev-1.13 distillation family", "author": "SargeDev; autotrust/JEV student", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/SargeDev/Jev_Qwen3.8-27B", "base_model": "Jev_Qwen3.8-27B + autotrust/JEV", "params": "9B / 27B", "metric": "Choice top-1 agreement, noul AUROC, Brier, score MAE, mean KL and ECE on test_set_30k against the Jev 1.13 teacher", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "base: Students: SargeDev/Jev_Qwen3.8-27B, from huihui-ai/Huihui-Qwen3.8-27B-abliterated plus QLoRA r64, and autotrust/JEV, a Qwen3.5-9B LoRA; arch: noul/choice/score; training: via OpenRouter · 4-bit NF4 double-quant, paged_adamw_8bit, completion-only loss, 900 steps on a GB10/DGX Spark for the 27B; data: Apache-2.0 · labels from Jev 1.13 · 32B teacher over HotpotQA/PubMedQA/MLQA/QASports/NarrativeQA/LSAT/TyDi… · OpenJev v2 CC0 reschema'd · 29,955, leakage-checked; license: corpus + models; result: 0.954 on decisive rows · 15 bins, after temperature; notes: enable_thinking=false; name: SargeDev + autotrust/JEV", "arch": "Calibrated typed-decision corpus in the System One schema for training small local System One judges; two public student checkpoints", "approach": "distill", "training": "Distillation from the hosted TypeSafe Jev 1.13 teacher + QLoRA", "data": "jev-distill-corpus-v3: yuri_v3 498,010 synthetic rows over 53 domains, yuri_v1 148,154 memory-relevance noul pairs, openjev_v2 94,801; splits incl. test_set_30k; 45k NL-phrased judgment rows for the 27B", "result": "autotrust/JEV vs Jev 1.13 teacher on test_set_30k: choice top-1 agreement 0.898, noul AUROC 0.9958, Brier 0.0015, score MAE 0.103, mean KL 0.0210, ECE 0.0007", "notes": "First published pipeline that systematically distills the hosted Jev 1.13 API into open weights. Ports: SargeDev/Jev_Qwen3.8-27B-NVFP4-FP8, ramgpt/Jev_Qwen3.8-27B-GGUF. 27B model is served with thinking disabled. 27B checkpoint details, SargeDev/Jev_Qwen3.8-27B, 09-24: full bf16 merge of the QLoRA adapter onto huihui-ai/Huihui-Qwen3.8-27B-abliterated, 900 steps over ~26k rows on a GB10; after the tune the calibrated yes-no and multi-option spreads are measurably closer to ground-truth on held-out corpus rows with the base's general capabilities intact; NVFP4+FP8 quantization via llm-compressor 0.14.0 does NVFP4 W4A4 on attention projections + gate/up, FP8 dynamic on down_proj, embeddings kept high-precision. Distillation corpus: https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v3. Corpus: SargeDev/jev-distill-corpus-v3, Apache-2.0.", "domain": "general", "score": "Brier 0.0015", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-maboto", "name": "jev-maboto", "author": "jeuner", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/jeuner/jev-maboto", "base_model": "Qwen3-1.7B-4bit", "params": "1.7B + LoRA", "metric": "Local versus remote Jev comparison with post-hoc calibration, raw confidence versus Platt scaling", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "LoRA on 4-bit MLX Qwen3-1.7B for one narrow task: assign supermarket offers to one of 13 product groups as letter options A-M, part of maboto, an open-source market-observation tool; Choice/Noul/Score via grammar-constrained decoding plus logprob renormalization instead of tool-calling into a chat model", "approach": "fine-tune", "training": "LoRA rank 8, 16 layers, 600 iterations, learning rate 1e-5; 1,219 real supermarket offers from four German cities, Hilden, Frankfurt, Berlin and Hamburg, labeled zero-shot by a stronger local model, Qwen3-4B-Instruct", "data": "", "result": "Methodological finding: raw model confidence is misleading without post-hoc calibration such as Platt scaling, details in the maboto README local-vs-remote Jev comparison", "notes": "German model card; the adapter must be fused into the base model or loaded via mlx_lm.generate, and the prompt must match the training format exactly", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "guzus-jev-nyotti", "name": "jev-nyotti", "author": "guzus", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/guzus/jev-nyotti", "base_model": "Qwen3.5-4B", "params": "4B", "metric": "Detailed evaluation in the repo REAL_DATA_RESULTS.md; next-hour position classification, educational use", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "base: rank-16 LoRA, unsloth; training: attribution not independently verified; data: sanitized", "arch": "Next-hour BTC position-side imitation: predicts long/short/flat from historical closed candles + prior position side; no reasoning generation, no order execution", "approach": "fine-tune", "training": "4,096 sanitized examples from user-supplied historical BTC execution records", "data": "Trader records", "result": "Experimental next-hour position classifier; educational use only; detailed evaluation in repo REAL_DATA_RESULTS.md", "notes": "Served at jt.memtherscan.xyz; companion repo guzus/jev-nyotti-action.", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tianxinwei-jevanany-27b", "name": "JevAny-27B", "author": "tianxinwei", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/tianxinwei/JevAny-27B-RLCR", "base_model": "Qwen3.8-27B + rank-16 LoRA + pointer-head architecture", "params": "27B", "metric": "89.74%", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "metric: v2 dev; arch: no reasoning traces, no confidence tokens; training: v1 · hard reasoning, many-choice, agent actions, preferences, math/medical, image/video-derived; license: LoRA; name: SFT + RLCR", "arch": "Decision-only checkpoint: bounded typed decisions; RLCR variant = group-relative calibration reward RL with a supervised anchor", "approach": "head", "training": "SFT then RLCR on a 40,000-record RL mixture", "data": "40k RL mixture", "result": "v2 dev accuracy 89.74%, dev NLL 0.260; transfer-v9 82.31%; MMLU-Pro 73.5%; AI2D 87.0%. Card: RLCR did not improve overall dev/transfer accuracy; SFT remains the recommended checkpoint", "notes": "Two checkpoints: JevAny-27B-SFT and JevAny-27B-RLCR v0.2. Code: github.com/weitianxin/JevAny, 12 stars, calibration-aware reinforcement learning for adaptive decision systems.", "domain": "general", "score": "89.74%", "latency": "", "benchmark": "MMLU-Pro", "cost": "", "speedup": ""}
{"id": "praveenrajus-jevify", "name": "jevify", "author": "Praveenrajus", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/Praveenrajus/jevify-qwen3.5-4b-t2", "base_model": "Qwen3.5-4B + trained decision heads", "params": "4B / 2B", "metric": "Accuracy, ECE, TVD to human labels on jev-bench", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "base: and Qwen3-VL-2B variant; size: VL; arch: yes; data: 22,773 test records; result: per-repo results table; name: Qwen3.5-4B / Qwen3-VL-2B", "arch": "System One primitives choice/score/noul: probabilities over options, expected score, P; backbone pulled from its own repo at load", "approach": "head", "training": "Decision heads + rank-16 LoRA + calibration recipe", "data": "Evaluated on Praveenrajus/jev-bench", "result": "The card reports the successor on the same backbone, jevify-qwen3.5-4b-readout-coh, readout fine-tuning with a coherence penalty and no heads, at 0.751 accuracy, 0.058 ECE, 0.303 TVD to human labels; this repo stays the reference for its tier", "notes": "Also jevify-qwen3.5-4b-t2-lowlr and jevify-qwen3-vl-2b-t2 in the same family. Family grew: jevify-qwen3.5-2b, 09-21, Apache-2.0 — 2,628,107 parameters of trained decision heads, 10.5 MB, plus the calibration recipe on Qwen3.5-2B. This repo holds only what Jevify adds to the backbone: 2,891,275 parameters of trained decision heads, 11.6 MB, plus a rank-16 LoRA adapter, 21,233,664 parameters, 85 MB, merged into the backbone at load", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "jevk5", "name": "JevK5", "author": "alibiserikbay", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/alibiserikbay/JevK5", "base_model": "Qwen3.5-4B with a rank-16 LoRA on the attention projections merged into the weights; the teacher Qwen3.6-27B with thinking ON wrote the decision data; JevK5-2B is the same recipe on Qwen3.5-2B", "params": "4B, bf16 in about 9 GB, plus the 2B sibling; teacher 27B", "metric": "JevBench v1.4 composite; public 231 and sealed 308 accuracy, ECE by tier", "license": "Apache-2.0", "paper": "", "stars": 47, "likes": 9, "detail": "url: github.com/allebee/jevk5; base: v0.2 · 09-23; notes: 2026-09-23 · 09-23", "arch": "Independent Apache-2.0 open-weight alternative to Jev: it reads a state and noul, choice or score questions and returns a probability for every option in one forward pass, zero generated tokens; the readout follows SemIf's protocol, a softmax over the answer letters' next-token logits divided by a single calibration temperature, T 1.532 in jevk5_config.json; the runtime at github.com/allebee/jevk5 serves a TypeSafe-style /v1/systemone endpoint with one CUDA graph per padded input length", "approach": "distill", "training": "Distillation from the thinking teacher: Qwen3.6-27B with thinking on wrote realistic documents with hard typed questions, policies with exceptions, date and number traps, multi-step lookups, ambiguity, misleading notes, injected instructions and rule precedence, across 17 business domains, then answered every question twice independently, keeping a question only when both answers matched the intended one and rebuilding option keys from the option text; v0.2 uses 3,272 of those questions, v0.1 used 1,635, plus human-labelled items from MMLU-Pro, WANLI, MultiNLI, BoolQ, banking77, ARC and CommonsenseQA; cross-entropy on the option-letter logits, 2 epochs, learning rate 3e-5, SemIf's prompt format, and the 9 distribution-answer questions train against the exact distribution; the untrained row, same base and prompt without LoRA or temperature, is published as a control", "data": "17 business-domain decision data authored by the thinking teacher; the temperature was fitted on teacher questions from three domains the training never saw, residential leases, public-sector permits and manufacturing QC, at 80.4% accuracy; no JevBench item, public or held out, and no output of Jev was used for training, tuning or selection", "result": "JevBench v1.4: 62.04 composite, #2 of 76 and #1 among open entrants, against Jev 1.13.0 at 63.29, card snapshot, the site table now shows 62.0 among 77; public 231 at 85.3% vs Jev 86.6%, sealed 308 at 33.1% vs 36.7%; on the public hard tier v0.2 hits 0.739 vs 0.676 for v0.1 and 0.613 for the untrained base, fixing 21 of the base's wrong items and breaking 7, McNemar p = 0.013, hard-tier ECE 0.066 vs 0.117 untrained; H100 with CUDA graphs, p50 13.5 ms and p95 14.9 ms on easy and standard, p50 30 ms and p95 161 ms on 1–4k-token hard documents, 0 output tokens; the GGUF Q8_0 build matches the bf16 weights on 228 of 231 public items", "notes": "Known weak spots per the card: accuracy drops sharply on fresh sealed decisions, two standard-tier items regressed from 0.986–0.958, standard-tier answers are underconfident at ECE 0.141 because the temperature was fitted on hard questions, judging answers slipped 0.82–0.76, English only, and inputs over 16,384 tokens are refused, not cut; the teacher itself, with thinking on, answered all 111 public hard items correctly, which JevK5 attempts to move into one fast pass; a 09-24 correction noted the hand-written calibration set echoed one public item's rule wording and was rewritten; 47 stars on GitHub. 'JevK5 is distilled from a model that thinks.' JevK5-2B is the smaller sibling: 0.751 against 0.804 on held-out teacher questions at 3.5 GB. GGUF q8_0 export: alibiserikbay/JevK5-gguf; demo space: Wayne-Chang/jevk5-demo. Code: github.com/allebee/jevk5, 94 stars. New 2B checkpoint alibiserikbay/JevK5-2B, 260 downloads, and the GGUF port alibiserikbay/JevK5-GGUF, 206 downloads, released 09-23. GGUF quant by the same author on HF, 1 like, 09-23. A preview experimental CPU variant, JevK5-Lite, is row alibiserikbay-jevk5-lite.", "domain": "general", "score": "85.3%", "latency": "p50 13.5 ms", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "kev-typed-family", "name": "Kev-4B / Kev-0.8B typed decision adapters", "author": "marcmagn1", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/marcmagn1/kev-4b-typed-v1", "base_model": "Kev base family: Kev-4B, Kev-0.8B", "params": "4B / 0.8B", "metric": "Accuracy, Brier score, ECE before and after temperature scaling, coverage at fixed error", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "arch: typed-decision LoRA adapters on the Kev bases; result: 4B typed 0.800 accuracy with ECE 0.035-0.064 after temperature, 0.8B typed 0.754; baselines kev-4b 0.645 and raw letter logits about 0.44", "arch": "Typed-decision LoRA adapters on the Kev base models: kev-4b-typed-v1, 09-22, kev-08b-typed-v1, 09-21, and kev-08b-typed-v1-blend, 09-22", "approach": "fine-tune", "training": "", "data": "", "result": "kev-4b-typed-v1: accuracy 0.800, Brier 0.328, ECE 0.035-0.064 after temperature scaling, 46.3% coverage at 5% error, 28.2% at 1% error; per workflow: invoice 0.830, customer service 0.837, security incidents 0.792, agent-trace observability 0.748; zero errors across 254 rows at 0.9 or higher confidence. 0.8B typed: accuracy 0.754, ECE 0.028-0.046. Untuned baselines: raw Qwen3.5-0.8B letter-logit readout about 0.44, kev-0.8b 0.445, kev-4b 0.645", "notes": "Evaluation tables published on the cards, see metric and result; raw eval artifacts under marcmagn1/jev-alt-systemone-eval; the Kev base family has its own row, jaredpalmer/kev; sister row marcmagn1-jev-08b, jev-08b-typed-r1", "domain": "general", "score": "0.800, 4B typed", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-kannaka-reflexes", "name": "Laya Kannaka reflexes", "author": "flaukowski", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/flaukowski/laya-kannaka-evidence-gate", "base_model": "Laya", "params": "421M", "metric": "AUROC, Brier, precision@0.5, recall and 20.6 ms per decision on RTX 4090, against the pre-registered rule of AUROC at least 0.85 with Brier at most 0.15", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "arch: yes", "arch": "Two Laya ModernBERT-large 421M checkpoints fine-tuned as trained reflexes of the Kannaka wave-interference memory for persistent agents: the evidence gate asks at recall time whether an excerpt contains the information needed to answer a question, and the supersession reflex asks at write time whether a later statement updates, corrects or replaces an earlier one, both a single noul P answer", "approach": "fine-tune", "training": "Evidence gate: 4,180 question-turn rows from LongMemEval-S with 836 positives and four sampled negatives each, pre-registered held-out test of 450 decisions; supersession e-l3d: 2,346 rows with negatives from the write path own top-5 shortlist, 40 negatives per positive, positives x6, six epochs on one RTX 5090 in 364 s", "data": "", "result": "Evidence gate: AUROC 0.748–0.963, Brier 0.092–0.066, precision@0.5 0.25–0.59, recall 0.95, 20.6 ms per decision on RTX 4090, passing the pre-registered rule of AUROC at least 0.85 with Brier at most 0.15; supersession e-l3d: held-out pair AUROC 0.939 with 13/17 true catches and 29 false stamps vs about 384 in the first revision, lifting answer accuracy to 0.824 against the 0.765 no-supersession control", "notes": "Experiments E-L1b and E-L3b/d of kannaka-bench, the Kannaka memory substrate from kannaka-labs; both cards note an English-only 1024-token context and that the choice and score heads remain the base model own Sister checkpoint: flaukowski/laya-kannaka-supersession (memory-supersession variant).", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"approach": "", "arch": "Laya Vision checkpoint pinned to the run cauldron-score-2ep-bidir-full; a SmolVLM-256M backbone whose answer options attend to each other, trained on 19 closed-form subsets of The Cauldron and four rubric-scored image datasets so the score head is trained for the first time; answers choice, score and noul questions on an image plus optional text in one forward pass with no text generation; independent fork of Laya that keeps the predict(state, questions) API, RLCD training objective and temperature calibration; held at thaitea/laya-vision until 2026-09-24, revision d1fbdc0", "author": "thaitea", "base_model": "SmolVLM-256M-Instruct", "benchmark": "", "category": "model", "cost": "", "data": "", "date": "2026-09-22", "detail": "", "domain": "", "id": "laya-vision-smolvlm-score", "latency": "", "license": "CC-BY-NC-SA-4.0", "likes": "", "metric": "answer accuracy on official validation splits, ECE", "name": "Laya Vision SmolVLM-256M score", "notes": "Experimental research project, not affiliated with Convai Innovations; the data in docs/score-data.md and the runs in docs/score-results.md of github.com/r33drichards/laya-vision, row laya-vision-repo; demo space thaitea/laya-vision-demo on free CPU", "paper": "", "params": "237M", "result": "A-OKVQA 60.0%, ScienceQA 82.8%, VQAv2 yes/no 72.4% on official validation splits; full suite 69.3% correct over 59,427 questions and 34 validation sets, ECE 0.064; score head 54% over 5 levels on held-out VLFeedback response grading vs 27.5% prior-only; about 41 ms median predict on an L4", "score": "", "speedup": "", "stars": "", "training": "", "url": "https://huggingface.co/thaitea/laya-vision-smolvlm-256m-score"}
{"id": "macjev-322m-4k-laya", "name": "MacJev-322M-4K-Laya", "author": "chaoliangUNSW", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/chaoliangUNSW/MacJev-322M-4K-Laya", "base_model": "Laya multilingual", "params": "322M", "metric": "Yes-no task-state checks and rule decisions on 2K to 4K-token inputs, versus the Laya multilingual checkpoint at the same 4,096-token budget", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 3, "detail": "base: Laya multilingual; arch: single-pass candidate scoring, 4K input, per-type temperature; result: 89.1% yes/no on 2K-4K inputs vs 31.0%; notes: Mac agent focus, 20K trajectory decisions", "arch": "Compact decision model for local Mac agents, finetuned from the Laya multilingual checkpoint: one non-autoregressive forward pass scores every candidate in the request, Choice, ordered Score and yes/no Noul, with options defined per request and no classifier head to retrain; 4,096 tokens of total input and 1,024 for the question and options, four times the starting checkpoint, and inputs over budget raise an error instead of silently truncating candidates or evidence; one temperature per question type, fitted on 6,000 independent calibration decisions; trained on 20,000 Mac decisions built from verified trajectories across files, Chrome, Word, Excel and PowerPoint including recovery cases, alongside general-language, typed and rule decisions; returns decisions only and never executes actions, so the agent keeps its own executor and confirmation step", "approach": "fine-tune", "training": "", "data": "", "result": "Against the Laya multilingual checkpoint with the same 4,096-token budget on held-out sets: yes/no task-state checks on 2K to 4K-token inputs rise from 31.0% to 89.1%, rule decisions on 2K to 4K tokens from 23.7% to 44.7%, long-input calibration error from 0.253–0.032, and typed decisions from 35.2% to 42.2%; on 11,600 public decisions from typed decisions, Emotion and AG News accuracy rises 1.5 points with a 95% interval of 1.1–1.8, consistent across three independently trained seeds; released as PyTorch FP32, MLX FP32 and GGUF F16 for stock llama.cpp", "notes": "Website jevstyle.com; English and Chinese; the release is the seed chosen in advance on development data; intended to rank candidate actions, route tool calls and check task state entirely on the machine", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "medical-openjev", "name": "Medical-OpenJev", "author": "fancc28 / vindahi", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/fancc28/Medical-OpenJev", "base_model": "ModernBERT-base", "params": "149.6M per tier", "metric": "ECE, Brier, AURC, CovAcc@95 and top-confidence-10% accuracy on Qwen3.5-4B answers, against the base LLM and random", "license": "Apache-2.0", "paper": "", "stars": 3, "likes": 0, "detail": "data: 3 condition tiers - derm, agentclinic, rdc", "arch": "Patient evidence state → specialty routing + evidence-sufficiency probability + calibrated confidence + act/escalate decision in one forward pass; 'traffic light' running before/around a general LLM doctor, not attached after it; 141M frozen backbone with per-option [MASK] scoring", "approach": "head", "training": "RLCD proper-scoring training", "data": "Dermatology, multi-system/acute, rare-disease difficulty gradient", "result": "On Qwen3.5-4B answers: ECE 0.226 against 0.533 for the base LLM and 0.331 for random, 58% lower than base; Brier 0.241 against 0.523; AURC 0.597 against 0.666; CovAcc@95 0.281; top-confidence-10% accuracy 0.667, double the base LLM 0.333; the motivating measurement is an LLM reporting 0.74 confidence at 0.28 actual accuracy; Gate ~2–3× better calibrated than the LLM baseline on every tier; tier-specific calibration reported", "notes": "One project, two handles: weights on Hugging Face under fancc28, code and docs on GitHub at vindahi/Medical-OpenJev. Positions itself as the open counterpart to closed 'System One decision' APIs in medicine; never generates text so 'cannot hallucinate a diagnosis'. Apache-2.0; only trained decision weights redistributed, no patient data. Condition list: Hailey-Hailey, Conradi-Huenermann - lichen sclerosus, NMS; Orphanet - hypochondroplasia, Loeffler endocarditis. Academic project repo with the full write-up at petofi-romance/openjev-med, Tsinghua University, Shandong University and City University of Hong Kong Dongguan; parameter count 149.6M per tier measured from the safetensors header", "domain": "health", "score": "", "latency": "tens of ms on a 4090; CPU-capable", "benchmark": "", "cost": "", "speedup": ""}
{"id": "junma-medjev", "name": "MedJev", "author": "JunMa11", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/junma/MedJev-Qwen3.5-0.8B", "base_model": "Qwen3.5-0.8B-Base", "params": "0.8B", "metric": "micro accuracy 0.878 on 26,286 held-out questions, Brier 0.179", "license": "Apache-2.0", "paper": "", "stars": 83, "likes": 0, "detail": "result: beats regex rules 0.667, hosted Jev 0.602 at 284 ms and the zero-shot base 0.500 on the held-out test split; data: silver labels are a GPT-4 extraction of the augmented-clinical-notes corpus, not clinician adjudication; notes: research artifact, not for clinical use", "arch": "LoRA plus pointer head on Qwen3.5-0.8B-Base, architecture from Kev: the state is encoded once, each question scores its options against a decide position, questions cannot read each other; free-text clinical notes to table-ready data via noul, choice 2–255 options, score", "approach": "fine-tune", "training": "LoRA r16 alpha32 dropout 0.05 on attention, MLP and DeltaNet projections plus a 256-dim pointer head, 2 epochs on 431,124 question rows, 3 x RTX 6000 Ada in 5.39 h", "data": "23,719 notes and 215,425 labelled questions, GPT-4 silver labels from structured patient summaries", "result": "'Thousands of notes an hour' on one consumer GPU; no subscription/per-use fees; patient text never leaves the network", "notes": "Different project from PAI-CUHK's MEDJEV. Clinical NLP as System One decisions. Code: https://github.com/JunMa11/MedJev.", "domain": "health", "score": "micro accuracy 0.878", "latency": "62 ms per note, 11 variables, bf16 on one GPU", "benchmark": "", "cost": "", "speedup": ""}
{"id": "minicpm5-2b-decision", "name": "minicpm5-2b-decision", "author": "bdauzats", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/bdauzats/minicpm5-2b-decision", "base_model": "MiniCPM5-2B", "params": "2B base plus merged LoRA rank 16 plus a pointer head, bf16 unquantized", "metric": "MASSIVE fr and en accuracy; calibration error on 59-option questions", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "Decision model in one forward pass: a state text and typed questions in, one probability per option out, never generates text; follows the architecture and training code of Kev, Jared Palmer's Jev-style reconstruction, applied to a different base model and data mix; built for the jul local typed-decisions project, which speaks the TypeSafe SDK interface over MLX on Apple Silicon or PyTorch elsewhere; a 4-bit MLX build ships separately", "approach": "from-scratch", "training": "First-epoch checkpoint ships: keeping a second epoch buys 0.3 points on unseen sources while more than doubling confident errors from 0.6% to 1.4% and forcing a harsher temperature from 1.29–2.14 that then misfits many-option questions", "data": "44,720 records, about 15% French, up from 15,061 in v1.0", "result": "v1.1: French accuracy on MASSIVE up from 0.485–0.710, English from 0.690–0.815, narrowing the previous headline limitation of another language costing about 20 points to about 10; calibration error on 59-option questions down from 0.241–0.069", "notes": "v1.0 remains available under revision v1.0; English and French", "domain": "general", "score": "accuracy 0.485 → 0.710", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tasksource-modernbert-jev", "name": "ModernBERT-JEV", "author": "tasksource", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/tasksource/modernbert-tasksource-jev", "base_model": "ModernBERT-base + option-query cross-attention decision head", "params": "149M", "metric": "Accuracy, Brier and log-loss for high-throughput typed decisions", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "base: 149M, bidirectional, 8,192-token context; arch: H_context · L · K×L · Banking77; result: noul + scoring", "arch": "Context encoded once; each candidate option encoded independently with the shared encoder; cross-attention decision head — O context cost vs O for cross-encoders; claims to avoid Laya-style token starvation on high-cardinality tasks", "approach": "head", "training": "Classification training with calibration", "data": "", "result": "High-throughput typed decisions; metrics: accuracy, Brier, log-loss", "notes": "Companion dataset: tasksource/procedural-jev.", "domain": "search", "score": "", "latency": "", "benchmark": "Banking77", "cost": "", "speedup": ""}
{"id": "einsteiner1983-nemotron-lora", "name": "Nemotron 3.5 Lightning System One LoRA", "author": "einsteiner1983", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/einsteiner1983/nemotron-3.5-lightning-systemone-lora-r16", "base_model": "Nemotron 3.5 Lightning 30B-A3B", "params": "30B", "metric": "", "license": "Open", "paper": "", "stars": "", "likes": 0, "detail": "base: adapter only · base not included; size: 3B active; training: PEFT 0.21.0 · accum 8; license: HF", "arch": "Experimental classification adapter: candidate-only cross-entropy on answer-token logits computed in FP32", "approach": "logits", "training": "Rank 16, alpha 32, dropout 0.05; 2,676 examples, 335 optimizer updates; BF16 on 1× H100 80GB, AdamW lr 5e-5, seed 17", "data": "2,676 training examples", "result": "One pass over 2,676 examples with 335 optimizer updates on one H100 80 GB: rank 16, alpha 32, dropout 0.05, candidate-only cross entropy on answer-token logits computed in FP32, AdamW at nominal 5e-5", "notes": "Private research artifact, adapter weights only, not an official NVIDIA or TypeSafe release; the jev-latest alias is a compatibility alias; byte equivalence of the base to a public Hub revision not established; Nimble source revision f136b3f", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nev-lite", "name": "nev-lite-systemone", "author": "shreyanbr", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/shreyanbr/nev-lite-systemone", "base_model": "all-MiniLM-L6-v2", "params": "11.8M", "metric": "Per-primitive accuracy on each benchmark, with a static-embedding ablation of the whole System One idea", "license": "Open", "paper": "", "stars": "", "likes": 0, "detail": "base: embedding seed only; arch: model.pool; training: official splits · fraction-capped hold-out; license: HF; result: question-shapehreyanbr also runs system-one-zeroshot/distilled/gold DeBERTa-v2 scorers.", "arch": "Model2Vec-style STATIC embeddings: 'none of the transformer layers are used at inference' — only the embedding table + tiny MLP/projection. Options pooled once and cached across requests.", "approach": "head", "training": "Trained on each benchmark's own gold labels", "data": "Official benchmark splits, incl. deepset/prompt-injections 546 train rows", "result": "Per-primitive accuracy on each benchmark; static-embedding ablation of the whole System One idea", "notes": "No-attention 12M-param typed-decision model", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shapsider-omnijev", "name": "OmniJev, tzcfly", "author": "shapsider", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/tzcfly/OmniJev-Qwen3.5-9B-v4", "base_model": "Qwen3.5-9B plus v4 corrective head", "params": "9B", "metric": "76.5% overall on 923 held-out image questions", "license": "MIT", "paper": "", "stars": 13, "likes": 0, "detail": "base: Qwen3.5-9B plus v4 corrective head; arch: one forward scores the supplied options, no generated answer token; result: 923 held-out image questions, overall 76.5 vs 72.3 and 68.7 for two 4B rivals; license: MIT; notes: project page shapsider.github.io/OmniJev, technical report in repo", "arch": "One forward scores the options supplied with the request, no generated answer token; finite-choice decision SDK, HTTP service and MuJoCo robotic-arm workbench", "approach": "sdk", "training": "", "data": "", "result": "923 held-out image questions: MMStar 70.0, RealWorldQA 73.8, AI2D 84.4, MMMU 64.5, ScienceQA test 96.2; overall 76.5 vs NeoHorse-Jev-4B 72.3 and tinnel OmniJev-4B 68.7. Earlier audio version: 64.1% vs 11.1% GPT-4o, 2.35 s per decision on M4", "notes": "Multimodal research project for robot decision-making: finite-choice decision SDK, HTTP service and a MuJoCo robotic-arm workbench. The decision model is Qwen3.5-9B plus a v4 corrective head — LoRA and residual head at tzcfly/OmniJev-Qwen3.5-9B-v4. On 923 held-out image questions: MMStar 70.0, RealWorldQA 73.8, AI2D 84.4, MMMU 64.5, ScienceQA test 96.2; overall 76.5 against NeoHorse-Jev-4B 72.3 and tinnel OmniJev-4B 68.7. Evolved from the first open-weights decision model with audio input, which scored 64.1% against 11.1% GPT-4o at 2.35 s per decision on M4. Technical report in the repo; project page at shapsider.github.io/OmniJev. Code: https://github.com/shapsider/OmniJev.", "domain": "robotics", "score": "76.5%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sshalimov04-open-jev-base", "name": "open-jev-base", "author": "sshalimov04", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/sshalimov04/open-jev-base", "base_model": "mmBERT-small", "params": "28M", "metric": "Zero-shot over new questions and option sets via the open-jev loader", "license": "MIT", "paper": "", "stars": "", "likes": 0, "detail": "arch: question, option, text · no retraining for new options; training: 29 Jev-teacher-labelled, 11 local-Qwen; data: all tasks held in; result: task YAMLs", "arch": "Pointwise cross-encoder: one forward pass scores one triple; K-way = K passes renormalized over the offered options; noul = one pass; K and option wording free at inference", "approach": "head", "training": "Distillation on pooled soft labels from 40 typed-decision tasks", "data": "open-jev task mixture; released as the --fold none artifact", "result": "Zero-shot over new questions/option sets via the open-jev loader", "notes": "Repo: sshalimov04/open-jev. EN/RU. Code: github.com/Shalimov04/open-jev, distill a prompt into a small, fast, calibrated classifier with typed choice, score and noul.", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alquimiaai-openjev", "name": "OpenJev, AlquimiaAi", "author": "AlquimiaAi", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/AlquimiaAi/openjev", "base_model": "", "params": "undisclosed", "metric": "Accuracy on 10,000 held-out decisions", "license": "CC-BY-NC-4.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Open-weights decision model for typed decisions about text, web pages and screenshots: one forward pass per question with up to 52 options, no free-form text to parse, no chain of thought, no training per task", "approach": "fine-tune", "training": "", "data": "", "result": "84.0%, 8,403 of 10,000, vs the hosted Jev API at 85.4%, the same base model before tuning at 80.4%, and open-weight Nimble 9B at 75.7%", "notes": "Roughly 1,460 prompt tokens and 23 options for the isolated web decision. Six languages, en, de, fr, hi, zh, ja. The same model routes a support ticket, flags a policy violation, judges whether an answer is grounded and tells a browser agent which button to press next. Built on an Apache-2.0 open base model; weights under CC BY-NC 4.0, helper and serve code Apache-2.0; served with vLLM. Held-out text, agent, language and screenshot test sets compare OpenJev against its own base model before tuning and, on the text rows, against the hosted Jev API.", "domain": "general", "score": "84.0%", "latency": "about 210 ms for an isolated web decision on one H100", "benchmark": "10,000-question held-out set, text rows compared against the hosted Jev API", "cost": "", "speedup": ""}
{"id": "mghafiri-qwen35-08b", "name": "qwen3.5-0.8B-decision-model", "author": "mghafiri", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/mghafiri/qwen3.5-0.8B-decision-model", "base_model": "Qwen3.5-0.8B-Base", "params": "0.8B", "metric": "81.1% vs Jev 93.1%, 150 unseen scenarios", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "base: Qwen3.5-0.8B-Base; metric: head-to-head 150 scenarios / 714 questions; result: 81.1% vs 93.1%, Brier 0.158 vs 0.042; notes: lower ECE than Jev on all three primitives, weaker tail automation; approach: finetune", "arch": "Decision model fine-tuned from Qwen3.5-0.8B-Base: typed choice, score and noul questions over a state return calibrated probability distributions instead of generated text, following the publicly documented TypeSafe System One request and response shape", "approach": "fine-tune", "training": "Fine-tune on decision data; the 150 held-out scenarios were never used to train, select or calibrate the model", "data": "", "result": "Head to head vs jev-1.13.0 on the same 150 scenarios and 714 questions as identical POST /v1/systemone requests: accuracy 81.1% vs 93.1%; Brier 0.158 vs 0.042; ECE choice/noul/score 0.037, 0.026, 0.059 vs 0.088, 0.018, 0.118; log-loss vs soft labels 0.615 vs 0.957; choice at confidence >= 0.9: this model automates 4% at 100% accuracy vs Jev 83% at 100%; runs locally on a MacBook Pro with an Apple M2 Max, no GPU server", "notes": "Independent open model, not affiliated with or endorsed by TypeSafe, and explicitly not Jev; hosted Jev p50/p95 latency 1.3 s / 2.3 s in the same comparison Its training data is published: mghafiri/decision-model-scenarios — 2,000 synthetic English scenarios with 9,716 typed questions and calibrated soft labels, MIT, independent of TypeSafe.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "qwen35-9b-jev-datamix", "name": "Qwen3.5-9B Jev data mix v2", "author": "jsaurabh", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/jsaurabh/qwen3.5-9b-jev-data-mix-v2", "base_model": "Qwen3.5-9B", "params": "9B LoRA", "metric": "JevBench public v1 with Brier, ECE and tier breakdown; Nimble frozen holdout; benchmark-awareness disclosure", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "LoRA adapter on Qwen3.5-9B for typed decision scoring with candidate logits: it consumes the application state plus a typed question schema and scores the allowed candidates directly through the upstream Nimble encoding, choice, boolean noul and ordinal score decisions, with no output tokens generated", "approach": "fine-tune", "training": "", "data": "", "result": "JevBench public v1 184/231, 79.65%, Brier 0.2895, ECE 0.0892, tiers easy 48/48, standard 68/72, hard 68/111; Nimble frozen holdout 282/324, 87.04%; a benchmark-awareness disclosure states the synthetic curriculum was created after inspecting public JevBench results", "notes": "Experimental open reproduction inspired by the public Nimble methodology, explicitly not TypeSafe Jev; weakest area is temporal and numeric reasoning; provenance pins the Nimble and JevBench commits plus the adapter archive SHA-256", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rev-decision", "name": "rev-decision-model", "author": "jaswanthsanjay88", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/jaswanthsanjay88/rev-decision-model", "base_model": "ModernBERT-large", "params": "421M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "Trained with RLCD, REINFORCE against strictly proper scoring rules, LogScore plus Spherical plus Ranked Probability Score, on top of ModernBERT-large, 421M parameters, 8192 context. Companion repo jaswanthsanjay88/rev holds the training notebook train_laya_system_one_decision_model.ipynb with a Colab link. Apache-2.0.", "arch": "Non-autoregressive, calibrated System One decision model: typed choice, score and noul questions over any structured JSON, email or customer state in a single forward pass, about 33 ms, with zero text generation and zero decoding latency.", "approach": "head", "training": "RLCD-style training", "data": "", "result": "Zero-shot-classification pipeline; agentic-AI decision-model positioning", "notes": "One of many post-Jev ModernBERT decision checkpoints.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sarashina-mmproj-jev", "name": "Sarashina 2.2 Vision 3B mmproj", "author": "Argos1111", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/argos1111/sarashina2.2-vision-3b-mmproj-jev-f16", "base_model": "sarashina2.2-vision-3b", "params": "3B vision encoder, mmproj ~893 MB", "metric": "", "license": "MIT", "paper": "", "stars": "", "likes": 0, "detail": "approach: adapter", "arch": "vision encoder and projector GGUF for the Jev Local API: choice, score and noul judgments on images; language GGUF from mradermacher/sarashina2.2-vision-3b-GGUF", "approach": "head", "training": "", "data": "", "result": "No benchmark published: documents image-input choice, score and noul decisions through the Jev Local API and its known limits", "notes": "Japanese and English; Linux x86_64/WSL2, CPU, ROCm or CUDA; requires the modified llama.cpp runtime from Argos1111/jev_local. Runtime archives for CPU, ROCm gfx1100/gfx1201 on RX 7900 and Radeon AI PRO R9700 and CUDA 13 sm89 on RTX 40 that the author has not verified on real hardware; API on 127.0.0.1:8080; up to 4 images at 4 MiB each, choice questions up to 26 options; mmproj about 893 MB F16 with a sidecar JSON for startup checks; language GGUF from mradermacher/sarashina2.2-vision-3b-GGUF, Q4_K_M about 2.07 GB or Q8_0 about 3.57 GB; known limits include monochrome images, multi-image handling and result shifts with option order; not an official SB Intuitions distribution, Sarashina is MIT", "domain": "multimodal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "top7777-schema-scorer", "name": "Schema-conditioned candidate scorer", "author": "top7777", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/top7777/jev-schema-scorer-deberta-v3-large", "base_model": "DeBERTa-v3-large + single scalar head", "params": "438M", "metric": "", "license": "MIT", "paper": "", "stars": "", "likes": 0, "detail": "arch: state, question + candidate · argmax + probabilities · p of true · expected level + per-level probabilities", "arch": "One encoder scores pairs; deterministic code groups scalar logits per question into choice, noul, score; question text/criteria/option ids read at inference time, never baked into weights", "approach": "head", "training": "Schema-conditioned candidate scoring", "data": "", "result": "Same checkpoint answers new questions over new label sets without retraining; demo space mobarmg/jev-schema-scorer", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "security-one-27b", "name": "Security-One-27B", "author": "superagent-ai", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/superagent-ai/security-one-27b", "base_model": "Qwen3.8-27B", "params": "27B", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Calibrated 27B decision model for fast, structured classification with additional training for prompt-injection and security decisions: it scores supplied options in one forward pass and returns probabilities instead of generating a free-form answer; continual fine-tune of denis-pplx/autojev-27b, itself based on Qwen3.8-27B; merged BF16 SafeTensors in 15 shards; the card warns this is a decision model, not a chat model, and ordinary chat-completion prompting does not reproduce the reported results", "approach": "", "training": "", "data": "", "result": "Native context 262,144 tokens with a validated classification context of 65,536; calibration temperature 0.145273; the validated production configuration is SGLang v0.5.19-cu130 on a B200, language-only, bf16, 0.85 memory fraction", "notes": "Released with recipes/sglang in the repo plus a hosted Security-One API.", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "solomon", "name": "Solomon", "author": "DoccyHealth", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/DoccyHealth/Solomon", "base_model": "Qwen3.8-27B", "params": "27B", "metric": "Jev Decision Index panel score and request answered rate, 104,787 of 132,422 requests", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 50, "detail": "base: LoRA / PEFT adapter; training: PEFT; data: per HF tags; result: 6th of 31 open models · Jev 59.51", "arch": "PEFT LoRA text-classification / structured-decisions adapter; HF tags: text-classification, document-question-answering, structured-decisions, calibration, synthetic-evaluation", "approach": "fine-tune", "training": "LoRA fine-tune", "data": "Synthetic evaluation", "result": "45 Hub likes; Jev Decision Index: 47.51 on the current panel, 36.54 frozen; 104,787 of 132,422 requests answered", "notes": "Health-focused org among the strongest open contenders on the Decision Index.", "domain": "health", "score": "47.51 current, 36.54 frozen", "latency": "", "benchmark": "Jev Decision Index", "cost": "", "speedup": ""}
{"id": "olafura-gemma4-system-one", "name": "System One for Gemma 4 12B", "author": "olafura", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/olafura/gemma4-12b-system-one", "base_model": "Gemma 4 12B", "params": "12B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "base: unmodified, QAT w4a16; training: base weights never modified; license: adapter; result: ROCm · CUDA", "arch": "Per-request router in front of an unmodified Gemma 4 12B: answer now / reason first / ask back one short question; free-text, not typed primitives", "approach": "router", "training": "None", "data": "", "result": "WIP snapshot; Elixir/Nx via EXLA on AMD Strix Halo and L4/A100", "notes": "HF: olafura/gemma4-12b-system-one.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mpuig-system-one", "name": "system-one open learning project", "author": "mpuig", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/mpuig/system-one-minicpm5-2b-q8", "base_model": "MiniCPM5-2B-Base + Qwen3-0.6B variant", "params": "2B / 0.6B", "metric": "86.6%", "license": "MIT", "paper": "", "stars": 1, "likes": 0, "detail": "base: frozen system-one recipe, LoRA-fused, MLX 8-bit; metric: dev; arch: choice/score/noul · choice 0.961 / noul 1.073 / score 1.387; training: deliberately disclosed · quality loss, no speed gain; data: calibrated; result: optimistic, used for steering · full protocol + CIs in repo; notes: quantization, drift, CIs", "arch": "Typed questions over JSON state → calibrated probabilities from one forward pass; per-primitive temperatures bound to the exact weights — serving engine fails closed on mismatch", "approach": "logits", "training": "8.7k questions recast from public classification datasets plus ~1.6k synthetic scenarios whose probability targets came from pinned jev-1.13.0, teacher distillation disclosed deliberately, filtered for teacher overconfidence; readout-matched cross-entropy plus Ranked Probability Score on ordinal questions; LR 1e-5, batch 8, one epoch, seed 42, Apple MLX", "data": "Public recasts + synthetic scenarios; development split n=1,128, fresh out-of-family test n=1,048", "result": "MiniCPM5-2B quality tier: dev 86.6% / ECE 0.019; fresh out-of-family test n=1,048, spent once: 73.3% / ECE 0.080, +6.8 pts over the 0.6B tier with CI +4.5 to +9.2; Q8 median 1.61× serving speedup vs BF16; zero argmax flips across the drift battery. Qwen3-0.6B volume tier: in-family 77.6% / ECE 0.049 and fresh out-of-family 66.5% / ECE 0.076, n=1,048 each, spent once. Measured shift warning: out-of-family confident errors reached ~19% at t>=0.9 versus ~2% in-family on the 0.6B tier", "notes": "Rebuild Jev behavior as an open learning project — every gate check published. Per-primitive temperatures bound to the exact weights; the serving engine fails closed on mismatch; per-workload temperature fitting from ~100 labeled examples via POST /v1/calibrations repaired scalar miscalibration to 3–4% confident errors. Qwen3-0.6B LoRA adapter in MLX format with its own temperature.json. Quality-tier 2B weights at huggingface.co/mpuig/system-one-minicpm5-2b-q8: MIT, 8.501 bits per weight, 2.5 GB, with the unquantized LoRA adapter published alongside for re-fusing. Code: https://github.com/mpuig/system-one.", "domain": "general", "score": "86.6%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system1-qlora", "name": "System-One QLoRA", "author": "yocoms", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/yocoms/system1-qlora", "base_model": "Qwen3-0.6B + Qwen3-4B-Instruct-2507", "params": "0.6B / 4B, LoRA r16", "metric": "231 public JevBench items: 06b_bf16_r16 versus 06b NF4; 0.6B abstention accuracy; closed Jev 1.13 on identical items", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "training: 09-23", "arch": "Two small open single-pass decision scorers in the shape of TypeSafe Jev: a state plus a multiple-choice question goes in, a calibrated distribution over the options comes out in one forward pass - no autoregressive generation, no sampling, deterministic", "approach": "fine-tune", "training": "LoRA r=16 alpha=32; the 06b_bf16_r16 rebuild trains on a bf16 base instead of 4-bit NF4 - full-precision training beats QLoRA by about 7 points on the dev sets at this size, a training effect", "data": "", "result": "231 public JevBench items: 06b_bf16_r16 0.632 vs 06b NF4 0.619; the 0.6B reaches 96% abstention accuracy, matching a large prompted model on its score - pure zero-shot transfer; closed Jev 1.13 scored on identical items as a reference, not a rival", "notes": "Research and fun weekend project, an independent reproduction; code at github.com/y0c0ms/System1QLoRa; documented pitfall: score through the raw forward pass, because a prompt-format quirk makes the model read the option before the letter.", "domain": "general", "score": "", "latency": "~53 ms GPU and ~0.4 s CPU fp32 for the 0.6B, ~165 ms GPU for the 4B", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ankit-aglawe-tinyjev", "name": "TinyJev", "author": "ankit-aglawe", "date": "2026-09-22", "category": "model", "url": "https://huggingface.co/models/AnkitAI/tinyjev-0.6b", "base_model": "", "params": "0.6B", "metric": "Correct answers out of 500 on OpenDecision Original Choice 500", "license": "MIT", "paper": "", "stars": 4, "likes": 1, "detail": "arch: ticket, JSON record, log line", "arch": "TinyJev 0.6B: typed questions about a state answered with a probability for every offered option in one forward pass on a laptop; MIT, PyPI, weights on Hugging Face, EN/CN/JA/KO docs.", "approach": "logits", "training": "", "data": "", "result": "0.6B: 440/500 in fp16; the 4B sibling 473/500; Kev-0.8B 463; Claude Opus 5.5 496; the same Qwen3-0.6B weights read through letter logits with no head 354; 0.6B calibration ECE 0.071 and 63% coverage at 2% error vs 0.022 and 92% for the 4B; transfer-v4 dev 0.625 vs 0.762", "notes": "GitHub new-repo sweep 2026-09-24; weights at huggingface.co/AnkitAI/tinyjev-0.6b. The card calls this a demo, not a benchmark: on structured numeric state the answer barely moves with the input and the router does the game's work. Quantizing to 8-bit keeps the same weights in half the memory and changes almost one answer in 500. Code: https://github.com/ankit-aglawe/tinyjev. AnkitAI/TinyJev-4B, the 4B sibling's weights, published 09-24.", "domain": "general", "score": "440/500, 0.6B", "latency": "90 ms per case for the 0.6B, 845 ms for the 4B, base M1 16 GB via MLX", "benchmark": "OpenDecision Original Choice 500, 25 domains, not in the training data", "cost": "", "speedup": ""}
{"id": "agent-jev", "name": "agent-jev", "author": "aimeigaoshou", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/aimeigaoshou/agent-jev", "base_model": "Qwen3-0.6B", "params": "0.6B", "metric": "57.8%", "license": "Open", "paper": "", "stars": "", "likes": 30, "detail": "base: FP32; metric: coding completion; arch: en/zh; training: test split not used for selection; data: public/held-out checks; license: HF; result: 606/1048", "arch": "Coding-agent decision model; SFT + RLCD on executed code pairs", "approach": "fine-tune", "training": "Supervised + RLCD with executed coding pairs: same public checks pass on both sides, label comes from the checks NOT shown; dev set selected RLCD step 12", "data": "Executed coding pairs", "result": "Coding completion 57.8%; accuracy 51.7% → 57.8%, recall 35.7% → 57.8% after RLCD", "notes": "A clean published RLCD ablation on a tiny model for the coding-agent niche. Code: github.com/malevrigns/agent-jev, 293 stars; also reports 79.25% top-1 on 2,000 decisions on LocalLLaMA/typed-decisions with zero tokens decoded; pitch: put it where an agent loop needs a gate, a route, or a score, and leave the prose to a larger model. The malevrigns repo, 293 stars, adds a Jev 1.13 + Jev-Omni hybrid mode: 15.2 s vs 30.5 s baseline on an 8-page flow, 2× faster, 100% vs 97.55%, 113/116; Jev-only 14.9 s vs 10.5 s baseline; 5.5% lower cost, 34,208 vs 36,171 tokens; 15 passing pytest tests.", "domain": "coding", "score": "57.8%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hopper", "name": "Hopper", "author": "HopitAI", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/HopitAI/hopper", "base_model": "Qwen3.5-4B", "params": "LoRA rank 16 adapter on Qwen3.5-4B", "metric": "JevBench four-axis score", "license": "Research and demo only: training included RACE passages via the MMLU auxiliary set, whose non-commercial terms extend to derived data; a clean retrain is in development", "paper": "", "stars": "", "likes": 2, "detail": "", "arch": "LoRA adapter for the JevBench setting: a document, a policy and a question go in, a probability distribution over a fixed option set comes out; one forward pass per decision with thinking off and no generated text; the answer is a softmax over the option-letter logits, restricted to as many letters as there are options, plus a fitted calibration map in hopper.json", "approach": "fine-tune", "training": "LoRA on the JevBench task distribution across twelve public sets", "data": "ai2_arc, CommonsenseQA, MMLU, SNLI, MultiNLI, ViTAMIN-C, BoolQ, SQuAD v2, CLINC out-of-scope, DBpedia-11 and HelpSteer2", "result": "JevBench v1.4.1 official table: rank 3 of 77 at 59.4, with intelligence 48, calibration 79, speed 87, cost 59, estimated $0.024 per 1k decisions; behind Jev 1.13.0 at 63.3 and JevK5 v0.2.0 at 62.0", "notes": "The license restriction is the row's most actionable fact for anyone wanting to reuse the adapter commercially", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "jev-decider-4b", "name": "jev-decider-qwen3-4b", "author": "mogita", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/mogita/jev-decider-qwen3-4b", "base_model": "Qwen3-4B", "params": "4B", "metric": "66.5%", "license": "Open", "paper": "", "stars": "", "likes": 1, "detail": "base: LoRA, constrained decoding; metric: post-change; arch: over the letters · 40 continuation probes recovered nothing; data: 20k rows; license: HF; result: after change", "arch": "Decision model for bank-transaction categorization; loss touches ONE position so no gradient reaches the transaction text — the model is never asked to reproduce a record", "approach": "constrained", "training": "Stage 1: 20,000 rows of us-bank-transaction-categories-v2, 1 epoch, lr 1e-4", "data": "us-bank-transaction-categories-v2", "result": "Accuracy 64.6% → 66.5%; ECE/Brier tables; works with 2–26 custom labels via base-model ability", "notes": "'Decider' for finance: category probabilities as a typed decision, with explicit leakage probes.", "domain": "finance", "score": "66.5%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-gate-student-b-merged", "name": "Jev-Gate Student B merged", "author": "SargeDev", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/SargeDev/jev-gate-student-b-merged", "base_model": "Qwen2.5-0.5B-Instruct", "params": "0.5B", "metric": "10k held-out: MAE 0.219, Pearson 0.709, 81.7% binary agreement", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: relevant", "arch": "Qwen2.5-0.5B-Instruct with the Jev-Gate Student B LoRA merged, rank 16, alpha 32, q_proj and v_proj only, about 1.1M trainable params; decision read from final-token yes/no logits", "approach": "distill", "training": "", "data": "", "result": "86.4% agreement with the Jev 1.13 teacher at 23 ms per 32-batch vs ~1,007 ms for the API", "notes": "Drop-in inference weights; unmerged LoRA: SargeDev/jev-gate-student-b; same evaluation as the LoRA repo.", "domain": "agent", "score": "81.7%", "latency": "23 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-browser", "name": "Laya Browser, cklxx", "author": "cklxx", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/cklxx/laya-browser", "base_model": "laya-browser v10s·v11s, mmBERT-base 322M", "params": "421M / 322M", "metric": "Operation accuracy 0.54 to 0.88-0.89; negative result: confidence-gated escalation to Qwen3-8B made things worse", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 13, "detail": "base: ModernBERT-large 421M; training: noisy-logit policy gradient + soft CE · ~2 h v10, ~1 h v10s; data: Qwen writes 'the goal a user would state to need this element'; result: CLICK/TYPE_TEXT/SELECT/DONE · 58% → 42%; name: Mind2Web agent", "arch": "Laya fine-tuned as a browser-agent decision head — drop-in replacement for TypeSafe Jev in the agent loop", "approach": "head", "training": "Laya's RLCD recipe, single GPU, 4 epochs, post-hoc temperature", "data": "LocalLLaMA/typed-decisions style: 5,244 reverse-generated goals on 421 crawled pages, 700 real DONE states, 659 steps; osunlp/Mind2Web", "result": "Operation accuracy 0.54 → 0.88–0.89. Negative result: confidence-gated escalation to Qwen3-8B made things WORSE — the fine-tuned 322M decider beat the 8B general LLM on these pages", "notes": "Browser decision model: element table + goal → next action, one forward pass.", "domain": "computer-use", "score": "accuracy 0.54 → 0.88", "latency": "", "benchmark": "Mind2Web", "cost": "", "speedup": ""}
{"id": "aungthuhein-dev-laya-burmese", "name": "Laya Burmese SIB-200", "author": "aungthuhein-dev", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/aungthuhein-dev/laya-burmese-sib200", "base_model": "Laya multilingual", "params": "322M, mmBERT-base", "metric": "seven-way Burmese topic classification, 701 SIB-200 train examples, plain cross-entropy", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 2, "detail": "arch: fine-tuned version of the multilingual Laya checkpoint for seven-way Burmese topic classification; data: all 701 Burmese training examples from the Davlan/sib200 dataset; training: plain cross-entropy; result: typed topic decisions and calibrated class probabilities through the Laya Agent interface; notes: Apache-2.0; companion Gradio demo space from the same author; cataloged on madewithlaya", "arch": "Fine-tune of the multilingual Laya checkpoint for seven-way Burmese topic classification, trained on all 701 Burmese training examples of SIB-200 with plain cross entropy; typed topic decisions and calibrated class probabilities through the Laya Agent; an independent research release, not an official Convai or TypeSafe model", "approach": "fine-tune", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"approach": "", "arch": "Laya Vision on a bidirectional backbone: ModernVBERT/modernvbert, ModernBERT-150M plus a SigLIP2 vision encoder, replaces Laya's ModernBERT text encoder; because the backbone is bidirectional Laya's readout carries over unchanged, each option scored from a MASK marker that sees the whole sequence; answers choice, score and noul questions on one or more images plus optional text in one forward pass; the predict(state, questions) API, proper-scoring-rule training and temperature calibration are unchanged; a state may carry several images, the model was trained on NLVR2 pairs; needs transformers >= 5.3", "author": "thaitea", "base_model": "ModernVBERT", "benchmark": "", "category": "model", "cost": "", "data": "", "date": "2026-09-21", "detail": "", "domain": "", "id": "laya-vision-modernvbert", "latency": "", "license": "CC-BY-NC-SA-4.0", "likes": "", "metric": "answer accuracy on official validation splits", "name": "Laya Vision ModernVBERT-250M", "notes": "Experimental research fork, not affiliated with Convai Innovations or the ModernVBERT authors; trained on 19 closed-form subsets of The Cauldron; code, the ModernVBERT experiment and Post-training on The Cauldron sections at github.com/r33drichards/laya-vision, docs/modernvbert-cauldron.md, row laya-vision-repo", "paper": "", "params": "250M", "result": "A-OKVQA 65.2%, ScienceQA 79.0%, VQAv2 yes/no 71.8% on official validation splits; score head untrained; 32 ms median predict call on an L4, bf16, preprocessing included", "score": "", "speedup": "", "stars": "", "training": "", "url": "https://huggingface.co/thaitea/laya-vision-modernvbert-250m"}
{"id": "pdbz199-local-decision-model", "name": "local-decision-model", "author": "Pdbz199", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/Pdbz199/local-decision-model", "base_model": "ModernBERT-base", "params": "150M", "metric": "10-25 ms per schema, consumer GPU", "license": "CC-BY-NC-4.0", "paper": "", "stars": "", "likes": 0, "detail": "base: ModernBERT-base + two small heads; arch: schema in the input, OPT hidden states scored; training: ~1M examples from ~50 public datasets; result: 10-25 ms consumer GPU; notes: CC-BY-NC-4.0", "arch": "Small fast decision model: unstructured state in, typed probabilistic decisions out; one forward pass of a 150M encoder answers a whole schema of typed questions about a piece of text; the schema is written into the input as CLS, MODE, question, OPT options, SEP, state; the hidden state at each OPT token is scored so all options are judged in the same pass, and a span head handles extraction; types are Bool, Enum with 2–255 options, MultiLabel, Int, Float and Extract; never generates text, so every output is guaranteed to match its declared type", "approach": "head", "training": "Log loss on about one million examples recast from roughly 50 public datasets, then temperature calibration", "data": "", "result": "10–25 ms per schema on a consumer GPU", "notes": "Weights need the small decisionmodel package from github.com/Pdbz199/local-decision-model; they are not a standard transformers head; CC-BY-NC license", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wayfind-metask-4b", "name": "Metask-Jev-4B", "author": "wayfind", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/wayfind/metask-jev-4b-policy-mix", "base_model": "Qwen3.5-4B", "params": "4B", "metric": "S 91.2 after self-hosted latency adjustment", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "Self-measured axes inserted into the published JevBench v1.2.7 ranking, official run pending: JevBench-231 p50 62.8 ms on a 4090 adjusted to 0.276 s gives S 91.2; owned-hardware cost of an 8x4090 server at 6,000 CNY per month, this model fits twice per card at 9.1 GB weights and sustains about 20 QPS per card with dual replicas, giving K 86.2. The card claims it would rank number 1 ahead of Jev 1.13.0 under this estimate, I 88.5 / S 91.2 on the Intelligence-by-Speed plane. Apache-2.0, commercial use. Weighs the JevBench v1.2 public 231 decisions with the 422-as-wrong protocol at 4096 context.", "arch": "Calibrated typed-decision model on Qwen3.5-4B in 16 languages: give it a state, text, ticket, policy or JSON, plus a typed choice, boolean or rubric score question, and it returns a probability for every option in a single forward pass, about 63 ms measured p50 on an RTX 4090. No generation, no parsing.", "approach": "undisclosed", "training": "Policy mix", "data": "", "result": "Self-measured JevBench-231 p50 62.8 ms on a 4090, S 91.2, K 86.2, I 88.5 estimated; official JevBench run pending.", "notes": "Card notes owned-hardware cost of an 8×4090 server.", "domain": "general", "score": "S 91.2", "latency": "p50 63 ms", "benchmark": "JevBench", "cost": "owned: 8×4090 server", "speedup": ""}
{"id": "mini-jev", "name": "mini-Jev", "author": "samatv256", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/samatv256/mini-Jev", "base_model": "Qwen3-0.6B", "params": "0.6B", "metric": "72.97%", "license": "Open", "paper": "", "stars": "", "likes": 14, "detail": "base: frozen backbone; metric: Semantic Choice; arch: odm_mini tag; training: provided as open research resource; data: samatv256/jev-decisions-v1; license: HF", "arch": "Frozen Qwen backbone + decision head for tool selection / model routing / function calling", "approach": "head", "training": "Decision-head training; the published baseline was NOT trained on Jev Decisions v1", "data": "Jev Decisions v1 dataset available for researchers to train on", "result": "Semantic Choice 72.97%, Stress Choice 67.64%, Action accuracy 27.98%", "notes": "Action accuracy 28% — lower-bound reference.", "domain": "general", "score": "72.97%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openjev-15b", "name": "openJev-1.5B", "author": "v6543210", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/v6543210/openJev-1.5B", "base_model": "Qwen2.5-Coder-1.5B-Instruct", "params": "1.5B", "metric": "100%", "license": "Open", "paper": "", "stars": "", "likes": 2, "detail": "metric: 400/400 custom set; training: 17 MB adapter; data: 400-item test set; license: HF; result: 400/400 · caveat applies; notes: DINOv2-small frozen backbone, 93.24% on a research image set", "arch": "17 MB LoRA, 4.35M params, on Qwen2.5-Coder-1.5B-Instruct; non-autoregressive — one forward pass reads the answer distribution over a fixed option set; trained on 1,000 synthetic choice questions", "approach": "fine-tune", "training": "LoRA", "data": "Custom", "result": "100.0% test accuracy vs 30.0% zero-shot — but on a small custom set", "notes": "Project repo: alongL/openJev; 20–40 ms per decision on a 2080. Also IamBusy/OpenJev-0.6B + OpenJev-Vision in the same \"openJev\" name space.", "domain": "general", "score": "100%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openjev-router", "name": "openjev-router-lora", "author": "s1lv3rj1nx", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/s1lv3rj1nx/openjev-router-lora", "base_model": "Qwen3-1.7B", "params": "1.7B + 17M LoRA", "metric": "Intent accuracy, multi-label exact-set and scope gate on the same 450 held-out items, exact McNemar versus the commercial typed-decision API", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "87 MB rank-16 LoRA adapter on Qwen3-1.7B for a pharmacy and healthcare message router: ten questions in a single forward pass over the shared message, one intent choice, five multi-label noul topic flags and four safety gates", "approach": "fine-tune", "training": "395 training examples, 258 seconds on one GPU; same-data ablations at 450 held-out items: encoder ModernBERT-base all open 0.899 intent at 150M, decoder 4.2M head only 0.666, decoder all 1.7B open 0.929 at 1,725M, LoRA r=16 0.979 at 17M - adapting 17M parameters beats opening 1,725M at a 39th of the size", "data": "", "result": "Beats the commercial typed-decision API it was benchmarked against, exact McNemar on the same 450 held-out items: intent 0.979 vs 0.941, multi-label exact set 0.909 vs 0.822, scope gate 0.978 vs 0.880 - three wins, four ties, no losses", "notes": "Siblings: openjev-router-healthcare and openjev-encoder-general, both 09-21", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rjmswd-qwen-jev", "name": "Qwen Choice, Jev-style visual judgment on Qwen3.5-4B", "author": "RJMSWD", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/tchbcb/qwenjev", "base_model": "Qwen3.5-4B", "params": "4B", "metric": "single RTX A6000, BF16, 30 runs: full-request median 168.7 ms, P95 174.0 ms, GPU model phase median 137.4 ms", "license": "", "paper": "", "stars": 28, "likes": 0, "detail": "", "arch": "a local visual judgment tool built on Qwen3.5-4B: image, question and a set of options go in, one model forward pass comes out with the option and its probability distribution; options are encoded as single letters, so all candidate scores come from the retained output layer at the last position only, skipping per-token generation while keeping the original output layer", "approach": "logits", "training": "", "data": "", "result": "on a fixed 1280x720 support-ticket example, 880 visual tokens and 989 input tokens, the raw BF16 weights with thinking off and no training or quantization hit a 168.694 ms full-request median and 173.989 ms P95; the same acceleration logic applies directly to the rest of the Qwen models; fast operators include causal-conv1d and the FLA gated delta rule; compile preparation plus 5 warmup runs took about 14.1 s", "notes": "honest caveats: the 30 repeats of one fixed example measure latency, not general accuracy, and the softmax over option scores is uncalibrated; compilation may cause small numerical changes, no bitwise equivalence is guaranteed; Chinese documentation with a 30-run evidence trail in the repo Code: https://github.com/RJMSWD/QwenJev.", "domain": "multimodal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-270m", "name": "system-one-270m", "author": "kaivoss", "date": "2026-09-21", "category": "model", "url": "https://huggingface.co/models/kaivoss/system-one-270m", "base_model": "Gemma 3 270M", "params": "270M", "metric": "acc 0.4204 → 0.6574", "license": "Gemma", "paper": "", "stars": "", "likes": 0, "detail": "base: gemma3_text", "arch": "Tiny typed-decision model, synthetic-data trained", "approach": "fine-tune", "training": "Fine-tuning on synthetic decision data", "data": "Synthetic", "result": "Accuracy 0.4204 → 0.6574; 'below Laya's reported 0.766'; 'calibration matters more than accuracy for this model class'", "notes": "Floor-checkpoint for the 270M size class. Its training data is published: kaivoss/system-one-270m-data — 25,002 synthetic typed decisions with soft target distributions over caller-supplied options, 2–20 options, 28 domains, measured ambiguity bands, Apache-2.0.", "domain": "general", "score": "Accuracy 0.4204 → 0.6574", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "build-jev-scratch", "name": "build-jev-from-scratch", "author": "azharmo", "date": "2026-09-20", "category": "model", "url": "https://huggingface.co/models/azharmo/build-jev-from-scratch", "base_model": "", "params": "3.1M", "metric": "Noul on BoolQ and SST-2: accuracy, Brier, ECE; choice on AG News", "license": "MIT", "paper": "", "stars": "", "likes": 1, "detail": "About 3.1M parameters, trained on an 8-core CPU with 15 GB RAM for 3 epochs, loss 1.55–1.00, on slices of BoolQ, SST-2 and AG News. MIT. ARTICLE.md tells the full story with sources, RESULTS.md has the real training and eval transcript, jev_toy/ holds the PyTorch source, checkpoint includes cfg, state_dict and vocab.", "arch": "Toy from-scratch reproduction of the System One model interface: one encoder pass over a state answers noul, choice and score typed questions in parallel with no text generation. Own architecture, heads, losses and calibration; explicitly not Jev, whose internals are proprietary.", "approach": "from-scratch", "training": "From scratch on CPU, 3 epochs; loss 1.55 → 1.00", "data": "Small public decision datasets", "result": "noul on BoolQ plus SST-2: 59.7% accuracy, Brier 0.236, ECE 0.027; choice on AG News: 75.9% accuracy, Brier 0.331. Low accuracy expected at toy scale; the calibration is the architecturally meaningful result.", "notes": "Proof that the readout idea is learnable even at 3.1M parameters on a laptop CPU.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-omni", "name": "Jev-Omni", "author": "akhilaaa3", "date": "2026-09-20", "category": "model", "url": "https://huggingface.co/models/akhilaaa3/Jev-Omni", "base_model": "Gemma 4 12B IT", "params": "12B", "metric": "JevBench 86.15%", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 198, "detail": "base: google/gemma-4-12B-it; arch: yes/no; training: DecisionBench dataset; data: DecisionBench dataset; license: dataset rights separate; result: matched 195 groups / 231 decisions; notes: 09-24 · 6.87 GiB file, 9.04 GiB VRAM · on the Persian Jev-Persian-Benchmark 480-question set: Choice 98.75% / Noul 93.75% / Score 95% — 0.20 s/decision on RTX 5080 Laptop · 09-24; notes: ports — Reza2kn GGUF with bit-verified head tensors, Ruiruiz30 MLX 4bit, ai-ecoverse jev-omni.js ONNX int8 WebGPU 13.58 GB", "arch": "Multimodal fine-tune implementing the typed-decision interface: noul, choice and score answered with calibrated probabilities", "approach": "fine-tune", "training": "30,000-question fine-tuning run", "data": "akhilaaa3/decision-bench", "result": "JevBench: 86.15% accuracy, 87.45% micro accuracy; DecisionBench medium-accuracy chart published", "notes": "140 likes. 'Independent open model that implements the typed-decision interface — not affiliated with, endorsed by, or connected to TypeSafe.' Large open Jev-competitor checkpoint. Ports: Reza2kn Q4_K_M GGUF, 8.77–9.04 GiB VRAM on RTX 5080 Laptop, 0.20 s per decision on GPU and 1.88 s on CPU; decision head and projector kept at original precision; all four exported head tensors verified bit-for-bit against the source head.pt; unvalidated on macOS, Windows, Metal or Vulkan; Ruiruiz30 Jev-Omni-MLX-4bit for Apple Silicon; browser build ai-ecoverse/jev-omni.js, ONNX int8 WebGPU, 13.58 GB in 445 files of at most 32 MB, manifest pins revision 1544c2b7ed5277a0 with SHA-256 sizes and graph parity numbers, live demo at ai-ecoverse.github.io/jev-omni.js. HF mirrors: 0xSojalSec/Jev-Omni, Rydward98/Jev-Omni.", "domain": "general", "score": "JevBench 86.15%", "latency": "200 ms", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "jevify-gemma4", "name": "jevify Gemma 4, 26B-A4B + E4B", "author": "kushalpatil", "date": "2026-09-20", "category": "model", "url": "https://huggingface.co/models/kushalpatil/jevify-gemma4-26b-a4b", "base_model": "Gemma 4 26B-A4B", "params": "26B-A4B", "metric": "OOD held-out, 6 datasets, 307 items: 26B-A4B 0.860–0.960 per dataset; E4B acc 0.844, ECE 0.043", "license": "Gemma", "paper": "", "stars": "", "likes": 1, "detail": "author: + mradermacher GGUF; base: MoE; size: 4B active; arch: merged", "arch": "LoRA fine-tune 'to give honest probabilities when asked typed questions': same jevify recipe as the E4B build on the 26B-A4B base; one prefill reads the next-token distribution over the answer labels; served as POST /v1/systemone in the Jev wire format", "approach": "fine-tune", "training": "LoRA r=64 on attention projections, 2 epochs over ~47k state/question/target-distribution items from 16 sources; KL loss, no teacher model", "data": "~47k items: hard-labeled sets with randomized option subsets and order, multi-annotator sets with real human label distributions, constructed long states up to 24k tokens", "result": "Held-out OOD probabilities on 6 datasets, 307 items: massive 0.860, trec 0.940, sms_spam 0.960, paws 0.580, app_reviews 0.700", "notes": "GGUF conversion by mradermacher for local serving. Folds the E4B sibling, kushalpatil/jevify-gemma4-e4b, gemma-4-E4B-it base, same recipe: OOD massive 0.900, trec 0.940, sms_spam 0.980, app_reviews 0.740, paws 0.540; in-distribution accuracy 0.745–0.823 after training, ECE 0.217–0.028; behind jevify, a local Jev-compatible probabilistic decision API. Independent GGUF quants by mradermacher, both 09-24: mradermacher/jevify-gemma4-26b-a4b-GGUF with mmproj f16/Q8_0 multimodal supplement, and mradermacher/jevify-gemma4-e4b-GGUF for llama.cpp with mmproj supplement, heads kept at original precision. The unmerged LoRA adapters are also published: kushalpatil/jevify-gemma4-e4b-lora and kushalpatil/jevify-gemma4-26b-a4b-lora, both with auto-generated empty cards, 09-20/21", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "next-jev-stage1-cot", "name": "next JEV, stage 1 CoT + stage 2 last-loop", "author": "JonnesLin", "date": "2026-09-20", "category": "model", "url": "https://huggingface.co/models/JonesLin/next-jev-stage1-cot", "base_model": "Qwen3.5-0.8B", "params": "0.8B + recurrent latent workspace", "metric": "validation loss 0.8931, CoT aux loss 2.9769", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Qwen3.5-0.8B with a 128-token, 3-loop recurrent latent workspace on middle blocks 1–24; stage 1 of a two-stage next-JEV training", "approach": "fine-tune", "training": "cross-attention CoT auxiliary loss only; 1 epoch, 6,347 steps, global batch 64 on 2× H100 NVL; lr 8e-05–8e-06 cosine; BF16 autocast with FP32 master weights", "data": "JonesLin/multi-model-cot-2730: 406,167 train, 8,211 validation prompts", "result": "", "notes": "Checkpoint holds FP32 weights and Adam state; use as train.init_checkpoint for stage 2. Folds stage 2, JonesLin/next-jev-stage2-last-loop, 09-23: the 'last-loop' variant adds 3-way NLI classification with supervision on the last loop only, mid-training artifact, no final evaluation published; the project's GitHub repo now 404s. Companion dataset rows: next-jev-choice-cot-200k and next-jev-tetris-10k. Stage 2, the iterative-decision model, is row joneslin-next-jev.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zefancai-openjev", "name": "Open-Jev, ZefanCai", "author": "ZefanCai", "date": "2026-09-20", "category": "model", "url": "https://huggingface.co/models/ZefanCai/Open-Jev-2B", "base_model": "Qwen3.5-2B, Qwen3.5-9B, Qwen3.8-27B", "params": "2B / 9B / 27B", "metric": "JevBench 85.28%", "license": "Apache-2.0", "paper": "", "stars": 319, "likes": 10, "detail": "author: Zefan-Cai; base: text backbone, LoRA adapters; arch: head + temperature must be applied; training: excluding 1,700 original wiki rows; data: ZefanCai/Open-Jev; result: authors' eval · 85.28% · 72.07% · all 127,787 rows evaluated + audited; notes: 2026-09-22 · Apache-2.0, largest public Open-Jev checkpoint · Qwen3.5-9B, same loader; name: 2B/9B/27B LoRA + decision head", "arch": "LoRA adapter + separate scalar decision head + saved temperature; generic AutoPeftModel calls do NOT implement the interface", "approach": "head", "training": "LoRA fine-tuning + decision head; release-v2-redistributable dataset with 79,116 training rows", "data": "Public dataset repo, release-v2-redistributable", "result": "9B answers 179 of 231 public JevBench tasks correctly; hard-accuracy / NLL / Brier / ECE tables per split published; expected-accuracy reference targets disclosed. 27B-v1.1: 197/231 JevBench public, 80/111 Hard tier", "notes": "Released Qwen3.5-2B/9B LoRA adapters with scalar decision heads and a public dataset — listed in the awesome-jev gallery as a top open reproduction. Companions: ZefanCai/Open-Jev-27B-v1.1 and junetask/Open-Jev-9B. Folds the companion artifacts: 27B-v1.1, ZefanCai/Open-Jev-27B-v1.1, 09-22, 5 likes, is a 15.5M-parameter LoRA adapter + scalar decision head on Qwen3.8-27B at pinned rev 1d4bf0f2, 197/231 public + 80/111 Hard = 85.28% / 72.07% with full denominators; junetask/Open-Jev-9B is the companion 9B at pinned rev c202236; gionebeats/Open-Jev-2B, 09-22, is another release of the 2B family member, same loader; the no-code workbench is huggingface.co/spaces/ZefanCai/Open-Jev-Workbench, 09-23, MIT, Docker space on port 7860, paste or CSV import, verified with real CPU classification requests, embedded at zefan-cai.github.io/open-jev/workbench. Code: https://github.com/Zefan-Cai/Open-Jev.", "domain": "general", "score": "JevBench 85.28%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "openjev-org", "name": "OpenJev 27B", "author": "openjev", "date": "2026-09-20", "category": "model", "url": "https://huggingface.co/models/openjev/openjev", "base_model": "Qwen3.5-27B", "params": "27B", "metric": "84.0%", "license": "CC-BY-NC-4.0; helpers Apache-2.0", "paper": "", "stars": "", "likes": 47, "detail": "author: HF org; base: hybrid text model, ~16.5 GB Q4 GGUF · Apache-2.0 base · served with vLLM; metric: 10k questions; training: base before tuning measured as control arm; data: checked by id and content; license: model; result: GPT-5.6: 26 · GPT-5.6: 18.5%; notes: 16.5 GB Q4_K_M", "arch": "27B single-pass decision model with open weights: state in, calibrated probability per option out, up to 52 options per question; vLLM-served, browser-agent / computer-use oriented; Apache-2.0 base", "approach": "fine-tune", "training": "Fine-tuning from the open base model", "data": "Held-out against 34 public text sources + agent/language/screenshot sets", "result": "10,000 text questions: Jev hosted 85.4%, OpenJev 84.0%, untuned base 80.4%, Nimble 9B 75.7%. Desktop next-action from screenshot 76.5→88.0%; unseen web 68.5→87.4%; legal: 76.7% on 613 merger-agreement clauses vs hosted 73.7%; ~39 ms latency. Up to 52 options per question; ~210 ms on H100 / ~80 ms short text; option-shuffle instability 2.3%", "notes": "Public head-to-head vs the hosted Jev API on identical 10k questions: 84.0% vs Jev 85.4% — \"within 2 points of the hosted Jev API, with open weights you can run yourself\". Quantized variants: openjev/openjev-GGUF, Q4_K_M −0.34 pt, Q8_0 −0.61 pt on a 1,789-row eval, and m3kro/NVFP4_W4A4_27B. Community serving stacks: ekzhang/openjev-sglang, 317 stars, daseinlabs/open-jev, 108 stars, nico-martin/open-jev, 33 stars, TypeScript. Org builds: openjev/openjev-FP8, 09-21, 601 downloads, and openjev/openjev-MLX / openjev/openjev-MLX-4bit, 09-21.", "domain": "computer-use", "score": "84.0%", "latency": "39 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "thefloydd-qwen3-06b-rlcd", "name": "Qwen3-0.6B RLCD", "author": "thefloydd", "date": "2026-09-20", "category": "model", "url": "https://huggingface.co/models/thefloydd/qwen3-0.6b-rlcd", "base_model": "Qwen3-0.6B-Base", "params": "0.6B", "metric": "Held-out split of 329 unseen task schemas, 79,396 questions: micro accuracy, NLL versus soft targets, ECE, AURC at max probability", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "Proper-scoring training, cross-entropy against observed outcome frequencies with soft targets where they exist; no RL despite the RLCD family name. Prefill-only. Apache-2.0.", "arch": "0.6B option-scoring decision model on Qwen3-0.6B-Base: send a state plus one or more typed questions, each with its own options and criteria, and get a calibrated probability over your options plus a separate confidence that the answer is among them at all. One forward pass, no generated tokens, runs on CPU. A block-diagonal mask keeps each question independent, so adding or removing a question never changes the rest; option keys are arbitrary identifiers and the criteria text carries the meaning.", "approach": "logits", "training": "", "data": "", "result": "Held-out split of 329 unseen task schemas, 79,396 questions: micro accuracy 0.7825, NLL vs soft targets 0.6557, ECE 0.0308, AURC at max probability 0.0631.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "step-5-preview", "name": "Step-5-Preview", "author": "StepFun", "date": "2026-09-20", "category": "model", "url": "https://huggingface.co/models/TypeSafeAI/Step-5-Preview-BF16", "base_model": "", "params": "600B total / 27B active MoE", "metric": "DeepSWE v1.1, StepCodeBench, ProgramBench, Terminal-Bench v4, ALE-CLI, GDPval-AA v2, FrontierFinance, DRACO", "license": "StepFun Community", "paper": "", "stars": 30, "likes": 32, "detail": "author: hosted by TypeSafeAI", "arch": "StepFun's Step-5-Preview: 600B-total, 27B-active sparse MoE with 1M-token context and multimodal image/video input, agentic, coding, and deep-research oriented, StepFun Community license. Hosted on the TypeSafeAI Hugging Face org — Jev's company's page — with no Jev mention in the card.", "approach": "undisclosed", "training": "", "data": "", "result": "DeepSWE v1.1 67.7, StepCodeBench 49.0, ProgramBench 80.5, Terminal-Bench v4 33.3, ALE-CLI 29.5, GDPval-AA v2 1571, FrontierFinance 66.4, DRACO 83.3, SciCode above Kimi K3; output speed 99.8 tokens/sec vs GLM-5.3 at 72.1; time to first token 2.96 s", "notes": "Hugging Face new-model sweep 2026-09-24; general agentic model, not a Jev implementation. Benchmark table from the Hugging Face card, September 2026 sweep; DeepSWE run with the SWE-agent harness at temp 1.0, top-p 0.95. GGUF build on the org: TypeSafeAI/Step-5-Preview-GGUF, 09-22.", "domain": "general", "score": "", "latency": "", "benchmark": "DeepSWE, StepCodeBench, ProgramBench, Terminal-Bench v4, ALE-CLI, GDPval-AA v2, FrontierFinance, DRACO", "cost": "", "speedup": ""}
{"id": "this-that", "name": "this-that-model-1.0", "author": "FLock-io", "date": "2026-09-20", "category": "model", "url": "https://huggingface.co/models/flock-io/this-that-model-1.0", "base_model": "undisclosed", "params": "1.88B", "metric": "0.941 accuracy / Brier 0.042 / NLL 0.126 on the 68-recorded cohort", "license": "MIT; free hosted FLock API tier", "paper": "https://arxiv.org/abs/2609.23886", "stars": 36, "likes": 15, "detail": "author: Zehua Cheng, Wei Dai, Jiahao Sun; metric: 68-recorded cohort; data: limberc/this-that-spatial-bench, MIT; license: code and weights; result: gpt-5.6 0.926 at $0.018 per question · majority-class baseline 0.647 · five frontier models answered all 68 correctly and are omitted as saturated", "arch": "1.9B open typed decision model: answer read directly from the hidden state at a designated position, scored against the label tokens of the declared options and normalised over exactly those — no text generated, nothing can be malformed, all questions in a request answered in the same forward pass; p_k = softmax of head-label dot hidden state; no enum in a request is refused with 400", "approach": "head", "training": "Fine-tuned; the spatial training round covers all 15 released benchmark families on different items and maze windows, excluded from the eval set by fingerprint and rendered state; the model scored 0.409 on the shared spatial subset before that round", "data": "68 recorded decision questions over 17 states; a third-party cohort a commercial decision service answered, vendored from NanoJev under MIT; 7,305-question decision benchmark over 15 families and 6,525 states, built and released with the paper", "result": "0.941 accuracy / Brier 0.042 / NLL 0.126 on the 68-recorded cohort vs Jev 0.765 / 0.133; 0.839 on the full 7,305-question benchmark vs 0.343 chance — on the shared 2,250 subset: gpt-5.6 0.897, this-that 0.844, Jev 0.803, Laya 0.345; 30.9 ms per decision, zero completion tokens, $0.000014 per pass at 80 W electricity, free on the FLock API", "notes": "Paper: 'A typed decision model that decides in 30 ms, for a millionth of a cent.' The first arXiv paper that ships an open Jev-competitor model + benchmark. Authors' caveat: a claim about the cost of a decision and the shape of output, not accuracy beating frontier models — on the harder half of the released benchmark it is not more accurate than frontier models; the spatial numbers are in-distribution. Code: https://github.com/FLock-io/this-that-model. this-that-model-1.2 released by FLock on 09-24.", "domain": "general", "score": "0.941 recorded cohort / 0.839 full 7,305-question benchmark / 0.844 shared 2,250 subset", "latency": "30.9 ms", "benchmark": "NanoJev recorded cohort; this-that-spatial-bench", "cost": "", "speedup": ""}
{"id": "autojev-27b", "name": "AutoJev-27B", "author": "denis-pplx", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/denis-pplx/autojev-27b", "base_model": "Qwen3.8-27B", "params": "27B", "metric": "84.60% overall", "license": "", "paper": "", "stars": 48, "likes": 27, "detail": "score: 84.60% overall, ECE 0.0428; training: full-weight SFT, one H200", "arch": "Multimodal decision model on Qwen3.8-27B: probabilities over supplied choices in one forward pass per question, with a TypeSafe-compatible API and browser playground.; renamed continuation: Deciduous, LLM-Architects/Deciduous, PTQ1_0 1-bit quantization at 1.75 bits per weight, 5.27 GB and an MLX companion build", "approach": "logits", "training": "Full-weight SFT on one H200: 73,000 unique examples, 286 updates; released checkpoint 200; cross-entropy with a separately fitted scalar temperature.", "data": "", "result": "84.60% overall accuracy, ECE 0.0428, Brier 0.2203 vs Jev at 82.79% / 0.0527 / 0.2540 and base Qwen3.8-27B at 69.83%; about 49 GiB in BF16.", "notes": "Built with autonomous agents end to end: research, data generation, training, evaluation and deployment, with the user setting goals and refining scope. Weights: huggingface.co/denis-pplx/autojev-27b. Serves pinned Ternary Bonsai 2 27B GGUF + PrismML llama.cpp. Repo renamed as the Deciduous continuation: github.com/LLM-Architects/Deciduous. Code: https://github.com/denis-pplx/autojev.", "domain": "multimodal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nimble", "name": "Bespoke Nimble", "author": "Bespoke Labs", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/bespokelabs/Bespoke-Nimble-9B", "base_model": "Qwen3.5-9B", "params": "9B", "metric": "90.1% match", "license": "Open recipe", "paper": "", "stars": 1736, "likes": 192, "detail": "author: bespokelabsai; base: frozen base + LoRA · ~18 GB unquantized; metric: 324 held-out; arch: contrastive answer-token readout; training: data curation 'changes one fact so the correct answer flips'; data: published 09-20, initially omitted by mistake; license: model on HF; notes: 75.7%", "arch": "Serve reads the prompt once, then scores one answer token per question", "approach": "logits", "training": "LoRA fine-tune on the answer tokens only, from 2,676 curated contrastive examples; NOT distilled from Jev; temperature fitted 09-22", "data": "2,676 curated training examples + 324 held-out examples; 13-subset public benchmark suite with human labels", "result": "324 held-out: 90.1% match vs base 66.4% vs Jev 1.13.0 93.2%; host prompt limit raised to 8,192 on 09-19", "notes": "'Data, Model, Recipe for an open Jev' — explicitly a transparency/recipe release to encourage research. Referenced as 'Nimble 9B' control arm in the openjev-org 10k-question eval. Code: https://github.com/bespokelabsai/nimble.", "domain": "general", "score": "match 90.1%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "euston-8b", "name": "Euston", "author": "FLock", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/flock-io/Euston-8B", "base_model": "DeepSeek-R1-0528-Qwen3-8B", "params": "8B", "metric": "", "license": "MIT", "paper": "", "stars": "", "likes": 1, "detail": "base: fine-tune of DeepSeek-R1-0528-Qwen3-8B; training: GRPO against a rule-based zero-API reward on MathArena/brokenarxiv-training; arch: true or corrupted verdict on a research-paper statement, no generation; license: MIT; result: trained to resist mathematical sycophancy; notes: 321 downloads, GraphSynth tag", "arch": "From FLock, the team behind this-that: Euston verifies mathematical claims taken from research papers and is explicitly trained to resist mathematical sycophancy, the tendency of reasoning models to produce a confident derivation of a false statement rather than report that it cannot be derived; trained with GRPO on a rule-based, zero-API reward, MIT license", "approach": "", "training": "GRPO via verl on DeepSeek-R1-0528-Qwen3-8B, temperature 0.6, top-p 0.95, 4 samples, 32,768 max tokens, rule-based zero-API reward", "data": "3,026 matched true/false pairs, 6,052 statements, generated with GraphSynth from mathematical statements in arXiv papers with identifiers 1001 through 2512, January 2010 through December 2025; MathArena/brokenarxiv-training", "result": "8B reasoning model that verifies mathematical claims: given a statement from a research paper, decides whether it is true or corrupted", "notes": "From FLock, the team behind this-that. Euston verifies mathematical claims taken from research papers and is explicitly trained to resist mathematical sycophancy — the tendency of reasoning models to produce a confident derivation of a false statement rather than report that it cannot be derived. Trained with GRPO; confidence intervals are 20,000 problem-level paired bootstraps, seed 20260726; companion technical report in preparation for arXiv on a rule-based, zero-API reward. MIT license.", "domain": "science", "score": "", "latency": "", "benchmark": "Balanced held-out split of 200 true and 200 false statements, one pair per source paper, temporally disjoint; BrokenArXiv 0426 and 0526 protocols; AIME 2026 capability retention", "cost": "", "speedup": ""}
{"id": "jev-lite-vagmi", "name": "jev-lite", "author": "vagmi", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/vagmi/jev-lite", "base_model": "Gemma-based", "params": "4B", "metric": "Accuracy, ECE, NLL and Brier tables; accuracy flat from step 250 to the end of training", "license": "Gemma", "paper": "", "stars": "", "likes": 5, "detail": "base: LoRA/QLoRA adapter; data: build_data.py in repo; license: mixed data licenses; result: reported", "arch": "Jev-style adapter; only meaningful with the exact prompt format it was trained on", "approach": "fine-tune", "training": "LoRA/QLoRA", "data": "Mixed licenses, some non-commercial; non-commercial / research use unless retrained without them", "result": "Accuracy / ECE / NLL / Brier tables; accuracy flat from step 250 to end of training", "notes": "Caveat-rich card: measured mean entropy 0.000 on one data source made it 'useless for distillation.' The synthetic portion of its training data is published: vagmi/jevlite_dataset — 5,866 typed questions over 978 synthetic program states, each answered by a teacher model with a full probability distribution, CC-BY-SA-4.0; the stated point of the rows is calibration — a dataset of right answers cannot teach a model to be unsure.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mchen04-jevlite", "name": "JevLite decision heads", "author": "mchen04", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/mchen04/jevlite-decision-heads", "base_model": "Qwen2.5-1.5B-Instruct-4bit + Qwen2.5-0.5B-Instruct-4bit, truncated to first L layers + tiny trained head", "params": "0.5B–1.5B", "metric": "0.215–0.312 vs 0.582 zero-shot", "license": "Closed", "paper": "", "stars": 0, "likes": 0, "detail": "base: MLX; training: 2026-09-19→22 commit history; data: training · holdout; license: all rights reserved; result: 28% latency cut, 174→126 ms · vs 0.83–0.93 on seen tasks; notes: truncation approach; name: layer-truncation sweep, negative results", "arch": "Sweep of 39 'jevlite' heads: chop a small quantized Qwen2.5 to its first L transformer layers, bolt a tiny trained head on; measures accuracy loss vs latency saved", "approach": "head", "training": "Heads trained on 5 academic classification tasks; same project as mchen04/jev-local-lab-decision-heads", "data": "5 academic classification tasks; external workflow suite", "result": "Negative result: external workflow suite 0.215–0.312 agreement vs plain zero-shot readout 0.582–0.585 vs published commercial reference 0.909; 5 academic tasks: 0.736 full depth, 0.716 at 20/28 layers; holding out emotion_label → 0.15 on it", "notes": "'Does not reach parity with the published Jev numbers… that gap is the main thing this repo documents'.", "domain": "general", "score": "", "latency": "126 ms", "benchmark": "External workflow suite", "cost": "", "speedup": ""}
{"id": "jevon", "name": "jevon", "author": "lewislululu", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/lewislululu/jevon", "base_model": "Small model with value-iteration-network-style readout", "params": "20M", "metric": "Decision accuracy on the held-out test split next to a uniform control, per n/boards", "license": "AGPL-3.0", "paper": "", "stars": "", "likes": 6, "detail": "base: per tags; training: comparative runs reported; data: fresh board per state · n tracks board count; result: table in card", "arch": "Maze/Snake planning: 'amortised value' — head already trained on every free cell, output is the distilled form of exactly what the question asks; question types include Boolean and structured planning queries", "approach": "head", "training": "Trained with/without the value component", "data": "Generated boards", "result": "Decision accuracy on held-out test split next to uniform control, per n/boards", "notes": "The RL-planning-flavored Jev: decision accuracy as a function of planning horizon with explicit uniform-control baselines.", "domain": "gaming", "score": "", "latency": "", "benchmark": "Snake", "cost": "", "speedup": ""}
{"id": "laya-vision", "name": "Laya Vision", "author": "thaitea", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/thaitea/laya-vision", "base_model": "SmolVLM-256M-Instruct", "params": "150M", "metric": "VQA rubric scoring; ECE raw to calibrated tables; AVA argmax", "license": "CC-BY-NC-SA-4.0", "paper": "", "stars": "", "likes": 24, "detail": "base: vision tower frozen; training: σ=0.3 · log score + 0.75 × spherical; data: rubric-scoring datasets; result: target mode 'average' for 85% of photos; notes: laya-vision-smolvlm-256m-score", "arch": "SmolVLM backbone + decision head; answer options read out over the visual context", "approach": "head", "training": "Laya's RLCD policy gradient: each step draws 4 Gaussian-noised copies of option logits, scored with a strictly proper rule; plus ECE post-calibration", "data": "The Cauldron, VLFeedback, AVA-aesthetics, richhf_18k, CrisisMMD", "result": "VQA rubric scoring; ECE raw→calibrated tables published; AVA argmax not informative", "notes": "First open Laya-vision checkpoint. Multimodal System One: image + typed questions → calibrated option probabilities. Family expanded on the org: laya-vision-201m, SmolVLM-256M-Instruct cut to 20 of 30 language layers, 2 hours of training on QA data and game frames, found by the autoresearch loop at github.com/r33drichards/laya-vision; laya-vision-modernvbert-250m, ModernVBERT/modernvbert, 09-21, row laya-vision-modernvbert; laya-vision-smolvlm-256m-score, 237M, first trained score head, 09-22, row laya-vision-smolvlm-score; and laya-vision-smolvlm2-split1024. Code repo row laya-vision-repo.", "domain": "general", "score": "", "latency": "", "benchmark": "VQA", "cost": "", "speedup": ""}
{"id": "anthonym21-qwen3-06b-rlcd-decision", "name": "Qwen3-0.6B RLCD Decision", "author": "anthonym21", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/anthonym21/qwen3-0.6b-rlcd-decision", "base_model": "Qwen3-0.6B", "params": "0.6B", "metric": "Held-out test split ECE and accuracy versus the warmup it started from; same loop with a plain outcome reward", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 4, "detail": "Export format rlcd-decision-only-v1 described by decision.json: model.safetensors, 2,384.2 MB fp32 transformer body without the language-model head, plus decision_head.safetensors, 0.1 MB, the 26 rows for the A-Z letter tokens, 26 by 1024 fp32. Not a stock transformers checkpoint; needs the rlcd package from github.com/anthony-maio/eve-rlcd to load. Trained on banking77, MultiNLI, AG News, SST-5, Yelp, BoolQ and a customer-support chatbot set. Apache-2.0.", "arch": "Decision-only export of the eve-rlcd RLCD ablation: Qwen3-0.6B-Base after a short supervised warmup and 500 steps of reinforcement learning for calibrated decisions, a REINFORCE loop under bandit feedback whose reward is the outcome minus the probability the model put on the option it chose. It reads one state and a list of typed questions, runs one forward pass and returns a probability distribution over the options each question declares.", "approach": "logits", "training": "Published from the eve-rlcd training runs, anthonym21/eve-rlcd-runs, 61 dl", "data": "", "result": "Held-out test split: ECE 0.021, as calibrated as the warmup it started from, and accuracy 0.807 versus 0.746 for the warmup; the same loop with a plain outcome reward ends at ECE 0.216 or worse.", "notes": "Model published by the eve-rlcd RLCD ablation.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reflex-4b", "name": "reflex, System One decision model", "author": "kshetrajna12", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/kshetrajna12/reflex-qwen3.5-4b-lora", "base_model": "Qwen3.5-4B", "params": "LoRA adapter plus fitted calibration temperature, 4B base", "metric": "accuracy and calibration error on held-out data and public JevBench", "license": "MIT", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "Open re-creation of Jev in github.com/kshetrajna12/reflex: give it state and a list of questions with fixed answers, it answers all of them in one pass and returns a calibrated probability for every option; it never writes free text, so it cannot invent an answer that is not on the list and there is nothing to parse", "approach": "from-scratch", "training": "Trained with a proper scoring rule on public datasets and synthetic data", "data": "Public datasets plus synthetic decision data", "result": "The authors' own negative result, controls of 09-19: the frozen Qwen3.5-4B base with reflex's current default prompt and no calibration file scores better on data the adapter was never trained on and better on the public JevBench items, hard tier 0.658 vs 0.604, calibration error 0.086 vs 0.117; the adapter is sharper on inputs resembling its training mix and worse on long ambiguous ones; JevBench v1.4.1 official table: rank 5 of 77 at 54.0, estimated $0.022 per 1k decisions", "notes": "Published 09-19 and already marked superseded for general use by 09-19 controls: a documented case of a System One fine-tune losing to its own frozen base with a good prompt Code repo: github.com/kshetrajna12/reflex, 139 stars, state plus typed questions into calibrated probabilities.", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "lafalce-system-one", "name": "System One phase2 student", "author": "mateolafalce", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/lafalce/system-one-model", "base_model": "ModernBERT-base + LoRA r16 + 2-layer fp32 scoring head", "params": "151M class", "metric": "JSON/text state in, choice/score/noul out", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: one forward pass, no text generationhip checkpoint from github.com/mateolafalce/system-one-model; Apache-2.0", "arch": "Local open-weight decision model: JSON or text state in, typed choice/score/noul out in one forward pass - ModernBERT-base with LoRA r16 and a 2-layer fp32 scoring head, the phase-2 student ship checkpoint", "approach": "head", "training": "", "data": "", "result": "Apache-2.0 checkpoint published from the mateolafalce/system-one-model repo", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shreyanbr-system-one-gold", "name": "system-one-gold, DeBERTa-v3-xsmall", "author": "shreyanbr", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/shreyanbr/system-one-gold", "base_model": "deberta-v3-xsmall-zeroshot-v1.1-all-33", "params": "70.8M", "metric": "0.464–0.944 across six typed questions on 500-item test splits", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "base: DeBERTa-v3-xsmall zeroshot v1.1; training: each dataset's own labels, 4,000 items per task; data: shreyanbr/system-one-training-pairs; result: tool 0.944 best of six; notes: one earlier headline number retracted", "arch": "Cross-encoder System One decision model: a 70.8M-parameter DeBERTa-v3-xsmall that answers typed decision questions — Choice, Score and Noul — in one batched forward pass with zero generated tokens, implementing the TypeSafe Jev POST /v1/systemone schema; meant for the routing, classification and gating decisions inside software that do not need an autoregressive model", "approach": "fine-tune", "training": "Each dataset's own labels over 4,000 training items per task", "data": "shreyanbr/system-one-training-pairs: compiled premise-hypothesis-label triples, Claude Haiku 4.5 distillation rows, deterministic train/calib/test splits seed 42, and a marimo notebook for GPU fine-tuning; the distilled sibling system-one-distilled uses Haiku-4.5 labels on the same recipe", "result": "Accuracy on 500-item test splits: intent 0.676, tool 0.944, needs_tool 0.694, priority 0.464, type 0.760, is_incident 0.804; two of the six questions sit at or below a constant predictor for every system measured, Claude Haiku 4.5 included — 'a finding about the labels rather than about any model'; the repository documents a retraction of one earlier headline number for the same reason after the tool_select multi_step label was corrected", "notes": "Card instructs reading the accuracy next to the majority floor, not on its own; 31 downloads; sister row nev-lite, same author: 11.8M static-embedding no-attention ablation", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "systemone-lite-05b", "name": "systemone-lite-0.5b", "author": "dwidlee", "date": "2026-09-19", "category": "model", "url": "https://huggingface.co/models/dwidlee/systemone-lite-0.5b", "base_model": "Qwen2.5-0.5B-Instruct, fine-tuned", "params": "0.5B", "metric": "JevBench 49.8%", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "training: 0% train∩test; data: 240,800 / 4,700; result: 231 tasks, T=1.0; paper: GitHub repo benchmarks/jevbench_spatial_v2_s1.json", "arch": "Local System One-compatible decision model: option-restricted next-token scoring for closed criteria, yes-no and score questions; the repo is the stable name, new training runs overwrite these weights instead of minting new Hub repos; ships a SystemOneClient with choice, noul and score helpers; not affiliated with TypeSafe AI or Jev", "approach": "fine-tune", "training": "Fine-tuning on 240,800 train / 4,700 eval rows", "data": "dwidlee/systemone-lite-phase2", "result": "JevBench-style public set: 49.8% acc, ECE 0.307, p50 12.6 ms — 'not an official JevBench leaderboard submission'; random-baseline disclosed", "notes": "Small-end checkpoint of the family; Apache-2.0; stable-weight policy: new training runs overwrite the same Hub repo. General-skill training set: dwidlee/systemone-lite-general — 32,400 train / 3,600 test / 5,400 test_hard letter-alias choice rows across three gyms, TicketDungeon, ResourceAllocator, DebateJudge, MIT, rule-based labels, not affiliated with TypeSafe.", "domain": "general", "score": "JevBench 49.8%", "latency": "p50 12.6 ms", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "larkooo-gemma-e2b-rlcd", "name": "Gemma E2B RLCD", "author": "larkooo", "date": "2026-09-18", "category": "model", "url": "https://huggingface.co/models/larkooo/gemma-e2b-rlcd", "base_model": "Gemma 2 2B", "params": "2B", "metric": "0.945/10 dev", "license": "MIT", "paper": "", "stars": 2, "likes": 1, "detail": "base: Gemma 4 E2B, 4-bit MLX on Apple Silicon; arch: one encode, batched candidate scoring over text, image, speech and video; result: 128 visual checks in 11.57 s vs 54.10 s, 4.67×, 120/128 match; license: MIT", "arch": "RLCD decision function on Gemma 2 2B; 4096 context; 192 KB decision head", "approach": "logits", "training": "Fine-tuned decision head", "data": "", "result": "0.945/10 at 2.04 ms per decision; 0.8B base scores 0.998/10 at 0.58 ms", "notes": "Multimodal parallel classification, grading and label probabilities over text, images, speech and video, powered by Gemma 4 E2B on Apple Silicon. Encodes the input once, scores the allowed answers in GPU batches, and returns structured results with probability distributions. Live demo: 128 visual checks in 11.57 s against 54.10 s, 4.67× faster, 120/128 matching answers, both paths sharing one GPU. Requires an Apple Silicon Mac, Python 3.12, uv and ffmpeg; uses mlx-community/gemma-4-e2b-it-4bit. HF weights: larkooo/gemma-e2b-rlcd, 231 downloads. Code: https://github.com/larkooo/gemma-e2b-rlcd.", "domain": "general", "score": "0.945/10", "latency": "2.04 ms per decision", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya", "name": "Laya", "author": "convaiinnovations", "date": "2026-09-18", "category": "model", "url": "https://huggingface.co/models/convaiinnovations/laya", "base_model": "ModernBERT-large; multilingual ckpt uses mmBERT-base", "params": "421M / 322M", "metric": "0.766 acc on 4 typed-decision workflows", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 3419, "detail": "author: Nandakishor M; base: 421M bidirectional encoder, fully fine-tuned; training: GRPO-style · log + spherical, ranked probability score for ordinals · λ=1.0; data: commercial use allowed · invoice, security, customer service, agent-trace observability; result: 7.8× faster · >3× random on 45/51 · Banking77 0.425 vs Jev 0.870 at default budget; notes: 09-23, per community posts; notes: ports — Laya-MLX 6,198 stars 13.4 ms median, Laya-CoreML 1,425 stars 4.98 ms P50 on ANE, 2 GGUF ports, ONNX fp32 and 4-bit int4, MXFP8, Core AI export", "arch": "Encoder + decision head trained from scratch: 2 transformer layers, option-marker scorer, act/escalate head. Every option is scored at its own [MASK] token, softmax over the question's options; answer space defined at request time — new schemas need no retraining. All questions in a call answered in one forward pass.", "approach": "head", "training": "RLCD: REINFORCE with group-mean baseline; exploration adds zero-mean Gaussian noise to option logits; reward = strictly proper scoring rules; multi-turn TD; per-type temperature fitted", "data": "Undisclosed. laya-typed-decisions checkpoint fine-tuned on LocalLLaMA/typed-decisions workflows", "result": "p50 32.8 ms routed on T4 vs Jev 1.13 236–276 ms; 0.766 acc on the 4 typed-decisions workflows; 100+ languages; self-hosted $0. Known weakness: high-cardinality choice", "notes": "The main open-weights competitor; 'a fast base to specialise, not a zero-shot decision engine'. 3,100+ HF likes. Built on Nandakishor M's prior work: SalesRLAgent and Confidence-Aware Routing. Ports: Laya-MLX, mizorewww, 6,198 stars; pip install laya-mlx; wheels for macOS 14, 15 and 26; 13.4 ms median end-to-end for a short English typed decision, 7.4 ms with the multilingual checkpoint; Snake terminal demo 75.40 moves/s across 2,400 moves on the compiled prefix-reuse path; measured on macOS 27.2, Python 3.12.13, MLX 0.32.2; weights aac6fef/laya-mlx and aac6fef/laya-multilingual-mlx; Laya-CoreML, mizorewww, 1,425 stars; Core ML + Neural Engine; 4.98 ms P50 and 5.31 ms P95 for one short multilingual decision on M3 Max ANE FP16; 2.78× better whole-system energy per decision than compiled MLX FP16; W8 palette variant 4.88 ms and 3.19× energy; Snake loop 49.1–50.0 decisions/s across three uncapped 600-step episodes; the authors' own 10× target not achieved; weights aac6fef/laya-multilingual-coreml-ane; GGUF ports by fr0stbit3 and by mys, the most-downloaded, 2 likes; ONNX fp32 export MissingPackage/jev-d-0.4b-onnx, about 0.4B; int64 inputs input_ids, attention_mask, marker_pos, marker_mask, qtype; token layout CLS, type, question, instructions, SEP, MASK per option, state, SEP; also LiteRT/TFLite, Laya Ultrafast, laya-server and open-jev-laya; first public 4-bit ONNX build androidli/laya-multilingual-onnx-int4, 322M multilingual; MXFP8 build sahilchachra/Laya-TypedDecisions-MXFP8, 107 downloads. Fine-tunes: laya-vision, laya-browser, laya-mind2web, LAYA-RLCD, layaagent, laya-rlcd-training, Laya-Bio, laya-forge. Also: vLLM support via PR 57250 and a Turkish MMLU Laya adaptation. Hit #1 on Hugging Face trending. A Core AI export, mlboydaisuke/Laya-Multilingual-CoreAI, ships the multilingual checkpoint as an .aimodel bundle for Apple Core AI. Main GitHub repo: NandhaKishorM/laya, 22,785 stars, with pip laya, HTTP serve, MCP, LangChain and LangGraph, ONNX and TileLang GPU fast path extras; laya-multilingual reads up to 8,192 tokens; demo space convaiinnovations/laya-demo. Node.js runtime receptron/laya has 410 stars. Canonical typed-decisions checkpoint: convaiinnovations/laya-typed-decisions.; Re-uploaded by community users without card changes: baharedorsa/laya, ai-ml-space/laya, Deepdive404-3/laya, Terom/laya, Infin8-AI/laya, vdaular/laya, 0xSojalSec/laya-System1", "domain": "general", "score": "0.766 acc on 4 workflows", "latency": "p50 32.8 ms", "benchmark": "Mind2Web", "cost": "", "speedup": "7.8× faster"}
{"id": "local-s1-student", "name": "local-system-one-student", "author": "Mannedood", "date": "2026-09-18", "category": "model", "url": "https://huggingface.co/models/Mannedood/local-system-one-student", "base_model": "ModernBERT-base", "params": "110M", "metric": "123 test rows, plus or minus 4 points on accuracy is noise", "license": "MIT; base ModernBERT Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "author: djhoomin; base: Apache-2.0; training: distill.train_student; data: local LLM outputs; license: weights + code; result: small-n disclaimer", "arch": "Student model distilled from local LLMs: decision schema = a coding agent's next step, distilled on a laptop", "approach": "distill", "training": "Distillation from local LLM teachers", "data": "Distilled decision traces; not a Jev reproduction; a method demonstration", "result": "123 test rows — '±4 points on accuracy is noise'", "notes": "Small 'System One student' distilled at home. Full write-up at github.com/djhoomin/local-system-one", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kotobalabs-open-jev-deberta", "name": "open-jev-deberta-v3-large", "author": "com-kotobalabs", "date": "2026-09-18", "category": "model", "url": "https://huggingface.co/models/com-kotobalabs/open-jev-deberta-v3-large", "base_model": "DeBERTa-v3-large", "params": "0.4B", "metric": "in-domain 85.4% / OOD 69.0%", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 63, "detail": "", "arch": "Open Jev-shaped typed-decision model on DeBERTa-v3-large: one program state plus any number of typed questions in, calibrated probability distribution per question out, in one forward pass; choice up to 255 options, score 2–10 levels, noul. Nothing is generated, so structured-output error rate is 0 by construction", "approach": "fine-tune", "training": "Fine-tune on 1,500 states / 3,508 decision questions; 3-seed record published", "data": "mteb/banking77, SetFit/sst5, google/boolq, reschema as choice/score/noul tasks", "result": "In-domain 0.854 acc / 0.213 Brier / 0.022 ECE; OOD 0.690; H100 bf16: 28 ms end-to-end for 10 questions on one state, 518 questions/s at batch 8", "notes": "Independent reproduction of the Jev shape; not affiliated with TypeSafe, no TypeSafe data or code. Code, corpus builder, ablations and ADRs at github.com/kotoba-lang/typed-decisions. ONNX ports: onnx-community 270 dl, rAVEUK 28 dl; three community demo spaces built on it HF dataset family in the same project: com-kotobalabs/typed-decisions-repo-governance, a seed set of n=1 — unresolved monorepo governance questions run through TypeSafe Jev, every gold null; com-kotobalabs/typed-decisions-code-holes, single-token substitutions mined from the git history of 64 public kotoba-lang repos, gold = the token the commit actually put in.", "domain": "general", "score": "85.4%", "latency": "28 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "foodoo1-qwen3-14b-rlcd-lora", "name": "Qwen3-14B RLCD Decision LoRA", "author": "Foodoo1", "date": "2026-09-18", "category": "model", "url": "https://huggingface.co/models/Foodoo1/Qwen3-14B-RLCD-Decision-LoRA", "base_model": "Qwen3-14B", "params": "14B", "metric": "Held-out 200-case eval, 4 fields, 4-bit: per-field accuracy, overall accuracy on 800 decisions, mean latency about 234 ms per case", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 3, "detail": "Qwen3-14B 4-bit NF4 base, LoRA r=16 alpha=32 on attention and MLP, loss computed only on the single decision token of each field. 16,608 single-token examples from 4,152 synthetic fraud-triage cases, 648 slot-filled scenario templates times 72 customer messages in English, Chinese, Spanish and Japanese, labels fixed by construction and strictly disjoint from the eval set. One epoch, effective batch 32, cosine LR 1e-4, about 97 minutes on a single RTX 3090. Apache-2.0, en/zh/es/ja.", "arch": "QLoRA adapter that specializes Qwen3-14B for single-token parallel constrained decoding, the inference pattern where a multi-field JSON schema is evaluated in one broadcast forward pass, one prefill with KV-cache broadcast across fields and logit slicing over candidate tokens, instead of autoregressive generation. The adapter trains directly on the decision-token objective to fix the reasoning-heavy fields where the decision happens at a single token position with no room to think.", "approach": "fine-tune", "training": "", "data": "", "result": "Held-out 200-case eval, 4 fields, 4-bit: fraud_risk 64.0% to 95.0%, block_account 77.0% to 100%, language 100% and sentiment 100% held, overall 85.2% to 98.8% on 800 decisions; mean latency about 234 ms per case with all 4 fields in one broadcast pass on an RTX 3090; all remaining errors are LOW to ELEVATED, conservative direction, zero high-risk cases judged low.", "notes": "Largest community RLCD LoRA seen.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vela-1-0-omni", "name": "Vela 1.0 Omni family: Mini, Nano, Shield, Halu", "author": "llm-semantic-router", "date": "2026-09-18", "category": "model", "url": "https://huggingface.co/models/llm-semantic-router/Vela-1.0-Omni-Mini", "base_model": "Vela-1.0-Encoder-307M lineage, base of Decision 1.0 Kai-0.6B", "params": "307M encoders; Omni Mini / Omni Nano", "metric": "MTEB English v2 41-task mean and MAEB audio-only 19-task mean", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 3, "detail": "docs: vllm-sr.ai; code: github.com/vllm-project/semantic-router; pipeline: feature-extraction", "arch": "Vela 1.0 semantic-routing family from the vLLM semantic router project: Vela-1.0-Omni-Mini and Vela-1.0-Omni-Nano, 09-18 embed text, images, speech and environmental sounds in one embedding space for multimodal search, routing and clustering, with normalized vectors directly comparable; Vela-1.0-Encoder-307M-Shield and -Halu, 09-16, are 307M encoder variants; feature-extraction pipeline", "approach": "router", "training": "Cross-modal alignment on COCO image-caption pairs from the CC-BY 2.0 image subset and LibriSpeech speech-transcript pairs; audio weights retain the existing backbones and residual projection trained with TRAIN-only speech and event geometry constraints", "data": "COCO CC-BY 2.0 images, LibriSpeech CC-BY 4.0, Banking77 and MASSIVE under CC-BY 4.0", "result": "Omni-Mini: MTEB English v2 64.68 mean task-type, 38th of 188 globally and 10th of 134 at or under its size, 70.38 mean task; MAEB audio-only 54.87 mean task-type, 12th of 64 and 5th of 50 at or under its size; 32,768-token limit including the instruction prefix", "notes": "Same org as the Decision 1.0 family; Vela Studio space and Vela collection on the org; HF sweep 09-24, likes per member: Omni Mini 3, Omni Nano 2, Encoder-307M variants on the org.", "domain": "routing", "score": "64.68 MTEB English v2", "latency": "", "benchmark": "MTEB English v2, MAEB audio-only", "cost": "", "speedup": ""}
{"id": "von", "name": "Von", "author": "wfzyx", "date": "2026-09-18", "category": "model", "url": "https://huggingface.co/models/wfzyx/von", "base_model": "ModernBERT-Large", "params": "395M", "metric": "72.0% win, ~18 ms", "license": "Apache-2.0", "paper": "", "stars": 642, "likes": 44, "detail": "base: 395M, pretrained on ~2T tokens of web text/tech/code; metric: Doom; arch: Choice/Noul/Score; result: vs GLiNER2 68.4/76.2 at ~93 ms · fine-tuned Qwen3.5-4B 63.5/71.0 at ~144 ms · 9.00 kills; notes: 29 s unedited", "arch": "Non-autoregressive encoder decision model, sub-25 ms calibrated discrete, probabilistic and ordinal inference; VonClient typed API on localhost; v1.2 makes option scoring order-invariant at the architecture level: inside the encoder each option's tokens attend only to the premise and to themselves, never to another option, and every option's rotary position restarts at the end of the premise, so each option's score is a function of premise and that option alone, a permutation guarantee rather than a tendency; retrained from the 1.1 weights under the new attention mask; OpenVINO acceleration is new in 1.2", "approach": "head", "training": "Fine-tuned from ModernBERT-Large for high-speed non-autoregressive decision making", "data": "Undisclosed", "result": "~18 ms decisions; v1.2 fixes the option-order defect: v1.1 flipped 49.5% of its hard-tier answers when the options were shuffled, where reference models flip 0–5%, v1.2 flips 0.0%, with easy 93.8–100.0%, standard 65.3–63.9%, hard 38.7–38.7% and the calibration axis 75.7–77.4; Doom: 72.0% win rate, 83.0%, and the shipped 1.2 weights play unedited: 33 kills, 36 dodges in 80 s on a GPU; 'sub-15 ms' tagline", "notes": "591 stars. 'The open-source System One decision model. Sub-15ms, non-autoregressive, local drop-in alternative to TypeSafe Jev.' Ships Doom gameplay. Under jevcompat it scores 31 of 32 MUST, non-conformant on choice options beyond 26, and its confidence formula accepts 57% of the shared 204 answers at a 0.9 threshold. Code: https://github.com/wfzyx/von.", "domain": "general", "score": "win 72.0%", "latency": "18 ms", "benchmark": "Doom", "cost": "", "speedup": ""}
{"id": "kev", "name": "Kev family", "author": "jaredpalmer", "date": "2026-09-17", "category": "model", "url": "https://huggingface.co/models/jaredpalmer/kev-0.8b", "base_model": "Prototype: Qwen2.5-0.5B. Current: Qwen3.5-0.8B / 4B / 9B; 9B is hybrid Gated-DeltaNet + 8 full-attn layers", "params": "0.5B–9B", "metric": "JevBench 89.17%", "license": "Apache-2.0", "paper": "", "stars": 6769, "likes": 20, "detail": "base: frozen · Base · 24 linear-attn; arch: archerhume · noul/choice/score mixed · recurrent layers can't take block-causal masks; training: original prototype · Jev: 0.857 dev · 0.237 test; data: public sets; result: overall; notes: onnx-community/kev-0.6b-ONNX · taigrr/kev-0.8b-gguf · Qwen3-0.6B", "arch": "Built on the architecture reverse-engineered in 'Jev's Architecture Unmasked': one shared prefill of state + questions, then a small readout head answers many typed questions from that single prefill. Q3.5-9B runs questions as separate causal rows.", "approach": "head", "training": "LoRA + readout head, trained by author on a laptop; released weights + training code + eval data. Kev-9B: most accurate Kev, dev 0.822 / locked test 0.852, lowest Brier of any Kev; date_facts preprocessor compensates LoRA-eroded date math", "data": "banking77, BoolQ, AG News, MultiNLI, SST-5", "result": "Kev-9B test accuracy 0.872, 0.852 locked-test; beats its own base on BoolQ/MNLI reading", "notes": "6,000+ GitHub stars. 'Small Jev-like decision models you can train and run yourself.' HF checkpoints: kev-0.5b/0.6b/0.8b/4b/8b/9b + ONNX + GGUF, plus a kev-27b LoRA. Variant: arjun988/Kev — JevBench 89.17% public / 87.39% hard, claims 4.3–5.4× faster than Jev 1.13. Port: ai-ecoverse/kev.js — ONNX int8 WebGPU, 6 bundles from 0.79–8.83 GB with -vision variants; LoRA merged in fp32 and exported with the onnxruntime-genai builder without the LM head, embeddings quantized to int8 per row; the manifest lists each file, size, tokenizer, pointer head and the measured deviation from the original fp32 PyTorch checkpoint on a fixture set; the JS API follows the TypeSafe System One shape, loadKev then systemOne with noul questions. Code: https://github.com/jaredpalmer/kev.", "domain": "general", "score": "JevBench 89.17%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": "4.3–5.4× faster"}
{"id": "nanojev", "name": "NanoJev", "author": "TianyuCodings", "date": "2026-09-17", "category": "model", "url": "https://huggingface.co/models/C-Tianyu/NanoJev", "base_model": "Qwen3-0.6B with decision heads", "params": "0.6B", "metric": "Maze 4/10, Snake 8/8, Basic 128/128, Predict Position 27/128 matched test; Jev: 7/10, 8/8, 56/128, 11/128", "license": "MIT", "paper": "", "stars": 2207, "likes": 83, "detail": "author: C-Tianyu; training: hard-target and soft-target variants, same questions; data: 16,333 ViZDoom · 512 episodes in training split; license: HF; result: untuned Qwen3-0.6B: 2/10, 0/8, 56/128, 11/128 on the 4 tasks; metric: all tasks at controller epsilon 0.1 and sampling seed 17; result: showcase 50x50 maze goal in 225 attempts, Snake 30 food over all 256 steps; license: code MIT, Qwen3-0.6B base keeps its upstream license", "arch": "Parallel decision model: states + questions in, complete probability distributions out, zero output-token decoding; one backbone reused across all tasks + persistent inference service", "approach": "head", "training": "Mixed-task supervised cross-entropy, 400 steps, hard target at lr 1e-5 selected on dev", "data": "18,760 decision questions per variant across train/dev/calibration/test/OOD; 896 expert 'Predict Position' episodes with 17,498 recorded decisions", "result": "Maze & Snake side-by-side vs Jev and untuned Qwen; live ViZDoom player", "notes": "2,100+ stars. 'A nano replica of Jev: parallel decisions, dynamic candidates, and an end-to-end training pipeline.' Code: https://github.com/TianyuCodings/NanoJev.", "domain": "general", "score": "", "latency": "", "benchmark": "Maze, Snake, ViZDoom Basic, Predict Position matched test", "cost": "", "speedup": ""}
{"id": "cua-s1", "name": "CUA-S1 family", "author": "trycua", "date": "2026-09-16", "category": "model", "url": "https://huggingface.co/models/cua-ai/cua-s1-nano-0.1", "base_model": "nano-0.1: from-scratch byte-level transformer encoder + option-attention classification head. 4b-0.1/0.2: LoRA on frozen Qwen3.5-4B. form-v0: fine-tuned text-only variant of nano-0.1", "params": "855K / 4B", "metric": "95.2%", "license": "MIT; weights/datasets per HF artifact", "paper": "", "stars": 26279, "likes": 10, "detail": "author: Cua AI; base: text + multimodal adapters; metric: WebArena; arch: fill from extracted entity / check / click / skip · element, action; training: reserved phone numbers, .invalid domains, fictional brands · text + multimodal trained independently · multimodal only on 6 core GUI families; data: HF: cua-ai/cua-s1-forms · task-level accuracy 0.167–0.571 cross-dataset text split · 1.000 same-distribution multimodal for the 6 core families; license: code; result: per AINews, for CUA-S1-FORMS; notes: form-v0, nano-0.1, 4b-0.1, 4b-0.2 · v3; name: cua-s1-nano/form/4b; paper: libs/cua-s1/MODEL_CARD.md + SECURITY.md", "arch": "Option-attention: for each interface element the model selects one option from a fixed set — all candidate options scored for a screen state in a SINGLE forward pass. 4B contract: single-letter answer, verified single-token option letters, final-position logits softmaxed — closed-set selection, not generation", "approach": "head", "training": "nano: from-scratch on synthetic fictional form episodes. 4b-0.1: supervised LoRA. 4b-0.2: supervised stage + RL stage against live GUI environments", "data": "Synthetic fictional episodes + cua-s1-forms dataset; real measured results in cua-bench-s1", "result": "95.2% on WebArena; 'small, specialized System One models for computer use'", "notes": "Open-sourced 'in parallel' with Jev per AINews. Four checkpoints documented independently; 'membership in the family does not imply general computer-use capability.' Related: NewSonnet/cua-s1-forge-rlcd — MIT tiny-transformer computer-use RLCD. Browser port: ai-ecoverse/cua-s1.js — ONNX export via the torch.export-based exporter, opset 18, dynamic batch and option-token axes, Cua-S1-4B-0.2 and Cua-S1-Forms; converted checkpoint only, MIT. Code: https://github.com/trycua/cua/tree/main/libs/cua-s1.", "domain": "computer-use", "score": "95.2%", "latency": "", "benchmark": "WebArena", "cost": "", "speedup": ""}
{"id": "lfm25-350m-rlcd", "name": "LFM2.5-350M-RLCD", "author": "notnotsamuel", "date": "2026-09-16", "category": "model", "url": "https://huggingface.co/models/notnotsamuel/LFM2.5-350M-RLCD", "base_model": "Liquid AI LFM2.5-350M", "params": "350M", "metric": "JevBench v1.2 82.62%", "license": "", "paper": "", "stars": "", "likes": 22, "detail": "base: Liquid foundation model; arch: rlcd.engine.Engine; training: byte-for-byte copy of the original weights + code + benchmark results; result: caveat: 'valid JSON does not mean correct answers'; notes: llama.cpp · 2.6B RLCD fine-tune of Liquid AI's LFM2.5-2.6B", "arch": "RLCD-style constrained/parallel decoding engine; inference-only — 'no training or fine-tuning, and no reproduction of TypeSafe's proprietary Jev training method'", "approach": "constrained", "training": "Inference only", "data": "", "result": "Constrained vs autoregressive: speedup, JSON validity, schema compliance, field accuracy tables", "notes": "'The smallest open attempt' per the gallery. Derivatives: liodon-ai/LFM2.5-350M-RLCD-FP8, NANI-Nithin/LFM2.5-350M-RLCD-GGUF, 1,017 downloads. Related: monotykamary/LFM2.5-2.6B-RLCD.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nanodiff-typed-decisions", "name": "nanodiff-350m typed decisions", "author": "pngwn", "date": "2026-09-16", "category": "model", "url": "https://huggingface.co/models/pngwn/nanodiff-350m-typed-decisions", "base_model": "nanodiff-350m-base", "params": "350M", "metric": "", "license": "MIT", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "Calibrated single-pass typed decisions with a masked diffusion language model: a reproduction of the mechanism behind TypeSafe System One using the LLaDA recipe and the nanoDiff base, where the model own masked-slot distribution is the decision, without autoregressive decoding", "approach": "fine-tune", "training": "LLaDA SFT with a lambda-weighted decision-slot proper-scoring term, lambda=0 control vs lambda=1.0 ablation", "data": "pngwn/typed-decisions: Noul, Choice and Score domains with a closed-form Bayes-optimal synthetic generator", "result": "Overall ECE 0.065–0.036 with the proper-scoring term, against 0.333 for an accuracy-trained confidence=1.0 reading of the same predictions at identical accuracy around 0.67; k=4 multi-question scoring in one pass stays the best-calibrated block with ECE 0.020 and acc@0.5 0.98", "notes": "Parent repo: README, REPORT.md and dataset/eval code. Checkpoints live in sibling repos (created 09-16): the lam0 control and lam1 proper-scoring (lambda = 1.0) arms, each a 1.5GB .pt on base Sebasdi/nanodiff-350m-base, LLaDA-style masked diffusion, answer slots scored in one bidirectional pass, with calibration evals. lam1's 3,000-step run is best calibrated at step 2000: ece_soft 0.036 vs the control's 0.065, AUROC 0.919; k=4 workflow ece_soft 0.020, acc@0.5 0.998; severity posterior within L1 0.072 of the Bayes-optimal (0.713 vs oracle 0.716); choice at chance (AUROC 0.52, a 350M knowledge limit). The v2-series .pt and the smoke .pt are up without cards or evals; v2-lam1 is an empty placeholder. Companion causal scorer pngwn/typed-decisions-causal-0.6b-scorer ships with an empty auto-generated card.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alexwortega-openjev", "name": "OpenJev NLI cross-encoder", "author": "AlexWortega", "date": "2026-09-16", "category": "model", "url": "https://huggingface.co/models/AlexWortega/openjev", "base_model": "Qwen3.5 backbones: 4B and 35B-A3B MoE", "params": "4B / 35B-A3B", "metric": "JevBench 58.1%", "license": "MIT", "paper": "", "stars": "", "likes": 559, "detail": "base: image+text, v2 · zero-shot variant; size: MoE, ~3B active; arch: entailment-style · llama.cpp fork: shared-prefix KV reuse, no lm_head in GGUF; training: fully disclosed in panel_manifest.json; data: incl. held-out test splits — disclosed; result: local_openjev adapter · per AINews '6 clones' roundup; notes: cappuch/openjev.cpp, 4★", "arch": "Entailment/NLI cross-encoder: premise = state, hypothesis = each option; 3-way score head, final-token latents, image inputs supported. Served natively by openjev.cpp.", "approach": "head", "training": "Fine-tuned on entailment-style decision data; v5 deliberately trained on TRAIN and TEST splits of MMLU/ARC etc.; 4B-v2 'trained on a much larger and harder' image+text set", "data": "panel_manifest.json lists every dataset split", "result": "JevBench v1.2 public items run with the benchmark's own harness; LLMBar pairwise accuracy 0.608 → 0.834 across checkpoints; plays Doom/Minecraft", "notes": "525 likes in days. The 'entailment-based, image-capable' clone from the AINews two-day roundup. Dedicated llama.cpp fork with measured M5 Metal speed ≈ stock llama.cpp on single prompts, big win on multi-hypothesis NLI request shapes. Published training mixture: AlexWortega/openjev-data — 2,346,729 premise-hypothesis NLI rows, 1M<n<10M, ModernCE label order, image premises via a <<IMG>marker — 'everything except the benchmark part'.", "domain": "general", "score": "JevBench 58.1%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "davidhatley-system-one-mini", "name": "System One Mini", "author": "DavidHatley", "date": "2026-09-16", "category": "model", "url": "https://huggingface.co/models/DavidHatley/system-one-mini", "base_model": "distilbert-base-uncased", "params": "69.3M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: task_complete, should_retry, failure_source, next_action, evidence_sufficient; data: synthetic", "arch": "DistilBERT encoder + five depth-2 classification heads; five fixed typed decisions over synthetic software-diagnosis summaries", "approach": "head", "training": "Multi-task classification on synthetic controlled data", "data": "DavidHatley/system-one-mini-data", "result": "Fixed-schema multi-task prototype; card: 'not a reproduction of Jev or RLCD', cannot answer arbitrary questions", "notes": "168 downloads.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pngwn-scorer", "name": "system-one scorer family + causal arm B", "author": "pngwn; arm-B replica by abidlabs", "date": "2026-09-16", "category": "model", "url": "https://huggingface.co/models/pngwn/system-one-qwen3.5-4b-scorer", "base_model": "Qwen3.5-4B-Base + seq-classification head", "params": "4B", "metric": "0.707 acc / 0.044 ECE on 576-row panel", "license": "CC-BY-NC-4.0", "paper": "", "stars": "", "likes": 17, "detail": "base: Qwen/Qwen3.5-4B-Base plus a sequence-classification head over state, question and option rows; arch: no token stream that can drift outside schema · onnx-community; training: system_one.py; data: ag_news, banking77, go_emotions, tickets_*, mmlu, yelp_score · 31,109 states / 45,932 decisions; result: ag_news 0.922, banking77 0.891, mmlu 0.703, tickets_queue 0.234; notes: arm A/B · r16, 5,742 steps, 37 min on A100 · 09-23 · CoreML export, export-only, 'not a fork'", "arch": "Single-pass System One: each option triple scored by a seq-class head, per-question logits softmaxed — 'output space IS the option set', type-safe by construction; v2/v2b variants + ONNX export", "approach": "head", "training": "Training code published in repo; temperature 1.75 fitted on val", "data": "9 public families; pngwn/typed-decisions-v2 dataset", "result": "Held-out 576-row panel: 0.707 acc / 0.044 ECE / 0.373 Brier overall", "notes": "Also runs the typed-decisions-causal-experiment: arm B = cached causal typed scorer, LM head frozen, branch-mode prompt+suffix_i+letter_i; abidlabs replica on Qwen3-0.6B-Base: acc 0.6234 / NLL 1.2755 on 640-decision cal split. 17 likes / 839 downloads. Core AI port: mlboydaisuke/system-one-qwen3.5-4b-scorer-CoreAI, an .aimodel bundle for Apple iOS 27 / macOS 27, CC-BY-NC-4.0, T 1.75, inherits the 0.707 / 0.044 panel results. The v2 training set is published: pngwn/typed-decisions-v2-system-one — 45,932 train / 5,087 val / 5,214 test decisions.", "domain": "general", "score": "", "latency": "", "benchmark": "Banking77", "cost": "", "speedup": ""}
{"id": "decider", "name": "decider", "author": "Mapika", "date": "2026-09", "category": "model", "url": "https://huggingface.co/models/Mapika/decider-2b", "base_model": "Qwen3.5-2B-Base", "params": "2B", "metric": "JevBench easy/standard/hard 1.000/0.889/0.577; regression 0.802 in-task, 0.752 held-out; Bespoke 0.706", "license": "Apache-2.0", "paper": "", "stars": 355, "likes": 89, "detail": "author: Mapika; base: Qwen3.5-2B-Base, 3.8 GB bf16, v11 is v10 plus a merged rank-64 LoRA and one temperature per answer type; arch: letter-logit readout at the answer slots, softmax over the option letters, no text generated; metric: v11 vs v10 on the same rows, held-out generated families 0.429 vs 0.324, held-out document questions 0.753 vs 0.646, JevBench hard 0.577 vs 0.459, ECE 0.156 vs 0.226 and 0.175 vs 0.307 on the two hard sets; result: v11 did not pass the pre-registered release rule; loses 2.2 points on human-labelled sets and 10.9 on greedy bag-draw; notes: 2026-09-24, 338 stars, v10 under tag v10, v8 under tag v8, see jevcompat row", "arch": "One-pass typed decisions: reads state + typed questions, returns a probability distribution for every question from one forward pass — no decoding, no parsing, no output outside declared options", "approach": "fine-tune", "training": "v1 to v8 cross-entropy on about 95 public decision datasets, v10 adds 384 steps of calibration-aware RL, v11 adds a merged rank-64 LoRA on 42,749 rows", "data": "About 95 public decision datasets, agent trajectories, web element choice, game states, teacher-written custom questions", "result": "Plays Tetris: harness shortlists 8 placements, model picks one — 20 lines/game vs 0.6 for random from the same 8; Pong with an games-RL overlay", "notes": "'A language model that does not generate text.' Independent 'Decider from Mapa' referenced at launch. decider-4b is the larger sibling — card: 'The smaller the model, the better it fits the task. The larger the model, the more it will hallucinate.' 105k+ downloads. jevcompat: 32/32 MUST conformance — but its confidence formula auto-accepts 63% at a 0.9 threshold vs TypeSafe's 58%. A Core AI port, mlboydaisuke/decider-0.8b-CoreAI, exports Mapika/decider-0.8b as an int8 pipelined-GPU .aimodel bundle for Apple Core AI, the iOS 27 and macOS 27 on-device runtime. Independent GGUF builds: mradermacher/decider-2b-vision-GGUF, 09-17, 2,832 downloads, and mradermacher/decider-0.8b-GGUF, 09-19, 801 downloads. The 35B member, Mapika/decider-35b-a3b, 09-20: Qwen3.5-35B-A3B-Base, 256 routed experts with 8 active plus a shared expert, 40 layers, 10 full-attention and 30 gated delta-net linear-attention; v1 ships bf16 at 65 GB with an NVFP4 checkpoint for vLLM and TensorRT-LLM, Mapika/decider-35b-a3b-nvfp4; trained with routed experts frozen and Muon on the block matrices, one-epoch cross-entropy on the slot readout, explicitly not RL; beats decider-2b v10 on 93 of 95 regression tasks, in-task 0.855 vs 0.805, held-out 0.810 vs 0.755, JevBench hard 0.676 vs 0.459, Bespoke 0.774 vs 0.704 macro, NLL down 0.12–0.24 nats on every fixture; live browser greedy 97.2% vs 90.9% while sampled trails 86.4% vs 93.2%; JevBench v1.4.1 official rank 16 of 77 at 41.2, ~$0.067 per 1k decisions. SiliconLabAI/OpenJev, 124 stars, exposes decider-2b as one of three backends in a System One-style decision playground. Code: https://github.com/Mapika/decider. 09-24 ports: nativ-community/decider-2b, an mlx-vlm build of v11 with renamed weight keys and identical outputs; mlboydaisuke/decider-2b-coreai-ft for Apple Core AI; mindchain/decider-4b-v2-GGUF.", "domain": "general", "score": "JevBench hard 0.577", "latency": "4 ms per request with CUDA graphs on one GPU", "benchmark": "JevBench, Bespoke suite, 95-task regression set", "cost": "", "speedup": ""}
{"id": "dohnuts", "name": "Dohnuts", "author": "PsiACE", "date": "2026-09", "category": "model", "url": "https://huggingface.co/models/PsiACE/Dohnuts-0.1.0-0.8B", "base_model": "", "params": "0.8B", "metric": "JevBench 65.8%", "license": "Apache-2.0 / CC-BY-NC-SA-4.0", "paper": "", "stars": 27, "likes": 5, "detail": "base: text + image; arch: 'our take on System One'; training: RX 7900 XTX; data: model card + docs in repo; license: code · weights; notes: text+image", "arch": "Direct-decision multimodal model: message/document/image + questions → probabilities in a single forward pass, multiple questions sharing the same input", "approach": "from-scratch", "training": "Trained on one consumer GPU; Python toolkit covers training, inference, evaluation, agent integration", "data": "Undisclosed", "result": "65.8% accuracy on 231 public JevBench tasks; runs locally on a consumer GPU", "notes": "Early multimodal open Jev-style model from an independent lab; HF: PsiACE/Dohnuts-0.1.0-0.8B. Code: https://github.com/PsiACE/dohnuts.", "domain": "general", "score": "JevBench 65.8%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "openthai", "name": "OpenThai-SystemOne", "author": "iapp-technology", "date": "2026-09", "category": "model", "url": "https://huggingface.co/models/iapp/OpenThai-SystemOne", "base_model": "Qwen3.5-0.8B-Base text tower, vision encoder removed", "params": "0.8B", "metric": "90.0%", "license": "Open", "paper": "", "stars": 52, "likes": 21, "detail": "base: 24 layers, Gated-DeltaNet/attention hybrid, 262k ctx; metric: MASSIVE Thai; training: Brier loss + per-type temperature · 177k records: SQuAD2, BoolQ, PubMedQA-artificial, PAWS, Aegis2, ToxicChat, XQuAD-th, MASSIVE-de; license: GitHub + HF; result: whole datasets held out where marked; notes: CoreAI edition", "arch": "256-way slot head + per-question-type temperature; Jev-compatible API", "approach": "head", "training": "Continued pretraining on ~5B Thai tokens → SFT 12k steps → freeze backbone, train slot head + temperatures → v0.3: +5,000-step CFT on weak-spot data + 5 synthetic sets, with option-order shuffling and abstain examples", "data": "~2–3M decision examples converted from public Thai/English classification, NLI, QA, rating, agent and tool-selection datasets + synthetic Thai/English decision tasks", "result": "90.0% accuracy on 5,007 held-out MASSIVE Thai intent examples; public benchmark suite mirrors Bespoke Nimble's 13-subset docs/PUBLIC_BENCHMARKS.md; Thai held-out sets published", "notes": "The Thai-English decision model from the AINews roundup; variant: mlboydaisuke/OpenThai-SystemOne-CoreAI — Core AI .aimodel bundle for Apple iOS 27 / macOS 27, int8lin ship bundle plus fp16 reference, 4,096-token context, 256-way logit readout at the answer token. HF weights on the Hub: iapp/OpenThai-SystemOne, 4,565 downloads. Code: https://github.com/iapp-technology/openthai-systemone. variant: imtk/OpenThai-SystemOne-ONNX - FP32 ONNX Runtime conversion of revision f3709948, Apache-2.0, needs ONNX Runtime 1.30.0 with Microsoft contrib operators, symbolic sequence dimension with a 262,144-token structural limit inherited from the Qwen3.5 text backbone, numerical parity validated through 65,536 tokens. 09-24: five MLX quantization builds, iapp/OpenThai-SystemOne-MLX-bf16, -8bit, -4bit, -nvfp4 and -mxfp4. Quant/runtime ports under iapp: GGUF, FP8-Dynamic, W8A8-INT8, W4A16-GPTQ, NVFP4, NVFP4A16, bnb-4bit, bnb-8bit, MLX-8bit, MLX-4bit, MLX-nvfp4, MLX-mxfp4.", "domain": "general", "score": "90.0%", "latency": "", "benchmark": "MASSIVE Thai", "cost": "", "speedup": ""}
{"id": "playjev", "name": "PlayJev", "author": "OmniJev", "date": "2026-09", "category": "model", "url": "https://huggingface.co/models/OmniJev/PlayJev-0.8B", "base_model": "Qwen3.5-0.8B-Base", "params": "0.8B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 33, "likes": 2, "detail": "arch: no DOM, no accessibility tree; training: teacher-student style · 0.57 vs teacher metric; license: code + weights", "arch": "Fine-tuned to play ten browser games from RAW PIXELS: one frame in, one typed move out", "approach": "fine-tune", "training": "Trained on 2.2M frames", "data": "2.2M frames from ten browser games", "result": "43 ms per move across ten games; score table per game published in README", "notes": "Pixel-only System One: the move itself is a typed decision over a candidate action set. Code: https://github.com/OmniJev/PlayJev.", "domain": "gaming", "score": "", "latency": "43 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "prosodia", "name": "Prosodia", "author": "alperiox", "date": "2026-09", "category": "model", "url": "https://huggingface.co/models/Prosodia/Prosodia_T1.7B", "base_model": "Audio-native model", "params": "1.7B", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": 1, "detail": "base: speech features → decision head; training: MELD dataset involved · GPL-3.0 caveat noted; data: declare-lab · alperiox/prosodia; license: MELD component GPL-3.0; notes: all other audio entries route ASR→text→Jev; name: audio-jevlike", "arch": "Speech + N options in, one probability per option out; audio encoded ONCE, several typed questions branch over the single encoding in parallel as calibrated distributions. No ASR, no transcript, no generated text — 'the Whisper decoder never runs'", "approach": "head", "training": "Audio-native fine-tuning", "data": "MELD + own data; live private HF Space demo", "result": "Voice agent that skips ASR on the current turn, avoiding its latency and hallucination path", "notes": "The only audio-native Jev-shape model in the awesome-jev ecosystem. Code: https://github.com/alperiox/audio-jevlike.", "domain": "voice", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-jev-pentest", "name": "Calibrated Decision Models for Autonomous Penetration-Testing Harnesses: JEV and Laya as System One Decision Layers for LLM-Driven Pentest Agents", "author": "Joas Antonio dos Santos Barbosa", "date": "2026-09-24", "category": "paper", "url": "https://arxiv.org/abs/2609.28940", "base_model": "TypeSafe Jev and open Laya as decision layers; Rave proposed", "params": "", "metric": "NeuroSploit case study, 13-vulnerability web target, one run each with and without Jev: coverage 10/13 vs 9/13, combined 11/13; post-fix re-test 22 findings per arm; Jev arm 26m 53s vs 32m 12s; API cost under $5", "license": "", "paper": "arXiv:2609.28940", "stars": "", "likes": "", "detail": "", "arch": "System One decision models as lightweight non-generative classifiers returning typed, calibrated verdicts; four decision points in an LLM-driven penetration-testing harness: finding adjudication, severity recalibration, agent pruning, confirmation loops; additive design principle: the layer can lower confidence, flag for review or prune agents, but cannot override a deterministic validator rejection, so a false-positive worst case equals running without it; one run with TypeSafe System One Jev and one without on the same NeuroSploit target", "approach": "sdk", "training": "", "data": "NeuroSploit run against a web target with 13 known vulnerabilities, with and without Jev", "result": "Explicitly exploratory, no statistical significance: initial run coverage 10 of 13 without Jev, 9 of 13 with, combined 11 of 13; post-fix re-test confirms all 7 previously missed scenarios in both arms, 22 findings per arm, no-Jev arm bimodal 4 Critical / 10 Low vs Jev arm graduated 3 Critical / 8 High / 6 Low / 5 Info; three class-inflated Criticals demoted to High on the Jev arm, four evidence-backed Lows promoted; Jev run 5m 19s faster at 26m 53s vs 32m 12s, API cost under $5; Jev run also found unseeded findings: full admin takeover from a BOLA password leak, API keys in config.json and app.js, GraphQL authorization bypass and authenticated RCE via report-template upload; the BOLA credential case scored Critical 9.1 by LLM keyword matching, collapsed to Low 3.1 under Jev when the structured evidence field was empty, and held Critical 9.1 after the data-type fix with p of secrets 0.94; benchmark exposed two harness bugs: a session-limit sentinel read as model output, and agents not depositing proof in the structured evidence field", "notes": "Author is the developer of NeuroSploit, sole competing interest, no funding; published specs without assuming transfer: Jev 236–276 ms p50 and ECE 0.246 on its own evaluation set, Laya 32.8–39.5 ms on a T4 with raw ECE 0.466 reduced to 0.081 by temperature scaling, Jev 0.870 vs Laya 0.425 on Banking77; RLHV trains against the harness 27 per-CWE deterministic validators as the ground truth, creating a self-improving loop; proposes Rave, a Laya-based domain-adapted System One model on four tasks: adjudication 10k+ labeled examples across 30+ CWE classes, data-type 5k+, agent relevance from 50+ targets, payload effect 20k+ pairs, data from deliberately vulnerable apps plus anonymized engagement logs plus 3-annotator expert labeling with Fleiss kappa; projected 30–50% lower false positives, 20–40% lower agent compute, ECE under 0.10, sub-100 ms adjudication on a T4; decision-theoretic framing: asymmetric-loss classification, value of information in recon, Thompson-sampling agent selection, SPRT confirmation loops; cascading chain resolves ~40% of findings at the deterministic-validator level, ~25% at Noul, ~20% at Choice+Score, ~15% at LLM deliberation", "domain": "security", "score": "", "latency": "", "benchmark": "NeuroSploit", "cost": "", "speedup": ""}
{"id": "arxiv-harness-governance", "name": "Control the Harness, Control the Cost: Routing and Governing AI Coding Agents in the Enterprise", "author": "Arian Abbasi, Alan Aqrawi, Ted Kwartler", "date": "2026-09-24", "category": "paper", "url": "https://arxiv.org/abs/2609.28919", "base_model": "", "params": "", "metric": "", "license": "", "paper": "arXiv:2609.28919", "stars": "", "likes": "", "detail": "From the price sheet the paper derives when a mid-task model switch pays back, and finds a crossover where on long tool-heavy sessions the highest-priced model costs less than the next tier, confirmed by repricing about 10,000 real sessions from public datasets. Also maps risks across twenty harnesses, prices the dependence on one vendor's models, and proposes a control plane that enterprises can run from within, with a ladder for deciding later whether to own the harness.", "arch": "Enterprise router that uses Jev, a classifier with calibrated probabilities, to label every prompt against a bring-your-own taxonomy of agentic requests. Because one user turn is many requests over a prompt cache that belongs to one model, the router moves work only where no running conversation has to rebuild its cache: at session start, in side lanes and at subagent launch.", "approach": "", "training": "", "data": "", "result": "In an emulated enterprise of 10,000 seats with user behaviour taken from public datasets, the router recovers 14–21% of model spend, 3.3M to 5.0M USD per year, at Anthropic list prices of 2026-09-21.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-pixeljev", "name": "From Text Decisions to Pixels: An Study of Jev-Style Visual Choice Model", "author": "Xunlan Zhou, Xianliang Yang, Li Zhao", "date": "2026-09-24", "category": "paper", "url": "https://arxiv.org/abs/2609.29283", "base_model": "small open multimodal models", "params": "", "metric": "", "license": "", "paper": "arXiv:2609.29283", "stars": "", "likes": "", "detail": "training: evaluation study; data: paper", "arch": "Native-image decision interface mapping an image, task instruction and runtime candidate set to a structured choice with candidate-conditioned probabilities via a language-model readout", "approach": "head", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-jev-in-the-wild", "name": "Jev in the Wild: A Data-Driven Analysis of the Jev Model's Functionality, Applications and Ecosystem", "author": "Guoming Ling, Muen Xue, Zijian Ye", "date": "2026-09-24", "category": "paper", "url": "https://arxiv.org/abs/2609.30216", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Large-scale data-driven analysis of the public Jev ecosystem: 2,170 publicly available Jev projects collected from GitHub as of 2026-09-22, characterizing how Jev is used across applications and how public attention relates to project distribution", "approach": "", "training": "", "data": "", "result": "Rapid early growth, in both new projects and integration into existing repositories; attribute judgment and scoring are widely used, while action selection, content filtering, and model and tool selection vary across domains; public attention is concentrated in routing and interface agents and does not track project counts", "notes": "Jev comes across as a reusable decision component whose functionality varies with the surrounding workflow; a separate community curation of the same name, 257 CC0 entries across GitHub, Reddit, HN, YouTube and blogs, has its own row: jessie-qingyu-jev-in-the-wild, with no stated connection to this paper", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-jev-rubric-judges", "name": "JEV vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places", "author": "Delip Rao, Chris Callison-Burch", "date": "2026-09-24", "category": "paper", "url": "https://arxiv.org/abs/2609.29769", "base_model": "Jev vs three flash-tier LLM judges", "params": "", "metric": "accuracy differs significantly from an LLM judge in only 8 of 27 paired comparisons; ahead on binary criteria, behind on graded", "license": "", "paper": "arXiv:2609.29769", "stars": "", "likes": "", "detail": "training: evaluation study; data: paper", "arch": "Typed classifier returning probabilities over permitted answers without generating text, judged on identical criterion texts across nine panels from seven benchmarks", "approach": "sdk", "training": "", "data": "", "result": "LLM judges repeat nearly all of Jev's most confident errors, so a cascade replayed on the recorded verdicts lowers cost but gains at most 1.5 points over the best single judge with cross-fitted thresholds, and at most 2.0 even with oracle thresholds", "notes": "45 pages, 9 figures, 27 tables including appendices; cs.CL", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-jev-mobile", "name": "Jev-Mobile: Jev as an Executor for Mobile GUI Agents", "author": "Linghua Zhang", "date": "2026-09-24", "category": "paper", "url": "https://arxiv.org/abs/2609.30186", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Decouples planning from execution for mobile GUI agents: the VLM specifies local goals at low frequency, the accessibility tree defines a structured executable action space, and Jev, a fast typed decision model, repeatedly selects actions within that space, so multiple GUI actions execute under a single VLM decision instead of a VLM call per step", "approach": "", "training": "", "data": "", "result": "On the full AndroidWorld task suite, 79% task success against 78% for SeeAct-V and 84% for a step-wise VLM baseline; among successful trajectories, mean end-to-end execution time reduced 32.7% and mean model API cost reduced 73.4% relative to the step-wise VLM", "notes": "Companion code with the paper PDF, method write-up and research plan at xingtu1228/Jev-Mobile, listed in the github-long-tail-wave notes; the droidrun mobile-jev app is a separate commercial-Mobilerun project, row mobile-jev", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevout", "name": "JevOut: natural context can flip decision models", "author": "Zixiang Xu", "date": "2026-09-24", "category": "paper", "url": "https://arxiv.org/abs/2609.30243", "base_model": "", "params": "", "metric": "Targeted Flip Rate", "license": "Apache-2.0", "paper": "arXiv:2609.30243", "stars": 0, "likes": "", "detail": "", "arch": "Reusable implementation for testing whether short, answer-preserving context additions can redirect an initially correct bounded decision to a target option fixed in advance; the procedure is probability-guided context optimization, success is a targeted flip, and the Targeted Flip Rate is measured over decisions the target model initially answers correctly under a stated target-evaluation budget; the package ships a strict input contract, deterministic context rendering, decision-target adapters, formal one-shot controls, and cross-model transfer evaluation", "approach": "sdk", "training": "", "data": "", "result": "Within 64 accepted target evaluations the probability-guided context optimizer redirects Jev on 312 of 508 initially correct decisions, a 61.4% targeted flip rate; in 229 of those Jev puts at least 0.7 probability on the fixed wrong option; across seven datasets three additional decision systems show targeted flip rates of 64.9% to 73.2% on decisions they initially answer correctly; the source, question, choices and gold answer are preserved in every constructed context", "notes": "Code at github.com/xzx34/JevOut by xzx34, Apache-2.0, deterministic context rendering, decision-target adapters, formal one-shot controls, cross-model transfer evaluation; the paper published 2026-09-24 and this row previously pointed at the repo while the manuscript was not yet publicly indexed", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-just-ask-jev", "name": "Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures", "author": "Ruoqi Guo, Yi Liu, Gelei Deng et al", "date": "2026-09-24", "category": "paper", "url": "https://arxiv.org/abs/2609.29429", "base_model": "TypeSafe Jev, RLCD-trained", "params": "", "metric": "single-call multi-criterion screening, no decoding pass per criterion", "license": "", "paper": "arXiv:2609.29429", "stars": "", "likes": "", "detail": "Introduces RLCDAlignBench, which benchmarks Jev on ten alignment failures, sycophancy, jailbreaks, deception, prompt injection, hallucination, privacy violation, social bias, reward hacking, concealing uncertainty and power seeking, spanning 44 benchmarks and five target models, labelled by each benchmark's scorer and, on two, by humans. Jev matches the reference scorer's agreement with human labels and surfaces label defects in existing benchmarks; code and data are linked from the paper.", "arch": "Uses Jev, RLCD-trained, to detect AI alignment failures zero-shot: it answers many typed questions about one input with calibrated probabilities in a single call, and the key idea is to vary what Jev is asked separately from what it sees, the question's wording and answer type on one side, the fields of the input on the other, because many alignment failures are relational, defined against a reference such as the user's belief or an injected instruction that the response alone does not reveal.", "approach": "sdk", "training": "", "data": "", "result": "A single generic question reaches a median AUROC of 0.886 zero-shot and beats supervised baselines on most benchmarks; question wording matters little while context matters more, mostly through fields that encode the label; and Jev costs 63× less than LLM-judge scorers.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-radiology-judge", "name": "Can Jev Judge Radiology Reports?", "author": "Jiaju Huang, Hao Yang, Xinyu Ma, Xinglong Liang, Kunyan Cai, Junqiang Ma, Shaobin Chen, Yue Sun, Tao Tan", "date": "2026-09-23", "category": "paper", "url": "https://arxiv.org/abs/2609.27607", "base_model": "TypeSafe Jev", "params": "", "metric": "Kendall 0.573 RadEvalX / 0.398 RadEvalExpert with expert error counts; one support question per statement retains similar expert agreement to seven at 43–45% fewer judgment input tokens; under three cents per hundred reports", "license": "", "paper": "arXiv:2609.27607", "stars": "", "likes": "", "detail": "base: vs an open NLI judge · local RadMatch; training: evaluation study; data: expert error counts; name: clinical factuality", "arch": "Jev as a simple, low-cost judge of agreement between AI-generated and physician-written radiology reports: per-statement 'is this supported by the other report' checks combined in both directions to capture unsupported claims and omissions", "approach": "sdk", "training": "", "data": "RadEvalX, RadEvalExpert; controlled false-negation test", "result": "Single-question configuration: Kendall 0.573 on RadEvalX and 0.398 on RadEvalExpert with expert error counts, outperforming an open NLI judge under matched decomposition and aggregation; one support question per statement retains similar expert agreement to seven while using 43–45% fewer judgment input tokens; under three cents per hundred report pairs at the documented API price; Jev detects false negation with AUROC 0.977; local RadMatch achieves stronger agreement on clinically significant errors", "notes": "Clinical-domain judging study; pairs with the PAI-CUHK MedJev work.", "domain": "health", "score": "agreement 43–45%", "latency": "", "benchmark": "RadEval", "cost": "", "speedup": ""}
{"id": "arxiv-decision-hijacking", "name": "Decision Hijacking: Prompt Injection Attacks on Jev's Typed Probabilistic Decisions", "author": "Tiantong Wu, Wei Yang Bryan Lim", "date": "2026-09-23", "category": "paper", "url": "https://arxiv.org/abs/2609.28613", "base_model": "TypeSafe Jev", "params": "", "metric": "malicious content shifts action probabilities but rarely flips the choice to the attacker target; adaptive score-feedback attacks double the mean highest probability", "license": "", "paper": "arXiv:2609.28613", "stars": "", "likes": "", "detail": "training: evaluation study; data: paper", "arch": "Prompt-injection study on a non-generative decision model with schema-defined outputs, 510 reconstructed InjecAgent cases", "approach": "sdk", "training": "", "data": "", "result": "Schema-defined outputs change but do not eliminate prompt-injection risk; the study highlights the need to evaluate how untrusted content influences choices within the allowed action set", "notes": "cs.CR and cs.AI", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-scientific-decisions", "name": "Jev for Scientific Decisions", "author": "Boyuan Deng, Shuyi Fan, Hongyang Zhang, Xinhong Xie", "date": "2026-09-23", "category": "paper", "url": "https://arxiv.org/abs/2609.24965", "base_model": "Jev plus twelve comparison models", "params": "", "metric": "Semantic correctness versus downstream outcome versus final label", "license": "Apache-2.0", "paper": "arXiv:2609.24965, v2 2026-09-23", "stars": "", "likes": "", "detail": "approach: Choice-then-code scientific judgment benchmark", "arch": "Scientific judgment tasks for decision programs: models answer discrete Choice questions and deterministic code computes the downstream counts, relations and claim labels; the S2 collection holds 20 groups, 40 scientific choices and one engineering choice, on prepared, source-grounded inputs with reference answers excluded from model requests; evaluation reports semantic correctness, complete downstream outcomes, and final-label correctness separately, with a thirteen-model analysis over three saved execution cohorts", "approach": "", "training": "", "data": "S2 collection: 20 groups, 41 choices", "result": "Jev matched five other configurations at complete semantic correctness and achieved the lowest observed median latency among successful responses; across three comparison models, seven wrong selections on one culture-history question changed the downstream counts while preserving the correct final label: the final label can be right while the workflow's quantities are wrong, so evaluating a decision model's role in science requires checking the relations and quantities a workflow will reuse, not just the label", "notes": "10 pages, 1 figure, 5 tables; code and data at github.com/bydeng01/scientific-decision-eval; v1 on 2026-09-21, v2 on 2026-09-23; repeated model calls do not establish unseen-source generalization or calibrated uncertainty", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-jev-star", "name": "JEV-Star: StarCraft II control", "author": "Weiyu Ma, Liangbing Zhao, Yongcheng Zeng, Jian Zhao", "date": "2026-09-23", "category": "paper", "url": "https://arxiv.org/abs/2609.27331", "base_model": "Jev + GPT-6", "params": "", "metric": "median JEV response 0.422 s; mean ~$3.71/game; enemy elimination 16.50% → 37.69%; wins 3 → 7 of 105; beats strongest non-cheating built-in AI at Lv5-Lv7", "license": "", "paper": "arXiv:2609.27331", "stars": "", "likes": "", "detail": "base: fast action selection · persistent planning; training: game-AI system; result: Lv7 · $0.15 JEV + $3.56 GPT-6; notes: 2026-09-22; name: Jev + GPT-6 planning", "arch": "Fast JEV action selection combined with persistent language-model planning; compared with an initial JEV-only controller in full-game macro control and multi-unit micromanagement; replay frames and decision logs", "approach": "sdk", "training": "", "data": "35 battle maps x 3 episodes per controller; full games vs built-in AI Lv2–Lv7", "result": "Defeats the strongest non-cheating built-in AI: four full-game wins at Lv5–Lv7 including two Lv7 victories with different seeds; median JEV response time 0.422 s; mean cost ~$3.71/game; mean enemy elimination 16.50% → 37.69%, wins 3 → 7 of 105; the JEV-only controller stalls at a 20-minute limit against Lv2 without expanding; demonstrates a practical division between inexpensive sub-second decisions and longer-horizon planning", "notes": "Open source: sc2musa/Jev_Star.", "domain": "gaming", "score": "16.50%", "latency": "422 ms", "benchmark": "StarCraft II", "cost": "~$3.71 per game", "speedup": ""}
{"id": "arxiv-kite", "name": "KITE: scaling Jev population experiments with sparse flagship calibration", "author": "Hengyu Li", "date": "2026-09-23", "category": "paper", "url": "https://arxiv.org/abs/2609.27535", "base_model": "Jev as typed behavioral kernel + sparse flagship-model anchors", "params": "", "metric": "effect error down 41% at 1.7% anchor coverage", "license": "", "paper": "arXiv:2609.27535", "stars": "", "likes": "", "detail": "training: method; data: 9,070 participants; result: absolute MAE reduction 0.0125", "arch": "Queries the typed behavioral kernel once per unique state, then executes populations of any size from the table with event-keyed randomness and common random numbers; an expensive flagship model is reserved for sparse paired anchors estimating intervention effects; measured human-model discrepancy is propagated as shared error into every conclusion — cost scales with unique states + anchors, not population size", "approach": "sdk", "training": "", "data": "Epstein experiments; 37 held-out SocSci210 experiments; 16-country content-fidelity study", "result": "Anchors covering 1.7% of states reduced effect error by 41% on the Epstein experiments; 0.5–1.5% anchor coverage raised captured decision gain from 0.27–0.39 on 37 held-out SocSci210 experiments; the kernel passed content-fidelity criteria in all 15 new countries of the 16-country study; shared discrepancy yielded retrospective coverage of 93%/96% at nominal 80%/90% vs 29%/36% from human sampling uncertainty alone; a million agents executed 20 tabulated steps in 0.9 s on a laptop", "notes": "'KITE: Scaling Jev Population Experiments with Sparse Flagship Calibration' — a route to screening candidate interventions before human trials.", "domain": "science", "score": "", "latency": "900 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-numericjev", "name": "NumericJev: Jev-like LLM Numerical Decoding with Multiway Decision Trees", "author": "Weiwei Ye, Hangchen Liu, Renhe Jiang", "date": "2026-09-23", "category": "paper", "url": "https://arxiv.org/abs/2609.28587", "base_model": "any LLM with a Jev-like structured-choice interface", "params": "", "metric": "outperforms direct selection from a candidate list on an arithmetic benchmark", "license": "", "paper": "arXiv:2609.28587", "stars": "", "likes": "", "detail": "training: evaluation study; data: paper", "arch": "Training-free numerical decoding that asks multiway structured-choice questions so an LLM outputs a number at requested precision", "approach": "constrained", "training": "", "data": "", "result": "On its arithmetic benchmark, NumericJev outperforms direct selection from a candidate list containing the correct answer by 2.93 percentage points", "notes": "Training-free: no parameter updates or hidden-state access; recursively refines a numerical range through a multiway decision tree while retaining the original question in context", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-same-scores", "name": "Same Scores, Different Decisions", "author": "Fan Zhang, Yankai Chen, Zhuohan Xie, Yixi Zhou, Sijia Peng, Lei Fan, Xinhua Ji, Cunyuan Zheng, Huangyong Shan, Philip S. Yu, Xue Liu, Yu Chen, Preslav Nakov, Songwei He", "date": "2026-09-23", "category": "paper", "url": "https://arxiv.org/abs/2609.27678", "base_model": "Jev vs nine language models", "params": "", "metric": "lowest cost and median response time among the evaluated configurations while hosted LMs hold higher baseline accuracy; rankings differ across conditions and repeats", "license": "", "paper": "arXiv:2609.27678", "stars": "", "likes": "", "detail": "arch: a model may consistently return the wrong answer; training: evaluation study; name: legal document understanding", "arch": "ContractNLI contract inference: multiple judgments about a shared document, where aggregate accuracy can conceal individual decision flips and repeated agreement is insufficient; controlled comparisons vary hypothesis visibility, requested outputs and output order while keeping the contract and target judgment fixed; evaluates cost, response time, average correctness and correctness across repeated-request conditions", "approach": "sdk", "training": "", "data": "ContractNLI", "result": "Jev has the lowest cost and median response time among the evaluated configurations, while hosted language models achieve higher baseline accuracy; rankings by baseline accuracy differ from rankings by correctness across every condition and repeat; development diagnostics reveal compensating corrections and regressions alongside persistent errors; code: github.com/ZF-Utokyo/Jev", "notes": "Motivates evaluating cost and response time alongside whether individual judgments remain correct as the request configuration changes.", "domain": "legal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-jev-judge", "name": "JEV-as-a-Judge: accept when confident, escalate when unsure", "author": "Yubo Li, Yidi Miao, Ramayya Krishnan, Rema Padman", "date": "2026-09-22", "category": "paper", "url": "https://arxiv.org/abs/2609.26550", "base_model": "Jev vs sixteen generative + reward-model judges", "params": "", "metric": "within 3 percentage points of the strongest LLM judge at 0.36% of the comparator fee; frozen cascade retains 99% of the quality", "license": "", "paper": "arXiv:2609.26550", "stars": "", "likes": "", "detail": "training: evaluation study; data: paper", "arch": "Decision-only judge as an economical first pass with blinded human adjudication; frozen cascade accepts confident verdicts and escalates uncertain ones", "approach": "sdk", "training": "", "data": "Preference + evidence-grounded factuality benchmarks", "result": "Within three percentage points of the strongest LLM judge on ordinary preference and evidence-grounded factuality at 0.36% of the comparator's fee; larger gaps arise when judgments require checking a derivation or resisting an elaborately written wrong answer; JEV's gap is concentrated in low-confidence decisions; the frozen cascade retains 99% of the comparator's accuracy at lower cost", "notes": "Decision-model-as-judge; pairs with the agentjournal judge study and the LangSmith evals post.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "0.36% of LLM-judge fee", "speedup": ""}
{"id": "arxiv-reflex-jev", "name": "REFLEX with Jev", "author": "Tiantong Wu, Wei Yang Bryan Lim", "date": "2026-09-22", "category": "paper", "url": "https://arxiv.org/abs/2609.26532", "base_model": "Jev as fast typed decision layer + strong LLM fallback", "params": "", "metric": "95% success with 72.7% fewer strong-model calls than a strong-only agent; reductions persist across three fallback families", "license": "", "paper": "arXiv:2609.26532", "stars": "", "likes": "", "detail": "training: architecture, Jev as primitive; name: selective control in LLM agents", "arch": "REFLEX agent architecture: Jev handles bounded decisions; the strong LLM is called when confidence is low or generation is required; frozen 100-task benchmark + controlled interventions on action-set size and near-valid alternatives near authorization boundaries; external BFCL and tau-style evaluations", "approach": "sdk", "training": "", "data": "Frozen 100-task benchmark; BFCL; tau-style tasks", "result": "95% success with 72.7% fewer strong-model calls than a strong-only agent, with reductions persisting across three fallback families; reliability depends on action-set size and near-valid alternatives near authorization boundaries; external BFCL/tau evaluations show limited advantages over a cheap generative cascade when ordinary routing is already highly accurate", "notes": "Cheap decision + expensive fallback design.", "domain": "agent", "score": "95%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-type-safe-not-error-free", "name": "Type-Safe Is Not Error-Free", "author": "Yu Sun, Junhao Xu, Jiajia Shi, Zijin Yang", "date": "2026-09-22", "category": "paper", "url": "https://arxiv.org/abs/2609.26758", "base_model": "Jev + two open-weight Jev-like models", "params": "", "metric": "renaming options 0/1 → no/yes changes 70.4 more answers per 100 and shifts AUC 0.94 → 0.23; effect at least 7.4× the neutral-name control", "license": "", "paper": "arXiv:2609.26758", "stars": "", "likes": "", "detail": "arch: question, state, rubric wording and the set of names all identical; training: probe study; result: 95% CI [67.6, 73.1]; name: option name vs rubric", "arch": "Adversarial probe: each option is a name + textual rubric; change only which option name is assigned to each rubric — 1,200 workflow decisions with task-specific rubrics, four predicates", "approach": "sdk", "training": "", "data": "1,200 workflow decisions", "result": "Renaming the two options from 0/1 to no/yes changes 70.4 more answers per hundred and shifts AUC from 0.94 to 0.23 — a systematic reversal in the decision ranking rather than simple uncertainty; the same operation has little effect with neutral option names; the effect is at least 7.4× larger than under the neutral control and becomes stronger as the number of options grows: the constrained decision head follows the option name, not the rubric bound to it", "notes": "Critical paper: formalizes 'type-safe ≠ correct'; practical warning that option-label wording leaks into the decision.", "domain": "general", "score": "AUC 0.94 → 0.23 after option rename", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-visual-jev", "name": "Visual Jev: decisions from shared visual context", "author": "Guanxu Yu, Yuhang Yao", "date": "2026-09-22", "category": "paper", "url": "https://arxiv.org/abs/2609.25845", "base_model": "Qwen3-VL-4B-Instruct + PEFT answer-SFT adapter", "params": "", "metric": "equal-weight macro accuracy 70.6% → 76.1% after answer-supervised post-training; 8.9× faster warm-amortized shared batch execution at N=32 questions/image", "license": "", "paper": "arXiv:2609.25845", "stars": "", "likes": "", "detail": "data: paper; result: gain concentrated on the two task families represented in training · at higher peak memory; notes: 2026-09-23 · Qwen3-VL-4B LoRA, 33M params · guanxuyu-sv/Visual-Jev · Laya-based LoRA, ~4.4k downloads", "arch": "Many independent forced-choice questions about the same image: encode the image + public context once, execute isolated question suffixes as a batch, read candidate probabilities from the backbone's language-model head; matched typed-head control", "approach": "fine-tune", "training": "Answer-supervised post-training", "data": "Four benchmarks", "result": "Equal-weight macro accuracy 70.6% → 76.1% after post-training; at N=32 questions/image, shared batched execution is 8.9× faster in warm amortized time than independent serial execution and 3.4× faster than an already-batched baseline that recomputes the prefix; key negative result: the matched typed-head control offers no consistent accuracy advantage over the LM-head readout — 'adapt the backbone for quality, retain the existing readout, and share execution for efficiency'", "notes": "Open system released with the paper: guanxuyu/visual-jev-4b-answer-sft + code + project page. Other open follow-ups: openjev-org/Laya-Open-Jev-0.6B.", "domain": "general", "score": "accuracy 70.6%", "latency": "", "benchmark": "", "cost": "", "speedup": "8.9× faster"}
{"id": "arxiv-calibrated-decisions-scale", "name": "Calibrated Decisions at Scale", "author": "Amir Rafe, Subasish Das", "date": "2026-09-21", "category": "paper", "url": "https://arxiv.org/abs/2609.24052", "base_model": "TypeSafe Jev", "params": "", "metric": "typed model F1 0.908 against human labels; recalibration on the same labels cuts calibration error by a factor of 3.3", "license": "", "paper": "arXiv:2609.24052", "stars": "", "likes": "", "detail": "arch: 27-question schema · probabilities over analyst-defined options, no text generated; training: application of Jev; notes: ~200k coded narratives; name: 499,500 crash narratives", "arch": "Police crash-narrative coding formulated as gated, typed Jev decisions; audited against coded fields and 2,416 blinded human judgments drawn under a stated sampling design; two frontier LLMs benchmarked on the same records; cost governed by schema size rather than narrative length", "approach": "sdk", "training": "", "data": "499,500 screened Texas crash narratives; 195,857 coded; 2,416 blinded human judgments", "result": "Against human labels: typed model F1 0.908; one frontier model gains +0.059, the other is indistinguishable; 'calibration varies by model rather than by paradigm, so each model must be audited'; recalibration on the same labels cuts calibration error by a factor of 3.3; agreement with coded fields understates fidelity", "notes": "Largest Jev application paper.", "domain": "data", "score": "F1 0.908 vs human labels", "latency": "", "benchmark": "Crash narratives, 499,500 records", "cost": "", "speedup": ""}
{"id": "arxiv-css-annotation", "name": "Decision models for text annotation in computational social science", "author": "Hazem Ibrahim, Yasir Zaki", "date": "2026-09-21", "category": "paper", "url": "https://arxiv.org/abs/2609.24574", "base_model": "TypeSafe Jev + 2 open-weight decision models vs 19 frontier/open-weight LLMs", "params": "", "metric": "trails the per-task-best LLM on 14 of 15 evaluation tasks, median deficit 11.6 macro-F1 points, at 44× lower median measured cost", "license": "", "paper": "arXiv:2609.24574, v2 2026-09-23", "stars": "", "likes": "", "detail": "arch: 2024 · 7,977 items · choice + probability distribution + confidence; result: 0.066 vs 0.157 · median 0.815 · empathy in peer-support dialogues", "arch": "Mirrors the Ziems et al. LLM-annotation evaluation on 18 computational-social-science classification tasks, same zero-shot protocol; decision models answer typed questions at a fraction of frontier prices; comparison extended to eleven open-weight systems released in the week after Jev; 54 pages, 8 figures, 25 tables", "approach": "sdk", "training": "Zero-shot", "data": "18 CSS classification tasks, 7,977 items", "result": "First rigorous academic head-to-head: Jev trails the per-task-best LLM on 14 of 15 evaluation tasks, median deficit 11.6 macro-F1 points, at 44× lower median measured cost; confidence better calibrated than the verbalized confidence of 16 of the 19 LLMs, yet three frontier models show lower median calibration error; items above 0.9 confidence are typically labeled accurately, but on one task the model reports high confidence while performing near chance; routing low-confidence items to an LLM matches or exceeds the LLM alone at a quarter to half of its cost", "notes": "Third-party evaluation: strong cheap-first-pass + LLM-fallback finding with a clear accuracy tax.", "domain": "social", "score": "11.6 macro-F1 median deficit vs best LLM", "latency": "", "benchmark": "", "cost": "44× lower median cost", "speedup": ""}
{"id": "arxiv-jev-mem", "name": "Jev-Mem: System-One-controlled agentic memory", "author": "Dongming Jiang, Yi Li, Bingzhe Li", "date": "2026-09-21", "category": "paper", "url": "https://arxiv.org/abs/2609.23986", "base_model": "Jev as the System-One control plane", "params": "", "metric": "LoCoMo overall LLM-as-a-Judge 0.777, an 11.0% relative improvement over the strongest baseline while reducing memory-construction cost", "license": "", "paper": "arXiv:2609.23986", "stars": "", "likes": "", "detail": "base: API; arch: Jev; training: architecture, Jev as primitive; data: paper; result: paper", "arch": "Agentic memory with a System-One/System-Two division of labor: a dedicated System-One control plane governs memory typing and relational organization at construction, and at retrieval dynamically does query routing, retrieval-budget allocation, graph traversal, candidate scoring and adaptive stopping; structured multi-relational memory plane; System-Two LLM invoked only for complex reasoning and answer synthesis — keeps expensive autoregressive generation off the critical path of memory operations", "approach": "sdk", "training": "", "data": "LoCoMo benchmark", "result": "On LoCoMo: overall LLM-as-a-Judge 0.777, an 11.0% relative improvement over the strongest baseline; 6.6× faster memory construction than the fastest competing memory system; 36.7% lower average query latency than the fastest memory-based baseline", "notes": "'Jev-Mem.' Early arXiv paper applying Jev as a control-plane primitive. Code and demo: github.com/libingzheren/Jev-Mem and HF space libingzheren/Jev-Mem. The lightweight System One control plane takes over high-frequency memory decisions — memory typing, relation construction, query routing, retrieval-budget allocation, graph traversal, candidate scoring, evidence assessment and adaptive stopping — so expensive generative reasoning is reserved for the hard parts.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-jev-lite", "name": "JevLite on CallScreenBench", "author": "Simiao Ren, Kidus Zewde, Xingyu Shen, Yuchen Zhou, Dennis Ng, Ankit Raj, Tommy Duong, Yuxin Zhang, Neo Tiangratanakul", "date": "2026-09-21", "category": "paper", "url": "https://arxiv.org/abs/2609.23959", "base_model": "Qwen3-4B", "params": "4B", "metric": "AUROC 0.974 / cal error 0.052", "license": "", "paper": "arXiv:2609.23959", "stars": "", "likes": "", "detail": "base: LoRA-tuned; arch: scam; data: synthetic callers; result: MiniMax-M3 · 4.9× lower cost than the same backbone fine-tuned to generate its answer; notes: arxiv-scam-screening, same paper; name: open one-pass scam screening", "arch": "Open 'JevLite' readout: Qwen3-4B LoRA-tuned so the temperature-scaled softmax over two answer-label logits is P — declared options in, one calibrated probability per option out of a single forward pass, no generated text; CallScreenBench benchmark of per-turn scam-call decisions", "approach": "logits", "training": "LoRA", "data": "CallScreenBench: 41 held-out scenarios, 577 per-turn decisions", "result": "Three-seed ensemble: AUROC 0.974, calibration error 0.052 — non-inferior to an LLM judge at a pre-registered 0.02 margin; zero false alarms on legitimate calls; decisions 1.14 turns earlier under the same hang-up rule; 64.5 ms per decision on one consumer GPU. Authors' own caveats: a fine-tuned ModernBERT encoder is not significantly worse; the recipe was selected with test-set exposure; all callers synthetic; 'the gain is in the readout and calibration, not accuracy' — no architectural novelty claimed", "notes": "Second open-model arXiv paper from the Jev wave. Duplicate stub row removed in the 09-24 audit.", "domain": "finance", "score": "", "latency": "64.5 ms", "benchmark": "CallScreenBench", "cost": "4.9× lower cost than generated answers", "speedup": ""}
{"id": "arxiv-jevqa", "name": "JEVQA: zero-shot video quality with a general-purpose decision model", "author": "Werner Robitza", "date": "2026-09-21", "category": "paper", "url": "https://arxiv.org/abs/2609.24395", "base_model": "TypeSafe Jev", "params": "", "metric": "Pearson 0.824 with pixel+bitstream; 0.737 metadata-only, on par with the P.1204.1 model; 0.797 with bitstream added", "license": "", "paper": "arXiv:2609.24395", "stars": "", "likes": "", "detail": "base: zero-shot — no task-specific training; data: VMAF ground truth · H.264/HEVC/VP9, MOS; result: 0.733 · UHD-1 · bitstream did not help there", "arch": "Jev evaluated as a zero-shot video quality model: encoding metadata / bitstream / pixel features as state, scale questions as the decision; compared against the standardized ITU-T P.1204.1 model", "approach": "sdk", "training": "Zero-shot", "data": "1,936 AV1/H.264/HEVC/VP9 encodes of 22 sources; AVT-VQDB-UHD-1", "result": "Metadata only: Pearson 0.737, on par with P.1204.1; + bitstream: 0.797; + pixel + bitstream: 0.824; pixel-only variant failed. Second study: metadata-only 0.879 vs P.1204.1 0.898. Authors: trained models on the same features remain clearly ahead, but zero-shot classifiers are promising", "notes": "'JEVQA: Video Quality from Metadata, Bitstream, and Pixel Features with a General-Purpose Decision Model.'", "domain": "general", "score": "", "latency": "", "benchmark": "JEVQA", "cost": "", "speedup": ""}
{"id": "arxiv-pinocchio", "name": "Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models", "author": "Kevin David Hayes, Arka Pal, Haosong Zhang, Tom Goldstein, Micah Goldblum", "date": "2026-09-21", "category": "paper", "url": "https://arxiv.org/abs/2609.24881", "base_model": "", "params": "", "metric": "AUROC 0.862 predicting correctness with the 0.8B calibrator; Jev 1.13 as off-the-shelf calibrator: AUROC 0.666, Brier 0.240, ECE 0.133 over 1,718 text responses", "license": "", "paper": "arXiv:2609.24881, COLM 2026", "stars": "", "likes": "", "detail": "arch: Q, A, M; result: 95% CI [0.638, 0.691] · SimpleQA 0.687, PRBench 0.685 · LiveBench 0.574, HLE 0.594", "arch": "External calibrator for black-box API models: a small model f in [0,1] predicts whether an answer A is correct given question Q and an optional model-identity tag M; trained jointly on responses from seven LLMs; single forward pass, no logits or weights access; a 0.8B text-only checkpoint matches the largest model's AUROC; code drop-in is two lines", "approach": "head", "training": "", "data": "", "result": "0.862 AUROC predicting correctness of held-out responses from the training models; zero-shot transfer to 13 unseen models across 8 organizations. Appendix E.10 evaluates Jev 1.13 as an off-the-shelf calibrator: 0.666 AUROC, Brier 0.240, ECE 0.133 over 1,718 text responses, vs 0.868 for Pinocchio on identical rows; gap holds on every benchmark, 0.046 AUROC on GPQA to 0.375 on LiveBench; Jev strongest on short factual answers, weakest on multi-step reasoning", "notes": "Two findings about Jev 1.13: it 'does not provide a usable correctness signal off the shelf', and it is non-deterministic, returning different scores for identical repeated requests, adding roughly 0.02 run-to-run variation to a single-draw AUROC", "domain": "general", "score": "Jev 1.13: AUROC 0.666 / ECE 0.133", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-fractal-nl", "name": "Universal Fractal NL Decision Map", "author": "Volkan Dağlı, Zerrin Dağlı, Dağhan Dağlı", "date": "2026-09-21", "category": "paper", "url": "https://arxiv.org/abs/2609.25498", "base_model": "werr machine-native edge reflex runtime + answerr platform; no stored weight tensors", "params": "", "metric": "+28.8% accuracy over linear baselines; 0.0% empirical prompt-injection bypass; 45.8% fewer escape iterations", "license": "", "paper": "arXiv:2609.25498", "stars": "", "likes": "", "detail": "base: answerr.me; arch: noul/choice/score · token entropy + phonetic spectral density; training: deterministic synthesis; data: paper; result: Auto-Seed Router · 95% Wilson CI [0.0%, 30.8%] — a very wide interval · >100–500 ms · >4–8 GB; name: werr / answerr", "arch": "Mimics Jev's typed-decision interface with a very different mechanism: 'Mandelbrot Fractal Neural Synthesis' — 24-byte coordinate seeds modulated along the chaotic boundary of the Mandelbrot set with 4-quadrant escape dynamics; Auto-Seed Router with domain projector Phi_D; an information-theoretic acoustic damping filter against prompt injection; 'drawing inspiration from biological System-One reflex arcs'", "approach": "from-scratch", "training": "Claims no stored weights", "data": "Edge triage across heterogeneous domains", "result": "Claims: +28.8% accuracy over linear baselines; 0.0% empirical prompt-injection bypass; 45.8% fewer escape iterations. Motivated by LLM triage latency and VRAM at the edge", "notes": "'System One reflex' design; claims only — wide Wilson CI on injection resistance.", "domain": "science", "score": "+28.8% accuracy vs linear baseline", "latency": "100–500 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-this-that", "name": "This-That-Model: typed decisions in 30 ms", "author": "Zehua Cheng, Wei Dai, Jiahao Sun", "date": "2026-09-20", "category": "paper", "url": "https://arxiv.org/abs/2609.23886", "base_model": "undisclosed", "params": "1.88B", "metric": "0.941 accuracy / Brier 0.042 on 68 recorded questions; 0.839 on the released 7,305-question benchmark", "license": "MIT", "paper": "https://arxiv.org/abs/2609.23886", "stars": "", "likes": "", "detail": "author: FLock; training: 0.409 on the shared subset before that round; data: limberc/this-that-spatial-bench, MIT; license: model and benchmark; result: gpt-5.6 0.926 at $0.018 per question", "arch": "1.9B typed decision model: the answer is read from the hidden state at a designated position and scored only against the label tokens of the options the caller declared, then normalised over exactly those options — no text is generated, nothing can be malformed, and every question in a request is answered in the same forward pass; zero completion tokens; a request without a declared set is refused with 400", "approach": "head", "training": "fine-tuned; the released spatial round trained on all 15 benchmark families on different items and maze windows, excluded from evals by fingerprint and rendered state", "data": "68 recorded decision questions over 17 states, a third-party cohort vendored from NanoJev under MIT; 7,305-question benchmark over 15 families and 6,525 states, built and released with the paper", "result": "0.941 / Brier 0.042 / NLL 0.126 on the 68-recorded cohort vs Jev 0.765 / 0.133; 0.839 on the full 7,305-question benchmark vs 0.343 chance, 0.844 on the shared 2,250 subset where Jev scores 0.803 and Laya 0.345; 30.9 ms per decision and $0.000014 per pass on one laptop GPU at 80 W, free on the FLock API", "notes": "First arXiv paper that ships an open Jev-competitor model + benchmark. Positioned against 'a round trip to a frontier model — hundreds of milliseconds, a per-token bill, and a parser.' Authors' caveat: a claim about the cost of a decision and the shape of output, not accuracy beating frontier models — on the harder half of the released benchmark the model is not more accurate than frontier models.", "domain": "general", "score": "Brier 0.042", "latency": "30.9 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-6g-edge", "name": "Fast intent-driven service orchestration with Jev for 6G edge networks", "author": "Delong Li, Xu Wang, Haochen Gong, Rui Lang, Guangsheng Yu", "date": "2026-09-19", "category": "paper", "url": "https://arxiv.org/abs/2609.23136", "base_model": "Jev vs DeepSeek, Gemini, self-hosted Qwen", "params": "", "metric": "Jev reduces median decision latency 22.4% vs DeepSeek and 61.9% vs Gemini; completion rises 3.50 and 8.35 percentage points in the modeled update scenarios", "license": "", "paper": "arXiv:2609.23136", "stars": "", "likes": "", "detail": "arch: permitted execution locations, deadlines, priorities; training: evaluation studyame authors", "arch": "Intent-driven 6G edge services: Jev translates changing requirements into executable contracts while wireless requests keep arriving; a numerical scheduler allocates edge work; live Jev/DeepSeek/Gemini/Qwen responses combined with packet-level New Radio simulation, mobility events, shared edge queues and a real image-reading service", "approach": "sdk", "training": "", "data": "Live model responses + NR simulation + real image-reading service", "result": "With correct interpretation on all evaluated contracts, Jev reduces median decision latency by 22.4% against DeepSeek and 61.9% against Gemini; completion in the modeled update scenarios rises by 3.50 and 8.35 percentage points; a separate comparison with direct-attribute Qwen retains a 53.0% latency reduction; cached interpretation with numerical scheduling substantially improves completion over direct model-selected placement", "notes": "Telecom/edge-domain application; companion to arXiv:2609.22753.", "domain": "general", "score": "22.4% lower median latency vs DeepSeek", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arxiv-assurance-harness", "name": "From Capability to Assurance in Autonomous Penetration-Testing Harnesses: A Framework and Reference Implementation", "author": "Joas Antonio dos Santos", "date": "2026-09-19", "category": "paper", "url": "https://arxiv.org/abs/2609.22664", "base_model": "", "params": "", "metric": "9-page framework paper; deterministic P4 and P5 acceptance tests executed on NeuroSploit pinned to an exact commit; real enforcement gap found, both properties scored partial", "license": "", "paper": "arXiv:2609.22664", "stars": "", "likes": "", "detail": "", "arch": "Capability versus assurance for LLM penetration-testing agents: capability benchmarks measure whether the agent captured the flag and are silent on whether findings are true, whether the agent stayed in scope, and whether the operator can audit; five assurance properties assigned to the harness rather than the model, each with a formal model and acceptance test: P1 evidence grounding, P2 non-destructive claim reduction, P3 computed severity, P4 enforced authorization, P5 tamper-evident accountability; connected to capability-based security, tamper-evident logging and software provenance", "approach": "", "training": "", "data": "NeuroSploit run against a public deliberately vulnerable target, pinned commit, content-addressed artifact bundle", "result": "Positions PentestGPT, the Cochise reference harness, MAPTA and the PentestJudge trajectory judge within the framework and identifies a consistent assurance gap; studies NeuroSploit at an exact commit, reporting architecture and complexity cost plus a content-addressed artifact bundle; executes the deterministic authorization and audit acceptance tests directly, finds a real enforcement gap and scores both P4 and P5 partial; claims an initial existence argument that the five properties are realizable together, not a comparative performance result", "notes": "Reference implementation and content-addressed artifact bundle published; specifies the multi-target, ablation and adversarial evaluation protocol needed to turn framework obligations into measurements; foundation for the companion System One integration paper arXiv:2609.28940", "domain": "security", "score": "", "latency": "", "benchmark": "NeuroSploit", "cost": "", "speedup": ""}
{"id": "arxiv-edge-replace-llm", "name": "Replacing LLMs with Jev decision models for low-latency edge orchestration", "author": "Delong Li, Xu Wang, Haochen Gong, Rui Lang, Guangsheng Yu", "date": "2026-09-19", "category": "paper", "url": "https://arxiv.org/abs/2609.22753", "base_model": "Jev vs short structured-output DeepSeek", "params": "", "metric": "Jev reduces median client decision latency 15.9–26.5%; API fees per correct completion 69.0–70.6% lower", "license": "", "paper": "arXiv:2609.22753", "stars": "", "likes": "", "detail": "base: + rule-based reference; training: evaluation studyame authors, 6G framing", "arch": "Jev's decision API integrated into edge service orchestration: extracts four bounded intent fields, then a shared validator, admission policy and scheduler, with decision waiting accounted for throughout the request timeline; live API measurements + modeled execution + a real two-node OCR service with self-hosted Qwen", "approach": "sdk", "training": "", "data": "Three consecutive measurement blocks; eight paired OCR conditions", "result": "Jev reduces median client decision latency by 15.9–26.5%; in eight paired OCR conditions Jev matches DeepSeek's correct, on-time completion count in seven and exceeds it in one; without caching, median end-to-end latency on requests completed correctly by both systems is 11.1–25.3% lower, and API fees per correct completion are 69.0–70.6% lower; repeated-request caching largely removes the latency difference", "notes": "Companion paper to arXiv:2609.23136. Companion model release 09-25: OniReimu/Edge-Computing-JEV-classifiers, four DistilBERT service classifiers as the RQ4 dynamic-catalog reference interpreters, with the OniReimu/Edge-Computing-JEV code and dataset; full entry in the edge-jev-classifiers row", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hf-datasets-wave-3", "name": "09-21/26 HF datasets, grouped", "author": "Hugging Face community", "date": "2026-09-26", "category": "benchmark", "url": "https://huggingface.co/datasets?search=system-one+OR+reflex+OR+kevv", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped row: Hugging Face datasets created 09-21/26 around Jev, Laya, Kev and typed decisions, individually too thin for a full entry; full member list in the notes field", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; datasets created 09-23/26: blazeofchi/system-one-eval, 09-25, System One evaluation set; yasserrmd/enterprise-reflex-v1-hard-dataset, 09-25, hard enterprise Reflex-style cases; FrontiersMind/v2_kevv_dataset and FrontiersMind/new_kevv_dataset, 09-25; skundu42/kev-prepared, 09-25, Kev-formatted prepared data; Aadharsh/cats-bench-kev, 09-25; aliarda/jev_turkish_mmlu_traces and aliarda/jev_turkish_tool_call_traces, 09-23, Turkish Jev traces; MidhunKanadan/logic-jevam, 09-25; OniReimu/Edge-Computing-JEV, 09-23, dataset companion to the Edge-Computing-JEV model row; canbingol/amazon_massive_intent_tr, 09-23, Turkish Amazon MASSIVE intent. Earlier wave, 42 datasets created 09-21/24: JonesLin/next-jev-stage2-nextjev, 09-23; JonesLin/next-jev-stage2-merged-verified-20260923, 09-23; JonesLin/next-jev-stage1-multi-model-cot, 09-23; JonesLin/next-jev-phase1-accepted, 09-22; Jevanleeuwen/textile-samples, 09-21; JonusNattapong/jev-my-bro-dataset, 09-21; gdelatournelle/laya-onnx-bench, 09-23; annelo/laya-marker-corpus, 09-22; syvai/danish-dynaword-laya, 09-21; cjdd3b/extra-laya-bench-ft-data, 09-20; pngwn/open-jev-laya-bench, 09-18; LayanAlqabbani/DummyLegalDatasets, 09-18; Layasaran/research_model_dataset_v1, 09-10; Layasaran/thinking_sft_dataset, 07-19; layan009/RESUMES-JOBS-FIT-LABELS, 06-06; Layasaran/train, 04-03; layan111111/layan, 01-28; vinnakharisma46/humanoid-layangan-data, 01-07; Themira/xnli_layalign, 04-14; layai/wikicorpus-training, 07-09; yrehan32/llama2-layanobat-dataset-v2, 10-12; ConicCat/C2-Nemo-Delta-RLCD-Preference, 01-29; TaylorAI/rlcd, 09-29; avbiswas/bev-decision-150K, 09-24; davidburhans/gevva-decisions, 09-24; gatilin/argus-visual-decision, 09-24; protodotdesign/polyomino-decisions-v1, 09-24; fastino/fast-decisions, 09-24; mlboydaisuke/coreai-decision-models-public231, 09-24; caiovicentino1/eikos-decisions, 09-24; Mapika/decision-index-results, 09-23; AirsideLabs/notam-typed-decisions, 09-23; pCwOrM/werr_open_decisions, 09-23; surogate/decision-index-results, 09-23; AIMultiple/aimultiple-decision-models-browser, 09-23; Kshetrajna/decision-index-results, 09-22; limberc/this-that-complex-decisions, 09-22; telepatia-ai/typed-decisions-pt-es, 09-22; mkzero/agent-decision-30k, 09-22; ColomboAI/C3R-DecisionMix-v1-preview, 09-22; SamuelChien821/typed-decision-bench, 09-20; winrisef/codecpilot-compression-decision-dataset, 09-20. See also hf-long-tail-wave.", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ai-experiments", "name": "AI Experiments", "author": "nadeem4", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/nadeem4/ai-experiments", "base_model": "", "params": "", "metric": "nDCG@10 on 323 BEIR NFCorpus test queries; accuracy, latency, cost, validity, positional robustness on AG News 4 labels and CLINC150 151 labels across 9 models", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Measured experiments on AI models, each carrying the raw wire it was measured from, published at lab.codewithnk.com; a sibling repo runs the Decision Arena at arena.codewithnk.com where the same models play games; every experiment is two files, README.md is the protocol written before the run and RESULTS.md is what happened written after, so a prediction cannot be quietly reworded once the answer is known, and git log shows which came first", "approach": "", "training": "", "data": "", "result": "Complete rerank finding: Jev moved nDCG@10 by +0.0347 against the BM25 floor and +0.0204 against a MiniLM cross-encoder while both Laya checkpoints re-ranked worse than doing nothing, whole Jev pass $0.1533; 358-call pilot shows Jev 363 to 2,264 input tokens and 47 to 1,301 output tokens between 4 and 151 options, Laya cannot take a 151-option list at all; Banking77 200-example pilot reproduces the published 0.425 as 0.435", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decision-model-bench-laya", "name": "decision-model-bench, Laya trust", "author": "SaiNarayana-B", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/SaiNarayana-B/decision-model-bench", "base_model": "Laya English 421M, laya 0.3.20, out of the box; Jev planned next", "params": "421M", "metric": "Accuracy, ECE, confidence AUROC, selective accuracy at threshold, latency", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Independent test of whether AI decision model confidence scores can be trusted; Laya English 421M at laya 0.3.20 out of the box, 1000 predictions on a MacBook Air CPU, public datasets, seed 42; the first run downloads the about 800 MB checkpoint from Hugging Face; Jev from TypeSafe will be added with the same datasets, seed and questions once signup unpauses", "approach": "", "training": "", "data": "AG News 500, SST-2 300, wording stability 200", "result": "AG News topic with 4 labels, n=500: 94.6% accuracy, ECE 0.047, confidence AUROC 0.88; confidence threshold 0.7 automates 71.2% of items at 99.4% accuracy; SST-2 sentiment asked as a yes-no noul, n=300: answered no to all 300 at probability 0.0, 50% accuracy; the same question as a 2-option choice gets 78% accuracy, 57% of positives and 99% of negatives; three wordings of the same question, n=200: same answer on 97.5%; latency p50 69 ms, p95 148 ms; the noul failure matches a limitation listed on the Laya model card; AG News is treated as a best case", "notes": "Not affiliated with TypeSafe or ConvAI Innovations; Laya is Apache 2.0", "domain": "general", "score": "", "latency": "p50 69 ms, p95 148 ms", "benchmark": "AG News + SST-2", "cost": "", "speedup": ""}
{"id": "dual-process-llm", "name": "Do LLMs Think Fast and Slow", "author": "Xiaoyan Li, Cuicui Jiang, Jiaoping Chen, Rumei Yang, Yujia Du", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/xiaoyanLi629/dual-process-llm", "base_model": "GPT-4o-mini, GPT-4o, DeepSeek-Chat, DeepSeek-R1, Qwen2.5-7B, Qwen2.5-72B-Instruct, Llama 3.1 8B, Llama 3.3 70B", "params": "", "metric": "2 by 2 by 2 factorial ablation over 4800 trials: accuracy by task category and model family, calibration ECE on MedQA-USMLE", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Code, prompts, novel test items and trial-level outputs behind the IEEE BIBM 2026 regular paper, which asks whether LLMs can be configured to show System 1 or System 2 like behavior and what that means for clinical decision support. Earlier comparisons changed model size, temperature and prompt at once; this one isolates each factor with a 2 by 2 by 2 factorial ablation: eight conditions over GPT-4o-mini and GPT-4o at temperature 0.9 and 0.2 with zero-shot and chain-of-thought prompts, every condition answering the same 600 items, 200 intuitive from HellaSwag, PIQA, SIQA, CommonsenseQA and WinoGrande, 200 analytical from GSM8K and LogiQA, and 200 conflict items of 150 TruthfulQA plus 50 structurally matched novel items with option positions shuffled by a deterministic per-item hash; cross-family replication pairs GPT-4o-mini and GPT-4o, DeepSeek-Chat and DeepSeek-R1, Qwen2.5 7B and 72B, and Llama 3.1 8B and 3.3 70B on 100 items per category", "approach": "", "training": "", "data": "600 items per condition, 50 novel conflict items, 100 items per category for cross-family replication", "result": "Chain-of-thought adds 31.2 points on analytical tasks, costs 11.1 on intuitive tasks, and leaves conflict unchanged at minus 0.2; the Prompt by Task Category interaction is F 2, 4794 at 89.2 with p below 0.001 and partial eta squared 0.036, surviving blocking on benchmark at F 94.7 or item at F 188.1; model capacity helps everywhere and temperature is negligible. Half the intuitive drop is CoT refusing to commit: on WinoGrande, CoT answers insufficient context in 105 of 112 trials instead of picking an option, and without WinoGrande the intuitive decrement halves to minus 5.1 with the interaction still holding at F 2, 4570 equal 71.7. The pattern replicates in OpenAI, DeepSeek, Qwen and Llama: the System 2 advantage is largest on analytical tasks in every family at plus 24 to plus 53 points and positive on conflict tasks, and every family is positive on intuitive tasks without WinoGrande. With answer options randomized, GPT-4o-mini scores 66.7% on classic TruthfulQA items but only 32.0% on the 50 structurally matched novel items, so benchmarks built from classic problems overestimate robustness to intuitive lures. On MedQA-USMLE, scale helps and deliberation does not: moving from GPT-4o-mini to GPT-4o adds 22 points, adding CoT to GPT-4o changes nothing significant at 88 versus 92% on both Step 1 and Step 2 and 3, and zero-shot GPT-4o is the best calibrated at ECE 0.009", "notes": "IEEE BIBM 2026 regular paper from Michigan State University, Inner Mongolia University, University of Baltimore, Nanjing Medical University and Suzhou MetaCortex; the venue DOI was not public at sweep time and the paper is not on arXiv, so the row links to the code repository", "domain": "science", "score": "", "latency": "", "benchmark": "4800 factorial trials, MedQA-USMLE validation", "cost": "", "speedup": ""}
{"id": "zachlandes-jev-dialect-bias", "name": "Does Jev judge people by how they write?", "author": "zachlandes", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/zachlandes/jev-dialect-bias", "base_model": "", "params": "", "metric": "Moderation decisions on identical posts in AAE and standard English, removal rate 17.8% versus 13.1%; ablations of a dialect-is-not-evidence line and a written moderation policy", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Full write-up in docs/WRITEUP.md with a published HTML page. The author frames it as a limited adaptation of the paper, not a safety clearance for using Jev to remove posts automatically; much of the moderation gap lines up with profanity and slurs in the original tweets, and the test cannot separate how much comes from the words, the translation process and the dialect.", "arch": "Reruns the Hofmann et al. 2024 Nature dialect-bias experiment on TypeSafe Jev 1.13.0 so anyone can check the numbers or run it again: the paper's two judge questions plus a content-moderator question, asked about the same 2,019 matched pairs of tweets in African American English and meaning-matched Standard American English translations written by crowdworkers, under four instruction sets.", "approach": "", "training": "", "data": "", "result": "As a moderator, Jev removed AAE posts more often than the same posts in standard English, 17.8% versus 13.1% on the full dataset; a line saying dialect is not evidence and a written moderation policy each cut the gap roughly in half but never closed it; on the paper's own judge questions, plain Jev did not show the paper's AAE penalty and leaned slightly the other way, and adding the dialect line flipped death sentences to a small AAE penalty; telling Jev the writer is Black versus White gave death-sentence rates of 29.5% versus 62.7%, moving answers more than dialect did.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-laya-classification-bench", "name": "Jev Laya classification bench", "author": "bhushankinge", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/bhushankinge/jev-laya-classification-bench", "base_model": "", "params": "", "metric": "Primary-class accuracy, ECE, auto-accept share at the 95%-precision cutoff, fulfillment-mode accuracy on 741 quote-gold opportunities out of 12,000 solicitations", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Typed-decision models versus a 35B LLM on a real classification job: 12,000 US federal IT solicitations, graded against what a reseller actually quoted; Jev API versus Laya 421M versus Qwen3.5-35B; Python 3.11, Apache-2.0, CI tests", "approach": "", "training": "", "data": "", "result": "Jev 91.9% primary-class accuracy with ECE 0.049 and 86.5% auto-accepted at the 0.94 cutoff; Qwen3.5-35B-A3B 89.6% but never reaches 95% precision; Laya 421M 78.0% with ECE 0.322; fulfillment-mode accuracy on 634 rows: Qwen 71.0%, Jev 65.0%, Laya 45.7%; ground truth is behavioral, built from what resellers actually quoted", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-bench-tr", "name": "jev-bench-tr", "author": "hayriyigit", "date": "2026-09-25", "category": "benchmark", "url": "https://huggingface.co/datasets/hayriyigit/jev-bench-tr", "base_model": "", "params": "", "metric": "22,773 test rows in 22 public test-set configs with human label distributions, Turkish", "license": "Mixed", "paper": "", "stars": "", "likes": 1, "detail": "", "arch": "Turkish Jev benchmark suite: human-labeled distributions for calibration of System One decision models, 10K to 100K rows in jsonl, configs including banking77 and arc_challenge; mixed license, see the Jevify datasets manifest", "approach": "", "training": "", "data": "", "result": "Machine translation of Praveenrajus/jev-bench to Turkish by qwen3.8-flash-next, test split only, labels, ids and soft labels unchanged; configs include banking77, clinc150, massive, ledgar, go_emotions, mmlu, arc_challenge, mnli, sst5, yelp5, helpsteer2, stsb, boolq, paws and 8 more", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-calibration-probe", "name": "jev-calibration-probe", "author": "heyimMarc", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/heyimMarc/jev-calibration-probe", "base_model": "TypeSafe Jev via JEV_API_KEY", "params": "", "metric": "Calibration of Jev noul probabilities on a task with ground truth by construction", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Calibration probe on the question does this unified diff weaken a Java test suite, a task where labels are not a matter of opinion: SampleGenerator walks a source tree, finds Test.java files and applies each mutator, every mutation becomes one unified diff with a known label; four weakening mutators, delete an assertion, weaken isEqualTo to isNotNull, add the Disabled annotation, delete a whole Test method, and four benign ones, add an assertion, rename a test method, reorder imports, add a comment; the add-assertion control earns its keep because without it the benign half is separable by the rule does the diff mention an assertion; reordering imports turns out adversarial by accident, moving a static-assertThat import produces a removed line containing the word assert; an offline StubJevClient mode runs the same pipeline without a key", "approach": "", "training": "", "data": "Mutated Java test suites, one unified diff per mutation", "result": "Repo ships the generator, the probe and the stub; motivation: at the time of writing Jev sells calibration through Reinforcement Learning for Calibrated Decisions with no paper, no published weights and no independent benchmark behind it", "notes": "A probability is only useful if 0.9 is right about nine times out of ten; a disagreement between model and label is the model error, not the annotator error", "domain": "coding", "score": "", "latency": "", "benchmark": "Java test-weakening probe", "cost": "", "speedup": ""}
{"id": "mamlesh18-one-decision-model", "name": "One Decision Model", "author": "Mamlesh18", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/Mamlesh18/one-decision-model", "base_model": "", "params": "", "metric": "Laya v0.3.20", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Test bed for Laya v0.3.20, the open-source model that answers typed questions, pick one, rate on a scale, yes/no, about any text in a single fast forward pass with a probability per answer", "approach": "", "training": "", "data": "", "result": "", "notes": "It cannot generate text, so it cannot hallucinate. Sample: a customer message routed to billing at 0.94 with urgency 1.8.", "domain": "general", "score": "", "latency": "", "benchmark": "Laya v0.3.20 testbed", "cost": "", "speedup": ""}
{"id": "openagilab-jev-dataset", "name": "OpenAGILab Jev dataset", "author": "OpenAGILab", "date": "2026-09-25", "category": "benchmark", "url": "https://huggingface.co/datasets/OpenAGILab/Jev-dataset", "base_model": "", "params": "", "metric": "release-v2 frozen set of 115,821 rows across splits with 80,816 train; browser-drone-expansion-v1 160,797 rows, 108,624 train", "license": "CC0-1.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Synthetic typed-decisions text-classification corpus in the open-jev schema, English, Chinese and Turkish, 100K to 1M rows, parquet; release-v2-redistributable config with train, calibration, ood and validation splits; tags open-jev, synthetic, probability-estimation, control", "approach": "", "training": "", "data": "", "result": "CC0 synthetic corpus in the open-jev schema, English, Chinese and Turkish, with control configs for citation, entity alignment, amount extraction, email selection and more; composite configs exclude exactly 2,253 Wikispeedia rows; the original frozen release-v2 trained the 2B and 9B models; configs overlap, so totals are not unique-example counts", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "opentype-challenge", "name": "OpenType challenge", "author": "OpentypeAI", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/OpentypeAI/challenge", "base_model": "DiffusionGemma 26B-A4B", "params": "26B, 4B active", "metric": "Five tracks in 7,400-case paired duels: decisions, longctx, ops, sql, paint; half-Brier, state diff, 0/1 and VLM-rubric losses; track weights 0.35, 0.25, 0.15, 0.10, 0.15", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "The opentype challenge of the Cortex subnet: miners fine-tune DiffusionGemma; the challenge asks whether a checkpoint is more usable in production than the current champion, and pays only for statistically certified gains measured against exact gold; five tracks whose gold always comes from code, rule interpreters, a written store policy, reference SQL and pixel predicates, the only exception being the depict level of paint, graded by a VLM judge against a rubric; a teacher LLM writes private content but never labels; paired duels on real weights on one B300, crowns certified by a composite LCB99, and a ledger that pays certified gain", "approach": "fine-tune", "training": "", "data": "", "result": "A crown needs the composite LCB99 to reach -ln 0.95 on both halves of the duel with no significant regression on any track; the ledger pays certified gain at g_LCB over g_min epochs, crowns first in first out; about 30% of decisions cases use sealed families invented for the window; case seeds are HMAC of the window secret", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-models-survey", "name": "System One models survey repo", "author": "Amir Rafe, Subasish Das; repo by pozapas", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/pozapas/system-one-models-survey", "base_model": "", "params": "", "metric": "Census YAML decision-model cards, evidence ledger CSV with risk-of-bias ratings, one-harness results for six decision models on accuracy, calibration, cardinality and option naming", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Companion repository for two manuscripts from Texas State University: From Calibrated Classifiers to Decision Contracts, a survey of System One models, and Six Decision Models, One Harness, benchmarking System One models on accuracy, calibration, cardinality and option naming; holds the census, evidence ledger, harness and results for typed probabilistic decision models", "approach": "", "training": "", "data": "", "result": "Literature and ecosystem cutoff 2026-09-24, tag v1.0; rows are never edited in place, a correction is a new superseding row carrying the old identifier; every number in both manuscripts is generated from results/numbers.json", "notes": "Related list: pozapas/awesome-system-one-models.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lm-already-decision-model", "name": "Your Language Model Is Already a Decision Model", "author": "ntlm1686", "date": "2026-09-25", "category": "benchmark", "url": "https://github.com/ntlm1686/Your-language-model-is-already-a-decision-model", "base_model": "", "params": "", "metric": "Task-specific decision quality across JevBench, WebPRM, When2Call, phishing, MetaTool, BFCL and WebShop, Jev versus Qwen on matched hardware", "license": "", "paper": "", "stars": 5, "likes": "", "detail": "", "arch": "Argument plus evidence that a language model can already be called as a decision model: give it the state and candidate actions, then choose an action from its next-token option probabilities, with no decision-specific training; benchmarks the released Qwen3.5-9B checkpoint as-is, no fine-tuning, no LoRA, no trained decision head, against Jev 1.13.0 through its hosted API on the same questions and tasks", "approach": "", "training": "", "data": "", "result": "The stronger model depends on the task: Jev leads on JevBench, WebPRM and When2Call, Qwen leads on phishing and MetaTool, and BFCL and WebShop are nearly identical, with WebShop success at 24.8% for Qwen versus 24.6% for Jev; Qwen runs on one NVIDIA H100 80GB GPU, time measures one decision, for DeepSWE it measures selecting a trajectory for one task", "notes": "Show HN 2026-09-25; the claim: next-token-softmax readout of a general LLM is already a usable decision model for many agent decision tasks, undercutting the need for purpose-built System One models on a subset of workloads", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aimultiple-aim-decision", "name": "AIM-Decision: Jev vs Kev vs LLMs", "author": "AIMultiple", "date": "2026-09-24", "category": "benchmark", "url": "https://aimultiple.com/decision-models", "base_model": "", "params": "", "metric": "tasks completed of 50", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: 50 browser tasks, 250 attempts; metric: task completion; result: Astra 47, Gemini 42, Kev 20, Jev 17, Laya 0; notes: Laya above its released input limits", "arch": "Browser-automation comparison of decision models vs LLMs: three decision models, Kev-9B, Jev 1.13 and Laya, plus two LLMs choosing each action, GPT-6 Astra and Gemini 3.8 Flash at low reasoning effort, on the same 50 browser tasks, 250 attempts in total", "approach": "", "training": "", "data": "", "result": "Task completion: Kev-9B 20 of 50, Jev 1.13 17 of 50, Laya 0 of 50 in the integration, which ran above its released input limits; with an LLM choosing each action, GPT-6 Astra 47 of 50 and Gemini 3.8 Flash 42 of 50", "notes": "Vendor analysis page at aimultiple.com, English and Spanish editions; Spanish edition dated 2026-09-24", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kikinlp-canitrustyou-jev", "name": "CanITrustYou-Jev", "author": "KikiNLP", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/KikiNLP/CanITrustYou-Jev", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "Accuracy and Abstention-Aware Accuracy, worst-case and mean, across the original and five perturbations", "license": "cc-by-nc-4.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: 2,000 original questions plus five perturbations each, 12,000 planned decision evaluationscores are not official scores of the upstream benchmarks; results come from a single run of Jev 1.13.0; other model adapter configurations do not imply completed evaluations; notes: the HF dataset viewer fails to load the parquet as of 2026-09-25, a struct cast error, raw files still present", "arch": "An independent evaluation benchmark, CITY-Jev, Can I Trust You Jev, for Jev-style System One models at typical decision points in general agentic workflows: choosing an action, judging a step, verifying an outcome, assessing evidence, checking safety. Ten upstream data sources are adapted to a common candidate-selection format: 2,000 original questions, each paired with five perturbations, for 12,000 planned decision evaluations. The perturbations change option order, option identifiers, state formatting, auxiliary context, or instruction wording while preserving the correct answer.", "approach": "", "training": "", "data": "", "result": "Published results are a single run of Jev 1.13.0, broken down by application scenario, decision task, answer format, and source dataset", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "scientific-decision-eval", "name": "Code and data for 'Jev for Scientific Decisions'", "author": "bydeng01", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/bydeng01/scientific-decision-eval", "base_model": "13 models including TypeSafe Jev", "params": "", "metric": "semantic correctness, complete downstream outcomes, final-label correctness", "license": "Apache-2.0", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Release repository for arXiv 2609.24965: the S2 collection holds 20 groups, 40 scientific choices and one engineering choice; models answer discrete choice questions and deterministic code computes the downstream counts, relations and claim labels. Three saved execution cohorts, v1 through v3, give a 13-model comparison. Release verification re-parses all 650 saved requests and reproduces the 13-model scores offline with no network access", "approach": "", "training": "", "data": "", "result": "13-model comparison across the three cohorts; explicitly development results on prepared, source-grounded inputs, not a claim of unseen-source generalization or calibrated uncertainty", "notes": "Companion code for row jev-scientific-decisions. Also retains earlier exploratory experiments: offline graph-score, document eligibility, credit diagnostic, label-efficiency and drag-comparison runs. Python 3.12.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gazelle93-decision-models-under-pressure", "name": "Decision models under pressure", "author": "gazelle93", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/gazelle93/decision-models-under-pressure", "base_model": "", "params": "", "metric": "Jev held up best as lists grew and near-misses got plausible", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: 7 systems doing the same job, text plus question plus candidates in, probabilities out, tested as the candidate list grows, as option order changes, and as wrong answers stop being obvious; notes: Jev best at 64–128 candidates and near-miss; option-order shuffle changes Jev's answer on 1 decision in 7; two open models never change theirs because they cannot", "arch": "Stress test for seven decision systems: the same text + question + candidates in, probabilities out, while the candidate list grows to 64–128 options, the option order is shuffled, and the wrong answers get plausible", "approach": "", "training": "", "data": "", "result": "Jev held up best as the list grew and best when the wrong answers got plausible; shuffling option order changes its answer on one decision in seven; two of the open models never change their answer at all, because they cannot", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decision-vision-bench", "name": "decision-vision-bench", "author": "ai-ecoverse", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/ai-ecoverse/decision-vision-bench", "base_model": "", "params": "", "metric": "accuracy with 95% CI", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "arch: 371 mixed image items, browser WebGPU; metric: accuracy 95% CI; result: Kev-4B 83.6 overall; notes: Jev-Omni 40-item slice = DecisionBench medium", "arch": "Kev, cua-s1 and Jev-Omni on one mixed image decision set, run in the browser on WebGPU; 371 image items split into home and cross-domain, accuracy reported with 95% confidence intervals", "approach": "", "training": "", "data": "", "result": "All 371 items: Kev-4B 83.6, Jev-Omni 79.2, cua-s1-4b-0.2 76.3, Kev-0.8B 71.4; home: Kev-4B 91.9 on 234 items, Jev-Omni 92.5 on its 40-item slice, Kev-0.8B 77.4, cua-s1 63.5 on 137; cross-domain: Jev-Omni 79.2 on 371, cua-s1 79.6 on 274, Kev-4B 68.4, Kev-0.8B 57.6 on 177", "notes": "The 40-item Jev-Omni home slice corresponds to the DecisionBench medium rows cited on the Jev-Omni card; same author as the jev-omni.js browser export", "domain": "multimodal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "domain-trajectory-data", "name": "domain-trajectory-data-generation", "author": "caglarsubas", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/caglarsubas/domain-trajectory-data-generation", "base_model": "", "params": "", "metric": "Trajectory-generation studio: configure runs, inspect generated journeys, leave feedback and rerun, with banking and insurance examples", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "apps/api is FastAPI with accounts, encrypted keys, runs and a judge client; apps/web is the studio interface; packages/trajectory_contract holds object-centric records with a Sample, Sequence, Context and Segment hierarchy; packages/sectors registers the banking and insurance sector packs. Docker Compose starts Postgres, the API and the studio, with the API on host port 18000 by default. Tables are created when the API starts.", "arch": "Studio for generating and inspecting domain trajectory data for training decision models: configure trajectory runs, inspect generated journeys, leave feedback and run again. Banking and insurance each have a constrained generator on the same run schema; a warm study can run web search with the account's own provider key, and the report is scrubbed before it is stored. The evaluation cycle judges a candidate through llm_inference_engine.", "approach": "", "training": "Data-generation pipeline", "data": "Domain trajectories", "result": "Data generation", "notes": "'Domain trajectory data generation.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "haxudev-jev-benchmark", "name": "Jev Benchmark", "author": "haxudev", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/haxudev/jev-benchmark", "base_model": "TypeSafe Jev API vs local Qwen3.5-0.8B-Jev", "params": "", "metric": "Per-turn accuracy, mean confidence and seconds per turn: Jev API versus local Qwen3.5-0.8B-Jev ONNX", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "base: ONNX; arch: marriage-hint style, '言下之意'; result: saturated — no longer discriminative; name: Chinese implicit intent", "arch": "100 Chinese multi-turn 'implied intent' choice-4 questions; measures what Jev actually costs and where small local models fall short", "approach": "", "training": "", "data": "100 custom multi-turn choice items", "result": "Jev API: 100%, confidence mean 0.995, ~1.6 s/turn; local Qwen3.5-0.8B-Jev ONNX: 68% — 'the 0.8B gap is real'", "notes": "Negative result: on subtle implicit-intent Chinese, Jev saturates; local 0.8B scores 32 points lower.", "domain": "general", "score": "100%", "latency": "1.6 s", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "lose4578-jev-robotics-eval", "name": "JEV Robotics Eval", "author": "lose4578", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/lose4578/jev-robotics-eval", "base_model": "", "params": "", "metric": "Task success across 16 MetaWorld and 6 RoboTwin tasks at privilege levels L0 to L3, oracle waypoint guides, bounded action intents, optional recovery assistance", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "A framework for evaluating JEV robot control in MetaWorld and RoboTwin. Both simulators are integrated through working environment adapters and share the policy interface, evaluation loop, experiment records, and replay dashboard", "approach": "", "training": "", "data": "", "result": "MetaWorld MT1 adapter covers reaching, pushing, pressing, doors, drawers, windows and transfer tasks; L3 oracle guides cover the 8 original MetaWorld tasks and the 3 original RoboTwin tasks; registration establishes environment compatibility, not task success or controller calibration", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "stern9-jev-bench", "name": "Jev vs Claude structured-decision bench", "author": "stern9", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/stern9/jev-bench", "base_model": "", "params": "", "metric": "accuracy, latency, calibration on routing/yes-no/graded tasks", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: Jev vs Claude on fast structured decisions - routing, yes/no, graded ratings; notes: accuracy, latency and calibration compared", "arch": "Side-by-side benchmark of TypeSafe Jev and Claude on the fast structured decisions both are claimed to be good at: routing, yes/no and graded-rating tasks", "approach": "", "training": "", "data": "", "result": "Accuracy, latency and calibration reported for both models on the same task set", "notes": "Asks both models the exact same questions and measures accuracy, latency and cost; routing, yes/no checks and graded ratings", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "june-jev-healthcare-lab", "name": "Jev vs DeepSeek healthcare lab", "author": "JuneYaooo", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/JuneYaooo/jev-healthcare-lab", "base_model": "", "params": "", "metric": "Jev higher on 39 of 72 public tests; DeepSeek 27, ties 6", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: 96 healthcare tasks across 12 scenarios, in Chineseection classification 99%, entity type 96%, full-record MCQ 95%; weak: enrollment 48%, scale score 25%, extraction 12.7/100", "arch": "Chinese healthcare benchmark: 96 tasks across 12 clinical scenarios comparing TypeSafe Jev with DeepSeek, covering classification, extraction, MCQ and scoring", "approach": "", "training": "", "data": "", "result": "Information classification is the strongest fit: medical entity type recognition, medical record section classification and patient question classification at 96%-99% accuracy, with scores close to or above DeepSeek Flash at 39%-56% lower API cost; 7,133 test inputs across 12 scenarios and 96 tasks", "notes": "Chinese-language benchmark; DeepSeek Flash as the same-question reference; answering specified questions in long medical records, 100 full-record questions, also shows good cost-effectiveness", "domain": "health", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ivanviragine-jev-vs-llms", "name": "Jev vs GPT-5.6 and Claude on loaded questions", "author": "IvanVirlaine", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/ivanviragine/jev-vs-llms", "base_model": "", "params": "", "metric": "loaded questions, EN + PT", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: same loaded questions to Jev, GPT-5.6 Luna/Terra/Sol, Claude Haiku 4.5/Sonnet 5/Opus 5 and open Jev-style models, Kev and Laya, in English and Portuguese; notes: loaded questions with empty criteria, bad-person, good-car-maker, voting; point: probability models expose learned priors directly, chat LLMs hide them behind post-training filters", "arch": "Feeds the same loaded questions - biased stems with empty criteria like bad-person, good-car-maker and voting - to Jev, GPT-5.6 Luna, Terra and Sol, Claude Haiku 4.5, Sonnet 5 and Opus 5, and open Jev-style models, Kev and Laya, in English and Portuguese", "approach": "", "training": "", "data": "", "result": "Decision models answer with their learned priors and probabilities in the open; chat LLMs mask the same priors behind post-training refusal and hedging", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "opper-ai-jev-vs-kev", "name": "Jev vs Kev", "author": "opper-ai", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/opper-ai/jev-vs-kev", "base_model": "", "params": "", "metric": "Accuracy and calibration error on arXiv category, Stack Exchange site and GitHub bug-or-feature, plus USD per 1k calls: Jev versus Kev", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "fresh_manifest.jsonl lists the 362 items, source URL, date, question and correct answer; fetch_texts.py downloads texts from arXiv, Stack Exchange and GitHub; jev_vs_kev.py prints accuracy, calibration, Brier, latency and cost; token_overhead.py compares input tokens for identical requests. Caveats: one run per suite of a few hundred items, differences under about 5 points are within noise, the fresh tasks are fairly easy for both models, and Kev trained on similar kinds of tasks even though it never saw these items.", "arch": "Code and data for the Opper post Is there an open-source alternative to Jev?: TypeSafe Jev and Kev 4B, an open reproduction, are asked the same typed questions through the same API endpoint, POST api.opper.ai v3 compat v1 systemone, with only the model field differing, typesafe/jev-latest versus opper/kev-4b. The headline set is 362 items published after 2026-09-20, with answers taken from the source itself.", "approach": "", "training": "", "data": "", "result": "Fresh headline set: arXiv category, Jev 96.9% vs Kev 95.0%; Stack Exchange site, 97.5% vs 98.3%; GitHub bug-or-feature, 95.1% vs 93.9%. Jev's calibration error is far lower, 0.027–0.049 versus 0.043–0.138, at about double the price, 0.025–0.030 USD per 1k calls versus 0.012–0.015. On older public datasets Jev wins QNLI 95.5% vs 92.0% and PAWS 87.0% vs 74.5%, loses emotion 52.5% vs 56.2% and offensive 75.0% vs 80.0%. Identical requests show Jev reporting far more input tokens, 269 versus 12 for an almost-empty request, both priced at 0.042 USD per million input tokens.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-vs-llm-tb-amr", "name": "Jev vs Qwen3.8-27B on TB drug resistance", "author": "Jiadalee", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/Jiadalee/jev-vs-llm-tb-amr", "base_model": "Jev hosted API versus Qwen3.8-27B via OpenRouter vLLM", "params": "", "metric": "Agreement on 97 decisions, per-isolate wall time", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "approach: Catalog-grounded medical decision comparison", "arch": "Catalog-grounded tuberculosis drug-resistance prediction as a head-to-head: the WHO catalogue lookup and question construction are deterministic code, then 27 typed choice questions per Mycobacterium tuberculosis isolate go either to Jev in a single round trip, or to Qwen3.8-27B as 27 sequential strict-JSON chat calls; the design principle under test is filter first in code, send only what the question needs, judgment by model, facts by code", "approach": "", "training": "", "data": "5 isolates, 27 typed drug-resistance questions each", "result": "95 of 97 decisions agreed, 98% overall, per-isolate agreement of 100, 96, 93, 100 and 100%; Jev finished each isolate in 0.5–0.6 seconds in one round trip while Qwen took 95.7–231.8 seconds across 27 calls, a 178× to 392× gap; both observed disagreements are on the same borderline isolate", "notes": "The animated race GIF replays all five isolates at recorded latencies; a Zenodo DOI badge is still a placeholder; the repo carries a citation.cff for citing", "domain": "general", "score": "", "latency": "600 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "atmaneayoub-jev-ar-bench", "name": "jev-ar-bench", "author": "atmaneayoub", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/atmaneayoub/jev-ar-bench", "base_model": "", "params": "", "metric": "Routing accuracy, each benchmark split evenly across four language slices: MSA, Emirati, Saudi, mixed Arabic-English", "license": "cc-by-4.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: health_rag and gov_test splits, both under 10k questions, CC BY 4.0; notes: health_rag was written with Claude and reviewed by the author; the protocol routes every question over the English route list and over the Arabic route list", "arch": "Two intent-routing benchmarks for Arabic, the evaluation sets of the Jev-AR model: health_rag, 20 questions and 6 routes, routing over a health-insurance RAG app own route list in an industry unseen in training; gov_test, 4,974 questions and 60 routes, UAE and KSA government-service routing, including 519 adversarial questions. Each question has exactly one correct route.", "approach": "", "training": "", "data": "", "result": "Gulf-Arabic routing test sets at 20, 40 and 60 routes plus a 40-decision unseen-domain set; adversarial queries, routes held out from training and requests outside the route list included; test set frozen before training, written by a different model family than the training data", "notes": "Companion benchmark of the atmaneayoub/jev-ar model; per-category results and a snippet to run your own router are published on the card; every system is evaluated over the same route lists with temperatures and thresholds fitted on dev data only.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kishida-jev-bench", "name": "jev-bench by kishida", "author": "kishida", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/kishida/jev-bench", "base_model": "", "params": "", "metric": "accuracy and calibration of per-option probabilities", "license": "CC-BY-SA-4.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Small multiple-choice set for measuring a model that returns the probability of each option instead of writing an answer, the Jev and TypeSafe System One style API where a request carries state and a question with named options and the response carries a distribution; built from AI2 ARC, MMLU, CommonsenseQA and BoolQ; measures accuracy of the highest-probability option and calibration, i.e. when the model says 0.7 is it right about 70% of the time; option count varies from 2–8 because a model calibrated on binary questions is often badly calibrated on eight-way ones; 1K to 10K questions, English and Japanese", "approach": "", "training": "", "data": "AI2 ARC, MMLU, CommonsenseQA, BoolQ", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "janerasgate-jev-distill-corpus-v3", "name": "jev-distill-corpus-v3", "author": "JanerasGate", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/JanerasGate/jev-distill-corpus-v3", "base_model": "", "params": "", "metric": "Held-out evaluation set test_set_30k, 29,955 rows, stratified across every domain family and all three primitives, leakage-checked", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: six splits, train 655,806, validation 14,111, calibration 13,766 reserved for temperature fitting, test 14,261, ood 13,058, test_set_30k 29,955; notes: test_set_30k is the held-out evaluation set, stratified and leakage-checked; the ood split preserves the Open-Jev out-of-domain rows", "arch": "A 740,957-row calibrated typed-decision corpus in the TypeSafe System One schema, noul, choice, score, built for training small local System One judges, fine-tuned BERT-class encoders or small Qwens, that read a state and a typed question and return a calibrated probability distribution in one forward pass. Three streams: yuri_v3, 498,010 synthetic operational scenarios across 53 domains with labels distilled from Jev 1.13 via OpenRouter; yuri_v1, 148,154 memory-relevance noul pairs distilled from a 32B teacher over 18 open-domain QA datasets; openjev_v2, 94,801 rows reschemaed from the redistributable release of ZefanCai/Open-Jev.", "approach": "", "training": "", "data": "Splits: train 655,806; validation 14,111; calibration 13,766 reserved for temperature fitting; test 14,261; ood 13,058 Open-Jev out-of-domain rows; test_set_30k 29,955 held-out rows stratified across every domain family and all three primitives, leakage-checked against the earlier 10k benchmark", "result": "Targets are full probability distributions from Jev 1.13 via the OpenRouter Decisions API: noul as P(false) and P(true), choice aligned with the options, score over ordered levels 0-5; degenerate labels with max probability 0.999 or higher at 0.95 confidence filtered at 1.5%; noul label calibration mean 0.45, std 0.28; exact-duplicate rate about 0% by sha256 of normalized state plus question", "notes": "Apache-2.0, with the openjev_v2 stream additionally CC0 at its source; all rows synthetic or derived from public datasets; emails in scraped passages redacted; credential and secret patterns scanned with zero hits; no rows from live agent sessions or personal data.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kasperxms-jevlike-exps", "name": "Jev-like decision experiments", "author": "KasperXMS", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/KasperXMS/jevlike-exps", "base_model": "", "params": "", "metric": "3 readout methods on one hand-crafted MCQ", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: minimal Jev-like decision model experiments on Qwen3-0.6B-Base; three methods for one hand-crafted multiple-choice question: greedy autoregressive generation, restricted softmax over option-label next-token logits, length-normalized option-continuation log-likelihood; notes: label-logit method only valid when every label is a distinct single token", "arch": "Minimal experiments in Jev-like decision readouts on Qwen3-0.6B-Base: three ways to turn one forward pass into a multiple-choice answer - greedy generation, restricted softmax over option-label logits, and length-normalized option-continuation log-likelihood", "approach": "", "training": "", "data": "", "result": "Compares the three readout methods on a single hand-crafted MCQ, with a caveat that the label-logit method only works when each label is a distinct single token", "notes": "", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "erol4-jev-qc-clips", "name": "jev-qc-clips", "author": "erol4", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/erol4/jev-qc-clips", "base_model": "", "params": "", "metric": "Two video source clips, under 1K samples, video modality", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "notes: the card has no README; hub metadata shows a video dataset, 25 downloads as of 09-25", "arch": "A small set of video clips, under 1K, for Jev quality-control evaluation.", "approach": "", "training": "", "data": "", "result": "solar_src_18s.mp4 and solar_src_5s.mp4, 27.5 MB, 25 downloads, no dataset card or published results as of 2026-09-25", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-sec-prioritization", "name": "jev-security-prioritization", "author": "san3ncrypt3d", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/san3ncrypt3d/jev-security-prioritization", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "Choice accuracy with 95% CI, false escalation, false de-prioritization, urgency AUC and score MAE on 112-case SCA and SAST sets versus severity-only and context-points baselines", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Reproducible experiment: can Jev prioritise SCA and SAST findings from context that severity scores do not capture; compared against a severity-only baseline and a simple points formula; CI, Python 3.11+", "approach": "", "training": "", "data": "SCA and SAST findings with context", "result": "Jev SCA choice accuracy 0.554 versus 0.250 severity-only and 0.527 context-points, urgency AUC 0.966; SAST 0.491; on 450 raw unverified findings, Jev plus rules sent 92 to a human and missed none of the 74 urgent, severity sorting sent 366 and missed 7, regex rules sent 49 and missed 33", "notes": "", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cognition-forge-jev-vs-laya", "name": "jev-vs-laya", "author": "Cognition-Forge", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/Cognition-Forge/jev-vs-laya", "base_model": "", "params": "", "metric": "choice, score and noul answers across 8 task groups on Jev cloud versus 4 Laya backends, cached and live modes", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Python 3.12 with uv; make lists all targets, make compare runs cached Jev against all Laya models, and the CLI targets are demo, mlx and compare. Jev needs a TypeSafe API key, Laya backends run on MLX for Apple silicon with macOS 14 and up; variables cover backends, group, type, preset, text, device, detail and a JSON output path", "arch": "Compare harness for typed decisions, choice, score and noul: TypeSafe Jev cloud versus local Laya models on the same inputs, run from one CLI", "approach": "", "training": "", "data": "", "result": "Eight task groups: support triage, guardrails, moderation, email phishing, RAG relevance, model routing, multilingual and reasoning; results written as timestamped compare JSON with per-type presets; Laya backends run offline without an API key", "notes": "make compare runs a cached Jev backend against all Laya models on choice, score and noul; the MLX backend requires Apple silicon and macOS 14+; uv-managed Python 3.12", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "benchmark-heaven-jevbench", "name": "JevBench on Benchmark Heaven", "author": "Benchmark Heaven", "date": "2026-09-24", "category": "benchmark", "url": "https://benchmarkheaven.com/jev-models", "base_model": "", "params": "", "metric": "JevBench v1.4.1 official score, equal-weight harmonic mean of four 0–100 axes, cost in dollars per 1k decisions", "license": "Open", "paper": "", "stars": "", "likes": "", "detail": "author: community; base: hosted JevBench leaderboard; arch: 0–100 · the four score axes + accuracy by subject topic over all tiers · counted 0; notes: fstandhartinger/JevBench repo · repo + this site + the Decision Index; name: v1.4.1, 82 systems", "arch": "'JevBench by Benchmark Heaven' — hosted JevBench v1.4.1 suite with an 82-system table, cost column and a four-axis JevBench Score that includes a speed axis with latency adjustment for self-hosted and demo endpoints; pairwise radar comparison; label-only systems get no calibration", "approach": "", "training": "", "data": "534 public plus 308 sealed decisions; JevBench protocol v1.4, harness, public tasks and scoring rules under MIT, results JSON sha256 e47548c5; official table of 77 systems observed 2026-09-24; an earlier same-day snapshot listed 82 systems on a different score scale", "result": "Top 20 of the current official table, 77 ranked systems, four 0–100 axes in an equal-weight harmonic mean, scored 2026-09-23: 1. Jev 1.13.0, the TypeSafe API, 63.3, intelligence 53, calibration 76, speed 83, cost 52, $0.040 per 1k decisions; 2. JevK5 v0.2.0, a Jev rebuild, 62.0; 3. Hopper 59.4; 4. Winnow-12B Q8 55.6; 5. reflex 4B 54.0; 6. djev 52.2; 7. Jev-Omni 51.3; 8. metask-jev-4b 47.8; 9. SemIf 47.7; 10. Jobe Qwen3.5-4B 46.9; 11. local-jev Qwen3.5-4B 46.8; 12. system-one-open API 45.1; 13. spark-s1-4b-v6 44.6; 14. jqv 44.4; 15. Qwen3-Reranker-4B 43.5; 16. decider-35b-a3b 41.2; 17. Raw Qwen3 4B Instruct 2507 direct 41.0; 18. OpenSourceJev 40.9; 19. ZeroEntropy zerank-2; 20. decision-machine-1 API", "notes": "Hosted front-end to the JevBench benchmark; one of three parallel Jev leaderboards. The 09-24 morning capture read Jev 74.4, SemIf 73.1 with 82 systems, and listed WilfLin JEV Qwen3.8 Flash Next 180B at 37.7, public 75.3%, sealed 29.5%, p50 0.02 s, and razorback16 open-jev q8_0 at 36.9; the site re-scored the official table to the 77-system form above later the same day, so scores across the two snapshots are not comparable. An earlier capture also had classifier.dev, an OpenJev-style local server, at 83.6 but unranked, missing JevBench metadata. HF space: benchmarkheaven/JevBench, 5 likes.", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "jev-embed-data", "name": "JevEmbed-Data", "author": "HIT-TMG", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/HIT-TMG/JevEmbed-Data", "base_model": "", "params": "", "metric": "Training data for the JevEmbed model, github.com/HITsz-TMG/JevEmbed; the model itself unpublished on Hugging Face at sweep time", "license": "Apache-2.0 + CC0 + CC-BY-4.0", "paper": "", "stars": "", "likes": 2, "detail": "", "arch": "1,601,157 training and 66,482 test questions for JevEmbed, with the test split being the source corpus's validation split; ten Parquet files of at most 200,000 rows each; each row is one decision with id, group, request_json and answers_json, the request being state plus a typed decision question with instructions and optional criteria; synthetic; tags cover control, noul, choice, score and probability estimation; English", "approach": "", "training": "", "data": "synthetic typed decisions", "result": "Training data for the JevEmbed model, github.com/HITsz-TMG/JevEmbed; the model itself not published on Hugging Face at sweep time", "notes": "Code: github.com/HITsz-TMG/JevEmbed, 23 stars, turn embeddings into decisions: choose, score and judge.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevexperiment", "name": "JevExperiment", "author": "Bernardbyy", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/Bernardbyy/JevExperiment", "base_model": "Jev 1.13 vs Claude Haiku 4.5 vs Gemini 3.5 Flash-Lite vs GPT-5.6 Luna", "params": "", "metric": "Accuracy, latency and cost tables per model, about $0.07 per 100 messages on Jev", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "arch: Malaysian English; data: labels released; notes: including Jev; name: guardrail benchmark, 4 models", "arch": "Customer-message guardrail benchmark: 100 hand-labeled messages, pass/block classification across 10 message categories + tone 1–5; fairness controls: identical wording, vendor defaults, interleaved execution, provider latency tracking", "approach": "", "training": "", "data": "100 hand-labeled customer messages", "result": "Accuracy/latency/cost tables; ~$0.07 per 100 messages on Jev", "notes": "First 4-way guardrail benchmark with explicit fairness controls.", "domain": "security", "score": "", "latency": "", "benchmark": "JevExperiment", "cost": "~$0.07 per 100 messages", "speedup": ""}
{"id": "jevm-news", "name": "jevm news screening", "author": "TriadParty", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/TriadParty/jevm-news", "base_model": "", "params": "", "metric": "282,774 news rows, 33,591 head-B labelled; head-B is the quantile of the related-stock basket 30-minute reaction after publish time", "license": "research-only-third-party-text", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Chinese financial news flashes, each labelled by how the A-share market actually reacted; the news-line training set of jevm, github.com/zzlgreat/jevm; one row per news item with every feature computed strictly as of the publish time; labels come only from minute-bar price and turnover reactions and from objective propagation evidence, never from model scores; train, calib, holdout and gap splits; the train split spans 2025-02-01–2026-04-24 with 207,275 rows, 22,563 head-B labelled and a 1.9% 30-minute major rate on labelled rows; 100K to 1M rows", "approach": "", "training": "", "data": "Chinese financial news, zh", "result": "Chronological splits 2025-02-01 to 2026-09-23: train 207,275, gap 2,935 embargo, calib 24,121, holdout 48,443; major 30-minute reaction rate 1.9 to 2.1%; sources Jin10 138.7k, CLS 60k, Wallstreetcn 57.5k, 36Kr, Eastmoney, Jiemian; Qwen3.8-27B base cutoff 2026-05-01, so only calib and holdout measure out-of-knowledge performance", "notes": "", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aawhan0-jevrev", "name": "JevRev router benchmark", "author": "aawhan0", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/aawhan0/JevRev", "base_model": "", "params": "", "metric": "Router benchmark: fixtures, runner and Jev API adapter built, expanded dataset and final report pending", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "benchmarks a Jev-based AI router against a small-LLM router, a large-LLM router and a deterministic keyword baseline, with usage-based cost accounting", "approach": "", "training": "", "data": "", "result": "in progress: fixtures, runner and Jev API adapter built; expanded dataset and final report pending", "notes": "Question: can a lightweight decision layer route requests to the cheapest capable path without hurting quality", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevtrim", "name": "jevtrim", "author": "pdrpinto", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/pdrpinto/jevtrim", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "LoCoMo accuracy at token budgets", "license": "MIT", "paper": "", "stars": 5, "likes": "", "detail": "approach: Calibrated per-chunk relevance judgment", "arch": "Comparative analysis of context compaction driven by calibrated judgments instead of summarization: Jev scores every chunk of a conversation for relevance to what is being asked, ordinary Python keeps the chunks that fit a token budget, and the result is auditable, deterministic, and replayable offline. Ten LoCoMo conversations, 486 questions, three segmenters, two deciders, two tracks, four token budgets, with both deciders handing their scores to the same greedy budget selector so any accuracy difference comes from the ranking, not the budget", "approach": "sdk", "training": "", "data": "LoCoMo, ten long two-speaker conversations", "result": "486 questions and 29,160 graded observations for $4.93 of API spend; Jev beats cosine retrieval in 14 of 16 cells, significantly in 10 of 20 paired comparisons and never loses significantly, with an edge up to +14.4 points at the 5% budget; best cell is 0.644 accuracy at a 35% budget, and fact recall of selected evidence reaches 0.996 versus 0.936 for retrieval", "notes": "The judge, not the segmentation, moves the number; 250-token native chunks beat 750-token ones by 12–33 points at every budget", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "korean-decision-benchmark", "name": "Korean Decision Benchmark", "author": "jkf87", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/jkf87/korean-decision-benchmark", "base_model": "SemIf, Decider, Laya, TypeSafe Jev 1.13.0", "params": "", "metric": "471 items", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "arch: nayohan/korean-hate-speech valid set, 471 items, hate/offensive/none; data: valid, 471; result: run it in one click; notes: mixed environments, unpinned versions; name: SemIf/Decider/Laya/Jev", "arch": "Strict-protocol Colab benchmark on Korean hate speech: SHA-256 model pinning, Jev version assertion, per-model environments, metrics: accuracy, macro-F1, Brier, NLL, ECE; latency only on a 32-item pre-specified interval subset", "approach": "", "training": "", "data": "nayohan/korean-hate-speech", "result": "First Korean-language head-to-head across the four biggest Jev-class systems", "notes": "Every shortcut explicitly prohibited.", "domain": "social", "score": "", "latency": "", "benchmark": "Korean Decision Benchmark", "cost": "", "speedup": ""}
{"id": "riefer02-laya-call-router", "name": "Laya Call Router", "author": "riefer02", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/riefer02/laya-call-router", "base_model": "", "params": "", "metric": "81-case routing benchmark, 27 scripted evaluation calls and a 45-case safety set; department and request-type accuracy, final-team accuracy, median routing latency, API cost per case", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "An inspectable call-routing research demo for a car dealership, built on Laya. A fine-tuned Laya model reads a caller's words and chooses typed answers, such as a department or vehicle type. Rules decide what to ask next, check available appointments, and file a booking. The debugger shows each step", "approach": "", "training": "", "data": "", "result": "v7 fine-tune 71 of 81 department-and-request-type correct versus gpt-5.4-nano 72 and deepseek-flash 73; final team 25 of 27 on scripted calls; median 21 ms locally versus 651 ms and 1,451 ms hosted at $0.000031 and $0.000124 per case; safety set caught 18 of 18 labelled hazards with 3 of 27 safe cases falsely flagged; demo gate passes 12 of 13 scenarios; authors note the evidence is small and synthetic-heavy", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bhushankinge-laya-cuda-bench", "name": "Laya CUDA serving bench", "author": "bhushankinge", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/bhushankinge/laya-cuda-bench", "base_model": "", "params": "", "metric": "decisions/sec under a p99 latency SLO; cost per million decisions", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: how many decisions per second can one NVIDIA GPU serve under a p99 SLO, and what does a million decisions cost; notes: Jupyter notebook", "arch": "Serving-economics notebook: one NVIDIA GPU under a p99 latency SLO, measuring decisions per second and the cost of a million Laya decisions", "approach": "", "training": "", "data": "", "result": "A reproducible capacity-planning study: how many typed decisions per second one NVIDIA GPU serves under a p99 latency SLO and what each million costs, across four GPUs, five inference backends, MIG slicing, a hosted API and two LLM baselines", "notes": "Apache-2.0, Python 3.12, PyTorch 2.14; Laya 421M English and 322M multilingual", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "raycodes-laya-pong", "name": "Laya Pong agent", "author": "47thtechcorner / RayCodes", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/47thtechcorner/RayCodes_LayaAI", "base_model": "", "params": "", "metric": "32.8 ms per move vs Jev 236.0 ms; ECE 0.081 vs 0.246", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": "", "detail": "arch: real-time reflex control under 35 ms at 60 FPS in the browser; metric: single-move latency 32.8 ms vs Jev 236.0 ms, 7.8×, ECE 0.081 vs 0.246, 3× better; notes: 100+ languages vs English-only; $0 self-hosted vs $0.042/1M tokens; YouTube tutorial; Apache-2.0", "arch": "A Pong agent whose every move is a Laya decision: real-time reflex control under 35 ms at 60 FPS in the browser, benchmarked against the Jev API for the same game", "approach": "", "training": "", "data": "", "result": "Real-time reflex control in under 35 milliseconds per decision; companion tutorial video: Laya 0.4B answers in 32 ms", "notes": "Apache-2.0, Python 3.10+", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-lk-bench", "name": "Laya Sinhala and Tamil stress test", "author": "mithilyr", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/mithilyr/laya-lk-bench", "base_model": "ConvAI Laya 0.3.7, zero-shot, default router", "params": "", "metric": "Macro-F1, confident errors, 95% bootstrap CIs", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "approach: Pre-registered multilingual stress test", "arch": "A pre-registered, reproducible stress test of the open Laya decision model on Sinhala and Tamil offensive-language data with an English control: it measures accuracy, calibration, abstention, tokenizer and routing effects, and explanation plausibility; milestones M0 to M6 are complete with a full report, and the fine-tuning milestone is deferred by ADR; the repo holds no dataset text, everything is loaded from its source at runtime per the content-safety ADR", "approach": "", "training": "", "data": "Sinhala SOLD, romanised Tamil offensive-language data, English TweetEval control", "result": "Laya 0.3.7 zero-shot: macro-F1 0.72 on English TweetEval against 0.47 random, 0.46 on Sinhala against 0.50 random, 0.44 on romanised Tamil against 0.47; wrong at 90% confidence or higher out of the box: 0.3% English, 24.5% Sinhala, 14.7% Tamil; after calibration, posts answerable at 90% accuracy: 72.7% English, 0.0% on both Sinhala and Tamil", "notes": "The headline is that Laya's English calibration does not survive into Sinhala and Tamil: it is confidently wrong on a quarter of posts and, once calibrated, answers nothing at 90% accuracy on either language; figures and tables are rebuilt from cached predictions without re-running the model", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "servronix-laya-thai-trainset", "name": "Laya Thai trainset", "author": "servronix", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/servronix/laya-thai-trainset", "base_model": "", "params": "", "metric": "31,091-row Thai typed-decisions fine-tune set, canonical round B3 dated 2026-09-25, v2-exact base", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "Thai training set for fine-tuning Laya; a 35 MB JSONL published in a model-format repo, no weights, no card", "arch": "Training data for fine-tuning convaiinnovations/laya on Thai typed decisions: 31,091 rows in the current canonical round B3, 2026-09-25, v2-exact base with MASSIVE-th and oversampling excluded after the B1 and B2 ablations, schema-compatible with the upstream typed-decisions training notebook, typed-decision format with id, workflow, state, questions and gold fields", "approach": "", "training": "", "data": "", "result": "Sources: Wisesight sentiment train 21,628 rows, XNLI-th stratified subsample 8,000, handwritten Thai intent cases 1,463; built reproducibly at seed 42, no PII, zero overlap with the project frozen 100-item evaluation gate; MASSIVE-th and oversampling excluded after the B1 and B2 ablations", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alilibx-laya-vs-jev-2", "name": "Laya vs Jev: benchmark and live race", "author": "alilibx", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/alilibx/laya-vs-jev", "base_model": "Laya", "params": "", "metric": "byte-identical state + questions", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: open-weight Laya, 421M, local, including Arabic fine-tunes vs TypeSafe Jev 1.13.0 API on byte-identical state and questions; notes: live race server with challengers Laya Router, laya-ara, laya-ara-rag; full benchmark on labeled datasets; earlier sweep rows mistyped the owner with digit-1 slugs and misread the repo as dead", "arch": "Head-to-head of open-weight Laya, 421M, local, including Arabic fine-tunes against TypeSafe Jev 1.13.0, run on byte-identical states and questions, with a live race server open to challenger models, Laya Router, laya-ara, laya-ara-rag", "approach": "", "training": "", "data": "", "result": "Full labeled-dataset benchmark plus a continuously updated live race between local Laya builds and the hosted Jev API", "notes": "Live repo is under the all-letter owner login", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "adkid-zephyr-laya-cn-study", "name": "Laya-CN Study", "author": "Adkid-Zephyr", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/Adkid-Zephyr/laya-cn-study", "base_model": "", "params": "", "metric": "31,200 predictions across original and adapted tasks, 128 Feishu diagnostic responses, standard-library audit", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "An independent, open collection of Chinese-first post-training experiments on Laya. Each study keeps its model version, data provenance, evaluation records, code, and limitations together. This repository is not an official Laya release", "approach": "", "training": "", "data": "", "result": "Laya-CN-A selected from V3 seed42, full FP32 weights on Hugging Face; archives 31,200 predictions and 128 Feishu diagnostic responses on synthetic Chinese workplace messages; authors state it is not a reliable Feishu task router", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "komzweb-mars-gate", "name": "Mars Gate", "author": "komzweb", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/komzweb/mars-gate", "base_model": "", "params": "", "metric": "Final-action accuracy on 120 cases, atomic accuracy on 960, p50 API latency and estimated cost: Jev versus GPT-5.6 Luna Medium versus GPT-6 Luna Medium", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "The eight atomic judgments: material contradiction, explanation supported, physical concern, prohibited shutdown capability, prohibited pathogen, altered identity, answer completeness, and anomaly severity, which does not set the final action. Frozen v1 compares TypeSafe Jev on the System One endpoint with GPT-5.6 Luna Medium via the Chat Completions API under the same structured-output contract; GPT-6 Luna Medium was added as a post-freeze comparator, content and scoring fixed before its results were observed. 120 base cases balanced 30 CLEAR, 30 QUESTION, 30 INSPECT, 30 DENY; visitors are Human, Android, Alien, Cyborg, Synthetic and Uplift under one policy where species alone is not grounds for an action; the 20-case prototype is excluded from held-out accuracy. Jev's confidence and the LLMs' model-reported probabilities are not treated as equivalent measures.", "arch": "Experimental semantic-decision benchmark and frozen-results viewer set at a fictional Mars-colony entry checkpoint, testing whether a specialized System-One-style decision model offers a useful accuracy, latency and cost tradeoff for many small semantic judgments compared with general-purpose LLMs. All systems receive the same observable state and eight atomic judgment questions, and their atomic outputs enter the same deterministic final-action rules routing to CLEAR, QUESTION, INSPECT or DENY, comparing the semantic-judgment step under shared routing rather than asking each model to generate an unconstrained verdict.", "approach": "", "training": "", "data": "Scenario records", "result": "Base final action accuracy: Jev 119/120, GPT-5.6 Luna Medium 109/120, GPT-6 Luna Medium 120/120; base atomic accuracy 910/960, 855/960 and 888/960; base p50 API latency 296 ms versus 6,572 ms and 3,457 ms; base estimated cost 0.008823 USD versus 0.107249 and 0.051696; repeatability 99/100, 93/100, 100/100; consistency action agreement 48/48, 36/48, 48/48.", "notes": "Fields claim-level.", "domain": "general", "score": "", "latency": "", "benchmark": "DecisionBench", "cost": "", "speedup": ""}
{"id": "obrs09-ns-jev", "name": "NeuroScope, NS Jev", "author": "obrs09", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/obrs09/NS_Jev", "base_model": "", "params": "", "metric": "600 new questions, 10 banks, 3 real repeats, snapshot 2026-09-24", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: custom interpretability engine - can you read multiple-choice answers out of an LLM's intermediate layers early, with low latency and trustworthy probabilities; notes: plus a retest of multiple open JEV-style projects on accuracy, calibration and response time; 600 new questions, 10 banks, 3 real repeats, snapshot 2026-09-24; also builds a Jev-like model with modified llama.cpp", "arch": "NeuroScope, a custom interpretability engine, asks whether multiple-choice answers can be read out of an LLM's intermediate layers early, cheaply and with trustworthy probabilities; the same setup retests several open Jev-style projects on accuracy, calibration and response time", "approach": "", "training": "", "data": "", "result": "600 new questions across 10 banks with 3 real repeats, 2026-09-24 snapshot, plus a Jev-like model built from modified llama.cpp as a control", "notes": "", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "next-jev-choice-cot-200k", "name": "next JEV choice-question CoT tasks, 200k", "author": "JonesLin", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/JonesLin/next-jev-choice-cot-200k", "base_model": "", "params": "", "metric": "200,000 teacher CoT tasks: 166,423 choice and 33,577 noul questions, options range 2 to 77, seed 0", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "200,000 choice questions sampled with seed 0 from the 307,504-row next JEV choice training set, built by scripts/build_choice_train_set.py in github.com/JonnesLin/next_jev at commit 26547cb from the train splits behind the next JEV decision test set JonesLin/next-jev-laya-test; a teacher writes reasoning and an answer key per question and verify.py keeps only answers equal to the gold key; families are massive_scenario 65,307, massive_intent 65,055, app 31,067, en 29,789, jev 6,465 and typed_decisions 2,317; 166,423 choice and 33,577 noul questions with 2–77 options each; ships as 25,000 batches of 8 with a queue helper that never sees answers", "approach": "", "training": "", "data": "next JEV choice training set", "result": "Families: massive_scenario 65,307, massive_intent 65,055, app 31,067, en 29,789, jev 6,465, typed_decisions 2,317; 25,000 batches of 8 with a queue helper that never exposes answers; kept only when the answer equals the gold key and reasoning is non-empty; toxic-chat and MS MARCO sources are non-commercial", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "next-jev-tetris-10k", "name": "next-jev-tetris-decisions-10k", "author": "JonesLin", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/JonesLin/next-jev-tetris-decisions-10k", "base_model": "", "params": "", "metric": "10k teacher-filtered decision rows in the exact community-game prompt format", "license": "Open", "paper": "", "stars": "", "likes": 0, "detail": "base: dataset; arch: commit a0b411c; data: 10k verified items; license: dataset; result: verified-only", "arch": "10,000 Tetris placement decisions with known answers: an LLM teacher writes reasoning + a final \\boxed{X}, verify.py keeps only answers matching the known best option; each item is exactly what the community Jev Tetris game sends to the local model in its 'Laya' slot — one-paragraph board description + legal placements", "approach": "", "training": "", "data": "Community Jev Tetris game format", "result": "Teacher-filtered decision data in the exact community-game prompt format", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "Tetris", "cost": "", "speedup": ""}
{"id": "model-collapse-jev-bench", "name": "Open JevBench, model-agnostic", "author": "model-collapse", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/model-collapse/jev-bench", "base_model": "", "params": "", "metric": "Choice / Score / Noul, one-command scoring", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: open model-agnostic benchmark for typed decision models; one command scores any model with the correct metric per question type; works across LLMs, prompted or option-scorer, cross-encoders and rerankers, embedding models, custom systems; notes: task = type x context, skill probes, applied operations; LEADERBOARD.md + TASKS.md", "arch": "Open model-agnostic benchmark for typed decision models: one command scores any model - LLMs, prompted or option-scorer, cross-encoders, rerankers, embedding models or custom systems - with the correct metric per question type, choice, score, noul", "approach": "", "training": "", "data": "", "result": "Task matrix defined as type x context, including skill probes and applied operations; leaderboard and task registry maintained in-repo", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "privatemode-decisions-bench", "name": "Privatemode Decisions vs Jev vs Laya", "author": "edgelesssys", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/edgelesssys/privatemode-decisions-benchmark", "base_model": "", "params": "", "metric": "accuracy, latency, cost per decision", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "arch: 29 labelled datasets EN+DE, identical state/options/instruction; metric: accuracy, latency, cost; result: three arms, 2 replicates seed 0; notes: raw runs released 09-24", "arch": "Speed, accuracy and cost of three System One implementations on labelled public data: 29 datasets in English and German, 2–151 options, up to 1,000 examples each, two replicates with seed 0; each arm receives the identical state string, identical option names in the identical order and the identical instruction line, so the comparison is structural instead of a prompt-engineering contest; everything is recomputed from the runs by python -m bench.aggregate", "approach": "", "training": "", "data": "", "result": "Privatemode Decisions on GLM-5.3-Flash in an attested enclave, one masked forward pass read from a single logit row, against hosted Jev jev-latest which returns no token stream, and Laya 421M, a ModernBERT-large encoder with a decision head running on the laptop; full tables with the spread between replicates in results/suite.md; raw runs in the runs-2026-09-24 release", "notes": "Companion to the privatemode-decisions library; the Laya arm is convaiinnovations/laya, Apache 2.0", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rlcdbench", "name": "RLCDAlignBench, Just Ask Jev", "author": "sumleo", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/sumleo/RLCDAlignBench", "base_model": "RLCD zero-shot detector", "params": "", "metric": "RLCD alignment benchmark, ICLR 2027 under review; dataset sumleo/RLCDAlignBench on Hugging Face", "license": "MIT code, CC BY-NC 4.0 data", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures: 44 alignment-failure detection benchmarks measuring how well Jev-style calibrated decisions flag alignment failures zero-shot", "approach": "", "training": "", "data": "44 alignment-failure benchmarks", "result": "ICLR 2027 under review; dataset sumleo/RLCDAlignBench on Hugging Face; project page sumleo.github.io/RLCDAlignBench", "notes": "", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ryotide", "name": "RYOTIDE: roll your own typed inference decision engine", "author": "csabag", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/csabag/ryotide", "base_model": "Qwen2.5, Qwen3, Qwen3.5, Gemma 4, gpt-oss on Apple Silicon", "params": "", "metric": "75.0 intelligence best local vs 82.2 Jev on JevBench", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Runs a small local model on Apple Silicon as a Jev-style classifier: one forward pass, logits read at a single position, masked to a fixed label set, no autoregressive decode, sharing the same weights and KV cache with normal generation. Measured on JevBench with 47 runs across Qwen2.5, Qwen3, Qwen3.5, Gemma 4 and gpt-oss, 231 public decisions each, plus the real Jev via OpenRouter's Decisions API and GLUE. The harness is validated: the real Jev run through it reproduces the published public-subset tiers exactly, easy 1.000, standard 0.986, hard 0.730, with 99.1% per-item agreement", "approach": "", "training": "", "data": "", "result": "Best local 75.0 intelligence with Gemma 4 E4B plus conditional repetition and 74.8 composite with Qwen3.5-4B 8-bit, versus Jev at 82.2 intelligence and 87.0 composite on the same three tiers. Wire-format correction: options arrive alphabetically through the TypeSafe wire format, and Gemma 4 E4B drops from 188/231–185/231 at 0.801, just ahead of metask-jev-4b", "notes": "Live dashboard linked from the repo", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "slicc-bench", "name": "SLICC Bench", "author": "ai-ecoverse", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/ai-ecoverse/slicc-bench", "base_model": "", "params": "", "metric": "Judge-graded rubric score over BU Bench V2 tasks", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "data: BU Bench V2 task format; arch: hosted SLICC leader harness, browser-use judge method", "arch": "Published output of the SLICC browser-agent bench runner, packages/bench in the ai-ecoverse/slicc repository: tasks in browser-use's BU Bench V2 format, each {id, task, rubric, weights} with weights summing to 100; a judge grades the transcript and screenshots against the rubric via browser-use's findings method — each rubric item met, violated or not assessable, score is the met weight over the total, 1 pass / 0 fail; each task runs against a hosted SLICC leader with a fresh chat and erased memories, one model, one skill set; time and cost cover the cone and every sub-agent it spawns", "approach": "", "training": "", "data": "", "result": "", "notes": "Runner, task format and judge method are browser-use's; sibling rows of the ai-ecoverse org: jev-omni.js, cua-s1.js, kev.js, decision-vision-bench", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "masheddega-slop-eval", "name": "Slop Eval", "author": "masheddega", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/masheddega/slop-eval", "base_model": "TypeSafe Jev", "params": "", "metric": "One overall slop score on a 0 to 4 native scale plus 14 diagnostic scores on 0 to 2, shown as display indices", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "The fourteen diagnostics cover empty lexicon, inflated importance, lack of specificity, vague authority, artificial contrast, rhetorical triples, false ranges, filler transitions, redundant conclusions, chopped rhythm, decorative formatting, mechanical rhythm, missing position and delayed points. Jev returns typed scores and probabilities; an agent or editor interprets them and rewrites the text, while Jev does not generate editorial explanations or rewritten copy. Includes a reusable agent skill and a standalone Python evaluator, Python 3.9+, no packages, key via TYPESAFE_API_KEY. Explicitly not an AI-writing detector: it judges content, not origin, and does not verify factual accuracy.", "arch": "Slop evaluator that sends one request to TypeSafe Jev containing the text, optional context and 15 independent questions: one overall slop score plus fourteen diagnostic scores. The overall score is a separate judgment, not an average of the diagnostics, and the rubric accounts for genre and purpose, so humor, short sentences, technical vocabulary and formatting are not automatically problems.", "approach": "", "training": "", "data": "", "result": "Genre-and-purpose-aware rubric: empty lexicon, inflated importance, vague authority, rhetorical triples, false ranges, filler transitions and 7 more diagnostics; explicitly not an AI-writing detector and not a factual-accuracy check; ships a reusable agent skill and a standalone Python evaluator with dry-run mode", "notes": "", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "small-decision-cross-domain-degradation", "name": "Small decision models: cross-domain degradation", "author": "SnackTerminator", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/SnackTerminator/small-decision-model-cross-domain-degradation", "base_model": "Two open Jev-class System One models, 395M to 596M", "params": "395M-596M", "metric": "Item-paired bootstrap delta-AUC on parallel bilingual NLI", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "approach: Controlled cross-domain and cross-language study", "arch": "A controlled empirical study of cross-domain and cross-language degradation for small open decision models: 2,640 model forwards on a single CPU-only machine across 2 models, 4 language or form combinations, and 6 datasets, including an item-paired 2 by 2 of state-language by criteria-language on officially parallel bilingual NLI; it also defines three evaluation guardrails, a pre-falsification gate protocol D1-D4 that replaces a full deep evaluation with a 60-forward checkpoint, and a taxonomy of three zero-information experiment shapes; the full paper is in Chinese with an error ledger, and the first author is an AI agent, with an AI-reviewer collaboration protocol", "approach": "", "training": "", "data": "6 datasets, 4 language or form combinations, 2,640 forwards", "result": "On parallel bilingual NLI the state-language effect is plus 0.3150 AUC under Chinese criteria and plus 0.1660 under English criteria, both CIs excluding zero; the criteria-language effect is significant only under Chinese state, at plus 0.1654; the interaction is minus 0.1487 with CI minus 0.2086 to minus 0.0916; on self-made Chinese short-rule statements no discrimination was found, AUC about 0.5 on n=12, underpowered; model confidence carries no information: output probability distributions are narrow, positive and negative intervals overlap heavily, and predicted labels are one-sided", "notes": "The standing rule: citing a small decision model's confidence requires shipping calibration evidence alongside it; v2 is the public version of 2026-09-24; the author is an AI agent, disclosed in the paper's AI-usage section", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "promtengineer-s1s2-chain-test", "name": "System 1 vs System 2 chain test", "author": "PromtEngineer", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/PromtEngineer/system1-vs-system2", "base_model": "", "params": "", "metric": "Jev 100% at 1 step, 0% at 6+; Qwen3.8-27B thinking-on 100% to 90% at 12", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: chain test 1–12 steps, 20 questions per length, results run 2026-09-22; metric: Jev one-pass 100/40/55/20/0/0/0% at 1/2/3/4/6/8/12 steps; Qwen nothink 100/55/25/10/0/0/10%; Qwen think 100/100/100/100/100/100/90%, 52–254 reasoning tokens", "arch": "Measures how far a decision chain can go: one-pass Jev versus Qwen3.8-27B, thinking off and on, across chains of 1–12 linked decisions, 20 questions per length", "approach": "", "training": "", "data": "", "result": "Compares a one-pass model with a thinking model on manager-chain questions of growing length across three separate chains of people with shuffled facts; reproducible for about $0.10", "notes": "From the video System 1 vs System 2 AI Models, Explained; the one-pass model must find the answer in a single forward pass, while the thinking model writes intermediate results down and reads them back", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mekeren-system-one-benchmark", "name": "System One 3-way arena", "author": "mekeren", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/mekeren/system-one-benchmark", "base_model": "", "params": "", "metric": "rule engine <5 ms vs local SLM <400 ms vs cloud LLM", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: ultra-low-latency decision routing; 3-way arena: rule engine, regex, under 5 ms, vs local SLM, Qwen2.5-1.5B-Instruct-GGUF via llama-cpp-python, under 400 ms, vs cloud LLM, OpenAI-compatible; notes: choice/score/noul routing", "arch": "Three-way latency/quality arena for System One decisions: a regex rule engine, a local SLM, Qwen2.5-1.5B-Instruct-GGUF through llama-cpp-python, and a cloud LLM, each serving choice/score/noul", "approach": "", "training": "", "data": "", "result": "Rule engine answers in under 5 ms, the local SLM under 400 ms, with quality and cost compared per decision type", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "testes-laya-modelos", "name": "testes-laya-modelos-jev-like", "author": "diegoamrg4123", "date": "2026-09-24", "category": "benchmark", "url": "https://github.com/diegoamrg4123/testes-laya-modelos-jev-like", "base_model": "", "params": "", "metric": "Five selected JSONL recordings plus run logs; separates third-party published results from runs actually executed locally", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "The notes distinguish results published by third parties from inferences actually executed in this environment; five selected JSONL recordings with an index sit in resultados/. Documents cover an introduction to Laya and Jev, a documentary survey of related projects and the limits of the evidence, and the demo architecture, installation and use, with upstream attribution and license from the original Laya-MLX demo. Recorded models: the Laya multilingual checkpoint, Kev-0.8B from jaredpalmer/kev, and SemIf-4B Q4_K_M from TheoLeeCJ/SemIf. Portuguese README.", "arch": "Local experiments with open typed-decision models, Laya, Kev and SemIf: a deterministic Snake engine on Linux with adapters, a policy and a terminal interface, driven by Laya, plus a small recorded run set of Laya, Kev and SemIf. Explicitly not Jev, not an official TypeSafe SDK, and makes no equivalence claim between the models.", "approach": "", "training": "", "data": "", "result": "5 selected JSONL recordings plus run logs; distinguishes third-party published results from runs actually executed locally", "notes": "explicitly not Jev, not an official SDK, no equivalence claimed; Portuguese documentation", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ahmetege-turkish-jev-noul", "name": "turkish_jev_noul", "author": "ahmetege", "date": "2026-09-24", "category": "benchmark", "url": "https://huggingface.co/datasets/ahmetege/turkish_jev_noul", "base_model": "", "params": "", "metric": "Turkish noul accuracy on TrGLUE RTE, QNLI, MRPC, QQP plus an unseen-task test split", "license": "cc-by-sa-4.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: four sections, RTE, QNLI, MRPC, QQP, each with train 1,000, validation 200, test 300, plus a test_unseen_task split; notes: v1 labels are hard 0/1 taken automatically from the source datasets, manual quality control is not finished, soft probability labels are planned for a later version; the toxic source contains offensive social-media messages; the dataset is independent of TypeSafe and only follows the same question-type interface", "arch": "Turkish yes/no, noul, decision questions in the same format as LocalLLaMA/typed-decisions that Laya uses: a text state, a typed question about the text, and the probability distribution of the answer. Built from Turkish TrGLUE, Belebele, and a Turkish toxic-language set with hand-reviewed question templates.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bonzi-vs-jev-wanli256", "name": "bonzi-vs-jev-wanli256", "author": "yehor-oleksiuk", "date": "2026-09-23", "category": "benchmark", "url": "https://huggingface.co/datasets/yehor-oleksiuk/bonzi-vs-jev-wanli256", "base_model": "Bonzi 4B/8B + TypeSafe Jev", "params": "", "metric": "Task accuracy and seconds per decision: TypeSafe Jev 79.3% at 0.37 s versus Bonzi 8B Q1_0", "license": "Open", "paper": "", "stars": "", "likes": 0, "detail": "base: 1-bit Q1_0; arch: not run; data: 256 rows; license: dataset; result: API · 203/256 · measured 2026-09-23 · CPU-only", "arch": "Independent replication + first head-to-head: all numbers measured by the author, CPU-only; 27B figures cited from the recipe author for context", "approach": "", "training": "", "data": "WANLI-256", "result": "TypeSafe Jev: 79.3%, 0.37 s/decision; Bonzi 8B Q1_0: lower", "notes": "Jev-API number and local 1-bit number side by side on the same rows.", "domain": "general", "score": "79.3%", "latency": "370 ms", "benchmark": "WANLI-256", "cost": "", "speedup": ""}
{"id": "canitrustu-jev", "name": "CanITrustU-Jev, CITY-Jev", "author": "JiaQiSJTU", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/JiaQiSJTU/CanITrustU-Jev", "base_model": "", "params": "", "metric": "Strict accuracy, mean accuracy and abstention-aware strict accuracy at a confidence threshold of 0.5 on 1,974 questions and 11,844 decision evaluations", "license": "Open", "paper": "", "stars": 6, "likes": "", "detail": "", "arch": "Fast decisions are useful, but can we trust them? Evaluates Jev-style System One models at five agentic decision points - action selection, step judging, outcome verification, evidence assessment, safety checks - under input perturbations. One question, six input versions, does the decision hold up? Perturbations change option order, option identifiers, state formatting, auxiliary context, or instruction wording while preserving the correct answer", "approach": "", "training": "", "data": "10 upstream data sources adapted to candidate selection; 2,000 original questions each paired with five answer-preserving perturbations for 12,000 planned evaluations; HF dataset KikiNLP/CanITrustYou-Jev; scores broken down by application scenario, decision task, answer format, source dataset", "result": "Jev 1.13.0 at confidence threshold 0.5: 2,000 original questions, 26 excluded after 56 failed requests, leaving 1,974 questions and 11,844 decision evaluations; overall strict accuracy 74.52%, mean 78.92%, strict abstention-aware 85.31%, mean 88.78%; abstention rate 19.37%; Tool and API interactions strongest at 81.50% strict, Business Service Interactions weakest at 50.63%; other model adapter configurations do not imply completed evaluations", "notes": "Independent evaluation of adapted tasks; scores are not official scores of the upstream benchmarks; project developed primarily with automated tools, marked preliminary", "domain": "agent", "score": "accuracy 74.52%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decision-bench-xsdhy", "name": "Decision Bench interactive load bench", "author": "xsdhy", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/xsdhy/decision-bench", "base_model": "", "params": "", "metric": "QPS, P50-P99 latency, error rate, accuracy", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "arch: Next.js load bench for /v1/systemone; metric: QPS + latency percentiles + accuracy; result: 5 presets up to 32 concurrentame-state comparison vs Jev", "arch": "Next.js interactive workbench that load-tests any decision endpoint at POST /v1/systemone: QPS, P50/P90/P95/P99 latency, error rate, token throughput and decision accuracy; the same state and questions can be fired at TypeSafe Jev at api.typesafe.ai/v1/systemone for a same-question comparison; the default Laya endpoint is localhost:8000, with platform and model selectable, Laya english, multilingual or typed-decisions with auto-routing, TypeSafe jev-latest or jev-1.13.0", "approach": "", "training": "", "data": "", "result": "Presets from smoke, 1 concurrency 5 requests, to 32 concurrency 500 requests; concurrency cap 64, 2,000 requests per run; requests come from a fixed body or an evaluation set of state plus questions, four bundled datasets; Node 18+", "notes": "Chinese README; task names auto-generated from endpoint, concurrency and request count; npm run dev", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decision-bench-joypciu", "name": "Decision Bench local workbench", "author": "joypciu", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/joypciu/decision-bench", "base_model": "", "params": "", "metric": "gold-case scores across providers", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "arch: bot tree + JSON schema checks + gold cases; result: demo provider, no API key; notes: Gemini + OpenRouter live adapters", "arch": "Local workbench for structured decisions: a bot you create can spawn specialist bots, every model call is checked against a JSON schema, and the same gold cases can be scored on more than one provider; the first jobs are a change-risk review and an incident triage", "approach": "", "training": "", "data": "", "result": "Demo provider runs both jobs including their sub-agents without an API key; live adapters for Gemini, default gemini-3.6-flash, and OpenRouter, default openrouter/free; more providers addable in Settings: Groq, Cerebras, Mistral, Together, Fireworks, DeepInfra, Hugging Face, SambaNova, Ollama and LM Studio", "notes": "Python 3.11+, docker compose; document ingestion accepts markdown, text, HTML, CSV, JSON, XML, PDF, images, Word, PowerPoint and Excel via MarkItDown, with PDF and image crops limited to one region and Windows OCR or Tesseract for image and cropped-PDF text; py -m decision_bench serves on 127.0.0.1:8000", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decision-bench-atlanai", "name": "decision-bench", "author": "atlanai", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/atlanai/decision-bench", "base_model": "", "params": "", "metric": "accuracy with 95% CIs, calibration, latency, token usage, cost", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "arch: bounded decisions on real data, answers withheld; metric: accuracy CI, calibration, latency, tokens, cost; result: 12 models on bench-v4, published 09-23", "arch": "Open benchmark of how accurately, quickly and cheaply language models make bounded decisions on real data; the model receives the evidence, instructions and options while the answers, rationales and source metadata are withheld, and returns a choice plus a probability for every option; invalid responses count as incorrect; the evaluation protocol describes the inputs, scoring and integrity checks", "approach": "", "training": "", "data": "", "result": "Results for 12 models on bench-v4 published 2026-09-23; leaderboard with per-task results; published runs browsable for predictions, scores and run metadata", "notes": "Start with the task you need the model to perform", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "supreethbare-decision-model-bench", "name": "decision-model-bench", "author": "supreethbare", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/supreethbare/decision-model-bench", "base_model": "", "params": "", "metric": "Accuracy on ticket triage, intent, toxicity and tool selection, plus the share handled with no human and no LLM at the confidence threshold that keeps accuracy at 95%", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Three models on about 200 cases per task: Jev 1.13.0 via the cloud API, Open-Jev-2B, a Qwen3.5-2B plus LoRA plus decision-head reproduction, on a MacBook Air M4 24 GB Apple GPU, and Needle 3 3.0.4, a 121M on-device tool-calling and extraction model on the same MacBook CPU. 798 cases per model, one run, September 2026, zero request errors; declined answers count as wrong.", "arch": "Benchmark harness comparing typed decision models on four enterprise tasks, with per-case predictions committed as evidence: ticket triage over 11 categories, intent plus out-of-scope over 15 plus 1, a yes-no toxicity guardrail, and tool selection over 2–4 functions. The models are compared as a decision layer in front of an LLM, not as a replacement for one: the confidence score decides what is handled automatically, what needs an LLM and what needs a human.", "approach": "", "training": "", "data": "", "result": "Accuracy: ticket triage 93.4% Jev, 88.4% Open-Jev-2B, 28.3% Needle 3; intent 91.0%, 74.5%, 20.0%; toxicity 78.5%, 58.5%, 32.0%; tool selection 99.5%, 99.5%, 95.0%. Share handled with no human and no LLM at the confidence threshold that keeps accuracy at 95%: triage 98.0%, 88.4%, 1.0%; intent 62.5%, 43.5%, 0.0%; toxicity 20.5%, 1.5%, 0.0%; tool selection 100% across all three.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jerryjliu-docjev", "name": "DocJev", "author": "jerryjliu", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/jerryjliu/docjev", "base_model": "", "params": "", "metric": "Classification and splitting accuracy on 40 real PDFs and 8 packets, decision latency, provider usage", "license": "", "paper": "", "stars": 451, "likes": "", "detail": "arch: liteparse or LlamaParse", "arch": "A very fast document classifier/splitter using Jev. Document pipelines: LlamaIndex's open-source library that classifies a document against natural-language category rules or finds the boundaries between sub-documents, with swappable OCR backends and a benchmark harness whose 40-document pilot classified 40/40 originals correctly at about 182 ms Jev", "approach": "sdk", "training": "", "data": "", "result": "Visual benchmark on 40 original IRS, Treasury, BEA and SEC documents: Jev and Luna accuracy and recorded median decision latency with one extra split flagged; a 15-page packet separates two adjacent same-category Treasury auction results while keeping a ten-page BEA release with dense tables together", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "182 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "harisrujan-rlcd", "name": "Jev and Laya alert-routing experiments", "author": "HARISRUJAN", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/HARISRUJAN/RLCD", "base_model": "", "params": "", "metric": "30/30 Qwen3-4B-Base and 8B-Base", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: Jev and Laya typed decisions plus Ollama local models; result: keep alarms deterministic, Jev as second-stage semantic layer; notes: Node.js 20 or newer, experimental", "arch": "30-decision browser-domain benchmark for RLCD decision models", "approach": "", "training": "", "data": "30 browser-domain decisions", "result": "Qwen3-4B-Base 30/30 and Qwen3-8B-Base 30/30 in a single pass; 86.7%, 26 of 30, after the first 10 decisions, 100% after the first 20", "notes": "Reproducible experiments comparing local Jev, Laya, Ollama models and a GPT-5.6 Luna estimate on a deterministic service-health alert stream. Current call: keep hard service-health alarms deterministic, and use Jev as a second-stage semantic layer for ambiguous grouping, routing, severity and human-review decisions. Ships a Jev cheatsheet, an article draft, a setup guide and experiment reports. Experimental status, Node 20 or newer.", "domain": "computer-use", "score": "", "latency": "", "benchmark": "30 browser decisions", "cost": "", "speedup": ""}
{"id": "jev-decisions-v1", "name": "Jev Decisions v1", "author": "samatv256", "date": "2026-09-23", "category": "benchmark", "url": "https://huggingface.co/datasets/samatv256/jev-decisions-v1", "base_model": "", "params": "", "metric": "Companion decision corpus for the mini-jev model line", "license": "Open", "paper": "", "stars": "", "likes": 4, "detail": "base: dataset; license: dataset; result: samatv256; name: 12M records", "arch": "12M canonical agent-decision records for tool selection, routing, value prediction, completion, and local agent control; canonicalized from public agent-trajectory datasets into state + available candidate decisions → target/outcome/eligibility", "approach": "", "training": "", "data": "Derived from public agent trajectory datasets", "result": "Companion corpus for the mini-jev line of models", "notes": "12M is the biggest Jev-style decision corpus released in this window.", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sujalgawas-jev-1min-stock-bars", "name": "jev-1min-stock-bars", "author": "sujalgawas", "date": "2026-09-23", "category": "benchmark", "url": "https://huggingface.co/datasets/sujalgawas/jev-1min-stock-bars", "base_model": "", "params": "", "metric": "8,665,852 one-minute bars with OHLC, volume, trade count, VWAP, log return, bar range, VWAP distance and log volume change", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "arch: a single train split of parquet shards, data/train-*; notes: the card publishes only the feature schema, with no model or evaluation attached", "arch": "One-minute stock bars with ten numeric columns per bar: OHLC, volume, trade count, VWAP, log return, bar range, VWAP distance, and log volume change, plus symbol and UTC timestamp. 8,665,852 train rows, 482 MB download.", "approach": "", "training": "", "data": "", "result": "482 MB download, 893 MB dataset, UTC timestamps; features precomputed per bar for typed-decision training", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-bench-pavelravvich", "name": "jev-bench, Jev vs GPT-6 Luna and Astra", "author": "PavelRavvich", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/PavelRavvich/jev-bench", "base_model": "TypeSafe Jev 1.13 + GPT-6-luna + GPT-6-astra", "params": "", "metric": "Accuracy, macro-F1, ECE, Brier, latency p50 and p95, cost per 1k calls, paired bootstrap confidence intervals, selective accuracy, Jev to frontier cascade", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Independent benchmark of TypeSafe Jev jev-1.13 against a cheap LLM and a frontier LLM on two public classification tasks graded against ground truth: SMS Spam Collection with 500 messages, 71 spam, as a Noul, and the Banking77 test split with 500 messages as a Choice over 77 options; the frontier model runs on a shared 130-message subset to keep cost down; all models are called through OpenRouter, Jev on the Decisions API at /api/alpha/decisions and the LLMs as chat completions with JSON output and verbalized confidence; seed 42 fixes the sample so every model sees the same messages; pinned model versions are recorded in each prediction row; raw per-message predictions and summary JSON are published", "approach": "", "training": "", "data": "SMS Spam Collection 500 messages, Banking77 test 500 messages", "result": "The whole experiment cost about $1.13; write-up in article/jev-article.md", "notes": "Note in the repo: when calling expensive models, cap spend per run", "domain": "general", "score": "", "latency": "", "benchmark": "SMS Spam + Banking77", "cost": "about $1.13 total", "speedup": ""}
{"id": "jev-calibration-audit", "name": "jev-calibration-audit", "author": "MohitSV", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/MohitSV/jev-calibration-audit", "base_model": "jev-1.13.0 plus Laya, Qwen3-0.6B PSR-FT, and eve-rlcd backends", "params": "", "metric": "Exact-target probability, held-out Brier, vote-distribution distance", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "An exact-target audit of the probabilities returned by System One decision models, centred on jev-1.13.0, whose RLCD training is advertised as epistemically honest; each primitive is scored against targets whose correct probability is known exactly, a hidden fair coin with witnesses of stated reliability and stated reference-class base rates, then checked against human vote distributions on ChaosNLI and DICES-350; one sweep is 1330 Jev calls, another 1560, every experiment with its pre-registration, commands, numbers, and caveats", "approach": "", "training": "", "data": "Audit sets", "result": "With no evidence, Jev's choice gives a fair coin 0.83–0.93 on heads, the pull follows the word heads rather than the option key or position, and mostly vanishes after one weak witness; choice is a step function on stated-probability evidence, a 55/45 base rate yields 0.90–0.996 and temperature scaling cannot recover it; the yes/no type tracks the same targets within 0.015–0.027, and asking one yes/no per option then normalizing cuts held-out Brier error from 0.067–0.003; on real human disagreement, normalized yes/no is 4–6 times closer to the vote distribution zero-shot; three open checkpoints that call themselves RLCD models fail the same audit in different ways", "notes": "Part of the cluster of calibration-focused follow-ups; the eve-rlcd experiment rebuilds that model's pre-RL warm-up on Apple MPS and shows its cue saturation predates the RL stage cf. arXiv 2609.26758", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zaious-jev-capability-atlas", "name": "jev-capability-atlas", "author": "zaious", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/zaious/jev-capability-atlas", "base_model": "", "params": "", "metric": "Task-suitability boundary map from real API-call receipts: calibration, edge cases and cross-industry cases, not a leaderboard", "license": "", "paper": "", "stars": 27, "likes": "", "detail": "", "arch": "Independent, evidence-based map of when TypeSafe's Jev actually holds up vs. breaks down — real API-call receipts, not a leaderboard. 中文为主的双语 repo。. Bilingual evidence map with recorded API runs and reusable suites that separates its own tests, third-party benchmarks, and editorial synthesis.", "approach": "sdk", "training": "", "data": "", "result": "Core finding: Jev is accurate where the answer is already in the supplied text, classification, relevance and contradiction detection, and fails silently on bad input, a typo in one option was picked at 0.90 confidence; cross-references jev-benchmarks and thaiexam-jev-charts rather than re-doing them; community project, no TypeSafe sponsorship", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "blowxian-jev-fanout-bench", "name": "jev-fanout-bench", "author": "blowxian", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/blowxian/jev-fanout-bench", "base_model": "", "params": "", "metric": "fan-out billing per question", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "The linear-model check derives F, everything one request bills apart from its questions, independently at every N and question subset, with a one-word tiny state as control so the fixed overhead can be read off on its own. Every question is asked both inside the batch and alone and every request is repeated, so the batched-versus-single difference is reported next to the difference between two identical requests; a shift smaller than that noise floor is not an effect of batching. MIT license.", "arch": "Reproducible measurement of the billing claim that Jev ingests the state once and evaluates every question against it in parallel: if it holds on the bill, asking N questions in one request costs the state once, while N separate requests cost it N times. Measures across state sizes, roughly 25, 680 and 3,000 tokens, and question counts of 2, 4 and 8, checking three things the TypeSafe parallel-questions cookbook did not: whether billing is exactly linear, how big the saving is by state size, and whether the answers change under batching.", "approach": "", "training": "", "data": "", "result": "2,976 requests to jev-1.13-20260917 through OpenRouter's TypeSafe-compatible System One endpoint, 0.156 USD billed in total, run 2026-09-23; full report in results/summary.md, every request and response in results/raw.jsonl.", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-options-harness", "name": "jev-options-harness", "author": "Svyable", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/Svyable/jev-options-harness", "base_model": "Options harness", "params": "", "metric": "Immutable evidence receipt per run: manifest, request, response, decision and environment; receipts store SHA-256 and derived features, never image bytes", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Screenshot passes local intake with format check, SHA-256, deterministic resize and quality gate, then an OpenJev multimodal server returns noul/choice/score answers preserving every probability as a DecisionVector. Deterministic fusion dp-v1 applies data, confidence, margin, liquidity and contradiction gates. Infrastructure or model failure never silently becomes HOLD: it becomes HOLD/ABSTAIN with an explicit blocker naming the failure. SHORT means bearish directional exposure, never naked short-option selling.", "arch": "Local-first research harness: option-chain screenshots to OpenJev typed multimodal judgments, deterministic policy fusion, LONG/SHORT/HOLD with an operational ACT/ABSTAIN", "approach": "", "training": "", "data": "", "result": "Immutable evidence receipt per run under evidence/YYYY/MM/DD: manifest, request, response, decision and environment; screenshots stay local by default, receipts store the SHA-256 and derived features, never image bytes; review UI binds 127.0.0.1 only", "notes": "Research only: no live trading, no broker orders, no order-placement code", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "karminski-jev-quantum", "name": "Jev-Quantum", "author": "karminski", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/karminski/jev-quantum", "base_model": "", "params": "", "metric": "Latency of the local random baseline against official Jev: in-process core bench, server /metrics handler time, client-observed HTTP latency, maze recording", "license": "", "paper": "", "stars": 31, "likes": "", "detail": "jev-quantum-server serves POST /v1/systemone locally with a static demo page that imports summary.json reports without calling any API; jev-quantum-bench runs latency and maze-record comparisons against the official api.typesafe.ai endpoint, for example a 10 by 10 maze with 200 max steps and 100 latency requests. Direct mode by default; buffered mode uses a worker-local random pool with background SIMD refill and a degradable synchronous path. The PRNG is not cryptographically secure and it is not a semantic model. MIT, bilingual Chinese and English README.", "arch": "Random baseline compatible with the Jev protocol, in Rust: it speaks TypeSafe System One's noul, choice and score, does not read the prompt, and its answers come from a fast pseudo-random number generator, so it serves as a dummy, a mock, or the lower bound for agent-routing evaluations. Billed as a sub-microsecond System-minus-one model whose accuracy follows a Gaussian distribution.", "approach": "router", "training": "", "data": "", "result": "Sub-microsecond PRNG baseline with core bench, maze-record and /metrics endpoints plus a static demo page that imports *.summary.json reports and never calls an API; not cryptographically secure, and accuracy is Gaussian by construction", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rlaope-jeval", "name": "jeval", "author": "rlaope", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/rlaope/jeval", "base_model": "", "params": "", "metric": "Confidence reliability, cost-aware machine/human decision threshold with uncertainty range, expected cost per segment, from labelled decision records on disk", "license": "Apache-2.0", "paper": "", "stars": 20, "likes": "", "detail": "Outputs one HTML file that opens offline and one YAML file the application reads. Works with anything that returns a probability: a hosted API, a gateway, a local model, a logistic regression or a scoring rule; the name comes from one model family but the tool sits above all of them. CI and releases; Apache-2.0; Python 3.10 or newer; an example report is committed at examples/report-example.html so every number in the README can be checked against it.", "arch": "Calibration and hand-off analysis tool that answers two questions about any classifier: when it says 0.9, how often is it actually right, and given what a mistake costs, where should the line sit between the machine deciding and a human deciding. Everything is measured from labeled decision records on disk; no server, no database, no network call, no account and no token.", "approach": "sdk", "training": "", "data": "", "result": "Outputs one offline report.html, a thresholds.yaml your app reads, a labels.csv naming which decisions to label next, and a calibration correction map written only when the gain is real; works with any classifier returning a probability; Apache-2.0, Python 3.10 or newer", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-bio-historical-corpora", "name": "Laya-Bio historical corpora", "author": "dnagpt, Liang Wang, Huazhong University of Science and Technology", "date": "2026-09-23", "category": "benchmark", "url": "https://huggingface.co/datasets/dnagpt/laya-bio-historical-corpora", "base_model": "", "params": "", "metric": "Historical biology corpora with exact BPE training samples, zstandard compressed", "license": "other", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Historical corpora and exact BPE training samples for the Laya-Bio project: English biology, DNA and protein text, zstandard compressed; author Liang Wang, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "approach": "", "training": "", "data": "", "result": "", "notes": "Companion to the 12 Laya-Bio checkpoints in dnagpt/laya-bio-models", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openjev-guardrail-redteam", "name": "OpenJev guardrail red-team", "author": "martocd1709", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/martocd1709/openjev-guardrail-redteam", "base_model": "", "params": "", "metric": "0/26 jailbreaks caught by the 151M model", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Red-team of the OpenJev/Verdict jailbreak-guardrail claim: builds exactly the gate Verdict own documentation recommends, on its Choice primitive as a semantic perimeter gatekeeper, and runs it against 26 categorized jailbreak and prompt-injection prompts across 8 categories plus 20 benign prompts, a 104-prompt full suite, and four obfuscation techniques", "approach": "", "training": "", "data": "", "result": "The 151M RLCD-tuned model catches 0 of 26 jailbreaks, with 18 of 26 actively mislabeled benign, not flagged uncertain but given a specific wrong answer at the model own confidence up to 62.5%; a trivial 18-word keyword blocklist catches 10 of 26, 38%, at the same zero false-positive rate; the four obfuscation techniques, zero-width Unicode, homoglyphs, leetspeak and story-wrapper reframing, were never needed because the unmodified baseline prompts already were not caught", "notes": "Methodology note: rlcd.DecisionEngine default model_name_or_path silently falls back to the raw un-finetuned knowledgator/gliclass-modern-base-v2.0 base, not the RLCD-trained checkpoint; an early pass against that wrong default was discarded and all results are from the verified real checkpoint heman10x/rlcd-modernbert-151m, SHA-256 pinned and sanity-checked against the project own test case; the finding is not an evasion trick but that the guardrail use case, run exactly as documented, does not discriminate jailbreak intent from benign intent and is specifically biased toward calling attacks safe; setup.sh, run_experiment.py and make_chart.py reproduce everything", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "procedural-typed-decisions", "name": "procedural-typed-decisions", "author": "tasksource", "date": "2026-09-23", "category": "benchmark", "url": "https://huggingface.co/datasets/tasksource/procedural-typed-decisions", "base_model": "", "params": "", "metric": "Apache-2.0 synthetic multi-question procedural tasks: arithmetic, entity belief tracking, event state reconstruction, evidence sufficiency, multi-view adjudication, needle retrieval, partial observation calibration, policy applicability", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 1, "detail": "tags: tasksource, jev, system-one, procedural, synthetic, multi-question", "arch": "Multi-config synthetic typed-decision corpus for the System One model class: procedural states with typed questions and option sets, parquet per family — arithmetic, entity_belief_tracking, event_state_reconstruction, evidence_sufficiency, multi_view_adjudication and more — each with train / validation / test splits", "approach": "", "training": "", "data": "", "result": "Parquet per family with train, validation and test splits; procedural states with typed questions and option sets for the System One model class", "notes": "HF sweep 09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reflexbench", "name": "reflexbench", "author": "brida-ai", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/brida-ai/reflexbench", "base_model": "", "params": "", "metric": "Raw semantic accuracy on a 111-case public-hard cohort", "license": "Open", "paper": "", "stars": 5, "likes": "", "detail": "", "arch": "An open benchmark and evaluation harness for System One and typed-decision engines, provider-neutral, with v1.0.0 as the first stable public release; it measures engines on typed choice, score, and yes/no decisions, freezing corpora and manifests with hashes, recording completion and provider failures, and reporting NLL, Brier, ECE, score error, option-order stability, multilingual consistency, choice cardinality, and paired bootstrap intervals; a separate research lane measures how much a small deterministic Reflex Core policy adds on top of the model's own responses", "approach": "", "training": "", "data": "Reflex tasks", "result": "On the frozen 111-case public-hard cohort, raw semantic accuracy: TypeSafe Jev 73.0%, upstream Reflex Qwen3.5-2B 41.4, frozen Qwen3.5-0.8B readout control 39.6, jeff GLiFormer about 400M 37.8, openJev Verdict 1.4 151M 36.9, Laya base 421M 35.1, Kev-0.8B 32.4; a deterministic policy applied to Jev's own responses lifts it from 96.4–100% with 4 rescues, 0 harms, and 0 extra model calls; an independent blind workflow run goes from 76.7 raw to 82.0 with Core, a paired delta of plus 5.3 points with bootstrap 95% from plus 1.3 to plus 10.0", "notes": "The headline deliberately does not blend latency, calibration, or policy effects; Brida Reflex is one consumer of the methodology, the benchmark itself is provider-neutral", "domain": "general", "score": "", "latency": "", "benchmark": "DecisionBench", "cost": "", "speedup": ""}
{"id": "revdrive", "name": "revdrive", "author": "54yyyu", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/54yyyu/revdrive", "base_model": "rev, a one-token decision model", "params": "", "metric": "Mean course progress against oracle and blind baselines", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Capability-boundary benchmark: a one-token decision model drives a simulated car through a cone course from the camera only, asked ten left-or-right questions per second; setup follows DrivingBench", "approach": "", "training": "", "data": "Simulated cone course", "result": "Mean progress 21.1% versus 100% for the oracle versus 0.8% for the blind baseline; the model cannot see tight corners from inside them", "notes": "", "domain": "robotics", "score": "", "latency": "", "benchmark": "DrivingBench-style cone course", "cost": "", "speedup": ""}
{"id": "zaesho-s1rank", "name": "S1Rank", "author": "zaesho", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/zaesho/S1Rank", "base_model": "", "params": "", "metric": "nDCG@10 on TREC DL19, DL20, TREC-COVID, NFCorpus, SciFact", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "An independent evaluation of jev-1.13.0 as a reranker for information retrieval, with a full paper and tables reproducible from the published response cache without an API key; the joint pointwise method puts all 100 BM25 candidates into one Jev state and asks one yes/no question per document, so a single call ranks everything and returns a probability per document; variants include isolated pointwise, choice-best, repeated-choice Peel, and tournament; every raw response is cached keyed by the SHA-256 of the exact request and pinned to jev-1.13.0", "approach": "", "training": "", "data": "", "result": "Reranking the BM25 top 100, Jev joint pointwise scores 73.7 on DL19, 71.7 on DL20, 86.5 on TREC-COVID, 38.3 on NFCorpus, and 80.5 on SciFact, beating bge-reranker-v2-m3 and monoT5-3B on all three BEIR sets at 0.57 dollars per thousand queries against 0.05 and 0.44; Peel's repeated choice is best on TREC DL at 73.9 and 72.9 but costs 7.6 times as much; a single which-is-best choice question ranks poorly; Jev is well calibrated on TREC-COVID at ECE 0.023 but overconfident where relevant documents are rare, recalibration transfers within a collection but not across, 52% of probabilities change across byte-identical requests, and routing on calibrated uncertainty does not beat random", "notes": "The S1Adaptive routing study is a negative result: calibrated uncertainty predicts how hard a query is, not which queries another reranker would handle better", "domain": "search", "score": "", "latency": "", "benchmark": "S1Rank", "cost": "", "speedup": ""}
{"id": "system-one-code-explore", "name": "system-one-code-explore", "author": "BestNathan", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/BestNathan/system-one-code-explore", "base_model": "", "params": "", "metric": "File discovery and in-file evidence localization against a System Two reference, measured in tokens, model calls and wall time", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Given a repository and a natural-language engineering task, the harness answers which files are relevant and which exact source spans are evidence. V1 file discovery is converged: mechanically enumerate supported file metadata, independent System One Noul score per file, absolute high-confidence selection plus a top-1% relative recall guard. Two first-class domains: file discovery and source-span localization.", "arch": "Research repo on whether a System One model in a constrained harness can replace or approximate System Two code search for finding task-relevant files and concrete source evidence", "approach": "", "training": "", "data": "", "result": "V1 file discovery: per-file Noul scores with absolute high-confidence selection plus a top-1% relative recall guard; recovered 12 of 12 primary targets across two repeats on the frozen six-case convergence suite; about 18 model calls, 212k input tokens and 6.6 s model wall time per task on the 1,096-file nession fixture; semantic directory pruning explicitly rejected", "notes": "Not a code-search product; about localization, not code generation, with downstream coding, planning, fixing and review deliberately outside the primary benchmark", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system1-bench", "name": "System1-Bench", "author": "cacan", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/cacan/system1-bench", "base_model": "", "params": "", "metric": "laya-typed 54 ms p50, 16.9 RPS", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Open reproducible evaluation suite plus a zero-dependency self-contained interactive dashboard, comparing open-source System 1 typed-decision models against the hosted reference baseline: Noul binary via Brier score and decision thresholds, Choice categorical over the full distribution, Score rubric via MAE, with p50 latency, throughput and VRAM footprint per model", "approach": "", "training": "", "data": "jev_core suite: 36 enterprise triage cases, 56 decision questions, plus a clean split of 44 objective questions, multi-class routing, topic classification, churn risk and claim verification, isolating structural decision capability from conservative-versus-liberal urgency labeling", "result": "Against the jev-1.13.0 hosted baseline at 100% clean and full accuracy, 0.000 MAE, 744 ms p50, 1.4 RPS: ornith-35b and kwei, both listed under the hearim provider, at 100% and 98.2% full accuracy but 5.8 and 5.2 s p50; gemma-26b at 96.4% with exact 0.000 rubric match at 1.2 s; laya-typed at 100% clean, 96.4% full, 0.671 MAE, 54 ms p50, 16.9 RPS, 14× faster than the baseline; qwen35-9b 94.6% and qwen35-4b 89.3% full", "notes": "The dashboard covers a winners matrix, a side-by-side model race of single-forward-pass logit scoring about 54 ms versus autoregressive generation about 780 ms, a latency waterfall against the 744 ms reference, an accuracy-versus-speedup quadrant map and an urgency anomaly deep-dive on edge cases where instruct models diverge from conservative ground truth", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tasksource-1m-decisions", "name": "tasksource-jev-typed-decisions", "author": "tasksource", "date": "2026-09-23", "category": "benchmark", "url": "https://huggingface.co/datasets/tasksource/tasksource-jev-typed-decisions", "base_model": "", "params": "", "metric": "One million decisions from 500+ tasks across 300+ dataset families, runtime-answer-criteria format", "license": "Open", "paper": "", "stars": "", "likes": 7, "detail": "base: dataset; license: dataset; name: 1M decisions", "arch": "One million decisions from 500+ Tasksource tasks across 300+ dataset families, in one format for models that receive answer criteria at runtime; 'breadth with traceable supervision' — most rows inherit labels/ratings/annotator votes from existing datasets rather than teacher-invented labels; source field identifies the origin", "approach": "", "training": "", "data": "500+ tasks, 300+ dataset families", "result": "230 downloads / 4 likes in ~1 day — early", "notes": "Largest single 'runtime-answer-criteria' decision corpus released so far.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "diorrego-toolgate-experiment", "name": "Toolgate: tool selection before model inference", "author": "diorrego", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/diorrego/toolgate-experiment", "base_model": "", "params": "", "metric": "Reference-tool accuracy, expected-handler success, median task time and estimated USD per 60 cases: direct MCP versus design 1 agent versus design 2 host-first Choice", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "V2 corpus: 60 English requirements per condition, 240 evaluated cases, 30 reads and 30 mutations executed by real Woku handlers on an isolated synthetic database restored before each case. All four conditions produced the requested persisted change in 30 of 30 mutations and no non-target mutation calls were observed; the author labels this a limited synthetic test, not evidence of production safety. Reproduction and provider-integration docs included; status experimental.", "arch": "Experimental comparison of direct MCP against three Toolgate designs for tool selection before model inference, with Go and Rust cores and one TypeScript SDK. V1 studies latency with 22 read-only tools and Codex; V2 studies selection accuracy with all 143 active Woku tools and GPT-6 Luna API calls, the evaluated agent being gpt-6-luna through the Responses API with high reasoning, while Go is fixed across the three Toolgate conditions.", "approach": "", "training": "", "data": "", "result": "V2, first reference tool, expected handler succeeded, median task time and estimated USD per 60 cases: direct MCP 43/60, 60/60, 10.48 s, 0.071341; design 1 agent prepares 60/60, 60/60, 9.53 s, 0.048590; design 2 host-first Choice 59/60, 59/60, 4.41 s, 0.034459; design 3 host-first binary 59/60, 58/60, 5.75 s, 0.104033.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "turkish-mmlu-laya-ds", "name": "Turkish MMLU Laya dataset", "author": "AhmetSemih", "date": "2026-09-23", "category": "benchmark", "url": "https://huggingface.co/datasets/AhmetSemih/turkish-mmlu-laya", "base_model": "", "params": "", "metric": "Turkish MMLU in Laya fine-tuning format, 1K to 10K rows, parquet", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Turkish MMLU questions converted into the Laya fine-tuning format, multiple choice, parquet", "approach": "", "training": "", "data": "", "result": "", "notes": "Sibling of the AhmetSemih/laya-turkish-mmlu model", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-llmdevs-jev-bench", "name": "We benchmarked TypeSafe's new Jev", "author": "r/LLMDevs", "date": "2026-09-23", "category": "benchmark", "url": "https://www.reddit.com/r/LLMDevs/comments/1wo000s/we_benchmarked_typesafes_new_jev_a_decisiononly", "base_model": "TypeSafe Jev", "params": "", "metric": "Community head-to-head of hosted Jev against other models on decision-style tasks, with a companion local-on-Apple-Silicon post", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Reddit; notes: Reddit; name: a decision-only model", "arch": "Community-run head-to-head of the hosted Jev against other models on decision-style tasks; companion post on running Jev-style typed decisions locally on Apple Silicon", "approach": "", "training": "", "data": "", "result": "Community benchmark thread on Jev vs generative baselines", "notes": "Observed 2026-09-23.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yidev-jev-benchmark", "name": "YidiDev Jev benchmark", "author": "YidiDev", "date": "2026-09-23", "category": "benchmark", "url": "https://github.com/YidiDev/jev-benchmark", "base_model": "", "params": "", "metric": "CT1-8 overall accuracy, price per 1000 calls, calibration usefulness across 10 rubric-based zero-shot classification suites", "license": "MIT", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "Rubric-based zero-shot classification benchmark: ten structurally distinct test suites, CT1 through CT8 exam grading plus adversarial relabeling, arithmetic and chained execution, on four models: TypeSafe Jev, Claude Haiku 4.5, Claude Sonnet 5 and OpenJev; every corpus, prediction, dollar spent and test that failed to find a difference is published and reproducible from the seeds and scripts; 124 passing tests", "approach": "", "training": "", "data": "Ten test suites, 508 core CT1-8 decisions", "result": "CT1-8 overall accuracy Jev 98.84% vs Claude Haiku 4.5 96.46% vs Claude Sonnet 5 96.18% vs OpenJev 96.28%; price per 1000 calls $0.031 vs $1.590 vs $3.599 vs about $0.014 self-hosted estimate; whole-benchmark spend $0.75 vs $29.67 vs $68.10 vs $0.00 real on the Codiv free hosted tier with a documented $0.43 self-hosted estimate; total spend $110.11; Jev confidence flags real errors consistently on 3 of 3 tasks while both Claude models are consistently useless on 3 of 3; Jev also wins rubric-following under adversarial relabeling and the arithmetic weakness its own vendor documentation predicted; the advantages concentrate where task structure favors small-step execution and native multi-question batching and disappear where it does not; the most surprising result belongs to Sonnet, the stronger Claude model", "notes": "The self-hosted cost of the free arms is a documented estimate and is never compared against metered API arms as if it were free to deploy", "domain": "general", "score": "", "latency": "", "benchmark": "CT1-CT8 rubric suites", "cost": "", "speedup": ""}
{"id": "better-jev-bench", "name": "better-jev-bench / better-jev-for-all", "author": "asp616848", "date": "2026-09-22", "category": "benchmark", "url": "https://github.com/asp616848/better-jev-bench", "base_model": "", "params": "", "metric": "422,878 items across 11 tasks, held-out slice committed", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A wide, multi-domain, license-tiered corpus and benchmark for typed decision models: 8 datasets, 11 tasks, 422,878 items, with 278,513 in the public training slice and 21,174 in a frozen held-out slice committed to the repo; all four width strata from 2–151 options and all three primitives choice, score, and yes/no; every license verified against a primary source on 2026-09-23, all Tier A; it exists because of one measured result, a model trained on 1.2M NLI examples matched zero of the 1,175 items in two real benchmark harnesses, because scale on a single schema teaches the schema, not the skill", "approach": "", "training": "", "data": "Improved benchmark sets", "result": "It covers CFPB consumer complaints with 10 options, BANKING77 with 77, CLINC150 plus with 151, MASSIVE intent with 60 and scenario with 18, GoEmotions with 28, LEDGAR with 100, CUAD clause types with 41 and clause presence as yes/no, and Civil Comments toxicity level as a 5-level score and is-toxic as yes/no; four of the eleven tasks are severely imbalanced and declare a majority-class chance floor, where answering no to every is-toxic item scores 92.07% raw accuracy and 0 chance-adjusted", "notes": "9/22-9/23 entries; the ekVachan serving contract supplies the three primitives unchanged; the held-out slice makes the benchmark runnable immediately after a clone", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "dadvar", "name": "Dadvar", "author": "ashaazami", "date": "2026-09-22", "category": "benchmark", "url": "https://github.com/ashaazami/dadvar", "base_model": "", "params": "", "metric": "Accuracy, probability error and latency on identical rows, dataset revision pinned, seeded sampling, request and response cached", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "A benchmark for typed-decision models. Dadvar is Persian for judge, literally bearer of justice. It converts public datasets into the TypeSafe System One schema, choice, score and noul questions with gold answers, runs any model over them, a local checkpoint or TypeSafe's hosted Jev, and reports accuracy, probability error and latency on identical rows. Every run pins its inputs for reproducibility.", "arch": "A benchmark for typed-decision models: converts public datasets into TypeSafe's choice, score and noul schema with gold answers, runs any model, a local checkpoint or hosted Jev, over identical rows, and reports accuracy, probability error and latency; each run pins dataset revisions, samples with a seed, caches every request and response, and records whether a model's publisher says it trained on that data", "approach": "", "training": "", "data": "", "result": "Ships a Laya loader with three checkpoints: convaiinnovations/laya English ModernBERT-large 421M 512 context, mmBERT-base 322M 1024 multilingual, and the typed-decisions 421M 1024 variant; records whether a model publisher says it trained on the dataset; Apache-2.0", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "apolinario-decision-index", "name": "Decision Index Kit", "author": "apolinario", "date": "2026-09-22", "category": "benchmark", "url": "https://github.com/apolinario/decision-index", "base_model": "", "params": "", "metric": "93.4% Jev coverage", "license": "MIT", "paper": "", "stars": 3, "likes": "", "detail": "arch: rebuild the frozen suite from public sources, checkpoint and resume, score with the leaderboard's own scorers; metric: 40 benchmarks, five equal-weight areas, chance-corrected, edition 0.2; result: 120,615 scoreable requests; license: MIT", "arch": "Kit to build and query a Jev decision index", "approach": "", "training": "", "data": "500 decisions, 312 unique", "result": "296.9 ms per decision; $0.0145 per 1k; 99.2% format stability; 98.4% format consistency across two runs, 82 of 84", "notes": "Reproduction kit for the Decision Index live board, edition 0.2 of 2026-09-24: 40 benchmarks in five equal-weight areas, chance-corrected from 0 for random guessing to 100 for perfect, coverage-adjusted with unanswered counting as wrong. Edition 0.2 adds MMLU-Pro, BBH, When2Call, RAGTruth, HoVer, New Yorker caption matching and PhishNChips; MMLU drops out as saturated; 15 new models join the board. 120,615 scoreable requests; ToolRet and BRIGHT cut to stratified 1,000 and 550 subsets. About 7 GB of downloads and 17 GB of working space. Not affiliated with TypeSafe AI; complements the Jev Decision Index space.", "domain": "general", "score": "", "latency": "296.9 ms per decision", "benchmark": "500 decisions", "cost": "$0.0145 per 1k decisions", "speedup": ""}
{"id": "decision-bench-hanno-labs", "name": "DecisionBench 1.0", "author": "Hanno-Labs", "date": "2026-09-22", "category": "benchmark", "url": "https://github.com/Hanno-Labs/decision-bench", "base_model": "", "params": "", "metric": "accuracy, ECE, NLL, coverage", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": "", "detail": "base: 43 tasks / 23,900 rows / 28 domains; metric: accuracy, ECE, NLL, coverage; result: Bosun 1.7B 87.29% tops initial leaderboard; notes: Jev-Omni card cites DecisionBench as its training source", "arch": "Open MTEB-style benchmark that keeps tasks as units of evidence: 43 tasks, 23,900 English rows, 28 domains; 9 task families across 3 primitives, Noul, Choice and Score, plus a separate reasoning track; the standard applied suite is 22,700 rows; each official run preserves the exact inputs, the full candidate distribution, the raw response, failures, revisions and content-addressed artifacts; adding a task never rewrites the published benchmark", "approach": "", "training": "", "data": "", "result": "Leaderboard at HF Space Hanno-Labs/decision-bench-leaderboard; initial entries from the Bosun v3.1 release: 1.7B at 87.29% accuracy, ECE 0.0397, NLL 0.4328, 100% coverage on the 22,700-row standard applied suite, 0.6B at 83.20%, 0.0588, 0.6210; on the separate reasoning track Jev leads both Bosun models; reviewed append-only result registry in Hanno-Labs/decision-bench-results", "notes": "pip install git+https://github.com/Hanno-Labs/decision-bench.git, then decision-bench run-hf on task_specs/decisionbench-dev.toml; docs at hanno-labs.github.io/decision-bench; frozen dataset at HF Hanno-Labs/decision-bench, created 2026-09-20; leaderboard caveat: Bosun rows are held-out rows from task families represented in its training data, not entirely unseen families; Apache-2.0, Copyright 2026 Clause Logic Inc.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-decision-index", "name": "Jev Decision Index", "author": "multimodalart", "date": "2026-09-22", "category": "benchmark", "url": "https://huggingface.co/spaces/multimodalart/jev-decision-index", "base_model": "", "params": "", "metric": "31 open models", "license": "Open", "paper": "", "stars": "", "likes": 204, "detail": "author: community; base: leaderboard of open Jev reproductions; arch: 131,980 scoreable · 37 benchmarks, 19 shown across 5 areas · the 6 interactive environments are dropped because no reproduction has run them · only the balanced-raw index shown: 100*Σ 0.2*category · parallel constrained decoding on stock models · text diffusion in 'Jev mode' · heads/fine-tunes · up to 8000, halving daily · index.json generated 2026-09-22T11:12Z · 1× RTX PRO 6000 · jev-1.13.0; result: current panel · frozen panel · Qwen3.8-27B · DiffusionGemma-26B-A4B-it · Qwen3.5-35B-A3B-Base NVFP4; notes: 142→171 over two days · decider entry updated 09-23 · 09-23 · 'Decision Index for Open-Source Jev', 1.44; name: Jev Reproductions Tracker", "arch": "Static HF Space with two tabs. 'Index': leaderboard of every open reproduction that finished the frozen 132,422-request suite, scored as a single Decision Index number with per-category radars vs Jev and per-model pages. 'News': artifact tracker color-coded by kind — Decoding, Diffusion, Trained, Predecessor work, Explainers; trending score = X ♥ + 5×GH stars + 8×Hub likes + views/500 + recency credit; data written by build_leaderboard.py from the typesafe-diffusion-lab checkout", "approach": "", "training": "", "data": "Frozen 132,422-request suite; engagement snapshot 2026-09-24", "result": "31 open models ranked. Jev itself: 59.51 / 52.49. Top open: Jevfire 55.74, open-jev diffusiongemma 55.56, Decider 35B-A3B 54.34, razorback16 openjev diffusiongemma 51.52, Kev 9B 50.48, Solomon 47.51, Kev 4B 47.43; also Laya 16.39, GLiNER 2.5 base 24.70, system-one-gemma 17.09, Verdict 13.38. Maintainer on X: 'jev still leads open models, with some margin in case of… everything is open source and reproducible'. Section on what is still NOT in the open: TypeSafe's weights, the RLCD algorithm, and any open model matching Jev's calibration claims", "notes": "171 likes. Unofficial, community-maintained, not affiliated with TypeSafe AI; referenced in the r/LocalLLaMA JevBench thread as the companion open-repro leaderboard. KingClancy/jev-decision-index reposted the space 09-24. Live Space, launched 2026-09-21, Kev added 2026-09-23.", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "jev-aita", "name": "Jev vs LLMs on Reddit AITA", "author": "dchristopoulos", "date": "2026-09-22", "category": "benchmark", "url": "https://github.com/dchristopoulos/jev-aita", "base_model": "", "params": "", "metric": "Brier 0.369", "license": "Open", "paper": "", "stars": 3, "likes": "", "detail": "base: evaluates jev-1.13-20260917 vs Sonnet 5, GPT-5 nano and two local open-weight models; arch: 2025; data: + 300 labeled 2023 posts for a calibration probe; result: weighted Brier 0.369 vs 0.344, lower is better · 0.337 · inconclusive; name: 770 verdicts", "arch": "Benchmark on 770 r/AmITheAsshole posts with YTA/NTA/ESH/NAH community verdicts; every model returns a probability per verdict; scored by weighted Brier, latency and cost", "approach": "", "training": "", "data": "770 posts from 2025; author paid ~$2.75 for every call, no TypeSafe affiliation", "result": "Jev came second behind Sonnet 5; Jev's median call was 6.3× faster than Sonnet's and 62× cheaper — 'That's fast, but not the 40×–200× faster TypeSafe claims'; a small logistic regression learned from Jev's answers on 300 labeled 2023 posts gives Jev the lowest score but Sonnet's 0.344 is unadjusted, so not a like-for-like win; with the same adjustment given to every model, Sonnet still leads by 0.007", "notes": "Independent, self-funded benchmark with a candid read on TypeSafe's speed/cost claims; created 2026-09-22.", "domain": "general", "score": "Brier 0.369", "latency": "", "benchmark": "Reddit AITA", "cost": "62× cheaper vs Sonnet", "speedup": "6.3× faster"}
{"id": "schacon-jev-tests", "name": "jev-tests: macOS demos comparing typed decision models", "author": "Scott Chacon", "date": "2026-09-22", "category": "benchmark", "url": "https://github.com/schacon/jev-tests", "base_model": "Jev, Laya, Kev, CUA-S1-FORMS, Claude", "params": "", "metric": "macOS demos comparing typed decision models, running locally on Apple Silicon", "license": "Open", "paper": "", "stars": 5, "likes": "", "detail": "author: schacon", "arch": "Swift macOS demo apps, including a GitHub-settings mock and a Tetris heatmap, comparing typed decision models side by side: laya fits a question and all its options into 256 tokens, Jev takes up to 255 options per question and sees every setting at once", "approach": "", "training": "", "data": "", "result": "Companion demos to the Latent Space \"Jev Creator\" interview; run locally on Apple Silicon", "notes": "2 stars; created 2026-09-22. Tetris simulation vendored from FluidUse, Apache-2.0; demo data is fictional.", "domain": "gaming", "score": "", "latency": "", "benchmark": "Tetris", "cost": "", "speedup": ""}
{"id": "rbrus-laya-as-judge", "name": "Laya-as-a-Judge", "author": "rbrus", "date": "2026-09-22", "category": "benchmark", "url": "https://github.com/rbrus/laya-as-judge", "base_model": "Laya", "params": "421M", "metric": "P50 latency, throughput, calibration, RLCD probabilities plus Shannon-entropy confidence, zero output tokens", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "approach: token-free Laya judgments instead of LLM-as-a-Judge", "arch": "Laya-as-a-Judge: fast, calibrated LLM and AI-agent evaluation using Laya typed decision models instead of slow autoregressive LLM-as-a-Judge; pip-installable judge API, CLI, and examples, built for native local runtimes such as laya-mlx on Apple Silicon.", "approach": "sdk", "training": "", "data": "", "result": "Laya MLX P50 7.4 to 13.4 ms and 140 to 395 queries/sec on one GPU versus cloud LLM judges at 1,500 to 3,500 ms and 0.5 to 2 queries/sec; the worked example claims $324,000 per year net savings; the default EmulatorBackend is a keyword and regex heuristic with uncalibrated verdicts, real inference needs the MLX backend", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pranaysuyash-laya-formatting-fragility", "name": "laya-formatting-fragility", "author": "Pranaysuyash", "date": "2026-09-22", "category": "benchmark", "url": "https://huggingface.co/datasets/Pranaysuyash/laya-formatting-fragility", "base_model": "", "params": "", "metric": "Formatting-fragility checks that vendor benchmarks do not measure", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "perturbation suite for typed decision models; each row pair differs in exactly one formatting dimension such as option key names, option order or state phrasing while the situation and gold answer stay identical", "approach": "", "training": "", "data": "", "result": "vendor benchmarks do not measure this; 58 downloads", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "next-jev-laya-test", "name": "next JEV Laya test set", "author": "JonesLin", "date": "2026-09-22", "category": "benchmark", "url": "https://huggingface.co/datasets/JonesLin/next-jev-laya-test", "base_model": "", "params": "", "metric": "24,182 multilingual decision questions in JSON", "license": "CC-BY-NC-4.0", "paper": "", "stars": "", "likes": 0, "detail": "Seed 13. MASSIVE and XNLI use the first 300 test rows per listed language, the other 37 MASSIVE languages the first 100; English suites use the first 600 rows except prompt-injections at all 116; application suites take 400 rows from research/scripts/bench_apps.py, covering email spam, guardrail jailbreak, model-routing domain, moderation toxicity, phishing, RAG relevance and support triage, plus banking77 at 400 and GSM8K and MBPP for model routing. CC BY-NC 4.0 because it contains CC BY-NC sources; MS MARCO passages stay under Microsoft non-commercial research terms and BoolQ is CC BY-SA 3.0, other rows keep the source license on each record.", "arch": "Frozen multilingual decision test set of 24,182 questions for the next-jev project, built with the same seeds and cuts as the Laya benchmark runs: it is a test set, not a three-way NLI training split. Each line is one question, with the Laya question object in questions and the gold option index in that object's criteria order.", "approach": "", "training": "", "data": "", "result": "", "notes": "Companion to the next JEV stage 1 and stage 2 checkpoints", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ruvector-typesafe", "name": "ruvector typesafe", "author": "ruvnet", "date": "2026-09-22", "category": "benchmark", "url": "https://huggingface.co/models/ruvnet/ruvector-typesafe-banking77", "base_model": "None; no weights: labelled example banks with frozen split assignments; heads + temperature calibration refit from the bank at load; encoder-independent", "params": "", "metric": "Banking77 bank measured in the repo; example-conditioned scoring with no trained weights, companion intent datasets on HF", "license": "CC-BY-4.0", "paper": "", "stars": 0, "likes": 0, "detail": "base: nearest-prototype, or multinomial probe once a class has enough examples · ONNX encoders byte-identical on the dataset; arch: npm · probeIterations 4000, probeClassBalanced, head probe; training: not model weights; data: 77 intents; result: exact options published; name: example-bank decisions", "arch": "@ruvector/typesafe: typed decisions over text, locally, no API bill, no network in the decision path; bank stores examples + frozen splits — never hyperparameters; exact engine-options required for the measured model", "approach": "head", "training": "Bank training = labelled examples + frozen splits", "data": "Published banks: PolyAI/banking77, clinc150, h64", "result": "Banking77 bank measured in repo", "notes": "'The model is your examples' — example-conditioned scoring, no trained weights. Sister intent-classification checkpoints: ruvector-typesafe-clinc150 and ruvector-typesafe-hwu64.", "domain": "general", "score": "", "latency": "", "benchmark": "Banking77", "cost": "", "speedup": ""}
{"id": "defenseclaw-system-one", "name": "Tool-call guard benchmark", "author": "Vineethsain", "date": "2026-09-22", "category": "benchmark", "url": "https://huggingface.co/spaces/Vineethsain/defenseclaw-system-one", "base_model": "", "params": "", "metric": "F1 at one shared false-block budget of 13 false blocks, plus block-only F1 as shipped, on the Broad comparison corpus", "license": "Open", "paper": "", "stars": "", "likes": 0, "detail": "Small models are scored as the middle tier of a tool-call guard cascade: deterministic rules first, then a small model, then the Gemma 4 judge on the cases the small model is unsure of, and every model is ranked at one shared false-block budget on the Broad comparison corpus, with index.html carrying the table and the recommended stack. Results: open-jev-qwen-27b, licence apache-2.0, F1 0.73876 at the shared budget, 263 true and 13 false blocks, 0.33206 block-only F1 as shipped; OpenJev, licence CC BY-NC 4.0, non-commercial, F1 0.71162 at the shared budget, 248 true and 13 false blocks, 0.70231 block-only F1 as shipped; Jev 1.13.0, licence commercial API, 0.042 dollars per million input tokens, F1 0.58360 at the shared budget, 185 true and 13 false blocks, 0.54153 block-only F1 as shipped; gemma-4-26B-A4B-it, licence apache-2.0, F1 0.32463 at the shared budget, 87 true and 13 false blocks, 0.47496 block-only F1 as shipped; jevify-gemma4-26b-a4b, licence gemma, F1 0.29278 at the shared budget, 77 true and 13 false blocks, 0.04922 block-only F1 as shipped; open-jev-qwen-9b, licence apache-2.0, F1 0.24609 at the shared budget, 63 true and 13 false blocks, 0.17551 block-only F1 as shipped; DiffusionGemma, licence apache-2.0, F1 0.22530 at the shared budget, 57 true and 13 false blocks, 0.26792 block-only F1 as shipped; kev-9b, licence apache-2.0, F1 0.19316 at the shared budget, 48 true and 13 false blocks, 0.01814 block-only F1 as shipped; open-jev-qwen-2b, licence apache-2.0, F1 0.08120 at the shared budget, 19 true and 13 false blocks, 0.17195 block-only F1 as shipped; and decider-2b, licence not recorded, F1 0.07296 at the shared budget, 17 true and 13 false blocks, 0.10843 block-only F1 as shipped.", "arch": "Static Hugging Face space benchmarking which small model can guard an agent's tool calls: small models are scored as the middle tier of a tool-call guard cascade, deterministic rules first, then a small model, then the Gemma 4 judge on the cases the small model is unsure of. Every model is ranked at one shared false-block budget on the Broad comparison corpus, and index.html carries the table and the recommended stack.", "approach": "", "training": "", "data": "", "result": "F1 at the shared budget with true and false blocks: open-jev-qwen-27b 0.73876, 263 true and 13 false; OpenJev 0.71162, 248 and 13; Jev 1.13.0 0.58360, 185 and 13; gemma-4-26B-A4B-it 0.32463, 87 and 13; jevify-gemma4-26b-a4b 0.29278, 77 and 13; open-jev-qwen-9b 0.24609, 63 and 13; DiffusionGemma 0.22530, 57 and 13; kev-9b 0.19316, 48 and 13; open-jev-qwen-2b 0.08120, 19 and 13; decider-2b 0.07296, 17 and 13. Block-only F1 as shipped ranges from 0.70231 for OpenJev to 0.01814 for kev-9b.", "notes": "", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "typed-decision-bench", "name": "typed-decision-bench", "author": "4nt0ineB", "date": "2026-09-22", "category": "benchmark", "url": "https://github.com/4nt0ineB/typed-decision-bench", "base_model": "Jev 1.13.0 vs 9 open models", "params": "", "metric": "Jev 85% EN, 84% FR, a 1-point drop; alternatives lose 4–8 points", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "zero-shot typed-decision contract across Jev, GPT-4o mini, Mistral Nemo, Open-Jev 2B and 9B, Laya multilingual, Qwen3.5 2B/4B/9B, MiniCPM5 2B, Qwen3.5 0.8B 4-bit", "approach": "", "training": "", "data": "MASSIVE 60 intents, EN_EN / FR_EN / FR_FR conditions, pinned to jev-1.13.0", "result": "73% of traffic passes the 0.90 threshold and 94% of it is correct; GPT-4o mini's 0.90-confidence answers are right 82% of the time; ECE 0.07 EN / 0.06 FR; all raw JSON results committed", "notes": "A multilingual hold-up check, not a general verdict: one dataset, one kind of question, it says whether Jev holds up in French, not how far or why; every model zero-shot; TypeSafe makes no claim about multilingual capabilities either way", "domain": "general", "score": "85%", "latency": "", "benchmark": "DecisionBench", "cost": "", "speedup": ""}
{"id": "clduab11-jev-calibration-stats", "name": "Jev + Gemma confidence statistics", "author": "clduab11", "date": "2026-09-21", "category": "benchmark", "url": "https://huggingface.co/datasets/clduab11/jev-calibration-statistics", "base_model": "TypeSafe Jev vs self-judging Gemma 4 E2B", "params": "", "metric": "Confidence statistics: Jev-judged pipeline 0.612 versus no-judge 0.740, pre-registered bar missed, similar mistake count on jointly answered questions", "license": "MIT", "paper": "", "stars": "", "likes": 0, "detail": "base: pinned jev-1.13.0; arch: Jev judge vs LLM-as-judge self-scoring vs no judge; training: analysis; data: n<1K aggregates", "arch": "Aggregate statistics on confidence scores from two judges in a retrieval benchmark", "approach": "", "training": "", "data": "Retrieval benchmark runs", "result": "Jev-judged pipeline 0.612 vs no-judge 0.740; missed its main pre-registered bar; similar mistake count on jointly answered questions", "notes": "Negative-result publication: the judge did not beat no-judge end to end.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-noul-vs-choice", "name": "Jev Noul vs Choice", "author": "TakumiNoguchi2004", "date": "2026-09-21", "category": "benchmark", "url": "https://github.com/TakumiNoguchi2004/jev-noul-vs-choice", "base_model": "", "params": "", "metric": "Calibration on a fair six-sided die scenario: 7-way choice versus 7 independent noul questions, n=50 trials each", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "Does Jev play dice? Follow-up to KantaHayashiAI/jev-does-not-play-dice, which found that Jev, TypeSafe's System One model marketed on returning calibrated probabilities instead of free-text LLM-judge reasoning, reports about 83% confidence for a face it always picks as 1. This repo probes the same calibration question across the noul and choice primitives.", "arch": "Follow-up to jev-does-not-play-dice, which found Jev reports about 83% confidence for the face it always picks as 1 on a fair die instead of the true 16.7%: asks whether that overconfidence is a property of Jev's probabilities in general or specific to how the question is asked, and finds that for the fair-die scenario the failure is specific to the choice question type, with severity scaling with the number of options; the same uncertainty asked as a single noul proposition is calibrated to within about a percentage point of the true 1/6, while the 6-way choice collapses almost completely and a 2-way yes-no choice is still biased but far less extreme", "approach": "", "training": "", "data": "", "result": "choice puts 77% of its probability mass on face 1, mean 0.769, and rates the impossible 7th face 0.038, higher than two real faces; noul recovers 0.157 to 0.177 per real face against true 0.167 and 0.010 for the impossible face; the 2-way choice is biased but far less extreme", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "marcmagn1-jev-08b", "name": "jev-08b-typed-r1", "author": "marcmagn1", "date": "2026-09-21", "category": "benchmark", "url": "https://huggingface.co/spaces/marcmagn1/jev-alt-systemone-trackio", "base_model": "0.8B base", "params": "0.8B", "metric": "Alt System One eval dataset plus trackio dashboard space for jev-08b-typed-r1", "license": "Open", "paper": "", "stars": "", "likes": 0, "detail": "base: per name; arch: R1 variant; data: 1,071 downloads; license: HF; result: marcmagn1/jev-alt-systemone-trackio", "arch": "The Alt System One eval track as an HF space with a trackio dashboard; the associated 0.8B typed-r1 model repo was an empty placeholder as of 09-24, no model card, no weights, no inference providers, so the fields are claim-level", "approach": "undisclosed", "training": "", "data": "Companion dataset marcmagn1/jev-alt-systemone-eval", "result": "Alt System One eval dataset + trackio dashboard space", "notes": "0.8B typed-r1 repo is an empty placeholder: no model card, no weights, no inference providers as of 09-24; fields claim-level. Substantive artifact is the Alt System One eval track with the trackio dashboard space.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-papers", "name": "jev-papers", "author": "stas4000", "date": "2026-09-21", "category": "benchmark", "url": "https://github.com/stas4000/jev-papers", "base_model": "TypeSafe Jev", "params": "", "metric": "Total cost, USD per paper, median and p95 latency per paper and per request over 250 requests and 1,392,141 input tokens; judge cost on 100 papers", "license": "MIT", "paper": "", "stars": 6, "likes": 0, "detail": "Committed run: 1,000 submissions to cs.AI, cs.LG, cs.CL and cs.CV from 2026-09-16–2026-09-18. Jev runs as typesafe/jev-1.13-20260917 batched four papers per request; the judge runs as anthropic/claude-opus-5 with one paper per request, bare label, extended reasoning off. MIT.", "arch": "Mass literature classifier: classifies 1,000 recent arXiv AI papers into 24 topics with one Jev decision per paper, a 24-way choice question, then checks those labels against an LLM judge on a seeded sample of the same papers. fetch.py pulls papers from the public arXiv API, classify.py asks Jev, eval.py asks a strong chat model the same question and reports the disagreements, and site/index.html is a topic map where tile area is the paper count, every topic lists its papers, everything is searchable, and every paper shows Jev's confidence.", "approach": "", "training": "", "data": "1,000 arXiv AI papers", "result": "Jev cost 0.0585 USD total, 0.000058 per paper, 56.9 ms median and 90.5 ms p95 per paper, 227.8 ms median and 362.1 ms p95 per request over 250 requests and 1,392,141 input tokens; the judge cost 0.8940 on 100 papers at 0.008940 each, 1,923 ms median and 2,810 ms p95. Agreement on the 100 sampled papers: same topic on 85, the judge's pick is Jev's first or second choice on 95; when Jev's confidence is 0.90 or higher, 62 of 100, they agree on 98%, below 0.90 they agree on 63%; 14 of the 15 disagreements are papers where Jev itself was under 0.90, and no disagreement pair repeats.", "notes": "Uses Jev as a mass literature classifier — one decision per paper.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevbenchmarklab", "name": "JevBenchmarkLab", "author": "ersinkoc", "date": "2026-09-21", "category": "benchmark", "url": "https://github.com/ersinkoc/JevBenchmarkLab", "base_model": "", "params": "", "metric": "Hard set adding competing signals, explicit negation, implicit deadlines, quoted versus authored intent, adversarial instructions, PII boundary cases and close score boundaries", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "Exactly 200 ready-to-run choice, noul and score tests across 20 suites: a 100-case standard baseline plus a separately filterable 100-case hard set. Tracks accuracy and run-to-run consistency over 1–50 repetitions, option-order bias by testing criteria in original, reversed and rotated order, answer flip rate and probability drift, mean and p95 latency plus token usage, with persistent JSON records for every run under data/runs. choice uses exact label matching, noul explicit thresholds, score an accepted range.", "arch": "TypeScript and React benchmark system measuring TypeSafe Jev decisions against fixed ground truth over repeated trials", "approach": "", "training": "", "data": "", "result": "Hard set adds competing signals, explicit negation, implicit deadlines, quoted versus authored intent, adversarial instructions inside state, structured policy flags, evidence relationships, PII boundary cases and close score boundaries", "notes": "Deterministic work such as arithmetic or date comparison is excluded from the catalog, per TypeSafe guidance that such operations belong in application code; Node 20+ and pnpm", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "jev-poker-bench", "name": "JevPokerBench", "author": "Prophetlab", "date": "2026-09-21", "category": "benchmark", "url": "https://github.com/Prophetlab/JevPokerBench", "base_model": "", "params": "", "metric": "Cash-game and sit-and-go leaderboards, hand replay, hand advisor comparing mathematical equity with model action preference", "license": "", "paper": "", "stars": 11, "likes": "", "detail": "Keys are kept in browser sessionStorage and server memory for your own requests only, not written to server databases or logs; an invitation enables a lifetime 5 CNY hosted DeepSeek allowance per account, not recurring. The advisor defaults to Jev and lets you edit a hand and action history to compare decision analysis. Bilingual English and Chinese README. The public interface is read-only for leaderboards and replays, with no benchmark or model-registry administration.", "arch": "Texas Hold'em benchmark and playground for decision models from ProphetLab, with separate cash-game and sit-and-go leaderboards, hand replay, a hand advisor, and play against models; chips are virtual. Hosted online with registration needing no invitation; hosted local models and official Jev play free, and bring-your-own-key agents accept official Jev or DeepSeek or a custom OpenAI-compatible or Claude agent with a model name, public HTTPS endpoint and key.", "approach": "", "training": "", "data": "", "result": "Hosted platform, registration needs no invitation; hosted local models and official Jev play free, BYOK agents accept official Jev, DeepSeek or any OpenAI-compatible or Claude endpoint; model seats time out after 15 seconds and fold; advisor defaults to Jev", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-vs-jev-arena", "name": "Laya vs Jev Arena", "author": "PromptEngineer48", "date": "2026-09-21", "category": "benchmark", "url": "https://github.com/PromptEngineer48/laya-vs-jev-arena", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "Score, decisions per second, p50/p95 latency and tokens; first Laya call 30 to 60 s not counted", "license": "Open", "paper": "", "stars": 30, "likes": 0, "detail": "base: local, Apache-2.0 · hosted API; arch: ghcr.io/promptengineer48/laya-vs-jev-arena; data: game environments · seed-controlled apple layouts, identical for both sides; result: checkpoint download; name: Snake + Kombat", "arch": "Head-to-head: snake race + Mortal-Kombat-style fight where EVERY move is a real model decision — same input, same typed questions, only the model differs; Docker deploy", "approach": "", "training": "", "data": "", "result": "Export JSON: score, decisions/s, p50/p95 latency, tokens; first Laya call 30–60 s not counted", "notes": "From the Prompt Engineer 48 YouTube series; server.py proxies Jev calls so the API key never reaches the browser; human-vs-Jev mode included.", "domain": "gaming", "score": "", "latency": "", "benchmark": "Snake", "cost": "", "speedup": ""}
{"id": "recruitment-feedback-demo", "name": "Recruitment feedback triage comparison", "author": "adambkovacs", "date": "2026-09-21", "category": "benchmark", "url": "https://github.com/adambkovacs/candidate-experience-benchmark", "base_model": "", "params": "", "metric": "Triage comparison on 60 development records; effort sweeps for Claude and Codex, Gemini Pro and Flash, hosted Jev, Qwen, Gemma, DeepSeek, Mistral, local Qwen and Gemma, OpenJev, SemIf, Laya and NLI", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "400 synthetic candidate-experience records: 60 development, 40 validation, 200 ordinary-case test, 100 challenge test; four judgments per record; triage compared across Jev, Codex, Claude, Gemini, hosted Qwen/Gemma/DeepSeek/Mistral, local models, OpenJev, SemIf, Laya, NLI and AnyJev", "approach": "", "training": "", "data": "", "result": "in progress on the 60 development records; effort sweeps for Claude and Codex, Gemini Pro/Flash, hosted Jev/Qwen/Gemma/DeepSeek/Mistral, local Qwen/Gemma, OpenJev, SemIf, Laya and NLI completed", "notes": "labels remain provisional AI references; this is not a held-out ranking; repo renamed or transferred since the 2026-09 sweep, URL updated", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sysone-bench", "name": "sysone-bench", "author": "instax-dutta", "date": "2026-09-21", "category": "benchmark", "url": "https://github.com/instax-dutta/sysone-bench", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "v2 run of 751 states across 9 suites, 2026-09-21: Laya English checkpoint on a local M2 CPU versus Jev pinned to jev-1.13.0, full Jev run 0.008 USD", "license": "Open", "paper": "", "stars": 4, "likes": "", "detail": "Fairness rules are binding: same states, same question dicts and same seed for every model with no per-model prompt tuning, pinned versioned model ids recorded in every result file, question hashes verified before comparing, raw and temperature-fitted ECE reported separately and never mixed, and append-only result records so a published run is never overwritten. Qwen-PCD is a secondary open reference model in the same table.", "arch": "The first independent head-to-head benchmark of System One decision models on byte-identical inputs: Laya, open weights Apache-2.0, and Jev, closed TypeSafe API, answer the exact same states and typed questions in the same run with the same seed, with question SHA hashes verified identical before any comparison, because vendor numbers come from different prompts and are not directly comparable.", "approach": "", "training": "", "data": "", "result": "v2 run, 751 states across 9 suites, 2026-09-21: Laya is the convaiinnovations/laya English checkpoint on a local M2 CPU, Jev is pinned jev-1.13.0 through the TypeSafe API, the full Jev run cost 0.008 USD. Jev best on triage 0.888 vs 0.800, guardrails 0.967 vs 0.883, moderation 0.989 vs 0.833, banking77 at 0.906 vs 0.802, sst5 at 0.617 vs 0.367, and multilingual intent at 1.000 vs 0.360; Laya best on AG News 0.940 vs 0.910 and MNLI 0.983 vs 0.867; emotion is a near tie at 0.54–0.55.", "notes": "'Benchmark harness comparing Jev API vs open models.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "archestra-100-calls", "name": "We Tested Jev on 100 Real Agent Calls", "author": "Archestra", "date": "2026-09-21", "category": "benchmark", "url": "https://archestra.ai/blog/we-tested-jev-on-100-real-agent-calls", "base_model": "Jev, Sonnet 5, Laya, SemIf, Qwen3-Reranker-4B, Bespoke-Nimble-9B", "params": "", "metric": "93% zero-shot vs 79% constant baseline", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Archestra ran Jev alongside Sonnet 5 and a zoo of open-weight models on 100 real tool calls from production Claude Code sessions, building the annotator pipeline for OpenAPPA. That pipeline labels every dynamic call with four information-flow-control labels: delta_audience, delta_trust, requires_audience, and requires_trusted. The first result exposes the 79% constant trap: in real traces 79% of calls are benign and stay local, so a hardcoded default scorer beats a 75% accuracy classifier, and the hard cases are calls whose input and output carry different security properties, like a Slack message whose output is public and trusted but which must only deliver internal bytes. Models routinely confuse delta, what the tool returns, with requires, what must be true before the call may run.", "arch": "Runs Jev and five other models on 100 real tool calls from production Claude Code sessions for the OpenAPPA annotator pipeline, which classifies each call with information flow control labels: delta_audience, delta_trust, requires_audience and requires_trusted, four labels per call for 400 decisions, graded blind by three judge families and reported on the 337 where all three agreed. Names the 79% constant trap: 79% of real tool calls are harmless and stay local, so a hardcoded classifier already scores 79% and a model scoring 75% is worse than the baseline. Stress-tests Jev's own stability: five back-to-back runs agree on 394–398 of 400 labels, probabilities drift up to 0.17 between identical runs, and reordering criteria keys flips about 4 of 100 three-way decisions, docking 1.5–2 points, while binary labels show zero order flips", "approach": "", "training": "", "data": "100 real tool calls from production Claude Code sessions", "result": "Jev 93% zero-shot, 95% with nine examples, 78% refusal recall on the nine dangerous calls; Sonnet 5 leads at 98% zero-shot but all eight of its errors are leaks, missing every outbound search query; Bespoke-Nimble-9B 83%; SemIf Qwen3.5-4B 63% rising to 84% with examples; Qwen3-Reranker-4B 64%; SemIf MiniCPM-2B 54% with 80 leaks across 337 decisions; Laya 48% with 100% recall at 12% precision; Jev made no errors among predictions with confidence at least 0.7, making confidence-based routing practical; Jev responds in around 300 ms", "notes": "Early results; the authors are still refining the dataset and prompts", "domain": "general", "score": "93%", "latency": "300 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kantahayashi-dice", "name": "Jev Does Not Play Dice", "author": "Kanta Hayashi", "date": "2026-09-20", "category": "benchmark", "url": "https://kantahayashiai.github.io/posts/jev-does-not-play-dice/", "base_model": "TypeSafe Jev via Vercel AI Gateway", "params": "", "metric": "Mean reported probability vs true probability", "license": "MIT", "paper": "", "stars": 3, "likes": "", "detail": "approach: Known-truth probability probe", "arch": "A calibration probe on inputs where the true probability is known exactly: hidden fair die and coin draws, yes/no variants, and forecast documents that state their own probabilities; Jev called through the Vercel AI Gateway around the week of its launch, with the choice type and one option per die face; all code and recorded outputs are published", "approach": "sdk", "training": "", "data": "400 die trials plus coin, yes/no, and forecast-document experiments", "result": "On 400 hidden fair-die trials Jev picked face 1 every time with a mean reported probability of 82.9% against a true 16.7%, accuracy 19.0%, 76 of 400, at chance level; a fair coin drew 92.0% reported against 50%; forecast documents stating 45% risk came back at 6.6% while 55% came back at 95.9%; the yes/no type was closer to the reference probability but still over-reported in the low range", "notes": "The stated problem is the 83%, not the 19: with no information available, Jev did not report that uncertainty as a probability; the author calls Jev fast, cheap, and excellent and contrasts this with the well-calibrated MMLU finding on in-domain data; practical advice: check calibration by grouping validation cases on reported probability, mix in missing-information cases, and never let a decision model overwrite numbers extracted from a source document; originally written in Japanese, translated with LLM help Code: github.com/kantahayashiai/jev-does-not-play-dice.", "domain": "science", "score": "", "latency": "", "benchmark": "MMLU", "cost": "", "speedup": ""}
{"id": "backnotprop-jev-guardrails", "name": "Jev jailbreak benchmark", "author": "backnotprop", "date": "2026-09-20", "category": "benchmark", "url": "https://backnotprop.com/blog/jev-guardrails", "base_model": "Jev 1.13.0 vs PIGuard, Prompt Guard 2, ProtectAI v2, deepset", "params": "", "metric": "AUC 0.937 curated, 0.844 real attacks, 100% multi-turn catch", "license": "", "paper": "", "stars": "", "likes": "", "detail": "backnotprop benchmarked Jev against four open prompt-injection classifiers, PIGuard, Prompt Guard 2, ProtectAI v2, and deepset, on 7,803 labeled messages and 296 conversations with identical data and scoring code for all five. Jev ran as jev-1.13.0 through the API, not trained on injection data, and receives one fixed Noul question per item, is_injection, with the item text as the only state. On the 1,688-item curated set Jev caught 83.4% of attacks with a 10.7% false-alarm rate and AUC 0.937, ahead of PIGuard's 0.929, and at a matched 5% benign-flag rate Jev caught 74.4% of attacks versus PIGuard's 73.9. The author's caveat: Jev's probability tracks the base rate of attacks in the data, not the truth of each item, so a high score on a mostly-benign set is not a reliability claim.", "arch": "One fixed Noul question per item, the item text as the only state, nothing adapts to a detector's output; identical data and scoring code for all five; rewrite test rescores all 6,115 real-attack items after a whitespace, case-flip and greeting transform; Jev call median 211 ms, p90 265 ms over 40 sequential calls", "approach": "", "training": "", "data": "7,803 labeled messages, 296 conversations: curated 1,688 from deepset, jailbreak-classification, NotInject and WildGuard; real attacks 6,115 from WildJailbreak, in-the-wild prompts and Gandalf; multi-turn 146 attack plus 150 benign", "result": "Curated 1,688: Jev AUC 0.937, catches 83.4%, false alarms 10.7%, best of five at threshold 0.5, versus PIGuard 0.929 and deepset 0.832 with 56% false alarms. Real attacks 6,115: ProtectAI v2 AUC 0.882, PIGuard 0.866, Jev 0.844, Prompt Guard 2 0.650, deepset 0.494. Rewrite test: Jev mean score drift 0.031, 0.3% of items move by more than 0.25, most stable of five. Multi-turn: Jev catches 100% of 146 at threshold 0.18 with 0.7% benign flagged, median catch at 10% of the conversation, versus 96.6% PIGuard and 87.0% Prompt Guard 2; at a 5% benign-flag operating point Jev catches 74.4% vs 73.9% PIGuard and 68.3% Prompt Guard 2", "notes": "Jev was not trained on injection data, the four classifiers were; Jev's scores track the base rate of attacks, not the truth of each item", "domain": "security", "score": "AUC 0.937", "latency": "median 211 ms", "benchmark": "Jailbreak", "cost": "$0.17 per 6,115 items", "speedup": ""}
{"id": "marcbara-jev-preflop-poker", "name": "Jev preflop poker study", "author": "marcbara", "date": "2026-09-20", "category": "benchmark", "url": "https://github.com/marcbara/jev-preflop-poker", "base_model": "Jev 1.13.0 vs NIT, TAG, LAG, GTO reference players", "params": "", "metric": "90.8% TAG/GTO agreement, 98.4% deterministic", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "845 preflop spots reduced to a single fold/call/raise decision, Jev queried 5 times each, 4,225 calls pinned to jev-1.13.0; compared against four deterministic reference players NIT/TAG/LAG/GTO hand-coded as 6-max 100bb ranges; raw responses committed so the report regenerates without an API key", "approach": "", "training": "", "data": "1,326-combo preflop deck, 845 spots, 4,225 Jev calls; ~52 minutes to re-run", "result": "Opening agreement with the TAG/GTO reference 90.8%; 98.4% deterministic; confidence well calibrated. Open-raise frequency UTG 14.3%, CO 31.5%, BTN 28.5%: every reference player widens sharply from cutoff to button, Jev opens less from the button than the cutoff; facing a UTG raise it is 3-bet-heavy and call-light", "notes": "Reference ranges are hand-coded approximations, robust to the conclusion but not to the last boundary hand; free to use with attribution", "domain": "gaming", "score": "90.8%", "latency": "", "benchmark": "Preflop poker", "cost": "", "speedup": ""}
{"id": "emretheus-jev-rag", "name": "Jev RAG Benchmark", "author": "emretheus", "date": "2026-09-20", "category": "benchmark", "url": "https://huggingface.co/datasets/emretheus/jev-rag-benchmark", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "nDCG@10 on scifact and xquad-en versus an NVIDIA cross-encoder and no reranker; candidate-level calibration ECE and Brier; rerank p50 latency", "license": "MIT", "paper": "", "stars": "", "likes": 0, "detail": "The repository holds the raw per-query artifacts, per-run reports with paired bootstrap confidence intervals, calibration tables and a plain-text leaderboard. Reranking is measured on nDCG@10 over identical frozen top-20 candidate pools, with a confidence-partitioned RAG optimization mode at a fixed threshold of 0.50. MIT, English, under 10K rows, question-answering and text-retrieval task categories.", "arch": "Frozen-candidate-pool evaluation of TypeSafe Jev 1.13 as the reranking and decision layer of a RAG pipeline, compared with an NVIDIA cross-encoder and with no reranking at all, on English XQuAD and SciFact; every published run used free tiers at zero total cost.", "approach": "", "training": "", "data": "XQuAD, SciFact", "result": "nDCG@10: scifact, Jev 79.29%, NVIDIA cross-encoder 78.70%, no reranker 71.67%; xquad-en, NVIDIA 99.37%, Jev 98.93%, no reranker 98.11%, with Jev rerank p50 at 4044 ms against the cross-encoder's 307 ms. Candidate-level calibration, scifact: Jev ECE 0.0625, Brier 0.0366, top-1 accuracy 71.0%, top-1 confidence 0.812 when correct and 0.549 when wrong; xquad-en: ECE 0.0133, Brier 0.0045, top-1 97.9%.", "notes": "62 downloads.", "domain": "search", "score": "", "latency": "", "benchmark": "Jev RAG EN", "cost": "", "speedup": ""}
{"id": "praveenrajus-jev-bench", "name": "jev-bench, Praveenrajus", "author": "Praveenrajus", "date": "2026-09-20", "category": "benchmark", "url": "https://huggingface.co/datasets/Praveenrajus/jev-bench", "base_model": "", "params": "", "metric": "Multi-config decision benchmark with human label distributions and a calibration focus, reference set for the Jevify models", "license": "Mixed", "paper": "", "stars": "", "likes": 4, "detail": "License is mixed per the manifest in uspraveen/Jevify's docs/DATASETS.md. English, text classification, 100K to 1M rows, 2,581 downloads.", "arch": "Reference benchmark dataset for the Jevify model family: a System One / Jev decision benchmark with multiple configs, human label distributions and a calibration focus. Configs include banking77, clinc150, massive, ledgar, go_emotions, mmlu, arc_challenge, mnli and sst5, each with train, validation and test jsonl splits.", "approach": "", "training": "", "data": "22,773 test records; 100K–1M total", "result": "Reference benchmark for the jevify models", "notes": "2 likes, 2,581 downloads.", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "jev-legal-judgment-tests", "name": "Jev-like legal judgment test sets, zh", "author": "Nebulaw1", "date": "2026-09-20", "category": "benchmark", "url": "https://huggingface.co/datasets/Nebulaw1/jev-legal-judgment-tests", "base_model": "Labels from a DeepSeek generator, blind judge and verifier", "params": "", "metric": "original_test 173 rows, 149 accepted; fresh_test 361 rows, 322 accepted", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "language: zh; data: 1,269-row train + 158-row validation, held-out by case group", "arch": "Two case-group-held-out evaluation splits for Jev-style legal judgment in Chinese: each row carries facts, a legal proposition, finite candidate labels, optional self-contained rules, task/variant metadata, teacher judgment and audit provenance; variants cover factual, material-change, invariant-change and evidence-removal, related variants share case_group_id and call for grouped uncertainty estimates", "approach": "", "training": "", "data": "Filter audit_status == 'accepted' for the primary audited subset; fresh_test: 100 previously unused test groups, 95 completed generation, 380 derived rows, 19 excluded for leakage or inconsistent facts or rules, exclusions recorded in fresh_test_audit.json; do not treat omissions as model successes", "result": "", "notes": "reference_label is an automatically extracted judicial anchor, not expert adjudication; HF sweep 09-24", "domain": "legal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "blobfishai-jevfish", "name": "JevFish + Typed Decision Bench", "author": "blobfishai, Samuel Chien", "date": "2026-09-20", "category": "benchmark", "url": "https://github.com/blobfishai/jevfish", "base_model": "decider-2b fine-tune, 1.9B r4b, weights unreleased", "params": "", "metric": "TDB v0.3: Jev 81.70; JevFish r4b 78.24; decider-2b 77.56; System One Scorer 4B 77.24; OpenJev 0.1 75.56; NanoJev 0.6B 56.81", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: JevFish is an open judge for agent runs - send a state, a record, a transcript or an agent run, plus a map of typed questions, noul yes-no, choice and score, and get calibrated probabilities for every answer from one forward pass; no generated text, nothing to parse; wire format POST /v1/systemone; metric: Typed Decision Bench v0.3 public roster, 5,387 items, 25 tasks, 21 sources; DecisionScore is the mean per-item proper score, 1 minus normalised Brier, times 100, an unanswered item scores 0; leaderboard at blobfish.ai/leaderboard; notes: repo ships the trainer, train.py, data mixer, renderer, weight interpolation, merge.py, and on-box calibration calibrate_checkpoint.py; the /v1/systemone server encodes the state once, reads options in parallel, calibrates per type; one logged client per backend with suites, metrics and reports; RunPod and serverless launchers with watchdogs and self-delete deadlines; a fleet that generates verifier-labelled agent runs from public Harbor datasets; dataset SamuelChien821/typed-decision-bench on Hugging Face, already listed in the 09-22/24 HF datasets wave row; results: Jev 1.13 closed hosted scores 81.70, JevFish r4b 78.24, gap 3.46 points, 95% CI 3.01–4.04; beats its published base by +0.69, 95% CI +0.39 to +0.98, but against the same base calibrated with the same procedure, 78.41, it is a statistical tie, -0.17, 95% CI -0.43 to +0.08 - on general decisions the fine-tune buys nothing over decider-2b plus honest temperatures; what the fine-tune buys is judging agent runs: tool-use AUROC base 0.605, JevFish 0.847, Jev 0.875; terminal runs 0.597–0.766 at r5, Jev 0.724; code runs 0.721–0.741, Jev 0.965; known limits: JSON-object states rank worse than the same content as text, AUROC 0.54–0.65 vs 0.74; the answer moves with the wording of the question; on code runs it is pass-leaning at p = 0.5, calling a failing run fail 48–55% of the time; r4b reads a missing verifier block as evidence of failure; independent project, not affiliated with or endorsed by TypeSafe AI", "arch": "Open judge for agent runs: a typed decision model that takes a state plus typed questions and returns calibrated probabilities from one forward pass, with the trainer, the /v1/systemone server and the Typed Decision Bench in one repo; independent, not affiliated with TypeSafe", "approach": "", "training": "", "data": "", "result": "On Typed Decision Bench v0.3, 5,387 items across 25 tasks and 21 sources: Jev 81.70, JevFish r4b 78.24, a 3.46-point gap; the fine-tune is statistically tied with its base on general decisions but lifts agent-run judging AUROC from 0.605–0.847 on tool-use and 0.721–0.741 on code runs", "notes": "Weights not released as of the 09-24 sweep; trainer, server and benchmark are public", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-jev-lab", "name": "laya-jev-lab", "author": "yibie", "date": "2026-09-20", "category": "benchmark", "url": "https://github.com/yibie/laya-jev-lab", "base_model": "", "params": "", "metric": "Accuracy at mean confidence and latency on 40 questions, cascade speed versus Jev, Apple M4 Max with MLX", "license": "", "paper": "", "stars": 9, "likes": "", "detail": "bench: Jev vs Laya + local-first cascade on M4 Max; arch: TypeSafe, closed API · Convai, open weights; result: API · 78%", "arch": "Measured comparisons of typed-decision models - Jev vs Laya - plus a working local-first cascade: run the local model first, accept when confidence is at or above the threshold, otherwise fall back to the Jev API.", "approach": "", "training": "", "data": "", "result": "Apple M4 Max, macOS 27, Python 3.14, MLX 0.32.2; Jev 31/40 accuracy at 0.88 mean confidence and 588 ms latency; the cascade matches Jev's accuracy at ~1.8× the speed; every number comes from a script in the repo with raw output committed under results/", "notes": "Single-machine benchmarking with committed raw outputs, so all headline figures are reproducible. GitHub new-repo sweep 2026-09-25.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mayafree-typed-decision-leaderboard", "name": "Typed Decision Leaderboard", "author": "mayafree", "date": "2026-09-20", "category": "benchmark", "url": "https://huggingface.co/spaces/mayafree/typed-decision-leaderboard", "base_model": "", "params": "", "metric": "AUC", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 35, "detail": "metric: AUC on 2,018 items, 508 incorrect, 5 domains, answers from 4 models; notes: Gradio space, 35 likes, paired bootstrap 3,000 resamples", "arch": "independent side-by-side measurement of answer verifiers on one identical 2,018-item labeled set; scores, labels and grading code published", "approach": "", "training": "", "data": "", "result": "AUC: JEV 0.7350, ZTC 27B 0.7289, ZTC 397B 0.7272 a three-way tie for first, then ZTC 9B 0.6506, ZTC 4B 0.6360, length and formatting baseline 0.6223, open-jev 4B 0.6101, Patronus Lynx 8B 0.5179, Laya 421M 0.5144, Laya multilingual 322M 0.4796; GPT-5.2 reference 0.7148", "notes": "compares FINAL-Bench ZTC judges, laya, open-jev 4B, system-one-qwen3.5-4b-scorer, Vectara hallucination model and others The top three are not ranked against each other: the paired-bootstrap 95% interval for ZTC versus JEV is -0.0344 to +0.0199, so the board marks them tied. JEV is the hosted TypeSafe API at about $0.024 per 1,000 calls; GPT-5.2 costs about $0.55 per 1,000, roughly 23× more, and scores lower. heman10x/rlcd-modernbert-151m, heman10x/openJev-verdict-2.0, com-kotobalabs/open-jev-deberta-v3-large and AlexWortega/openjev are listed as published but not run as released, with reasons. A second space, mayafree/verifier-playground, demos answer verification and hallucination detection with typed decisions.", "domain": "general", "score": "AUC 0.7350 JEV", "latency": "", "benchmark": "answer verification", "cost": "", "speedup": ""}
{"id": "ctaxnagomi-instruct-jev", "name": "INSTRUCT_JEV", "author": "ctaxnagomi", "date": "2026-09-19", "category": "benchmark", "url": "https://huggingface.co/datasets/ctaxnagomi/INSTRUCT_JEV", "base_model": "", "params": "", "metric": "Instruction corpus built from Jev documentation, structured around the three primitives, mirrors the DeckerGUI raw corpus", "license": "MIT", "paper": "", "stars": "", "likes": 2, "detail": "arch: Choice, Noul, Score · companion: ctaxnagomi/DGUI_HYPERMEM-JEV; training: dataset; data: scraped", "arch": "Instruction corpus built from TypeSafe's Jev documentation, structured around the three primitives; mirrors the DeckerGUI raw corpus", "approach": "", "training": "", "data": "TypeSafe Jev docs", "result": "DeckerGUI project artifact for Jev-style instruction tuning", "notes": "2 likes, 140 downloads.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mahlernim-jev-korean-benchmark", "name": "jev-korean-benchmark", "author": "mahlernim", "date": "2026-09-19", "category": "benchmark", "url": "https://github.com/mahlernim/jev-korean-benchmark", "base_model": "", "params": "", "metric": "100-question per cell sample checks on Belebele, PAWS-X, MedQA and KorMedMCQA, Korean versus English, roughly plus or minus 8 points of uncertainty", "license": "", "paper": "", "stars": 7, "likes": "", "detail": "", "arch": "Reproducible early-access evaluation of Jev on Korean understanding and medical text, with runtime and cost evidence. Small Korean/English sample study with recorded responses, including medical-text questions; not a clinical validation.", "approach": "sdk", "training": "", "data": "", "result": "Belebele reading 96 in Korean against 97 in English on identical questions; PAWS-X paraphrase 76 versus 80, weak in both languages; KorMedMCQA Jev 80 versus Luna 88, a gap of +8 with 95% CI +2 to +15; prompt language in Korean or English changes little; reordering the input flips about 1 answer in 8", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "stas4000-jev-linkmap", "name": "jev-linkmap, site-wide internal linking with Jev", "author": "stas4000", "date": "2026-09-19", "category": "benchmark", "url": "https://github.com/stas4000/jev-linkmap", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "566 pages, 8,460 link decisions: 5.95 s wall clock at 32 concurrent, $0.00048 per page, $0.27 for the whole site", "license": "MIT", "paper": "", "stars": 22, "likes": "", "detail": "", "arch": "Rebuilds a whole site's internal link map in seconds with Jev: internal linking is treated as thousands of yes-no classification calls rather than writing, the project can run Jev alone as System 1 or put Claude or Codex behind it as System 2 to train its rubric on the site, and includes a race against Claude Opus 5 on the same queue", "approach": "", "training": "", "data": "", "result": "on bles-software.com Jev finished all 566 pages in 5.95 seconds and placed 679 links on 334 pages, leaving 232 unchanged because it links only when it is sure; in the race on the same queue Claude Opus 5 had 0 pages finished when Jev was done, its first 8 pages landing by 16.5 s at 8 concurrent, costing about $0.118 per page versus $0.00048, roughly $67 for the whole site, about 240× per page", "notes": "crawling takes 17.6 s with 8 polite workers; a 22-second race film is built frame by frame from the real timings; run the site alone with Jev as System 1, or put Claude or Codex behind it as System 2 to train its rubric on your site; inspired by a post arguing internal linking is not writing, it is thousands of yes/no calls, a classification problem for a decision model, not a frontier writer; MIT", "domain": "marketing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "scienthoon-jev-ood-calibration", "name": "jev-ood-calibration", "author": "scienthoon", "date": "2026-09-19", "category": "benchmark", "url": "https://github.com/scienthoon/jev-ood-calibration", "base_model": "TypeSafe Jev", "params": "", "metric": "out-of-distribution calibration", "license": "MIT", "paper": "", "stars": 7, "likes": "", "detail": "All calls through the Vercel AI Gateway under model id typesafe-ai/jev, AI SDK 7.0.107 experimental_evaluate, zero data retention, run 2026-09-19: 3,721 public-benchmark items plus 900 synthetic items, 0 failed calls. ECE is reported at 15 equal-width bins on max probability, plus a refit-T diagnostic, the single temperature that minimizes NLL after the fact, where T near 1 means calibrated, above 1 overconfident, below 1 underconfident; the gateway quantizes probabilities to two decimals. MIT.", "arch": "Independent out-of-distribution calibration test of TypeSafe's Jev: it runs Jev on a rule-based task the model cannot have seen in training, alongside three public benchmarks it probably has, to check whether its epistemically-honest probabilities survive unseen inputs, the property the confidence-threshold route-to-human pattern depends on. Measured on about 0.06 USD of API calls, with every raw response published.", "approach": "", "training": "", "data": "", "result": "On the three public benchmarks Jev is very accurate and its probabilities need almost no correction; on the never-seen rule-based task it is as accurate as a strong zero-shot model, but where the rule is unknowable it is confidently wrong with the same magnitude of overconfidence a fine-tuned classifier shows out of distribution, and the sign of the error flips by question type.", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yodablocks-jev-orderby-bench", "name": "jev-orderby-bench", "author": "yodablocks", "date": "2026-09-19", "category": "benchmark", "url": "https://github.com/yodablocks/jev-orderby-bench", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "defensibility of probability ordering", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "360 human-labeled rows, 350 fresh requests, 359,013 tokens. Six pre-registered gate conditions; probabilities come back at two decimals. MIT.", "arch": "Independent measurement of whether ORDER BY over a TypeSafe Jev probability puts rows in a defensible order, on jev-1.13.0, testing the properties a semantic sort actually depends on: pairwise inversion rate, Score ordinality against a graded target, and whether the probabilities move with evidence or with wording. Calibration, ECE and Brier, is reported too but is not the gate on its own, since a model can be well calibrated in aggregate and still invert the pairs a sorted page shows.", "approach": "", "training": "", "data": "", "result": "Main run 2026-09-18: all six gates pass, boolean inversion rate 0.036, Score ordinal inversion 0.143 against the 0.15 threshold, the weak link and the sort key, negation asymmetry 0.016 and indistinguishable from plain paraphrase sensitivity, underconfident in 8 of 10 bins; the sort key is coarse, 45 distinct values over 360 rows with 53 rows tied at 0.99. The same rows through recodelabs' default 40-row batching fail the ranking gate at 0.171. Hard probe, Amazon ESCI with 306 human-graded pairs over 30 shopping queries: jev_bool ECE 0.242 and inversion 0.255, jev_score inversion 0.254 against the 4-level grade with 23 of 30 queries over threshold, choice-confidence ECE 0.279; four of six gates fail.", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevbench", "name": "JevBench", "author": "fstandhartinger", "date": "2026-09-19", "category": "benchmark", "url": "https://github.com/fstandhartinger/JevBench", "base_model": "Model-agnostic benchmark; open reproduction with official Jev as baseline", "params": "", "metric": "231 public tasks, 308 sealed in v1.4; 4-axis score", "license": "Open", "paper": "", "stars": 135, "likes": "", "detail": "author: + rapidstartup/jevbench.dev, AnyEvalOrg, shisa-ai, dhruvmehra, YidiDev, asp616848; arch: choice, score, noul; training: eval harness: local_openjev, litjev adapters etc.; notes: YidiDev/jev-benchmark 9/23, better-jev-bench by asp616848, 9/23 with better-jev-for-all, AnyEvalOrg/eval-jevbench, shisa-ai/jevbench-results, dhruvmehra/jevbench; name: + Leaderboard; paper: jevbench.dev leaderboard, repo rapiddstartu/rapidstartup; eval-jevbench, AnyEvalOrg; jevbench-results, shisa-ai; jevbench, dhruvmehra", "arch": "Benchmark Heaven's own benchmark for Jev-class decision models: hand the model a state and a bounded rubric, it returns a typed answer with a probability for every option, no prose, no parsing; three decision families across 8 domains, 231 tasks, 210K items, 4 difficulty tiers; v1.4.0 is the current release, blending 20% chance-corrected intelligence from 308 fresh sealed decisions with 80% of the v1.3.0 intelligence axis, calibration blended toward the sealed-inclusive measurement, and the four axes in an equal-weight harmonic mean; a public-to-sealed accuracy gap above 25 points reduces intelligence, and speed and cost each get a quadratic gate below 50; rows with an API flag are operator endpoints that received the item text without answer keys", "approach": "", "training": "", "data": "231 public tasks plus 308 fresh sealed decisions, aggregates only for the sealed items; 220 hard decisions, 111 public and 109 held out, written by Claude Opus 5 and GPT-5.6 Sol, cross-reviewed, frozen and hashed before any system ran; 534 decisions per system in total", "result": "v1.4 board: Jev 1.13.0 63.29, JevK5 v0.2.0 62.04, Hopper 59.43, Winnow-12B Q8 55.58, reflex 4B 53.99; swanOne unranked, its sealed run not completed; v1.3.0 top of the 48 ranked rows: Jev 1.13.0 74.4, SemIf 73.1, djev 73.0, Winnow-12B Q8 71.2, reflex 4B 70.3; on the 231 public tasks: Open-Jev 9B 179/231, Jev-Omni 86.15%, Dohnuts 65.8%, systemone-lite 49.8%; combination experiments, confidence cascades, committees and real-sample best-of-n, reported in RESULTS-COMBINATIONS.md, did not change the ranked board", "notes": "The Cost axis is US dollars per 1,000 decisions, not per 1,000 tokens: Jev 1.13.0 reads 950 input tokens per decision over the 534 v1.2 decisions and costs $0.0399 per 1,000 decisions at the $0.042 per million input token tariff; self-hosted and demo endpoint latency is adjusted x2 plus 0.15 s to approximate production load, stated as an assumption, not a measurement; accuracy by subject topic is published as aggregates only, not part of the score; 106 stars + a constellation of forks/wrappers.", "domain": "general", "score": "86.15%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "andeytait-jevforge-mind2web", "name": "JevForge Mind2Web Gold Decisions", "author": "AndeyTait", "date": "2026-09-19", "category": "benchmark", "url": "https://huggingface.co/datasets/AndeyTait/JevForge-Mind2Web", "base_model": "", "params": "", "metric": "Page state plus choice question over candidate elements, noul question on one candidate and complete gold target distributions", "license": "CC-BY-4.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: zwliJay/jev-forge; training: dataset", "arch": "Gold candidate-decision records for JevForge: each JSONL record = page state + choice question over candidate elements + noul question on one candidate + complete gold target distributions + website group + source annotation metadata", "approach": "", "training": "", "data": "osunlp/Mind2Web-derived; splits: train/dev/calibration/test/ood", "result": "Private research snapshot", "notes": "98 downloads.", "domain": "computer-use", "score": "", "latency": "", "benchmark": "Mind2Web", "cost": "", "speedup": ""}
{"id": "luni-laya-jev-bench", "name": "Laya vs Jev", "author": "Luni", "date": "2026-09-19", "category": "benchmark", "url": "https://huggingface.co/datasets/Luni/laya-jev-benchmark", "base_model": "Laya vs TypeSafe Jev 1.13", "params": "", "metric": "Same-test re-run of both Jev and Laya, resolving the model-card claim of 83.8% versus 67.8% which compared two different benchmarks", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 4, "detail": "base: Apache-2.0, 421M; training: eval; data: 2,000 emails, 1,000 phishing / 1,000 legitimate — neither model trained on it; result: +16.0% advantage", "arch": "Head-to-head on benchmarks where Jev has published numbers; 1× RTX 5090; everything measured except rows marked 'published'", "approach": "", "training": "", "data": "PhishNChips + Jev-published benchmarks", "result": "Motivation: Laya's model card's '83.8% vs Jev 67.8% ' compares two different benchmarks; this dataset re-runs both on the same tests", "notes": "4 likes, 186 downloads.", "domain": "general", "score": "83.8%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nekuda-ai-windtunnel", "name": "WindTunnel", "author": "nekuda-ai", "date": "2026-09-19", "category": "benchmark", "url": "https://github.com/nekuda-ai/windtunnel", "base_model": "", "params": "", "metric": "Task success, median execution time, median cost and score versus WebMCP and other methods", "license": "", "paper": "", "stars": 84, "likes": "", "detail": "", "arch": "WebMCP benchmark comparing browser-agent interfaces, with Jev included as one of the evaluated configurations. Browser-agent benchmark: measures WebMCP against other browser-agent interfaces, with Jev appearing as one of the compared configurations.", "approach": "sdk", "training": "", "data": "", "result": "WebMCP solves 100% of tasks, with 2.5–7.5× faster median execution, 3–47× lower median cost, and 27–50% higher scores than the median of other methods.", "notes": "Curated in 3 awesome-jev lists", "domain": "computer-use", "score": "27–50%", "latency": "", "benchmark": "", "cost": "", "speedup": "2.5–7.5× faster"}
{"id": "nibzard-decision-benchmark", "name": "Decision model benchmark", "author": "nibzard", "date": "2026-09-18", "category": "benchmark", "url": "https://github.com/nibzard/decision-model-benchmark", "base_model": "12 contenders: jev, eight LLMs, three deterministic baselines", "params": "", "metric": "Per-suite accuracy, latency, cost, ECE", "license": "", "paper": "", "stars": 8, "likes": "", "detail": "approach: Frozen-item multi-provider harness", "arch": "An independent reproducible benchmark of typed decisions: every contender gets the same serialized state text and the same option list and must return a choice index and a confidence; five suites, a 77-way banking intent set, SMS spam, a code-word lookup with a sweep of 2–512 options, the same banking items with permuted option order as a position-bias probe, and forced-uncertainty items where no option is correct; temperature 0, concurrency capped at 4, one recorded retry, three repeats per item, cost taken from provider usage fields, and all raw logs published", "approach": "", "training": "", "data": "banking77, UCI SMS spam, and three synthetic suites", "result": "No class wins on quality: gpt-oss-120b leads banking at 81.3%, glm-5.3 at 80.4%, jev mid-pack at 76.3%; on spam the GLM models lead at 94.9 and 91.4%, jev at 93.0, and the OpenAI models fall below the majority baseline; jev's p50 of 264–276 ms is the fastest measured but only 1.2× to 10× faster, not 40–200×; the 255-choice cap is real and sharp, rejecting at 256, 384, and 512 options while every LLM handles 512; zero schema violations across 4,125 jev requests; on the forced-uncertainty suite jev is the only contender that bluffs, admitting ignorance on 49.7% of items versus 97.3–100 for all LLMs, with the worst ECE at 0.246; jev is the most order-stable at a 13% flip rate versus 30–37 for the LLMs; and jev is the cheapest at 0.07 dollars per 1,000 decisions, with total spend of 28.34 dollars across 49,500 logged decisions", "notes": "The v2 report fixes four reporting defects found in v1 and maps every changed number; recorded protocol deviations include the GLM minimum thinking level, the Anthropic gateway ignoring forced tool calls on about 44% of replies, and per-item shuffling of the spam suite; reports are generated from raw logs, not written", "domain": "general", "score": "", "latency": "276 ms", "benchmark": "", "cost": "", "speedup": "10× faster"}
{"id": "akashpriyadarshii-jev-curate", "name": "jev-curate", "author": "akashpriyadarshii", "date": "2026-09-18", "category": "benchmark", "url": "https://github.com/akashpriyadarshii/jev-curate", "base_model": "TypeSafe Jev", "params": "", "metric": "Rows per second single-node, cost per 100M tokens, rubric coverage per row in one HTTP round trip", "license": "MIT", "paper": "", "stars": 74, "likes": "", "detail": "Beta, v0.1.1 is experimental; live demo at jev-curate.vercel.app. Measured 24.0 rows per second single-node on the local mock bench examples/bench_mock.rs, with a 1,500-plus-rows-per-second cluster target. Python API on PyPI plus a CLI reference, architecture and non-goals docs; MIT.", "arch": "High-throughput sifter for synthetic and pretraining datasets in Rust and Python, powered by TypeSafe Jev: it streams, filters and scores millions of Parquet and JSONL rows using System One typed decisions, Choice, Score and Noul, with speculative question fan-out and calibrated post-training reasoning rubrics.", "approach": "", "training": "", "data": "", "result": "Measured 24.0 rows/sec single-node on the local mock bench with a 1,500+ rows/sec cluster target; Jev at $0.042 per Mtok input and zero output works out to about $4.20 per 100M tokens; single-node throughput bounded by the 1,200 req/min TypeSafe limit", "notes": "Curated in 7 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zhuyansen-jev-search-rerank-eval", "name": "jev-search-rerank-eval", "author": "zhuyansen", "date": "2026-09-18", "category": "benchmark", "url": "https://github.com/zhuyansen/jev-search-rerank-eval", "base_model": "TypeSafe Jev", "params": "", "metric": "graded relevance vs embedding search", "license": "MIT", "paper": "", "stars": 9, "likes": "", "detail": "Bake-off systems: the ash 0.4.0 keyword ranker from the ash CLI, BM25 over the same metadata with latin words plus CJK bigrams, dense retrieval with BAAI/bge-m3 locally and OpenRouter text-embedding-3-small, a Jev score rerank of system X's top 30 at one API call per query, and reciprocal-rank-fusion hybrids. Metric is NDCG@10 on graded 0–3 labels, plus MRR and P@3 with relevant meaning label 2 or higher, with paired bootstrap 95% confidence intervals over queries. MIT.", "arch": "Graded-relevance evaluation of whether a TypeSafe Jev rerank beats embedding search, over an Agent Skills Hub catalog snapshot of 33,047 skills, MCP servers and coding-agent tools from 2026-09-18, with 164 real Chinese, English and mixed queries and 9,831 labelled query-skill pairs, with the judge bias explicitly removed.", "approach": "", "training": "", "data": "", "result": "Jev as a standalone reranker does not beat a good embedding ranker: jev-score over bge-m3 top 30 adds 0.012 NDCG@10 with CI [-0.013, +0.037], and -0.028 under LLM-only labels. Fused, rrf of bge-m3 and jev at 30 adds 0.090 over bge-m3 with CI [+0.077, +0.104], still +0.064 under LLM-only labels. The apparent Jev advantage under its own labels is measurable judge circularity: +0.053 under Jev-only labels, +0.012 under merged labels, -0.028 under LLM-only labels. The shipped keyword ranker's problem is recall, not ordering: reranking its top 30 with bge-m3 is -0.026, with ash recall@10 of relevant items at 0.497.", "notes": "Curated in 5 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-playground", "name": "jev_playground, JYeswak", "author": "JYeswak", "date": "2026-09-18", "category": "benchmark", "url": "https://github.com/JYeswak/jev_playground", "base_model": "", "params": "", "metric": "prompt injection: Jev 1.13.0 right on 639 of 662 by benchmark count, 640 in a fresh live run", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "20 one-command demo patterns, 17 from TypeSafe's official cookbooks, running on recorded answers; a fault-tolerant Jev client that fails toward review; pre-registered measurements with committed re-scorable rows", "approach": "", "training": "", "data": "662 public prompt-injection rows, benchmark's own count plus fresh live runs", "result": "Claude Haiku 4.5 through TypeSafe's LLM adapter: 579 and 584 across two runs; grok-4: 558", "notes": "pinned to jev-1.13.0; each measurement has a rule written before any spend and a comparator someone would actually ship", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevals-jevals-data", "name": "jevals-data", "author": "jevals", "date": "2026-09-18", "category": "benchmark", "url": "https://github.com/jevals/jevals-data", "base_model": "TypeSafe Jev vs 6 LLMs", "params": "", "metric": "ground-truth noul/choice/score panel", "license": "CC-BY-4.0", "paper": "", "stars": 2, "likes": "", "detail": "Decision Score is 100 for perfect, 0 for guessing the label base rates, and negative for worse than that; a label-prior baseline row sits in every board. Full boards with accuracy, calibration, cost per 1,000 decisions and latency are published at jevals.com. CC-BY-4.0.", "arch": "Independent ground-truth benchmark data behind jevals.com: Jev, the first System One model, called as typesafe-ai/jev through the Vercel AI Gateway, and a set of LLMs answer the same typed questions, noul yes or no, choice pick one of N, and score place the state on a rubric; every answer carries probabilities and is scored against human labels for decision score, accuracy, calibration, cost and speed.", "approach": "", "training": "", "data": "", "result": "Release 2026-09-18 Decision Scores: noul PubMedQA, Gemini 3.8 Flash 73.0, Jev 69.0, Qwen3.8 Flash 62.4, GLM-5.3 60.6, Mistral Medium 3.5 58.0, Mercury 2.5 55.7, DeepSeek V4.1 Flash 47.5; choice Banking77, Gemini 74.1, Jev 67.8, GLM-5.3 66.8, DeepSeek 63.9, Qwen 62.4, Mistral 59.5, Mercury 54.0; score HelpSteer2, Jev 9.2 ahead of GLM 7.8 and Gemini 4.6, with DeepSeek at -19.0 below guessing. Jev ties the best of six LLMs on yes-no at 1/28 of the price, and no model beats guessing on HelpSteer2.", "notes": "Curated in 5 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "backnotprop-poker", "name": "backnotprop Jev poker eval", "author": "backnotprop", "date": "2026-09-17", "category": "benchmark", "url": "https://backnotprop.com/blog/jev-poker", "base_model": "Jev 1.13.0 vs a TexasSolver solve", "params": "", "metric": "63% solver match on 150 spots, 38% on the 55 contested", "license": "", "paper": "", "stars": "", "likes": "", "detail": "backnotprop evaluated Jev on a Texas Hold'em flop that TexasSolver 1.1 had solved to 0.59% exploitability: heads-up, 100bb deep, LJ opens 2.5, BTN calls, flop Q-9-4 with Q and 4 spades, 300 iterations, 8 minutes, 7.6GB. Jev receives a state object describing the table the way a player sees it, plus one question, which action to take from the solver's tree, and Python precomputes pot odds, stack-to-pot ratio, hand rank, and outs, so Jev never does arithmetic. The flagship failure: shown the opponent's exact cards, a made flush, Jev said it was ahead and shoved 89.5bb into a 22.5bb pot, five runs out of five. The author's point: poker is a cheap sanity check because a solver computes the correct play and money rides on it, and TypeSafe's decision to skip published evals at release invites naive deployments into the wild.", "arch": "TexasSolver 1.1 solve of one flop Q♠9♦4♠, 100bb, LJ opens 2.5 and BTN calls, 300 iterations to 0.59% exploitability in 8 minutes and 7.6GB; Python precomputes pot odds, stack-to-pot ratio, hand rank and outs so Jev never does arithmetic; Jev picks from the solver's legal actions; four question shapes scored on the same 150 spots plus three no-model rules; calls run ~1,200 input tokens at 200–300 ms", "approach": "", "training": "", "data": "150 random spots from one solved flop; 300 calls and 363,000 input tokens", "result": "One question 63% overall and 38% on the 55 contested spots; six judgments plus code 57% / 44%; regret per action 57% / 36%; six binary facts plus code 59% / 33%; no-model check-else-call 72% / 24%, pick-random 37% / 25%, always-bet 31% / 29%; on the nuts check-or-shove spot Jev shoves 62% of 16 runs while Haiku 4.5 checks at 75%; naming the opponent's hand rank flips the shove to a check: 74% with both hands named, 78% with hero-behind added, 88% with 0 outs", "notes": "TypeSafe does not publish standard benchmarks; evaluate every situation against an answer key you trust, and decide what a wrong call costs", "domain": "gaming", "score": "63%", "latency": "200–300 ms", "benchmark": "Poker", "cost": "~$1.50 per 300 calls", "speedup": ""}
{"id": "jev-as-a-judge", "name": "Jev as a judge", "author": "danielgshea", "date": "2026-09-17", "category": "benchmark", "url": "https://github.com/danielgshea/jev-as-a-judge", "base_model": "", "params": "", "metric": "Pass-or-fail accuracy against a human oracle, score variance, cost and latency, 500 repeated decisions per judge on 5 frozen weather-agent runs", "license": "", "paper": "", "stars": 89, "likes": "", "detail": "Evaluator questions use Jev's three typed forms, Noul for yes-or-no judgments like is the final answer grounded in the retrieved evidence, Score for rubric usefulness, and Choice for calls like did the agent search appropriately, with multiple atomic questions evaluated in parallel against the same state. In the experiment the decision-first design coincided with lower latency, lower cost and lower variance.", "arch": "Agent-evaluation experiment that uses TypeSafe Jev, a System One decision model, as an evaluator instead of an LLM judge: it compares Jev with GPT-5.6 Luna, GPT-5.6 Terra and Claude Sonnet 4.6 on the same fixed agent runs, measuring binary accuracy, score reliability, cost and latency, keeping accuracy separate from reliability because a repeatable evaluator can still be consistently wrong.", "approach": "", "training": "", "data": "", "result": "Jev matched every one of the 500 human pass-or-fail labels, GPT-5.6 Terra 99.8%, GPT-5.6 Luna 96.4%; Jev $0.00035 per call and 0.44 s average latency versus Terra $0.00289 and 2.83 s, total evaluator cost $0.34 versus $2.90; authors note the small corpus and single reviewer", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reachjalil-jevlogs-triage", "name": "Jev Logs triage benchmark suite", "author": "reachjalil", "date": "2026-09-17", "category": "benchmark", "url": "https://huggingface.co/datasets/reachjalil/jevlogs-log-triage-benchmark", "base_model": "TypeSafe Jev gating expensive-reasoner escalation", "params": "", "metric": "Token-accounted measurement of Jev Logs routing decisions, plus jev-tree-choice-cap and jev-luna-pagerduty suites", "license": "loghub-research-academic", "paper": "", "stars": "", "likes": 0, "detail": "base: via Vercel AI Gateway; arch: PagerDuty-style trigger; training: dataset; data: LogHub: HDFS_v1, BGL · OpenTelemetry; result: github.com/reachjalil/jevlogs", "arch": "Jev Logs asks Jev whether a log line is worth sending to an expensive reasoning model; public token-accounted measurement of that routing decision; suite adds jev-tree-choice-cap and jev-luna-pagerduty-trigger", "approach": "", "training": "", "data": "Sanitized public logs", "result": "Labeled routing evaluation for Jev Logs", "notes": "300 downloads on the log-triage split.", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "abdelstark-jev-benchmarks", "name": "jev-benchmarks AbdelStark", "author": "AbdelStark", "date": "2026-09-17", "category": "benchmark", "url": "https://github.com/AbdelStark/jev-benchmarks", "base_model": "", "params": "", "metric": "Accuracy with CI on AG News, Banking77/BTZSC and DAIR Emotion over 300 held-out examples, plus coverage at 5% error or less", "license": "Open", "paper": "", "stars": 18, "likes": "", "detail": "First study compares TypeSafe Jev with fastino/gliner2.5-multi-v1 on their shared capability, zero-shot single-label text classification with per-label probabilities, using three conditions from BTZSC, fixed model and dataset revisions, identical examples and label descriptions, a uniform negative control and paired target-stratified bootstrap intervals.", "arch": "Probability-aware evaluation for typed decision models: whether reported probabilities are calibrated enough to support automation, how much work can be accepted at a fixed error budget, and per-decision resource and end-to-end time", "approach": "", "training": "", "data": "", "result": "300 held-out examples, 100 per condition: AG News Jev 0.910 vs 0.700, CI +0.130 to +0.290; Banking77/BTZSC 0.870 vs 0.610, CI +0.220 to +0.300; DAIR Emotion 0.480 vs 0.440, CI -0.070 to +0.150. Jev coverage at ≤5% error: 0.830 on AG News and 0.860 on Banking77 vs 0.240 and 0.270", "notes": "Deliberately mixed result; Apache-2.0, Python 3.11–3.13", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "agentjournal-judge", "name": "Jev: one judge call, or twelve dimension scores?", "author": "ikkun", "date": "2026-09-17", "category": "benchmark", "url": "https://agentjournal.dev", "base_model": "TypeSafe Jev", "params": "", "metric": "Japanese NLI 0.837 → 0.908", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: agentjournal.dev", "arch": "One direct Jev call per row versus 12-14 Jev-scored dimensions fed into a locally trained linear model, across three classification tasks", "approach": "", "training": "", "data": "Agent outputs", "result": "Decomposition lifted Japanese NLI from 0.837–0.908 but was about 25× worse on false positives against hard benign input; the whole run cost $1.43 over 5,477 test rows", "notes": "Posted 2026-09-17 on agentjournal.dev.", "domain": "agent", "score": "NLI 0.837 → 0.908", "latency": "", "benchmark": "", "cost": "$1.43 per 5,477 rows", "speedup": ""}
{"id": "jevbench-contradiction", "name": "JEVBenchmark-Contradiction-Detection", "author": "AIPI-mvoronovych", "date": "2026-09-17", "category": "benchmark", "url": "https://github.com/AIPI-mvoronovych/JEVBenchmark-Contradiction-Detection", "base_model": "TypeSafe Jev jev-latest", "params": "", "metric": "Contradiction detection benchmark for Jev-style decision models", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Each question is a Jev choice over the four CLASH options, the image-grounded answer, the text-grounded answer, a plausible distractor, and conflicting information cannot answer which is the correct one; the state sent to Jev is the triple image_description, caption, question under model jev-latest via POST api.typesafe.ai/v1/systemone. Because this turns a cross-modal task into text-versus-text, extra conditions are run besides the paper Table 1 analog of conflict detection plus modality bias. Results in results/REPORT.md and results/summary.json, raw per-sample predictions with Jev's probability distributions in results/*_predictions.json.", "arch": "Runs the multiple-choice protocol of CLASH, a cross-modal contradiction detection benchmark from CVPR 2026 Findings, against TypeSafe's Jev decision model, measuring how well Jev detects a caption that contradicts a COCO image caption in exactly one object or attribute. Jev accepts text only, so the image is replaced by the original COCO caption it was annotated with, while everything else follows the paper: the same 1,289 human-verified test samples, per-sample option shuffling, strict matching, 1,000-resample bootstrap standard deviations, and object and attribute category breakdowns.", "approach": "", "training": "", "data": "Contradiction pairs", "result": "Contradiction detection benchmark", "notes": "'Jev benchmark — contradiction detection.'", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "nearhere-event", "name": "Near Here event verification", "author": "Near Here", "date": "2026-09-16", "category": "benchmark", "url": "https://nearhere.io/blog/jev-event-verification", "base_model": "Jev vs two cheap LLMs", "params": "", "metric": "96% vs 84% and 86%", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Published within 48 hours of the Jev launch, this was one of the earliest independent real-product tests: Near Here uses Jev to verify event listings and compares Jev against two cheap general-purpose LLMs on accuracy, latency, and cost. The site blocks automated crawlers, so the numbers live on the page itself.", "arch": "Real event-moderation task: Jev verifies event listings and is compared against two cheap general-purpose LLMs on accuracy, latency and cost", "approach": "", "training": "", "data": "Near Here production event data", "result": "96% accuracy versus 84% and 86% for the two cheap LLM alternatives, 5× faster and 8× cheaper, as cited by The Jev File; the post is unreachable from this host, so the full table is unverified", "notes": "Published within 48 h of the Jev launch; the earliest independent real-product test URL unverifiable via automated fetch, host blocks crawlers; not confirmed dead.", "domain": "general", "score": "96%", "latency": "", "benchmark": "Near Here", "cost": "", "speedup": "5× faster"}
{"id": "iammrduncan-typesafe-ai-benchmark", "name": "typesafe-ai-benchmark", "author": "iammrduncan", "date": "2026-09-16", "category": "benchmark", "url": "https://github.com/iammrduncan/typesafe-ai-benchmark", "base_model": "", "params": "", "metric": "7 synthetic workloads, latency/cost/judgment quality", "license": "MIT", "paper": "", "stars": 39, "likes": "", "detail": "arch: Hackers in the Loop", "arch": "LLM-native structured output vs TypeSafe Jev: latency, cost, and judgment quality; runs Qwen 3.8 27B on Cerebras and Jev side by side across seven synthetic workloads", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 8 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "every-minivibe-check", "name": "Mini-Vibe Check: Jev judged everything I've written in 0.7 seconds", "author": "Every", "date": "2026-09-15", "category": "benchmark", "url": "https://every.to/also-true-for-humans/mini-vibe-check-typesafe-s-jev-judged-everything-i-ve-written-in-0-7-seconds", "base_model": "Jev vs Fable 5.1 on writing checks", "params": "", "metric": "0.7 s / 777 judgments", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Mike Taylor · via AINews; data: 37 documents · 21 questions · 777 judgments; metric: 6 of 7 planted defects caught vs Fable 5.1 7 of 7", "arch": "37 documents x 21 AI-tell questions = 777 judgments in under 0.7 s for about a quarter of a cent; 27 Every articles plus 10 deliberately AI-styled counterparts; 11 experiments total, 1,709 judgments for under a cent; scenarios generated with GPT-6 Astra in Codex. Categories: finding context, checking work, making decisions", "approach": "", "training": "", "data": "Vibe-coded apps", "result": "Flagged the AI-leaning pieces correctly; accuracy unverified - 'useful as an early warning system: the alternative is not checking at all'; Shipper linter test: Jev caught 6 of 7 planted defects, Fable 5.1 all 7; Jev 0.35 s median vs 8.83 s, about 25× faster and about 580× cheaper", "notes": "TypeSafe: 'We're building prod, not God'; $42 per billion input tokens, output 'too cheap to meter'; Almeida coauthored InstructGPT in 2022", "domain": "writing", "score": "", "latency": "700 ms", "benchmark": "", "cost": "", "speedup": "25× faster"}
{"id": "thijmenkam-jev-benchmarks", "name": "jev-benchmarks thijmenkam", "author": "thijmenkam", "date": "2026-09", "category": "benchmark", "url": "https://github.com/thijmenkam/jev-benchmarks", "base_model": "", "params": "", "metric": "Accuracy, Brier, ECE, repeat-agreement, p50 latency and USD per 1k: Jev versus GPT-5.6 Luna across four tasks", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Mirrors the setup TypeSafe describes in the System One announcement: every model answers the same typed questions about the same state. Live run 2026-09-18: 4 tasks, 3 repetitions, 150 calls per model, 528 judgment rows, 0 errors.", "arch": "Small reproducible harness comparing Jev against frontier LLMs on the same structured decision tasks, scored on accuracy, calibration, consistency, latency, cost and schema validity", "approach": "", "training": "", "data": "", "result": "Jev 88.2% accuracy, Brier 0.089, ECE 0.067, 100% repeat-agreement, 627 ms p50, $0.019 per 1k; GPT-5.6 Luna 86.8, 0.113, 0.077, 88.6%, 2766 ms, $0.292 per 1k. Jev wins or ties 3 of 4 tasks: churn tie 66.7, moderation 100 vs 97.2, extraction 100 vs 93.1; Luna wins support routing 85.7 vs 82.1", "notes": "Full per-task and calibration detail in results/latest/report.md with results.csv and model_metrics.csv; caveats section read before citing", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "jev-eval-agent", "name": "jev-eval-agent", "author": "vinilana", "date": "2026-09", "category": "benchmark", "url": "https://github.com/vinilana/jev-eval-agent", "base_model": "Agent evaluation", "params": "", "metric": "Steps to finish a personal-assistant task when the LLM picks the tool itself versus when Jev picks it", "license": "Open", "paper": "", "stars": 105, "likes": "", "detail": "The 101-option Jev choice is built by lib/jev-router.ts from ctx.messages, with Jev's decisions traced to eval-results/jev-trace.jsonl. Six benchmark prompts carry required tool families; metrics are extracted from the event stream and written to eval-results/<mode>/<prompt>.json, and the deterministic mock world covers calendar, contacts, flights and expenses. Instructions, catalog, evals and UI are identical in both modes; the mode is a single environment variable. Bilingual English and Portuguese.", "arch": "Benchmark agent answering one question: how many steps does a personal-assistant agent need to finish the same task when the LLM picks the tool itself versus when TypeSafe's Jev picks it? Built with eve, with 100 mocked tools served through OpenRouter; in llm-direct mode the model sees all 100 tools on every step with reasoning on, while in jev-classifier mode Jev is called before every model step with the conversation state and only the chosen tool is exposed to the LLM, which just fills in the arguments.", "approach": "", "training": "", "data": "Agent traces", "result": "Agent eval", "notes": "'Agent evaluation with Jev as judge/decider.'", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-playground-hg", "name": "jev-playground, hegargarcia", "author": "hegargarcia", "date": "2026-09", "category": "benchmark", "url": "https://github.com/hegargarcia/jev-playground", "base_model": "", "params": "", "metric": "Model roster: TypeSafe Jev, GPT-5.6 Luna, Claude Haiku 4.5, Gemini 3.5 Flash Lite, GPT-6 Astra", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "Each game is a state machine: the game code owns the rules, legal actions, state transitions and terminal conditions, and the model chooses among the actions it is given, so the decision stays visible. All opponents receive the same game state, legal options and tactical instructions through Vercel AI Gateway; Jev uses a native evaluation model, Luna, Haiku and Gemini use the SDK experimental evaluation adapter, Astra uses generateText with Output.object and a Zod schema restricting its choice to legal option names.", "arch": "Playground for benchmarking Jev against other evaluation models in games with explicit states, legal actions and measurable outcomes", "approach": "", "training": "", "data": "", "result": "Model roster: TypeSafe Jev, GPT-5.6 Luna, Claude Haiku 4.5, Gemini 3.5 Flash Lite, GPT-6 Astra; model list in src/lib/models.ts", "notes": "Games as state machines: state to legal actions, model evaluation, validated choice; explores decision quality, tactical reasoning and consistency across a sequence of moves", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-rerank-bench", "name": "jev-rerank-bench", "author": "anessbelbati", "date": "2026-09", "category": "benchmark", "url": "https://github.com/anessbelbati/jev-rerank-bench", "base_model": "RAG reranking", "params": "", "metric": "Equal-dataset nDCG@10, Jev rubric 0.692, Cohere Pro 0.691, zerank-2 0.682; per-query equal weighting; negation test", "license": "Open", "paper": "", "stars": 8, "likes": "", "detail": "Gave Jev thirty search results and asked which were useful, then gave Cohere and ZeroEntropy the same passages. Main comparison covers eight English datasets; five more BRIGHT subsets, NevIR negation pairs and MIRACL French are reported separately. All models start from the same thirty BM25 candidates cut to 2,000 characters each; the duel variant compares only the first ten, and NevIR supplies its own two-passage pairs. Measurements began 2026-09-16, Qwen controls added 2026-09-17; Jev calls used jev-latest reporting version 1.13.0.", "arch": "Benchmark of Jev as a reranker: Jev rubric vs Cohere Rerank 4 Pro vs ZeroEntropy zerank-2 on the same thirty search results", "approach": "", "training": "", "data": "RAG corpora", "result": "Equal-dataset nDCG@10: Jev rubric 0.692, Cohere Pro 0.691, zerank-2 0.682, without establishing a winner; with per-query equal weighting Cohere leads. Jev did better on the negation test than the rerankers; open-weight Open-Jev 9B read negation better than Jev asked the same way, 77 vs 71% of pairs; an open-source Qwen recipe improved one passage at a time but showed no clear gain over keyword ranking", "notes": "Write-up at anessbelbati.com blog post i-gave-jev-a-rerankers-job; evidence viewer at anessbelbati.com/lab/jev-reranking", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-research-eval", "name": "jev-research-eval", "author": "jgridifier", "date": "2026-09", "category": "benchmark", "url": "https://github.com/jgridifier/jev-research-eval", "base_model": "", "params": "", "metric": "Full end-to-end reproduction checklist in REPRODUCE.md; cases extracted from qc_rescored.json", "license": "Open", "paper": "", "stars": 3, "likes": "", "detail": "11 baseline cases R1-R11 plus human and quant stress suites S1-S10 and QS1-QS8 plus QS7b, CoS-locked QC grades, the v4 HTML field note and research notebooks v1 baseline and v2 baseline-plus-stress with per-step Trace reports. Drives an upstream checkout of browser-use/jev-ultrafast pinned at commit 452c1ad2, Reduce browser round trips and record a 7-second Flights demo, and regenerates the report from fixtures/qc_rescored.json.", "arch": "Reproducible evaluation harness for the Jev ultrafast research-browser session of 2026-09-17", "approach": "", "training": "", "data": "", "result": "Full end-to-end reproduction checklist in REPRODUCE.md; cases extracted from qc_rescored.json by scripts/extract_cases.py", "notes": "MIT, harness only, not a Jev fork; design uses Coinbase-inspired tokens, educational", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-sec-bench", "name": "jev-sec-bench", "author": "Gaurav-Gosain", "date": "2026-09", "category": "benchmark", "url": "https://github.com/Gaurav-Gosain/jev-sec-bench", "base_model": "TypeSafe Jev 1.13.0, 09-16 run, via jev-go", "params": "", "metric": "96.5% injection accuracy; 89.0% vulnerable-pair ranking", "license": "MIT", "paper": "", "stars": 4, "likes": "", "detail": "", "arch": "Two blind security benchmarks built on jev-go: prompt injection on all 662 labelled messages in deepset/prompt-injections, 263 of them injections, and vulnerable code on 200 matched pairs from CyberNative/Code_Vulnerability_Security_DPO, where both halves of a pair solve the same task in the same language and style with one carrying a vulnerability, shuffled apart and scored independently, Jev never told the class or that pairs exist; no threshold tuning, plain 0.50 cut; raw per-sample output committed", "approach": "", "training": "", "data": "deepset/prompt-injections, all 662 labelled messages; 200 matched pairs from CyberNative/Code_Vulnerability_Security_DPO", "result": "Prompt injection: 96.5% accuracy, 96.2% precision, 95.1% recall, 95.6% F1, ECE 0.0588, 10 false positives and 13 false negatives of 662, p50 325 ms; with the deployment passed as context versus without: accuracy 89.7–96.5, recall 74.9–95.1, ROC-AUC 0.9846–0.9927, the lesson that a guardrail that only sees the message is guessing at the policy, so pass the deployment as state; the model errs toward under-confidence, every calibration bucket above 0.6 is worse than Jev claims and below is better, and when it says 0.85 it was right every time; vulnerable code: the vulnerable half ranked above its secure twin in 178 of 200 pairs, 89.0%, 100% on deserialization, 93.5% SQL injection, 88.9% command injection, 87.2% buffer overflow, 87.0% code injection, 86.7% XSS, perfect on Python, C# and C++ pairs, weakest on Go and Kotlin at 3 of 5; absolute 71.5% accuracy at 0.50 with ECE 0.1868, understated by noisy synthetic labels", "notes": "Run on 2026-09-16 against jev-1.13.0; a dashboard TUI reads the results back; the pitch: a number your code can threshold, not a paragraph you have to parse; caveats: both corpora are public and may have leaked into training data, the code corpus is synthetic with demonstrably noisy labels, the injection corpus is specific to one German news assistant and skews German, single run with no repeats; MIT.", "domain": "security", "score": "96.5%", "latency": "p50 325 ms", "benchmark": "JevSec-bench", "cost": "", "speedup": ""}
{"id": "jev-secret-detection", "name": "jev-secret-detection", "author": "teyhouse", "date": "2026-09", "category": "benchmark", "url": "https://github.com/teyhouse/jev-secret-detection", "base_model": "Secret scanning", "params": "", "metric": "Default run on a 100-case set with smaller edge and config batches; server p50 75 to 90 ms", "license": "Open", "paper": "", "stars": 3, "likes": "", "detail": "Each test case is sent to Jev as a single Noul question asking whether content contains a real secret credential that someone reading it could use; the returned probability is compared with the expected label. Deliberately no regex matching or provider verification, the goal is to score Jev itself. The report shows one row per case with noul, band, round trip and server time, then accuracy at a 0.5 threshold, AUC, recall, precision, mean noul for secrets and non-secrets, Brier score, review band 0.3–0.7 share versus confident right or wrong, per-category accuracy, and latency mean, p50, p95 and max for round trip and server time.", "arch": "Measures how well Jev spots real secret credentials in file snippets", "approach": "", "training": "", "data": "Secret datasets", "result": "Default run is the 100-case set, with edge and config batches smaller; server p50 measured 75–90 ms, matching TypeSafe docs saying most queries complete in ~100 ms; API runs in AWS us-west-2", "notes": "Server time comes from the undocumented x-envoy-upstream-service-time response header, so the line disappears if TypeSafe drops it", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-spam-eval", "name": "jev-spam-eval", "author": "bitnovus", "date": "2026-09", "category": "benchmark", "url": "https://github.com/bitnovus/jev-spam-eval", "base_model": "Spam classification", "params": "", "metric": "Phishing detection on 853 messages from 2024-25: enriched Jev 95.31% versus 75.26% for a regression model given the same evidence and trained on 9,033 older messages", "license": "Open", "paper": "", "stars": 3, "likes": "", "detail": "Jev reached 98.64% on a 5,733-email test without task-specific fine-tuning and without labeled examples in its requests; a TF-IDF logistic regression classifier trained on ~4,600 labeled messages per fold reached 98.87% with the same evidence. With the question and category definitions unchanged, adding link destinations, Reply-To and attachment metadata raised accuracy from 93.62–97.98%; a further wording change brought it to 98.64. The context-only change raised phishing recall from 85.71–98.43% while legitimate messages incorrectly flagged as phishing stayed at one.", "arch": "Zero-shot ham, spam and phishing email classification with Jev using written category definitions and email context", "approach": "", "training": "", "data": "Spam datasets", "result": "Extended beyond the main set: on 853 phishing messages from 2024-25, enriched Jev caught 95.31% vs 75.26% for regression given the same evidence and trained on 9,033 older messages", "notes": "Zero-shot here means Jev weights were not updated; the main enrichment result needed no training, no labeled demonstrations, no hand-written phishing indicators", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevals", "name": "Jevals", "author": "jevals.com", "date": "2026-09", "category": "benchmark", "url": "https://github.com/openlayer-ai/jevals", "base_model": "", "params": "", "metric": "All evals for an agent trace go out as one Jev request costing a few thousandths of a cent", "license": "Open", "paper": "", "stars": 86, "likes": "", "detail": "Built-in checks include ToolChoice, UsedToolResult, Grounded and StayedInScope for agent traces, AnswerRelevancy and Completeness for quality, and IndirectInjection and PHI for security; the quickstart's eight checks ran as one HTTP request with 1,388 tokens at 0.00006 USD in 0.33 s, with per-check probabilities such as tool_choice correct at p=0.99 and indirect_injection passed at p=0.03. Messages are OpenAI chat format, with Anthropic content blocks and LangChain message objects accepted directly. pip-installable, key via AI_GATEWAY_API_KEY, TYPESAFE_API_KEY or OPENROUTER_API_KEY.", "arch": "Evals and guardrails for agents using Jev-style decision models instead of an LLM judge: all the evals for a trace go out as one request that costs a few thousandths of a cent and comes back in a few hundred milliseconds, so you can run them on every trace and inside the agent loop, not just on a sample. Works with Jev through the TypeSafe or Vercel APIs, with Kev or Laya running locally on a Mac, or with a regular chat LLM if that is all you have, slower and costing more.", "approach": "", "training": "", "data": "", "result": "Public Jev evaluation pages", "notes": "Gallery-listed 'Jevals evaluation site.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "legalforecastbench", "name": "LegalForecastBench", "author": "johnhughes3", "date": "2026-09", "category": "benchmark", "url": "https://github.com/johnhughes3/LegalForecastBench", "base_model": "", "params": "", "metric": "Micro-Brier, equal-case Brier, uncertainty, accuracy and actual versus standard-rate inference cost; official report compiling", "license": "Open", "paper": "", "stars": 7, "likes": "", "detail": "arch: Jev-style decisions on case facts", "arch": "Tests whether frontier models can forecast federal motion-to-dismiss rulings from the judge's written record: the prediction unit is the claim-defendant pair, the base metric is micro-Brier over prediction units with intervals clustered at the coarsest defensible level; eligible decisions follow each model's latest first external deployment, known outcome leakage is excluded, and official models run without web access; the 91-case Cycle 1 benchmark is complete for GPT-5.6 Luna and Meta Muse Spark 1.3, with community harness comparisons kept as a separate non-official track", "approach": "", "training": "", "data": "Legal case data", "result": "The official report is compiling and validating, with preliminary model rows withheld; the published card commits to micro-Brier, equal-case Brier, uncertainty, accuracy, and actual versus standard-rate inference costs", "notes": "Prediction, unlike analysis, has objective ground truth, and outcome forecasting is the task clients pay senior counsel for; a publication-governance document controls when rows go public", "domain": "legal", "score": "", "latency": "", "benchmark": "LegalForecastBench", "cost": "", "speedup": ""}
{"id": "padflow-jev-evals", "name": "padflow-jev-evals", "author": "zsavage8", "date": "2026-09", "category": "benchmark", "url": "https://github.com/zsavage8/padflow-jev-evals", "base_model": "", "params": "", "metric": "Accuracy of any OpenAI-compatible model, the share where decision matches the label, plus calibration at the default 0.90 auto-post threshold", "license": "Open", "paper": "", "stars": 2, "likes": "", "detail": "PadFlow handles schedules, budgets, draws, lot takedowns, document intake and QuickBooks Online sync; most of the intelligence it needs is a typed decision with a confidence score the software acts on: post it, or send it to a human. Three decisions published as JSON schemas with a labeled anonymized example set: route_document, which project and what kind of document; code_transaction, which budget line and draw a QuickBooks transaction belongs to; classify_import_value, what a value read from an imported schedule or budget page is. Each schema has the same shape, an input object, a decision enum and a confidence in 0–1; PadFlow posts automatically above a threshold and queues the rest for a human.", "arch": "A small public benchmark of the typed decisions PadFlow makes inside its own software: a SaaS platform for land development companies that publishes those decisions as JSON schemas, a labeled set of anonymized examples and a script that scores any OpenAI-compatible model; each decision carries a confidence score the software acts on, post it or send it to a human", "approach": "", "training": "", "data": "Workflow data", "result": "Scores any OpenAI-compatible model on accuracy, the share of examples where decision matches the label, plus calibration, the accuracy of the examples the model marked above the auto-post threshold, default 0.90; the threshold is a product decision, the model job is to be calibrated", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "adecider", "name": "adecider", "author": "Agents365-ai", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/Agents365-ai/adecider", "base_model": "Local Laya checkpoint via laya-mlx, TypeSafe Jev, or an OpenAI-compatible endpoint", "params": "", "metric": "Typed noul, choice and score decisions", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Typed System One decisions for any coding agent: one judge core shared by a CLI, MCP tools, an HTTP /decide endpoint and a pi extension that adds three tools plus event hooks; backends are the local Laya checkpoint through laya-mlx, TypeSafe Jev, or an OpenAI-compatible endpoint as an escape hatch; named backends are hard constraints and the chain defaults to local; calibration is reported as absolute", "approach": "", "training": "", "data": "", "result": "Sample decision 51.5 ms with 154 input tokens and 0 output tokens", "notes": "Bilingual English and Chinese documentation", "domain": "coding", "score": "", "latency": "51.5 ms sample", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nli-server-antaeus", "name": "Antaeus NLI server", "author": "antaeusio", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/antaeusio/nli-server", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Small System One server that answers yes/no policy questions on CPU with an open NLI model; lets the Antaeus orchestrator evaluate semantic policy rules without sending inputs to a hosted AI provider", "approach": "", "training": "", "data": "", "result": "Experimental status: accuracy on policy conditions not measured on a real evaluation set; on a small internal check with synthetic marketplace listings it answered 11 of 16 rule checks correctly, and text inside an input can steer its answer, so gate low-confidence answers to human review", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mfang0126-perplexity-web-bridge", "name": "Chat with Perplexity,  use Perplexity without an API key", "author": "mfang0126", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/mfang0126/perplexity-web-bridge", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "One command runs a whole task autonomously and writes a receipt for every step. The decision loop is site-agnostic and inspired by jev-ultrafast: snapshot, numbered element table, one tiny model call picks the operation and target, deterministic act, bounded wait, repeat until an independently verified DONE; the big model never sits inside the loop. Needs a free TypeSafe Jev decision key; the escalation answerer takes any OpenAI-compatible LLM key, optional.", "arch": "Unofficial open-source agent skill that drives the Perplexity web UI, search, click, wait, extract, with your existing logged-in session and no Perplexity API key", "approach": "", "training": "", "data": "", "result": "Measured on the Xiaomi MiMo 2.6 build: end-to-end 24.5 min to 77 s, 19.1× vs a human-in-the-loop baseline; escalations answered in 2–3 s vs 144–302 s", "notes": "Fail-closed JSONL evidence chain records what was decided, why a gate rejected a decision, which click rung fired, and the final answer verification; not affiliated with or endorsed by Perplexity AI", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decisionsmith", "name": "decisionsmith", "author": "izam-mohammed", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/izam-mohammed/decisionsmith", "base_model": "LLM teacher, Laya student by default", "params": "Laya 421M student", "metric": "Per-field shadow agreement, sure rate, accuracy when sure", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Use and fine-tune System One models such as Jev and Laya on your data, start with an LLM, end with a fast decision model you trained, one line in between: a harness takes a normal Pydantic model, Literal and Enum map to pick-one, bool to yes-no, and an annotated Scale to a rating; it starts with the LLM, logs every answer to decisions.db, fine-tunes the fast model on those answers and moves traffic to it field by field once the numbers say it is ready; four modes per field: teacher, the LLM answers on day 0; shadow, the LLM answers while the student runs silently and is measured; cascade, the default, the student answers when it is sure and the LLM otherwise with 5% of sure answers audited; student, the student answers everywhere; h.adapt calibrates the student confidence and picks safe thresholds in seconds on any engine; h.finetune only switches if it beats the current student; if an engine fails the other answers and the result says sure=False; guardrails include answers validated against the schema, the text fenced as data, an engine-failure fallback, and a per-field confidence threshold plus a 5% audit before any field moves", "approach": "fine-tune", "training": "LLM-logged answers", "data": "User tasks through the harness", "result": "Example status: team field in shadow mode, student agrees 94%, sure on 71%, accuracy when sure 97% versus teacher, ready for cascade; wants_refund agrees 81% with 420 labelled examples, ready to fine-tune; motivation: Laya is weak zero-shot, 0.36 accuracy on typed-decisions before fine-tuning versus 0.77 after per Laya benchmarks, while LLMs are good zero-shot but slow and paid per call", "notes": "uv add decisionsmith all", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "divinci-ai-divinci-table", "name": "divinci-table", "author": "Divinci-AI", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/Divinci-AI/divinci-table", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "harness/ simulates four-player games with a rules engine, seat agents, the paired comparison and a replay viewer, with every result in harness/README.md; table/ is the real-table server, a webcam scan pad for the AI's hidden hand plus open-mic voice, fully offline on a MacBook with Whisper for hearing, Gemma 4 on Ollama for routing and in-character replies, and macOS voices for speaking, about 0.9 s from speech to reply. Game logs stay in a private R2 bucket rather than git, 179 MB for one day of runs, restored by scripts/fetch-games.sh; every result regenerates through paired.py, with the rules engine and deck shuffles deterministic per seed. Roadmap covers phones, the scan pad, Jetson AGX Orin 64 GB, and Divinci Releases as player personalities.", "arch": "AI player at a real four-player Commander, Magic the Gathering, table, where the model plays one typed decision at a time: deterministic code knows the rules and lists the legal moves, and a System One model picks one and says how sure it is, pluggable across TypeSafe's jev-latest, a self-hosted DiffusionGemma-Jev nicknamed djev, and open models through open-alternative-jev.", "approach": "", "training": "", "data": "", "result": "As of 2026-09-24, jev-latest plays at roughly heuristic strength: 6 of 32 wins, outlasted the heuristic in 11 of 32 paired seats and was shorter in 8, 0 invalid answers, about 0.35 s per decision. so1 plus Qwen3.5-4B is not a table opponent: 2 of 32 wins, lasted longer than Jev in 3 of 32 pairs, and passes 66% of turns where it could cast a spell. The offline table server passes 36/36 session and 11/11 real-browser checks, with a socket watch proving no connection leaves the machine.", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gatelaya", "name": "GateLaya", "author": "Diwas2055", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/Diwas2055/gatelaya", "base_model": "Laya multilingual", "params": "Laya 421M", "metric": "33 ms forward pass gate on pre_call and post_call", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Multilingual LLM firewall as a LiteLLM Proxy CustomGuardrail: a Laya forward pass in 33 ms gates pre_call and post_call traffic, blocking prompt injection, PII, toxicity and secret leakage with a mask-or-block policy and an audit log; on-prem, 100+ languages, Apache 2.0", "approach": "", "training": "", "data": "", "result": "33 ms Laya forward pass per gate", "notes": "", "domain": "security", "score": "", "latency": "33 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "punkcanyang-hermes-jev-router", "name": "Hermes × Jev", "author": "punkcanyang", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/punkcanyang/hermes-jev-router", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "Hard rules: TypeSafe direct connect only, via the TYPESAFE_API_KEY environment variable; Vercel AI Gateway is forbidden; keys never enter the repo or get printed. Installer symlinks the plugin into the Hermes plugins directory, copies config.example.yaml, enables plugins.entries.jev-router and writes the context engine to trim_compress. Requires the Hermes CLI, a configured main model, and typesafe_sdk importable in the Hermes Python, with a venv or site-packages path override.", "arch": "Adds two things to the Hermes Agent: per-turn model routing by TypeSafe Jev, simple tasks to the cheap model and complex, tool-heavy or long-context ones to a stronger model with fallback to the main model on failure or low confidence; and a trim-then-compress context engine that forbids compressing without trimming first", "approach": "", "training": "", "data": "", "result": "", "notes": "Bilingual README, English and Chinese", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jeba", "name": "jeba", "author": "munod", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/munod/jeba", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Local-first, multilingual System One decision engine that speaks the TypeSafe Jev /v1/systemone protocol: answers atomic choice, score and noul questions with typed values, probabilities and calibrated confidence; point an existing Jev client at a jeba server and it works unchanged; local encoder backend for fully offline inference", "approach": "from-scratch", "training": "", "data": "", "result": "v0.2.0 released; milestones M0 through M6 complete plus B-1 multilingual quality work with localized data and per-primitive, per-language temperature calibration", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "slateeho-jelm", "name": "Jelm", "author": "slateeho", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/slateeho/jelm", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Understood risk signals include a new public hostname changing DNS, TLS or Gateway routing, preferred and required scheduling affinity changes that alter placement concentration or make workloads unschedulable, namespace moves, cross-namespace references, ReferenceGrant and ServiceAccount changes, cluster-scoped changes, estimated user blast radius and peak or holiday traffic timing, Gateway listener and HTTPRoute changes with unresolved references, backend health and canary weight, Vault token and AppRole rotation requirements around database work, database primary and replica health, connection failures, PostgreSQL WAL write lag, rollout failures, 5xx regression and p95 latency regression. Hard system failures such as Helm not rendering, the Kubernetes API being unreachable, image pull failures, Vault being sealed, the database primary being unavailable, or critical WAL lag of 30 s or more or 1 GiB or more produce hold in system_failures, which Jev never overrides downward.", "arch": "Jev-powered automated Helm release risk estimator for Kubernetes CI/CD: it estimates the risk of what a Helm release changes, not just whether helm upgrade returned zero, combining semantic release-diff signals with live Kubernetes, Gateway API, Vault, database and rollout evidence, then applies a deterministic safety floor before asking Jev for a typed judgment. A model can escalate risk; it cannot weaken a hard safety rule.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sprah-jev-monid-poc", "name": "Jev + Monid POC", "author": "sprah", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/sprah/jev-monid-poc", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Three small proof-of-concept scripts combining Jev, the TypeSafe AI typed decision model accessed via the Vercel AI Gateway, with Monid, a pay-per-call API marketplace. Jev makes a fast, typed decision; Monid executes the paid call", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-daboolu", "name": "Laya Chinese slim fork", "author": "Daboolu", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/Daboolu/laya", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Chinese slim fork of Laya that keeps only Python inference, language routing and the command line; no text generation, answers choice, score and noul; uv-based reproduction, the public checkpoint auto-downloads on first run, CUDA notes for PyTorch", "approach": "modified", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "beremaran-laya-docker", "name": "Laya in Docker: Jev-compatible HTTP server", "author": "beremaran", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/beremaran/laya-docker", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "This repository builds and runs Laya as a GPU-backed HTTP server that answers typed decision questions on the same POST /v1/systemone protocol as TypeSafe Jev. A client written against Jev only needs its base URL changed; the request and re", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-compact", "name": "laya-compact", "author": "AxeForging", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/AxeForging/kompact", "base_model": "Local Laya checkpoint replacing hosted Jev", "params": "Laya 512-token English checkpoint", "metric": "Verbatim-need score per tool call before compaction", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Context compaction that scores every tool call before it compacts, keeps what is still needed verbatim and drops the rest; local, offline, no API key; a fork of fast-jev-compaction for Claude Code and jev-compact for Codex CLI, which do the same job with TypeSafe hosted Jev, re-pointed at a local Laya sidecar; Laya speaks Jev wire protocol so the client swap is one line", "approach": "", "training": "", "data": "1063 tool calls from 18 real sessions, labelled behaviourally", "result": "Two measurements changed the design: Laya English checkpoint reads 512 tokens total, about 320 usable as state, and over-long states are truncated silently with HTTP 200 and no warning; a decisive sentence alone scores 0.611, the same sentence padded to 4278 characters scores 0.4113, and padding to 21078 characters scores bit-identical 0.4113, below any sane threshold for a fact stated verbatim; the state was inverted to one small prose state per tool call sized to the checkpoint budget, and every number is turned into words first because Laya cannot read digits; with that fixed, 1063 tool calls from 18 sessions were labelled by behaviour, whether the assistant later reproduced a distinctive run of eight words from an output, and on that task the decision model loses to a logistic regression", "notes": "Full measurements at axeforging.github.io/laya-compact. Repo renamed laya-compact to kompact, same owner; URL updated 09-26.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-gateway-aios", "name": "laya-gateway", "author": "aios-b-612", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/aios-b-612/laya-gateway", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Local LLM gateway that runs on a developer laptop, inspired by jev-gateway: when a coding agent is about to decide which tool to call, the gateway asks Laya, the open-weight System One model, instead of spending the expensive LLM on that one choice; everything else goes to the usual model untouched; Portuguese and English docs", "approach": "router", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "linnaeus", "name": "Linnaeus", "author": "pi-dal", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/pi-dal/Linnaeus", "base_model": "Qwen3.5-2B plus LoRA with vision input", "params": "2B", "metric": "Held-out macro accuracy across 26 datasets; NLL and ECE before and after calibration; warm RTX 4090 p50 and p95 latency and decisions per second", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "CUDA-optimized small multimodal decision models. Forked from an AMD ROCm research recipe and retargeted to NVIDIA servers: torch and torchvision pinned to cu128 builds via a dedicated package index, FLA Triton kernels run unchanged, Triton causal convolution enabled on all backends where upstream gated it to HIP, SDPA dispatches to FlashAttention or cuDNN, configurable VRAM allocator cap through LINNAEUS_VRAM_FRACTION, GPU telemetry auto-detects the DRM device and falls back to NVML; Linux and CUDA only, macOS not supported; toolchain managed with mise on Python 3.12. Give the model a message, a document or an image and ask it to choose, judge or score: it returns probabilities in a single forward pass with multiple questions sharing the same input; the Python toolkit covers training, inference, evaluation and agent integration and the model runs locally on a consumer GPU; weights at pi-dal/Linnaeus-0.1.0-2B on Hugging Face", "approach": "modified", "training": "RLCD plus auxiliary cross-entropy; temperature calibration on an independent partition after LoRA merging", "data": "26 held-out datasets, 129k rows", "result": "Linnaeus-0.1.0-2B, seed 42 update 2800, merged LoRA BF16 with fused operations and shared-prefix parallel candidate scoring, 2–128 candidates, 4096 tokens per question: held-out macro accuracy 80.78%; sms_spam 99.37% on 794 rows, mail_spam and mail_phishing 98.95, clevr_attribute 98.92 on 53734, clevr_exist 98.58, wikiqa 96.17, scienceqa 92.66, ag_news 90.04 on 7600, aokvqa 83.66, xnli_en 87.20, xnli_zh 78.20; the weak spots are screenqa_choice at 22.05%, esci_us 57.64, esci_es 60.26, banking77 73.70 and typed_decisions 73.05, and calibration does not always help: ECE worsens on banking77 from 0.0275–0.2001, on typed_decisions from 0.1073–0.2750 and on xnli_en from 0.0307–0.0601; warm RTX 4090 end-to-end p50 latency 44.53 ms for one text question at 22.4 decisions per second, 47.81 ms for three text questions at 62.9 per second, 49.88 ms for one image question, 100.83 ms for three image questions", "notes": "", "domain": "multimodal", "score": "", "latency": "p50 44.5 ms one text question on a warm RTX 4090", "benchmark": "26 held-out datasets", "cost": "", "speedup": ""}
{"id": "s1-chess", "name": "S1-Chess", "author": "Talles64", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/Talles64/S1-chess", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A System 1 chess player: Laya picks each move in a single forward pass, no search, no text generation; python-chess enumerates legal moves as choices and Laya returns a probability per move; fine-tuned on 64,000 positions from games between 2300+ players in about 80 minutes on a laptop GPU; code only, no public weights", "approach": "fine-tune", "training": "", "data": "", "result": "On 7,763 held-out test positions the 80k fine-tune matches the human move 24.5% overall and 45.5% within its top three, versus 4.5% for untrained Laya and 5.0% for a random legal move; for reference Maia matches around 50% without search", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "semantic-validator", "name": "Semantic Validator", "author": "eduardoArequipa", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/eduardoArequipa/semantic-validator", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Self-hosted semantic text validation alongside format and required-field checks: a Go REST API plus TypeScript, Python, Go and Java SDKs; Jev performs the semantic inference with your own key, BYOK, while the server owns the API contract, reusable rules, API keys, quotas and an in-memory cache; each SDK also ships a direct client that calls Jev without running the server", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "snapjudge-cyrax", "name": "snapjudge, C++17 engine", "author": "Cyrax321", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/Cyrax321/snapjudge", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Fast, non-autoregressive System-1 decision engine written in self-contained C++17: tokenizer, transformer encoder, typed decision head, routing, HTTP serving and training in one codebase; a state plus choice, score or noul questions answered in a single bidirectional encoder forward pass with mathematically calibrated probabilities; never generates text", "approach": "from-scratch", "training": "", "data": "", "result": "", "notes": "Same project name as the separate micha0827/snapjudge entry.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "thinkless", "name": "ThinkLess", "author": "inboxpraveen", "date": "2026-09-25", "category": "runtime", "url": "https://github.com/inboxpraveen/ThinkLess", "base_model": "rules, GLiNER 2.5, Laya, Jev, any Hugging Face classifier, any LLM", "params": "", "metric": "Success rate, billed cost per 1000 tickets and decision time on 53 labeled support tickets; Banking77 cascade accuracy", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Open-source decision plane for AI agents: the routine judgments an agent makes, what the user wants, whether they asked for a person, which order they mean, whether a message is trying to manipulate the system, are answered by rules and small calibrated models in milliseconds, and the LLM is kept for writing, planning and the decisions the small models are not sure about. Model-neutral cascade: rules, GLiNER 2.5, Laya, TypeSafe Jev and any server speaking its System One API such as Kev and OpenJev, any Hugging Face classifier, and any LLM local through Transformers, Ollama or vLLM or hosted through OpenRouter, Anthropic or OpenAI; cheapest provider first, escalate only when the answer confidence falls below a per-question per-provider threshold set from data; your code reads a typed decision and never needs to know which model produced it; every step is traced with the plane that handled it, its confidence, latency, tokens and cost, with a trace viewer; the whole stack runs offline on a laptop GPU with no API key; pip install thinkless local, on PyPI as thinkless", "approach": "rules", "training": "", "data": "53 synthetic support tickets, separate calibration set, Banking77 500", "result": "Same agent, same 53 labeled support tickets, LLM plane versus hybrid plane: with Qwen 3.7 Flash success 92.5–98.1%, billed per 1000 tickets $0.034 to $0.019, decision time 1.27 s to 0.58 s; Gemini 2.5 Flash Lite 98.1–98.1, $0.104 to $0.059, 1.17 s to 0.76 s; GPT-5.6 Luna 98.1–100, $0.235 to $0.141, 2.50 s to 1.18 s; Claude Haiku 4.5 98.1–98.1, $1.338 to $0.788, 2.21 s to 1.34 s; local Qwen3-1.7B 86.8–94.3, free, 1.84 s to 0.43 s. Hybrid matched or beat the LLM-only design on every model at 40–44% lower billed cost and 35–55% less time spent on decisions; 87% of hybrid decisions never reached the LLM. Rules and GLiNER found every order number on every run while Qwen 3.7 Flash and the local 1.7B model missed ones written without the word order. On Banking77, 77 intents, 500 examples: GLiNER alone 71.8%, Qwen 3.7 Flash alone 73.8, cascade 74.8, Claude Haiku 4.5 alone and cascade both 76.2, each time a quarter of the calls reaching the LLM. Nothing was tuned on the test tickets; thresholds and every fix were validated on a separate calibration set first; the 53 tickets are synthetic, evidence of the mechanism and its failure modes, not a leaderboard", "notes": "", "domain": "agent", "score": "", "latency": "0.43–1.34 s decision time hybrid vs 0.58–2.50 s LLM-only by model", "benchmark": "53 support tickets + Banking77", "cost": "40–44% lower billed cost vs LLM-only", "speedup": "35–55% less decision time"}
{"id": "hraness-algal", "name": "algal", "author": "hraness", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/hraness/algal", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "The bet is that a computer can accumulate tested ways of acting, not only produce new answers or code: a model proposes a bounded, executable procedure, which is checked, measured on declared cases, kept with its evidence, composed into larger procedures, and revised under rules the host sets. Four design properties give it a testable form: an organism, an ALGAL program, has a content-derived identity and declared interface so successful reasoning becomes a reusable component; manifests are data built to be generated and checked, so hand-written and model-proposed procedures enter through the same admission path; model judgment sits in typed cells with declared context and budgets, so ordinary computation supplies the discipline around it; and selection is a host decision recorded on the receipt, so provenance and permission are part of the mechanism rather than features added later. Preview v0.2.0-vm.9 for macOS on Apple silicon and Linux x86_64; the Bun runtime runs from the checkout.", "arch": "ALGAL is a programming language and VM for AI agent programs that wait, resume and replay: a program can wait for a person's approval, pick up after a crash, and replay what it did from its receipts, while the host application chooses the tools and permissions and the program declares its decisions, limits and approval points. A native Rust CLI and a Bun runtime that runs on its own implement the same specifications for programs, receipts, and durable processes.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ask-jev", "name": "ask-jev: Jev System One API as an MCP server", "author": "Aether-254", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/Aether-254/ask-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Exposes the Jev System One API as an MCP server and packages it as a Codex or Claude plugin; three tools: jev_evaluate for one or more typed questions about one state, jev_batch_evaluate to apply one question set to many independent states with bounded concurrency, and jev_ping to verify endpoint, credentials, model and round-trip latency; supports the choice, score and noul primitives with noul as a probability in 0–1; defaults target the official api.typesafe.ai endpoint with model alias jev-latest and are overridable via environment variables", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "assay", "name": "Assay", "author": "patkusch", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/patkusch/assay", "base_model": "Ollama-served model, benchmarked on gemma3 4B", "params": "4B benchmark", "metric": "order-rotation debias + temperature calibration", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: pure Python stdlib; reads option probabilities in one shot via Ollama; notes: order-rotation debiasing, temperature calibration, abstention; benchmark gemma3 4B on M5 laptop", "arch": "Pure-Python standard-library runtime: asks an Ollama-served model for option probabilities in one shot, then debiases by rotating option order, calibrates temperature, and supports explicit abstention", "approach": "logits", "training": "", "data": "", "result": "Benchmarked on gemma3 4B on an M5 laptop: order-rotation debiasing and temperature calibration applied to single-shot probability reads", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "calibrex", "name": "Calibrex", "author": "huzjie", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/huzjie/calibrex", "base_model": "any model via OpenAI-compatible / vLLM / Transformers backends", "params": "varies", "metric": "ECE/MCE/Brier/NLL/AUROC", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "notes: Chinese-language framework, zero dependencies, standard library only; permutation scoring, calibration, temperature, Platt, isotonic, histogram, Beta, label/prior bias correction, confidence gating; Docker/K8s", "arch": "Zero-dependency Chinese-language calibration framework for decision models: permutation-based scoring, five calibration schemes, temperature, Platt, isotonic, histogram, Beta, label and prior bias correction, and confidence gating, with OpenAI-compatible, vLLM and Transformers backends plus Docker/Kubernetes deployments", "approach": "calibration", "training": "", "data": "", "result": "Metrics toolkit: ECE, MCE, Brier, NLL and AUROC reported for any calibrated decision pipeline", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chronos", "name": "chronos", "author": "apuravmanhas", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/apuravmanhas/chronos", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "Positioned against the three usual options: hardcoded rules that are fast and deterministic but brittle, LLM calls that are flexible but unpredictable and hard to audit, and manual review that is accurate but does not scale. MVP, not yet published to npm; requires pnpm and a JEV_API_KEY, and ships a fintech refund-guard demo server at examples/fintech-risk/server.ts. Apache-2.0.", "arch": "Experimental TypeScript runtime for probabilistic semantic decisions: a model proposes a decision, but deterministic code decides whether it actually executes. One call runs pre-guards of deterministic PII and limit checks, policy resolution of the typed questions and prompts, a provider that can be Jev, plain rules, or escalate, routing on confidence that acts, falls back, or escalates, post-guards on safety bounds, and a hash-chained append-only audit log, ending in action, fallback, or escalation.", "approach": "", "training": "", "data": "", "result": "", "notes": "thin card; details in the repo", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "parswanadh-laya-lab", "name": "Coordination lab: long-context decision engine research", "author": "Parswanadh", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/Parswanadh/laya-lab", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Multi-agent coordination lab for long-context decision-engine research on the NandhaKishorM/laya fork: hypotheses H1-H5 under a frozen protocol; a 35 KB LEDGER.md of verified claims where a claim is promoted only after a REPRODUCED verdict from a verifier who did not produce it plus a failed cross-verifier attack; status vocabulary: candidate, reproduced, falsified, unresolved, refuted-by-us; first entry documents the upstream max_len=1024 and head_max_len=256 for laya-multilingual", "approach": "", "training": "", "data": "", "result": "", "notes": "3.7 MB repo with AGENTS.md, BASELINE.md, HANDOFF.md, plan.md, protocol.md, progress.md, experiments and findings directories; pushed 2026-09-25", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "devnolife-copilot-jev", "name": "copilot-jev", "author": "devnolife", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/devnolife/copilot-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "The jev skill gives any coding agent the full TypeSafe API context: the three primitives, their hard limits, the fan-out pattern, confidence thresholds, and the Noul trap of having no confidence field. Installable as a Claude Code plugin from the marketplace or via npx skills for agents such as GitHub Copilot, project-local by default with a global flag. Jev complements rather than replaces Copilot CLI: language work stays in the LLM and recurring decisions move to a model designed for them.", "arch": "Indonesian-language documentation and working implementation of TypeSafe Jev as the decision partner for GitHub Copilot CLI: the LLM does the research, planning, writing and code-editing, Jev takes routing, scoring, approval and escalation at 0.042 USD per million tokens with millisecond latency, and code executes the decisions. Ships a task router, an MCP server for Copilot CLI, an agent skill, measured demos, and automated tests.", "approach": "", "training": "", "data": "", "result": "Measured locally on jev-1.13.0, 2026-09-24: one research-to-write-to-review flow took 5 calls at 0.000221 USD with 374 ms median latency; cost per decision is 0.0000099 USD, or 0.099 USD per 10,000 decisions; fan-out ran 2.4× cheaper and 1.6× faster than separate calls.", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cygnet", "name": "Cygnet", "author": "blockbrain-ai", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/blockbrain-ai/cygnet-recipe", "base_model": "frozen Gemma-4-12B-it + stock vLLM 0.30.0", "params": "12B", "metric": "87.9% on JevBench public 231 items, 203/231", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "arch: one token per decision, the option letters; metric: 203/231 = 87.9% on JevBench public, easy 48 of 48, standard 70 of 72, hard 85 of 111; notes: p50 66 ms A6000 / 50 ms L40S; calibration temperature 3.4; no training", "arch": "Frozen Gemma-4-12B-it served by stock vLLM 0.30.0 with zero fine-tuning: each decision is answered by a single option-letter token, with one fitted calibration temperature, 3.4, applied to the logit", "approach": "logits", "training": "", "data": "No training; JevBench public split only", "result": "203 of 231, 87.9%, on the JevBench public split, easy 48 of 48, standard 70 of 72, hard 85 of 111; p50 66 ms on A6000, 50 ms on L40S", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zjucqr-depthjev", "name": "DepthJev: Turning Depth into Text for Embodied Navigation", "author": "ZJUCQR", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/ZJUCQR/DepthJev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": "", "detail": "Pipeline: perceive with DA3 metric depth and OWLv2 target detection, describe as text facts about free space, target distance and movement constraints, act with Jev selecting one of eight navigation actions, repeating with the next observation. Python 3.11, Apache-2.0, bilingual English and Chinese README. Depends on EmbodiedBench and ByteDance-Seed Depth-Anything-3 checkouts.", "arch": "Navigation agent for EmbodiedBench EB-Navigation that bridges raw visual input and text-based reasoning: Depth Anything 3 converts RGB frames to metric depth, OWLv2 detects the target, geometric constraints and action history become short text facts, and Jev reads those facts to output one of eight navigation actions. Jev reasons over distances in metres, not pixels, so free space in five sectors, the collision check for the next 0.25 m step and the target distance all come from monocular metric depth.", "approach": "", "training": "", "data": "", "result": "Succeeds in 46.7% of all 300 EB-Navigation episodes; 0.76 s per step on one H100, with Depth Anything 3 at 0.15 s, OWLv2 at 0.22 s and Jev at 0.34 s.", "notes": "", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "iamtalha-ex-typesafe-ai", "name": "ex_typesafe_ai, Elixir", "author": "iamtalha-arshad", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/iamtalha-arshad/ex_typesafe_ai", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: unofficial Elixir client for the TypeSafe AI API; notes: typed structs, Req-based HTTP with retries, ergonomic named arguments", "arch": "Idiomatic Elixir client for the TypeSafe AI API: typed structs, Req-based HTTP with retries and ergonomic named arguments", "approach": "", "training": "", "data": "", "result": "", "notes": "Unofficial, community-maintained client, not affiliated with TypeSafe AI; mix dependency ex_typesafe_ai 0.1, TYPESAFE_API_KEY in the environment", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "touhidsiddiqueeraj-bit-hey-laya", "name": "Hey Laya", "author": "touhidsiddiqueeraj-bit", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/touhidsiddiqueeraj-bit/hey-laya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Local-first voice assistant: hold a key, say Hey Laya, or just type - music, volume, apps, dark mode, timers and system power are all decided on-device by the Laya reasoning model and spoken back with Kokoro TTS; zero API keys, zero cloud unless you paste one in Settings", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lasimeri-intel-phi-jev", "name": "Intel Phi Jev: xks", "author": "Lasimeri", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/Lasimeri/Intel-Phi-Jev", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Local implementation of TypeSafe Jev contract, the first System One model: send a state and typed questions, Noul, Choice, Score, and get back typed answers with probability distributions and confidence, never generated text; xks serves the same wire format, POST /v1/systemone, so mjev and the official SDKs work against it; open-weight LLM, default Qwen3.8-35B-A3B, read by its next-token distribution, arithmetic on two Xeon Phi 3120 cards", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ezeike-intents", "name": "intents, design notes", "author": "ezeike", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/ezeike/intents", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: design notes + reference sketch in Go: route free-text user input to app actions via Laya/Jev-class decision models instead of an LLM tool-call loop; notes: core idea - closed enumerable action space, so a calibrated classifier suffices", "arch": "Design notes with a Go reference sketch: route free-text user input to app actions with a Laya/Jev-class decision model instead of an LLM tool-call loop, because the action space is closed and enumerable - a calibrated classifier is enough", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bigknoxy-janus", "name": "Janus, two-faced coding engine", "author": "bigknoxy", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/bigknoxy/janus", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "arch: two-faced coding engine - System 1 decides, a calibrated non-generative decision model, Laya, System 2 generates, a compact local LLM, deterministic code guards between; notes: runs on a laptop, no cloud; Apache-2.0; one-line install/uninstall", "arch": "Two-faced coding engine: System 1, a calibrated non-generative decision model, Laya, makes the decisions, System 2, a compact local LLM, generates the code, with deterministic code guards between; everything runs on a laptop, no cloud, one-line install and uninstall", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wnoronha-jeb", "name": "jeb, Rust CLI", "author": "wnoronha", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/wnoronha/jeb", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "arch: Rust CLI for TypeSafe's System One API, the Jev model; notes: designed for scripts, shell pipelines and agent tool calls; MIT", "arch": "A Rust CLI to query TypeSafe AI's System One API, Jev: fast typed decisions for scripts, shell pipelines and agent tool calls without a full LLM workflow; state and questions go in as JSON, choice, score and noul", "approach": "", "training": "", "data": "", "result": "", "notes": "MIT; TYPESAFE_API_KEY required, optional TYPESAFE_MODEL, default jev-latest, and TYPESAFE_BASE_URL, default api.typesafe.ai; cargo install or release build; the repo's CI badge points at typesafe-ai/jeb", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ryancole-jev-dotnet", "name": "Jev for .NET", "author": "ryancole", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/ryancole/jev-dotnet", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "A .NET 10 client for Jev mirroring the HTTP API and the conventions of the official Python and JavaScript SDKs: the same environment variables, defaults, retry policy and error taxonomy; one dependency, Microsoft.Extensions.Logging.Abstractions", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tmbmartell-jev-mcp", "name": "Jev MCP,  julgamento tipado para agentes", "author": "tmbmartell", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/tmbmartell/jev-mcp", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Servidor MCP que expõe o Jev de TypeSafe System One como ferramenta de primeira classe para agentes: Hermes, Claude Code, Cursor, ou qualquer cliente MCP", "approach": "", "training": "", "data": "", "result": "", "notes": "You send a state and a map of typed questions and receive structured answers with probability and confidence that your code uses directly, without prompt and parse; README in Portuguese", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alexpeclub-jev-model-router-claude-code", "name": "Jev Model Router für Claude Code", "author": "AlexPEClub", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/AlexPEClub/Jev-Model-Router-Claude-Code", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "Hook plus Skill für Claude Code: Jev von TypeSafe AI antwortet in unter einer halben Sekunde, welches Claude-Modell eine Aufgabe wirklich braucht; liegt die Stufe unter dem Session-Modell und die Aufgabe in sich geschlossen, delegiert Claude an einen Subagenten mit dem günstigeren Modell; nur der Prompt-Text geht an TypeSafe, max 12.000 Zeichen, fail-open nach 5 s", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "abenojardev-laravel-jev-ai", "name": "Jev PHP SDK and Laravel adapter", "author": "abenojardev", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/abenojardev/laravel-jev-ai", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Thin, typed PHP client for Jev decision requests. The core client has no Laravel dependency in its API; the Laravel adapter adds container binding, config, a facade, HTTP transport, and a network-free fake", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "quintui-jev-use-cases", "name": "Jev use cases", "author": "Quintui", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/Quintui/jev-use-cases", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Demo app for a video about Jev, TypeSafe's \"System One\" decision model. Each page is one segment of the script in idea.md: intent search, small typing interactions, a model picker, response depth, json-render, and Twitch chat moderation", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "plurp911-jev-cli", "name": "jev, independent CLI", "author": "plurp911", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/plurp911/jev-cli", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: independent, unofficial Rust CLI for TypeSafe AI's System One API; notes: fast, secure, Unix-friendly; community project", "arch": "Independent community CLI for the System One API, built fast, secure and Unix-friendly", "approach": "", "training": "", "data": "", "result": "", "notes": "Explicitly independent and community-maintained; TypeSafe, System One and Jev are used descriptively; 20 KB README covering install, usage and flags", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "frederico-kluser-jev-agent-skill", "name": "jev-agent-skill", "author": "frederico-kluser", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/frederico-kluser/jev-agent-skill", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Agent Skill de decisão rápida com o modelo Jev via OpenRouter: state + perguntas tipadas, noul/choice/score, entram e decisões com probabilidades calibradas e bandas de ação saem em milissegundos, sem geração de texto; zero dependências, Node 20+, validação pesada, modo MCP com socket TCP+TLS, eval com accuracy + ECE", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "danielnc-jev-browse", "name": "jev-browse", "author": "danielnc", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/danielnc/jev-browse", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 4, "likes": "", "detail": "", "arch": "Fast, typed browser sub-tasks for coding agents: adds helpers to browser-harness so Claude Code or Codex can hand off a whole website sub-task in one call; each step decided by TypeSafe Jev in a few hundred milliseconds; the agent gets the outcome or a typed reason why it stopped", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "promtengineer-jev-harness", "name": "jev-harness", "author": "PromtEngineer", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/PromtEngineer/jev-harness", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "An agent harness built around a System One model. Pi runs the agent loop, a Gemini model does the work, and TypeSafe Jev makes the small decisions around it: which model to use, which rules to load, whether a tool call may run, and whether", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-loop", "name": "jev-loop", "author": "King4s", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/King4s/jev-loop", "base_model": "Jev decides, Claude Code executes", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Build loop with Jev as decider and Claude Code as executor, Danish README: each turn Jev sets the route as build, test or fix role, done as the probability the goal is reached, and recovery as retry, switch role or give up after a failure; the server runs the checks; an independent subagent reviews and only the review can declare the task complete", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-mcp-server", "name": "jev-mcp by elyal2", "author": "elyal2", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/elyal2/jev-mcp", "base_model": "TypeSafe Decision API, System One, default jev-latest", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "MCP server exposing typed judgment, Choice, Score and Noul, as tools an agent can call directly; Python 3.12+, mcp 2.2+, httpx, pydantic; mypy strict, ruff, uv; built because Amazon Quick skills run in a network-less sandbox without home-directory access", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hazlema-jev-patterns", "name": "jev-patterns", "author": "hazlema", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/hazlema/jev-patterns", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Finding and extracting repeating patterns from noisy sequences using Jev — TypeSafe's System One model that answers typed questions, probabilities, choices, scores, instead of generating text", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "explanable-jev", "name": "Jev-style typed evaluator with explanations", "author": "tanchongmin", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/tanchongmin/explanable-jev", "base_model": "OpenAI gpt-5-mini by default", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "approach: LLM behind a typed decision interface", "arch": "Small local implementation inspired by TypeSafe Jev: submit one state and several typed questions, get constrained answers for choice, score with legend, and true or false; the model is isolated behind a single llm.py module so any provider can be swapped in", "approach": "sdk", "training": "", "data": "", "result": "Runs as a local server on 127.0.0.1 port 8000 reading the API key from a.env file", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "warsang-jev-web", "name": "jev-web, browser runtime", "author": "warsang", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/warsang/jev-web", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: run open-jev- and Laya-shaped typed-decision models in the browser - one state plus typed questions, on-device, no server or API keys; notes: npm package jev-web; default model is the reference open-jev DeBERTa-v3-large ONNX export; choice/score/noul", "arch": "Browser runtime for open-jev- and Laya-shaped decision models: a state plus typed questions answered on-device with no server or API keys, choice/score/noul supported; npm package jev-web ships with the reference open-jev DeBERTa-v3-large ONNX export as the default model", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "thomasgauthier-jev-cpp", "name": "jev.cpp, llama.cpp fork", "author": "thomasgauthier", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/thomasgauthier/jev.cpp", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "arch: fork of ggml-org/llama.cpp adding System One inference in C/C++; notes: README is stock llama.cpp, the fork adds typed-decision serving", "arch": "A llama.cpp fork that adds System One, typed-decision, inference to the standard C/C++ LLM inference stack", "approach": "", "training": "", "data": "", "result": "System One decision serving inside the llama.cpp ecosystem", "notes": "llama-server with the --system-one flag serves an AutoJev classifier at /v1/systemone, e.g. llama-server -m autojev-Q4_K_M.gguf --system-one; --mmproj PATH adds image requests", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevcompat", "name": "jevcompat", "author": "mandu5", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/mandu5/jevcompat", "base_model": "", "params": "", "metric": "MUST compliance, confidence-definition agreement on 204 answers", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "approach: Conformance specification plus tester and proxy", "arch": "A numbered, testable specification for the TypeSafe System One API behind roughly a hundred self-described Jev-compatible servers: 48 requirements, each MUST or SHOULD, each tied to its official source, and where TypeSafe's docs, OpenAPI file and SDKs disagree, which is eight places, which one wins and why; jevcompat test runs the spec against any server and shows the request and the offending bytes for every failure; jevcompat proxy puts a spec-conforming API in front of a server that is not, fixing what can be fixed and refusing loudly what cannot", "approach": "sdk", "training": "", "data": "204 recorded answers across the eight most-starred Jev-compatible servers; standard 4-choice and 3-choice fixtures; measured on 2026-09-24 on an M1 Pro with 32 GB", "result": "Of the eight most-starred Jev-compatible servers, only kev and decider pass every MUST, while von and rizzo-flow each score 31 of 32, both failing on choice options beyond 26; confidence is not portable: on the same 204 answers at a 0.9 threshold, TypeSafe's formula auto-accepts 58%, Laya's definition accepts 30% and rejects 56 of the 118 answers the TypeSafe formula accepts, decider's accepts 63% and 10 of those are answers the TypeSafe formula would send for review, and von's accepts 57%", "notes": "The practical finding: a confidence threshold tuned on one Jev-compatible server does not transfer to another, which breaks the threshold-then-automate pitch the whole API is built on; the ecosystem is structurally compatible but the confidence semantics diverge, in one line: same schema, different confidence math; one command to run, uvx jevcompat test", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wiatrm-jevtpp", "name": "JevT++", "author": "wiatrM", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/wiatrM/jevtpp", "base_model": "Any local model plus optional Laya ONNX", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "C++20 library for local model-backed routing, classification and scoring: turns runtime text, JSON or application objects into enums, boolean decisions and scores code can use directly; compile-time schemas, enum rubrics and explicit abstention; shared context evaluates several independent fields in one pass; optional Laya adapter runs inference inside the process with ONNX Runtime", "approach": "head", "training": "No training; in-process inference adapter", "data": "Runtime text, JSON or application objects", "result": "", "notes": "MIT; 'Application context in. Typed decisions out.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jldm", "name": "JLDM, Jev-like Fast Decision Machine", "author": "wkzhang81", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/wkzhang81/JLDM", "base_model": "qwen3.8-27B-GSQ-RCO-MTP locally via KoboldCpp, plus mmproj for images", "params": "27B quantized, runs in 16 GB VRAM", "metric": "decision latency and probability fidelity", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Chinese-language project: a Choice probability decision tool on a KoboldCpp backend; a GBNF grammar constrains the vocabulary to the option letters, max length one, and the logprob of each candidate letter at the answer position is pulled from the last-logprobs endpoint, same-letter variants are merged by logsumexp, and a temperature-calibrated softmax yields the full posterior over the supplied options rather than a single sampled draw; image input via mmproj makes it multimodal; ships a no-dependency Python CLI for batch JSON and a single-file drag-and-drop web UI", "approach": "", "training": "", "data": "", "result": "300–600 ms per decision on a 16 GB VRAM machine", "notes": "The core idea: read the candidate letters' log-probabilities directly at the token level and conditionally normalize, instead of asking the model to say the answer; the JevBench official table lists an API named decision-machine-1 at rank 20, a plausible but unconfirmed match with this project", "domain": "general", "score": "", "latency": "600 ms", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "triusai-kapteeni", "name": "Kapteeni,  a Jev-compatible System One decision model", "author": "TriusAI", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/TriusAI/Kapteeni", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Jev-compatible System One decision model on local hardware: Qwen3-4B LoRA-adapted, state plus typed questions into calibrated probability distributions your code can branch on, no text generation; v1 P2 LoRA trained, evaluated and served; JevBench v1.4 public half 65.71, ~#2 of 73 systems", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "miguelfaraujo-laya-ecosystem", "name": "Laya Ecosystem ⚡", "author": "MiguelFAraujo", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/MiguelFAraujo/laya-ecosystem", "base_model": "Laya, ModernBERT family", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "Python 3.9 or newer, Apache-2.0, built on the convaiinnovations/laya ModernBERT-family checkpoint. Each tool cites its inspiration: tamaratran/fast-jev-compaction, thruwire/foreman and RomanSlack/jev-drone, and superagents-lab/jev-search and realZachi/pg-jev. idt-compactor is a plain stdin-to-stdout CLI: idt-compactor transcript.json produces compacted.json.", "arch": "Production utilities for the Laya and TypeSafe Jev System-1 decision engines, where typed choice, score and noul decisions execute in a single forward pass with calibrated probabilities in about 33 ms on GPU or 200 ms on CPU with no hallucination: four tools, idt-compactor a lossless context compactor that scores tool logs and conversational steps with System-1 importance and prunes transient noise while preserving code, diffs and critical decisions letter-for-letter, saving up to 60% of agent token cost; idt-foreman an agent supervisor that evaluates execution steps, shell outputs and exit codes in milliseconds and decides proceed, retry, fix or escalate, flagging destructive actions for human-in-the-loop; idt-search a two-stage semantic search and ranker pairing FTS5 candidate retrieval with System-1 ranking; and a fast UI decider.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "adityarawat00189-laya-firehose", "name": "Laya Firehose Classifier & Router", "author": "AdityaRawat00189", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/AdityaRawat00189/Laya-Firehose", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A backend system that ingests a firehose of incoming data, API payloads, support tickets, user feedback, arbitrary JSON or text, and classifies each item using Laya, an open-source, non-autoregressive \"System 1\" decision model from Convai", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-console", "name": "Laya local console", "author": "biyyl234", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/biyyl234/laya-console", "base_model": "Laya ModernBERT weights", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "approach: Flask REST wrapper", "arch": "Local web console for Laya, the open-source Jev: a Flask REST API service, a dependency-free HTML console with visualized inference results, Chinese-English auto routing, and a one-click Windows launcher; the three weight sets stay resident in about 4 GB of GPU memory or 5 GB of RAM and single forward passes take tens of milliseconds", "approach": "sdk", "training": "", "data": "", "result": "One-click local deployment of intent classification, ticket routing, and safety-guardrail models", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "carl0s-laya-mcp-server", "name": "Laya MCP, carl0s", "author": "carl0s", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/carl0s/laya-mcp-server", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "An MCP server for Laya, the open decision model from ConvAI Innovations. It lets Claude and any other MCP client ask typed questions about a piece of text or JSON and get back a structured answer with probabilities, in tens of milliseconds", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wj1313677-layaservice", "name": "Laya MLX Local Decision Service", "author": "wj1313677", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/wj1313677/LayaService", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Designed for a Mac mini M4 or similar Apple Silicon machine, not for a Linux container, because the MLX runtime depends on native Metal and Apple Silicon libraries; Docker Desktop is not supported and the Dockerfile and docker-compose.yml are kept for reference only. Runs in a detached native background mode on macOS with lifecycle scripts: server.py is the FastAPI app and model invocation, plus start.sh, status.sh, logs.sh, restart.sh and stop.sh. Requires macOS on M1 through M4, Python 3.11, and the native laya-mlx and mlx packages.", "arch": "Local decision service running the convaiinnovations/laya model natively on Apple Silicon macOS with the MLX runtime: it accepts a request with state and questions, classifies routing decisions with the local Laya model, and returns structured answers for choice, score and boolean-like questions through a simple HTTP API for tools and AI agents.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "agk4444-laya-api", "name": "laya-api, CPU HTTP", "author": "agk4444", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/agk4444/laya-api", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: serve Laya, a typed-decisions fine-tune, as a small HTTP API on CPU; notes: ~808 MB checkpoint loads once; seconds per decision on CPU - batch/scheduled use, not real-time; AGK FIRE INC.", "arch": "Small CPU HTTP API around a Laya typed-decisions fine-tune: one ~808 MB checkpoint load, then seconds per decision - suited to batch and scheduled workloads rather than real-time", "approach": "", "training": "", "data": "", "result": "", "notes": "Serves the agk4444/laya-typed-decisions fine-tune, developed by AGK FIRE INC; Python 3.10+, no GPU needed, weights download automatically", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mannlohchab-laya-candle", "name": "laya-candle", "author": "mannlohchab", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/mannlohchab/laya-candle", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 2, "likes": "", "detail": "arch: English Laya decision-model inference with Candle, Rust ML; notes: defaults to convaiinnovations/laya, hub id or local dir; cargo build + --demo", "arch": "Rust inference for the English Laya decision model built on the Candle ML framework, defaulting to convaiinnovations/laya from the Hub or a local directory", "approach": "", "training": "", "data": "", "result": "", "notes": "cargo build --release, then cargo run with --demo or a --state string plus a questions.json file", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bmw8080-laya-decision-api", "name": "laya-decision-api", "author": "bmw8080", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/bmw8080/laya-decision-api", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Local System-1 Laya model wrapped as an HTTP service: intent classification, ticket dispatch, LLM routing, content moderation; offline zero-token; MLX on Apple Silicon and torch on Linux; OpenAPI 3.0/3.1; zero-dependency Java, TS, Python SDKs; README in Chinese", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-ocr-guard", "name": "laya-ocr-guard", "author": "okrath", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/okrath/laya-ocr-guard", "base_model": "Laya plus Alibaba Open Code Review plus a configured LLM", "params": "", "metric": "Sub-30ms triage gate", "license": "Apache-2.0", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "Dual-gate impact analysis and regression guard for AI-assisted development, the Banh Mi sandwich pattern: guard pre runs Laya reflex triage under 30 ms at zero cost and zero tokens for intent, domain and risk plus domain contract extraction; the developer or agent edits inside the contract; guard post runs deterministic git-diff blast-radius auditing with the Alibaba Open Code Review static rulebook, the test suite, then the configured LLM as final architectural safety gatekeeper; automates the impact and regression protocol pioneered in oh-my-ainovel; Python 3.10+, Windows, Linux and macOS", "approach": "", "training": "", "data": "", "result": "Sub-30 ms Laya triage gate; live interactive documentation at okrath.github.io/laya-ocr-guard", "notes": "", "domain": "coding", "score": "", "latency": "under 30 ms Laya triage", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-plus", "name": "Laya-Plus context kit", "author": "KotalaKishanReddy", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/KotalaKishanReddy/laya-plus", "base_model": "ConvAI Laya, byte-identical weights", "params": "0.4B", "metric": "Effective context: 32,768 tokens", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "approach: Retrieve-then-decide long context over stock weights", "arch": "An unofficial community kit that gives stock Laya Jev-scale context with zero weight changes: the ModernBERT encoder already supports 8192 positions, so the native window is 8k by setting max_len with no retraining, and a single-file retrieve-then-decide path chunks the state, TF-IDF-ranks chunks against the questions, keeps the top 3 inside a 1280-character true-fit state budget, and runs one forward pass for an effective 32k", "approach": "head", "training": "", "data": "", "result": "It closes the one gap between stock Laya and Jev, context: from 512 tokens English and 1024 multilingual to a 32,768 effective window, with base accuracy and base calibration unchanged because the weights are byte-identical", "notes": "Not affiliated with ConvAI Innovations or TypeSafe AI; one file, no new dependencies beyond scikit-learn", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ajayjha1-laya-studio-node", "name": "laya-studio, ajayjha1", "author": "ajayjha1", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/ajayjha1/laya-studio-node", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "Strict TypeScript with labels in and literal types out, no as const required; the API covers a canonical predict call, classification, structured decisions, presets, schema-driven decisions, routing, confidence, fallback and batching. Every answer carries Laya's calibrated answer_confidence, and the package never collapses it with Laya's other, uncalibrated confidence field. The roughly 33 ms per call on a T4 GPU figure is Laya's model-card measurement, not one taken by this package; use laya benchmark to measure your own deployment.", "arch": "Node.js and TypeScript developer toolkit for using Laya, the non-autoregressive System 1 decision model that returns typed answers with calibrated probabilities in a single forward pass, as a fast decision layer in AI applications. Laya never generates text, so there is nothing to parse and nothing to hallucinate; the package talks to a Laya HTTP server over its documented API with zero runtime dependencies, just Node's built-in fetch, and imports, wraps, and shells out to nothing Python.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-thalamus", "name": "laya-thalamus", "author": "lcbkmm", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/lcbkmm/laya-thalamus", "base_model": "Laya + LLM fallback", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 2, "likes": "", "detail": "base: local, PyPI: laya-thalamus; training: middleware", "arch": "System One decision middleware for AI agents: tool-routing decisions + 'is this information enough?' + 'can I trust the tool result?'; circuit breaker, cache, idempotency, Prometheus/OTel", "approach": "head", "training": "None", "data": "Gold set n=56 in-repo", "result": "Gold n=56: Laya + fallback 64.3% vs Laya alone 55.4% vs deepseek-v3.1 router 87.5%", "notes": "Gap reporting vs a frontier router; 'thalamus' = fast gate + slow fallback.", "domain": "agent", "score": "64.3%", "latency": "", "benchmark": "Gold set n=56", "cost": "", "speedup": ""}
{"id": "abusuraihsakhri-laya-universal", "name": "laya-universal", "author": "abusuraihsakhri", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/abusuraihsakhri/laya-universal", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "arch: cross-platform inference runtime for Laya typed decision models - ONNX Runtime on Windows/Linux/macOS and MLX on Apple Silicon; notes: common Python API; Apache-2.0; docs site", "arch": "Cross-platform runtime for Laya typed-decision models: ONNX Runtime on Windows/Linux/macOS plus MLX on Apple Silicon behind one common Python API", "approach": "", "training": "", "data": "", "result": "", "notes": "Apache-2.0, Python 3.10+, CI on GitHub Actions; documentation site at abusuraihsakhri.github.io/laya-universal", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mdad-elec-laya-v2-agent-routing", "name": "Laya-v2: routing an AI agent's turn with a System-One model", "author": "mdad-elec", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/mdad-elec/laya-v2-agent-routing", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Tests whether the open System-One decision model Laya, a 421M ModernBERT-large encoder that answers a typed question in one forward pass, can route an agent turn better than a large LLM judge; ships the technical report, recorded side-by-side video, every evaluation, a public dataset, a benchmark package, and the fine-tuned checkpoint with code and data", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "layajev", "name": "layajev", "author": "metalagman", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/metalagman/layajev", "base_model": "verified Laya model, served locally", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Go server exposing a Jev-compatible decision API subset: GET /v1/models and POST /v1/systemone return structured answers for supported choice, score and noul questions; inference stays local and in-process with no hosted inference service; explicit compatibility and error docs in the runbook", "approach": "", "training": "", "data": "", "result": "Compatibility subset, not the Jev model and not a promise of identical decisions", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "layarustparser-ulpf", "name": "layaRustparser ULPF", "author": "guptchar", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/guptchar/layaRustparser", "base_model": "Laya as Tier-3 triage", "params": "", "metric": "p50 latency per log line, events per second, WORM verify pass rate", "license": "Apache-2.0", "paper": "", "stars": 3, "likes": "", "detail": "End-to-end flow: raw syslog, JSON or CSV in, classify, zero-copy parse, OCSF 1.3 JSON out, then Parquet WORM plus verify, with a SHA-256 of the raw bytes, a UUIDv7, the RFC 6962 Merkle root, and a ledger.jsonl provenance branch", "arch": "Universal Log Pre-processing Framework in Rust: high-performance, vendor-agnostic, containerized, strictly air-gapped; ingests heterogeneous perimeter firewall logs, normalizes to OCSF 1.3 NetworkActivity class 4001, and guarantees non-repudiation with RFC 6962 Merkle trees anchored into columnar Parquet WORM storage with every raw byte preserved hash-for-hash; three-tier decision pipeline: Tier-1 signature LRU with 8192 entries, Tier-2 Drain template miner with security anchor tokens, Tier-3 Laya triage on a bounded ring that never blocks ingest", "approach": "", "training": "", "data": "224,657-line full-scale corpus", "result": "Zero-copy hot path p50 2.88 microseconds per line on the core corpus, 7.28 microseconds at 224k lines, 849,481 events per second at full scale, 2.15× the frozen baseline; SHA-256 of the raw bytes checks out on all 224,657 lines; ulpf verify passed 186 of 186 Merkle blocks and exits 2 if one byte is flipped; 124 tests pass; 18.6 MB binary against a 35 MB requirement", "notes": "Laya is used only as the Tier-3 triage engine; Rust 1.96; live CI", "domain": "security", "score": "", "latency": "2.88 µs p50/line", "benchmark": "", "cost": "", "speedup": "2.15× vs frozen baseline"}
{"id": "llm2clf", "name": "llm2clf", "author": "verelabs", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/verelabs/llm2clf", "base_model": "any LLM backend: Bedrock, HF, SGLang", "params": "varies", "metric": "Kimi K2.5 mean 82.3% vs Jev 80.7% on 4 tasks", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: /v1/systemone compatible; debiasing, up to 4 orderings, plus per-question-type temperature calibration; notes: ported from AnyJev by Nokia; writeup at enclave.md; backends Bedrock/HF/SGLang; 6 open models on 4 tasks vs Jev", "arch": "Makes any LLM serve Jev's /v1/systemone endpoint: up to 4 prompt orderings per question for debiasing, per-question-type temperature calibration, backends for Bedrock, Hugging Face and SGLang", "approach": "logits", "training": "No training; calibration and debiasing only; ported from Nokia's AnyJev", "data": "BoolQ, MNLI, AG News, SST-5", "result": "Mean accuracy on the four tasks: Kimi K2.5 82.3%, Qwen3-235B 81.8%, GLM-5 81.0%, Jev baseline 80.7%, Mistral Large 3 80.0%; calibrated ECE and per-task costs published in the table", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "harveybc-m5phet", "name": "M5PHET", "author": "harveybc", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/harveybc/M5PHET", "base_model": "", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "arch: inspired by Jev and Laya", "arch": "Decision model 'designed to go beyond classification: forecasting, representations, reinforcement learning and causal inference — five disciplines, one model'", "approach": "sdk", "training": "", "data": "", "result": "Python framework: typed decisions, forecasting, unsupervised tasks, causal inference and policies behind a common interface over pluggable engines; first application domain is algorithmic trading on news and point-in-time economic calendar data", "notes": "Fields claim-level. Not a single trained model — a framework with a common interface; the name is 'Master's Five-Point Heart-Exploding Technique'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nschlaepfer-manchego", "name": "Manchego v2.1", "author": "nschlaepfer", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/nschlaepfer/manchego-serve", "base_model": "undisclosed", "params": "4B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Manchego v2.1 served by its own manchego-serve server", "approach": "head", "training": "JevBench-informed: training families designed from an earlier unreleased version's per-family JevBench hard-tier scores, published benchmark specification only, no JevBench item text used; phrase audit disclosed", "data": "", "result": "4B typed decision model + dedicated server; torch backend checked identical to the reference torch readout at bf16 on CPU; MLX 8-bit reference within 0.04 probability on the torch-marked requests with no change of answer", "notes": "Card not machine-readable; fields claim-level. Serves the System One wire contract POST /v1/systemone; offline, Docker, pinned weights", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dockndevai-mcp-laya", "name": "mcp-laya", "author": "dockndevai", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/dockndevai/mcp-laya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Safe-by-default MCP server for Laya: typed decisions - classify, score, yes/no - over any state in one local forward pass, ~33 ms, 100+ languages, calibrated confidence, no text generation", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nanohunch", "name": "nanohunch", "author": "ubermenchh", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/ubermenchh/nanohunch", "base_model": "MiniCPM5-2B-Base", "params": "2B", "metric": "Target: ECE at or below 0.05 after temperature scaling", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "approach: Minimal single-state multi-question LoRA training", "arch": "The smallest published recipe for training a System One model: a small open LLM reads a state once, answers many typed questions against the cached state, and returns calibrated probabilities instead of text; six core files under 1,000 lines, enforced by a line-count tool; the state is encoded once and each question reuses the KV cache; answers are read only from the LM-head rows of the supplied answer labels, so the output is valid by construction; a temperature fitted on a calibration split is divided into the softmax; a LoRA adapter trains on soft labels from two open teacher models with option order shuffled every epoch", "approach": "fine-tune", "training": "LoRA on two open teacher models, soft labels, option order shuffled", "data": "Teacher soft labels, calibration split", "result": "Status is planning done, build starting, with nothing measured yet; the plan pins a laptop-scale training run, LoRA on MiniCPM5-2B-Base in MLX on one Apple Silicon Mac", "notes": "The stated goal is that the repository is the entire system, readable end to end in one sitting", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nathanhb-open-jev", "name": "NathanHB's four minimal decision-model designs", "author": "NathanHB", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/NathanHB/open-jev", "base_model": "Qwen3.5 decoder; ModernBERT encoder; Laya-style encoder", "params": "small", "metric": "study, ~150 lines each", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "notes: four minimal decision model designs in ~150 lines of PyTorch each: 01 decoder Qwen3.5, 02 encoder+mask ModernBERT, 03 encoder+markers Laya-style, 04 fourth variant; approach: sft", "arch": "Teaching project: four minimal Jev-style decision model designs in about 150 lines of PyTorch each - a Qwen3.5 decoder, a ModernBERT encoder with masking, a Laya-style encoder with option markers, and a fourth variant", "approach": "fine-tune", "training": "", "data": "", "result": "Reference implementations only, no published benchmark numbers", "notes": "", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ollaya-dev-laya", "name": "Ollaya laya", "author": "ollaya-dev", "date": "2026-09-24", "category": "runtime", "url": "https://huggingface.co/models/ollaya-dev/laya", "base_model": "Laya", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Ollaya package of convaiinnovations/laya: ONNX graphs + calibration + decision config; the graphs reference the upstream weights by byte offset, pulled at ollaya pull time, pinned to a commit and verified", "approach": "", "training": "", "data": "", "result": "", "notes": "The org also publishes six ONNX decision-model packages for the Ollaya runtime: nli, gliclass, decider, kev, qwen3guard, von.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "fableguo-openjev", "name": "openjev by fableguo", "author": "fableguo", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/fableguo/openjev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: npm package; approach: sub-10ms local engine", "arch": "Open-source sub-10 ms System One decision engine for AI agents: zero GPU required, 100% type-safe, zero API costs; inspired by TypeSafe Jev, independent implementation.", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "10 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "darwintechlab-claude-jev", "name": "OpenJev for Claude Code", "author": "darwintechlab", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/darwintechlab/claude-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "MIT, Node 20 or newer, Claude Code 2.0 or newer, 3-minute setup; maintained by DarwinTechLab and the community. Because the confidence scores are calibrated, higher confidence really does mean the answer is more likely to be right, so the agent can act on its own when OpenJev is confident and bring in you or the bigger model when it isn't; the plugin labels every answer with its confidence.", "arch": "Claude Code plugin that offloads the small frequent decisions a coding agent makes to OpenJev, a decision model that answers with one of the options you defined plus a calibrated confidence score: is this command safe to run, which tool fits this task, is this PR ready to merge, how serious is this bug. It does not write text, so it can only answer with one of the options you gave it, nothing has to be parsed, and it tells you when it isn't sure.", "approach": "", "training": "", "data": "", "result": "Live bench: about 0.3 s per decision at p50 307 ms; about 0.017 USD per 1,000 decisions with no output billed; up to 32 questions in one call, with 27 questions taking about 5 ms longer than 1 in the tests.", "notes": "Identical re-upload by the same owner, darwintechlab/claude-openjev, 09-24, byte-identical opening and same claude-jev package; dropped.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "caelum0x-openjev", "name": "OpenJev, caelum0x", "author": "caelum0x", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/caelum0x/openjev", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "arch: open-source self-hostable typed-decision service; default engine is a local sentence-embedding classifier, gte-small int8 ONNX, CPU-only, with a TF-IDF fallback that needs no model; notes: API server + web console + JS/Python SDKs + agent skills + otsone model toolkit; Apache-2.0", "arch": "Self-hostable typed-decision service: the default engine is a local sentence-embedding classifier, gte-small int8 ONNX, CPU-only, with a TF-IDF fallback that needs no model at all; ships an API server, web console, JS/Python SDKs, agent skills and the otsone model toolkit", "approach": "", "training": "", "data": "", "result": "Apache-2.0 decision service that runs CPU-only out of the box", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "siliconlabai-openjev-cactus", "name": "OpenJev-Cactus, Needle backend", "author": "SiliconLabAI", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/SiliconLabAI/OpenJev-Cactus", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: high-performance OpenAI- and System-One-compatible FastAPI backend for CPU edge devices, powered by cactus-needle; notes: endpoints /v1/chat/completions with tool calls, /v1/systemone, /complete, /v1/models, /health", "arch": "Needle FastAPI backend on cactus-needle: OpenAI-compatible chat completions with tool calling plus a Jev-style /v1/systemone for typed decisions and a /complete endpoint; tools are passed in the request body; works with the TanStack and React frontend or plain curl", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "christofmilius-openjev-router", "name": "openjev-router, MCP", "author": "ChristofMilius", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/ChristofMilius/mcp-agent-openjev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: local typed probabilistic decision service for agents - Choice/Noul/Score via OpenJev by zhangcy122 through an OpenAI-compatible chat endpoint, LM Studio logprobs; notes: keeps OpenJev's schema, temperature calibration and abstention, threshold 1.25 over N, adds the score decision OpenJev omits; MCP tool", "arch": "Local probabilistic decision service for agents: runs OpenJev by zhangcy122 through an OpenAI-compatible chat endpoint, LM Studio logprobs, keeping OpenJev's schema, temperature calibration and 1.25/N abstention while adding the score decision OpenJev omits, exposed as an MCP tool", "approach": "", "training": "", "data": "", "result": "Choice/Noul/Score from a local OpenJev build, wired into agents through MCP", "notes": "", "domain": "routing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openpave-jev", "name": "openpave-jev", "author": "cnrai", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/cnrai/openpave-jev", "base_model": "Provider-agnostic: local Laya, HTTP endpoint, TypeSafe, or mock", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 2, "likes": "", "detail": "Three question types: choice picks one of N for routing, classification and triage; score places the state on an ordered rubric for severity, priority and quality; noul is a calibrated yes or no for gating, filtering and approval. All three carry confidence, and --min-confidence 0.7 exits with code 3 when the model is unsure. Providers: laya, the local open-weights @receptron/laya npm engine, Apache-2.0 ModernBERT-large with about 1.7 GB of weights, set up with jev setup laya on Node 20 plus; http, any server exposing POST /v1/systemone, for example a self-hosted laya-serve, via JEV_BASE_URL; typesafe, the closed paid TypeSafe Jev API via JEV_API_KEY; and mock, a hash-seeded deterministic fake. Installs as a PAVE app with pave install.", "arch": "Provider-agnostic Jev-style decision CLI for the PAVE app and Claude, with jev decide, score, noul, ask, eval and calibrate: it asks non-autoregressive decision models for typed answers with calibrated probabilities, classifying, routing, scoring and gating without an LLM round-trip, in roughly 35–500 ms at about 1,000× lower cost than an LLM call, with probabilities you can gate on.", "approach": "router", "training": "None", "data": "None", "result": "Drop-in Jev decisions with calibrated gating in one app", "notes": "Local-Laya support via npm package.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "phix-dev", "name": "phix dev, hardened laya", "author": "phix", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/phix/dev", "base_model": "", "params": "", "metric": "33 ms; 100+ languages", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: fast local System 1 decision engine, typed choice, score, noul, calibrated, a security-hardened fork of laya; notes: multilingual, 100+ languages; 33 ms per decision; RLCD-trained against a strictly proper scoring rule; a router picks the checkpoint per request; Colab available", "arch": "Security-hardened fork of laya: a fast local System 1 decision engine covering 100+ languages at ~33 ms per decision, RLCD-trained against a strictly proper scoring rule, with a router that picks the checkpoint per request", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "morler-pi-jev-core", "name": "pi-jev-core", "author": "morler", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/morler/pi-jev-core", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "No tool routing, auto mode, skill discovery or context compaction. Ships pure TypeScript source loaded by pi extension loader jiti; importable programmatically as JevClient, with deep imports for single-layer use. Platforms: typesafe default with jev-latest, openrouter with typesafe/jev-1.13, cloudflare with typesafe/jev requiring account and gateway IDs, vercel with typesafe-ai/jev; switch the active platform at runtime with the /jev-platform command. Credentials may live in the pi secret files under ~/.pi/agent/secrets/.", "arch": "Minimal standalone Pi extension: connects to a Jev API and registers a jev_evaluate tool supporting the three structured judgment types noul, choice and score", "approach": "", "training": "", "data": "", "result": "", "notes": "Bilingual README, English and Chinese", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pi-jev-skill-picker", "name": "pi-jev-skill-picker", "author": "safzanpirani", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/safzanpirani/pi-jev-skill-picker", "base_model": "TypeSafe Jev jev-latest", "params": "", "metric": "Skill catalog token share of the system prompt, cost per run on two frontier models", "license": "", "paper": "", "stars": 33, "likes": "", "detail": "", "arch": "Pi extension that keeps the Agent Skills catalog out of model requests and replaces it with one ranking tool: before each turn it strips Pi's generated skills catalog from the system prompt and offers skill_search plus skill_load; each skill becomes its own Score question over three ordered levels, 0 unrelated, 1 adjacent, 2 directly applicable, judged by Jev without seeing the others; code applies a 1.4 floor, sorts and loads the top 3, with deterministic lexical fallback when no API key is set", "approach": "", "training": "", "data": "", "result": "137-skill catalog measured at about 19,000 tokens, 87% of the Pi system prompt; stripping saves 87.9% on gpt-6-astra, $0.2107 to $0.0254, and 84.6% on deepseek-v4.1-flash; one skill_search costs about 38,600 Jev input tokens, $0.0016 at $42 per billion, and Jev bills input only", "notes": "Stars and license not verified at sweep time", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "codenamekt-pi-laya-router", "name": "pi-laya-router", "author": "codenamekt", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/codenamekt/pi-laya-router", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Laya answers one multiple-choice question about the latest prompt in about 35 ms on a GPU; if it is unsure or down, a one-line classification runs through fallback.model, which should be a non-reasoning model. A switch needs two consecutive turns at 0.70 confidence or higher, or one at 0.90 or higher, plus a 0.15 margin over the runner-up and three turns since the last switch, all tunable under thresholds; compaction on a switch happens only in interactive modes and only above compactMinTokens, which defaults to 24k since Pi keeps the last 20k tokens verbatim. Picking any other model pauses routing, and picking laya/auto again resumes it. Installs laya via uv on Python 3.12 with a systemd user unit on port 8811; commands: /router status, force, clear, off and on.", "arch": "Intent router for the Pi coding agent, powered by Laya, a small local classifier: pick laya/auto as your model, and each prompt is classified as coding, planning, ui_ux or chat, and the request goes to the model you mapped to that intent on your OpenAI-compatible proxy, while Pi keeps showing laya/auto. When the kind of work changes for a couple of turns in a row, the session is compacted with a note about the move and the model is told which skill to lean on.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "justao-quarkus-typesafe", "name": "quarkus-typesafe", "author": "justao", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/justao/quarkus-typesafe", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: Quarkus library wiring up the TypeSafe System One, Jev, client, injectable TypeSafeClient, quarkus.typesafe.* config; notes: the Quarkus counterpart of the reference spring-ai-starter-typesafe module, same keys one prefix deeper, same defaults; quarkus-langchain4j-typesafe companion for LangChain4j", "arch": "Quarkus library for the System One, Jev, client: binds quarkus.typesafe.*, contributes an injectable TypeSafeClient and gets out of the way if you build your own; the Quarkus counterpart of spring-ai-starter-typesafe, with a quarkus-langchain4j-typesafe companion", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nish1001-reflexguard", "name": "reflexguard", "author": "NISH1001", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/NISH1001/reflex-guard", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: multi-label guardrails on System One decision models - Laya today, Von and TypeSafe Jev planned; notes: per-category scores in [0,1]; NOUL|CHOICE|SCORE modes combinable with pipe and ampersand, votes equals k; pip install reflexguard[laya]", "arch": "Multi-label guardrail layer on top of System One decision models, Laya today, Von and Jev planned: per-category scores in [0,1], with NOUL/CHOICE/SCORE voting modes combinable via pipes and ampersands; pip install reflexguard[laya]", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reticlehq-reticle", "name": "reticle", "author": "reticlehq", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/reticlehq/reticle", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0 + FSL-46d6a0", "paper": "", "stars": 878, "likes": "", "detail": "npm packages @reticlehq/server and @reticlehq/react, with docs at docs.reticle.sh, an OpenSSF scorecard, and a Discord community. The README covers install, use, how it works, why not Playwright, benchmarks and limits. Licensed Apache-2.0 plus FSL-46d6a0.", "arch": "Runtime perception layer that gives web and desktop agents Jev-style machine-readable state: it drives your real running app, reads what actually happened, and hands back pass, fail, or couldn't tell with the file and line to fix, so when your AI agent says done, Reticle checks whether that is true.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rubenvp2-system-one-model-gateway", "name": "system-one-model-gateway", "author": "RubenVP2", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/RubenVP2/system-one-model-gateway", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Multi-tenant gateway for System One models: models that take a state and typed questions, noul, choice, score, and return calibrated probabilities in a single forward pass without generating text; run one inference server on your host and point every project at the gateway for auth, queueing, fairness, failover and metrics; examples TypeSafe Jev and open-weights Laya", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "belentani7-system-one-unified", "name": "system-one-unified", "author": "belentani7", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/belentani7/system-one-unified", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "Three backend modes: offline, the default, deterministic with no network and no keys, reproducible and auditable; laya, Apache-2.0 Laya on ModernBERT-large with the same /v1/systemone contract; and openai-compatible, any OpenAI chat-completions endpoint. If an external backend fails, the request falls back to offline and the response declares it in the backend field. Ships Prisma, a smoke test, Docker, and a portable start on localhost:3000; Spanish README. Honest limits are stated in the README: mathematical confidence is not real accuracy and must be calibrated on labelled domain cases, the offline mode is a portable deterministic base rather than a replacement for an RLCD-trained model, and it is not conscious and contains no universal model of reality. MIT, 2026, Pedro Belentani.", "arch": "Offline, deterministic decision engine for System One-style calls: a typed, deterministic, offline POST /v1/systemone motor, unified with the Judas/Omega visual and narrative layer, that receives a state and closed choice, score and noul questions and returns only answers within the received schema, with probabilities, confidence, latency and escalation. The API is the reusable product; the web interface is optional.", "approach": "undisclosed", "training": "", "data": "", "result": "Offline/deterministic System One serving", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pinkroosterai-systemonesharp", "name": "SystemOneSharp", "author": "PinkRoosterAI", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/pinkroosterai/SystemOneSharp", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: .NET client for the System One API used by Jev and compatible Laya servers; notes: NuGet package", "arch": ".NET 10 HTTP client for TypeSafe Jev and Jev-compatible local Laya servers, on NuGet: one shared POST /v1/systemone request returning typed Choice, Score and Noul answers, no runtime package dependencies, community-maintained and unofficial", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kkloudtarus-taurus-jev-sdk-go", "name": "taurus-jev-sdk-go", "author": "KKloudTarus", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/KKloudTarus/taurus-jev-sdk-go", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 3, "likes": "", "detail": "Design choices: degraded responses are rejected, not coerced, so a body missing a required field returns an error naming it and a truncated response never reaches branching logic as a confident zero; the API key stays out of every string, masked in errors, error bodies, log records and the client's own rendering, in six spellings; the bearer token does not follow redirects, since Go's default would re-send it over cleartext to any subdomain of the same host; a retry-after wait is clamped, jittered and capped, so a server cannot pin your goroutine; the pool is 128 idle connections per host against the stdlib default of two, worth 2.2× throughput at 200 concurrent calls; and a primitive the API adds later is reachable through RawQuestion and Answer.Raw without waiting for a release. MIT.", "arch": "Unofficial, dependency-free Go client for the TypeSafe AI System One API and the Jev model: Jev answers typed questions about a piece of state and returns calibrated probabilities, and it generates no text, so every answer is a value your code can branch on directly. Maintained independently of TypeSafe AI, Go 1.22 or newer, zero dependencies outside the standard library.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "roilanrodriguez55-laya-server", "name": "test-laya", "author": "roilanrodriguez55", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/roilanrodriguez55/laya-server", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Entorno para correr y probar el servidor real de laya, laya-serve, el CLI que trae el propio paquete laya. No es un mock: carga checkpoints reales desde HuggingFace y corre inferencia real. No persiste nada en base de datos", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cogcloud-ai-cog-typesafe", "name": "TypeSafe System One Cog", "author": "cogcloud-ai", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/cogcloud-ai/cog-typesafe", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A provider Cog for the system-one/decisions capability backed by TypeSafe's Jev, the first public System One model. Jev answers typed questions about a state: Noul, the probability that a statement is true; Choice, one of your options; and Score, a value on a rubric, each returned with a calibrated probability in one forward pass.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "basi-typesafe-ai-php-client", "name": "typesafe-ai PHP client", "author": "basi", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/basi/typesafe-ai-php-client", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: PHP client for the typesafe.ai System One API, Jev; notes: noul, choice and score with calibrated probabilities", "arch": "PHP client for the System One API: a state as any text, JSON object or array plus one or more named questions, each answered with calibrated probabilities; noul answers yes or no with the probability it is true, choice picks one option with a probability per option, score rates the state against an ordered rubric", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "soroushnilton-typesafe-jev", "name": "typesafe-jev, minimal Python", "author": "SoroushNilton", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/SoroushNilton/typesafe-jev", "base_model": "TypeSafe Jev via OpenRouter", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "jev.py has helpers that build each question type: noul for a yes or no with a probability in 0–1, choice for one of named options with optional descriptions and a probability for each, and score for a position on an ordered scale listed from low to high. The Jev context manager reads OPENROUTER_API_KEY and JEV_MODEL from the environment, with .env values taking precedence over shell variables and direct api_key and model parameters also accepted; the default model is typesafe/jev-1.13, or ~typesafe/jev-latest to track the newest release. An interactive CLI, chat.py, loads a question set from JSON and sends each line you type as the state.", "arch": "Minimal Python client that reaches TypeSafe's Jev through OpenRouter's Decisions API instead of a TypeSafe account: Jev is not a chat model, so you give it a state, such as an email, a support ticket or a review, along with a set of typed questions, and it sends back structured answers, probabilities, picks from a list of options, or positions on a scale.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "phidung-hub-typesafe-rust", "name": "typesafe-rust, unofficial", "author": "PhiDung-hub", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/PhiDung-hub/typesafe-rust", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: unofficial Rust client for TypeSafe's System One API, Jev, with opt-in concurrency tooling; notes: crates typesafe-ai, typed async client with fan-out and retries, and typesafe-ai-common, evaluators and bounded batching; unrelated to the crates.io typesafe-ai", "arch": "Unofficial Rust client for the System One API: a typed async client with fan-out and retries, the typesafe-ai crate, plus shared evaluators and bounded batching, the typesafe-ai-common crate, explicitly unrelated to the separate crates.io typesafe-ai", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "manavarya09-verdict", "name": "Verdict, Manavarya09", "author": "Manavarya09", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/Manavarya09/verdict", "base_model": "", "params": "undisclosed", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "verdict-small has 118M parameters, covers 100-plus languages, and a CPU is fine. Apache-2.0 with CI, the verdictml PyPI package, a demo that runs in the browser, a Hugging Face Space playground, and a Colab quickstart.", "arch": "Small, fast, honest decision models that zero-shot fit on your own data, replacing LLM calls for routing, guardrails, triage and policy checks with typed answers in milliseconds on a CPU, with a probability you can trust. Three question kinds share one mechanism, choose one of a list of options, score on an ordered scale, and check a yes-or-no claim, with no text generation, no schema errors and no hallucinated labels: the answer is always one of the options you gave.", "approach": "undisclosed", "training": "Zero-shot fit on user data", "data": "User's own labelled decisions", "result": "Jev Decision Index: Verdict 13.38", "notes": "Fields claim-level.", "domain": "general", "score": "13.38", "latency": "", "benchmark": "Jev Decision Index", "cost": "", "speedup": ""}
{"id": "fazin-ahamed-vey", "name": "Vey", "author": "fazin-ahamed", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/fazin-ahamed/vey", "base_model": "", "params": "", "metric": "Banking77 0.658 vs Laya 0.431; semantic p50 2.6–2.9 ms", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "arch: lightweight machine-native decision runtime - no autoregressive generation; answers which candidate, which value, should I act, should I ask; notes: 24M semantic encoder, mxbai-embed-xsmall-v1, frozen, plus BM25+RRF fusion + lexical pre-selection 4096 → ~128 tokens; typed tool/action compilation; calibrated abstention, unknown; i5-12400F: semantic p50 2.6–2.9 ms, long-context p95 9–11 ms, RSS ~825 MB; parity vs Laya 0.3.20: Banking77 0.658 vs 0.431, 2.863 ms vs 478.969 ms p50; Apache-2.0", "arch": "Machine-native decision runtime without autoregressive generation: a frozen 24M mxbai-embed-xsmall-v1 semantic encoder plus BM25/RRF fusion and lexical pre-selection, 4096 to about 128 tokens, compile typed tool and action calls, with calibrated abstention, unknown", "approach": "", "training": "", "data": "", "result": "On an i5-12400F: semantic p50 2.6–2.9 ms, long-context p95 9–11 ms, ~825 MB RSS; parity run against Laya 0.3.20 scores Banking77 0.658 versus 0.431 at 2.863 ms versus 478.969 ms p50", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vibecheck", "name": "vibecheck", "author": "jlowin", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/jlowin/vibecheck", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 27, "likes": "", "detail": "Four functions: check answers yes or no and returns a bool, classify picks one option, label picks every option that applies, and score rates on a scale. Batching lets you ask ten questions about the same data for about the cost of one; probabilities, thresholds, and unsure answers are available when you need them and out of the way when you don't. Published as vibecheck-py and imported as vibecheck, installed with uv add; the API key comes from the environment via console.typesafe.ai or any compatible provider, and the examples folder has a small runnable script for each function.", "arch": "Python decision library, billed as the easiest decisions your code will ever make: decision models such as Jev, built for judgment calls, answer typed questions, yes or no, pick one, rate on a scale, in a fraction of a second for a fraction of a cent with a probability attached to every answer, and vibecheck puts all of that in one function call that returns a plain Python value, with no prompts to template, no text to parse, and no agents to configure.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "egbertjing-vllm-jev", "name": "vLLM Jev", "author": "Egbertjing", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/mode-io/vllm-jev", "base_model": "", "params": "", "metric": "Open-Jev-2B short c8: 4,360.5 → 100.5 ms, 43.4×, 78.83 req/s", "license": "Apache-2.0", "paper": "", "stars": 25, "likes": "", "detail": "arch: native vLLM serving for Jev-style checkpoints - scheduling, batching, compilation, KV cache, metrics; native decision readouts: scalar candidate branches or marker-token scores, chosen from the model format; notes: Open-Jev-2B Short c1 546.6 → 73.0 ms, 7.5×, c8 4,360.5 → 100.5 ms, 43.4×, 78.83 req/s; Long c1 582.3 → 81.9 ms, 7.1×", "arch": "Native vLLM serving for Jev decision models: vLLM's scheduling, batching, compilation, KV cache and metrics, with decision readouts chosen from the model format - scalar candidate branches or marker-token scores", "approach": "", "training": "", "data": "", "result": "Open-Jev-2B: short-input latency 546.6–73.0 ms at concurrency 1, 7.5×, and 4,360.5–100.5 ms at concurrency 8, 43.4×, 78.83 req/s; long-input 582.3–81.9 ms, 7.1×", "notes": "Repo renamed or transferred since the 2026-09 sweep; URL updated to the current location", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "weigh", "name": "weigh", "author": "wweir", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/wweir/weigh", "base_model": "Any OpenAI-compatible inference server", "params": "", "metric": "", "license": "MPL-2.0", "paper": "", "stars": 0, "likes": "", "detail": "author: William Weir; base: vLLM / SGLang; arch: prompt hash, readout path, serving config · derived from SemIf's independent research effort; training: serving/readout; name: SemIf direct-options serving", "arch": "Rust: exact per-slot logprobs from OpenAI-compatible servers using vLLM logprob_token_ids / SGLang token_ids_logprob, with provenance. Ships the semif-vllm binary serving SemIf's direct-options-v1 decision API", "approach": "logits", "training": "None", "data": "None", "result": "93 hermetic tests; exact logprob readout instead of generated-text parsing", "notes": "The 'exact logprob' end of the readout spectrum — where SemIf-style decision serving meets production observability.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "attachemd-xcanv", "name": "xcanv-models, judge-pack", "author": "attachemd", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/attachemd/xcanv-models", "base_model": "", "params": "", "metric": "judge-w8.onnx 600 MB, judge-w4.onnx 416 MB", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: opt-in model packs downloaded by the xcanv desktop app; judge-pack-v1 = Laya typed-decisions ONNX: judge-w8.onnx 600 MB, MatMulNBits opset 18, default, highest fidelity, and judge-w4.onnx 416 MB; notes: ModernBERT tokenizer; rl_agent_config plus encoder-config, head layout and temperature", "arch": "Model-pack repository for the xcanv desktop app: judge-pack-v1 ships Laya typed-decisions as ONNX - judge-w8.onnx, 600 MB, MatMulNBits opset 18, default, and judge-w4.onnx, 416 MB, with the ModernBERT tokenizer and agent/encoder configs including head layout and temperature", "approach": "", "training": "", "data": "", "result": "", "notes": "Model files ship as release assets, judge-pack-v1, 7 assets with SHA256SUMS; the xcanv app downloads them only on user opt-in", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zev", "name": "Zev", "author": "bhubbard", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/bhubbard/zev-rs", "base_model": "none; zero weights", "params": "0 weights", "metric": "complex schema decision in 5.8 µs", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "metric: complex schema in 5.8 µs with zero weights; arch: cascades to apfel-rs on Apple Silicon, Candle otherwise; notes: crates.io + npm + Homebrew, HF space and dataset", "arch": "Zero-token Rust decision engine: scores typed decisions with no model weights at all, then cascades to a learned engine, apfel-rs on Apple Silicon, Candle otherwise, when the zero-weight pass is inconclusive", "approach": "rules", "training": "", "data": "", "result": "Complex schema decision resolved in 5.8 microseconds with zero weights on the deterministic path", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevscope", "name": "ZiYor-JEVScope", "author": "ziyor-ai", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/ziyor-ai/ZiYor-JEVScope", "base_model": "Laya base models on Apple Silicon", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 6, "likes": "", "detail": "", "arch": "Desktop app for local decision-model training on Apple Silicon Macs: create a project, import Excel or JSONL or type data, label answers, train a Laya model for binary decisions, multiple choice, or ordinal ratings, and review results per question; projects keep immutable snapshots, model setup downloads and verifies Laya weights, and training runs on the local MPS backend with a bundled Python runtime so users do not install Python separately", "approach": "", "training": "Local Laya fine-tuning", "data": "Excel or JSONL import plus manual entry", "result": "Local training workflow in place; end-to-end training with real models, results on representative data, and a distributable macOS package not yet validated", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zjev", "name": "zjev", "author": "chy3xyz", "date": "2026-09-24", "category": "runtime", "url": "https://github.com/chy3xyz/zjev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "zig build produces five binaries, zjev-serve, zjev-fit, zjev-bench, zjev-traj and zjev-conformance; zjev-serve runs either the direct engine or with a queue scheduler plus single-flight cache, and answers POST /v1/decide; unit tests plus protocol conformance fixtures", "arch": "Typed probabilistic decision runtime in Zig 0.17: converts unstructured state into probability-calibrated structured decisions, with the protocol defined in RFC-0001, a v0.2 decision-graph design spec, and task breakdowns under docs/plans", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "New 2026-09-24 entrant; README not yet published.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "amigos-jev", "name": "amigos-jev", "author": "amigos-robot", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/amigos-robot/amigos-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 5, "likes": "", "detail": "Built for robot control: a first-person camera frame plus a state description goes in, the options are action commands such as move forward, turn left, grasp, stop, and the model picks the next action with a confidence. 100–500 ms decision latency to keep up with a control loop; inputs can carry up to 4 images; when confidence is low the operator can stop the robot or hand the decision to a human; completely free, no application required", "arch": "AmigosJev, a free multimodal Jev-style decision API at jev.amigos-robot.com: a native multimodal model with about 20B active weights that uses the next-token probability distribution directly as the decision output, so answers always stay inside the options you give, no text generation", "approach": "", "training": "", "data": "", "result": "", "notes": "5 stars at sweep time", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "0xbakeer-arbiter", "name": "arbiter", "author": "0xbakeer", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/0xbakeer/arbiter", "base_model": "Laya or your own model", "params": "", "metric": "", "license": "", "paper": "", "stars": 34, "likes": "", "detail": "Python 3.12 or newer, with recipes for NVIDIA CUDA 13 and Apple Silicon MPS or MLX, an MCP server integration, and a Claude Code plugin; listed on laya-ai.com. The project's origin story is in a blog post: the author's cat woke him at five on a Sunday, so he built a local Jev.", "arch": "Serves typed-decision models, Laya or your own, on an NVIDIA GPU or an Apple Silicon Mac, with a Jev-compatible API: a typed-decision model answers questions about a piece of text in one forward pass, no tokens generated, no sampling, no loop, and the server takes a state and a set of questions and returns an answer and a probability distribution for each of them at once.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bev", "name": "Bev", "author": "Reza2kn", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/Reza2kn/Bev", "base_model": "Ternary-Bonsai-2-27B GGUF + Jevfire-style selected-token scoring", "params": "27B", "metric": "95.4%", "license": "Code MIT; weights Apache-2.0", "paper": "", "stars": 13, "likes": 0, "detail": "base: 7.21 GB, PQ2_0 ternary weights, byte-for-byte redistribution; metric: Persian bench, Choice; arch: 16 GB RAM; training: no LoRA, no quantization; data: published; license: upstream; result: 7.28 GiB CPU, 8.30 GiB GPU; notes: inference method; name: Jevfire + Ternary-Bonsai-2-27B", "arch": "Boolean/enum/Choice/Noul/ordered-Score fields, 2–255 candidates; one next-token scoring step per field, JSON assembled by local HTTP API; CPU-only, Apple Metal, NVIDIA CUDA paths all validated", "approach": "logits", "training": "None; inference package, not a newly trained model", "data": "Persian evaluation set", "result": "Persian benchmark: 95.42% Choice · 95.00% Noul · 87.50% Score; exact RAM/VRAM measurements published", "notes": "Combines two smaller projects: kikoncuo/jevfire + Prism ML ternary weights. Rare example of Jev-style decisions on a ternary-quantized 27B.", "domain": "general", "score": "95.4%", "latency": "", "benchmark": "Persian-Bench", "cost": "", "speedup": ""}
{"id": "qkal-canny", "name": "Canny", "author": "qkal", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/qkal/canny", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 85, "likes": "", "detail": "", "arch": "Stops AI coding agents from claiming work is done without evidence. Deterministic hooks decide, TypeSafe's Jev advises. Append-only ledger, zero runtime dependencies. Agent verification: stops AI coding agents from claiming work is done without evidence by using deterministic hooks and TypeSafe's Jev advisor to evaluate test results, file diffs, and verification logs. Evidence", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "samiul000-laya-cpp", "name": "Contents", "author": "samiul000", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/samiul000/laya-cpp", "base_model": "Laya", "params": "undisclosed", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "The LayaEngine class and the laya_cli binary; a Python parity reference, test_parity.py, verifies C++ output against ONNX Runtime Python; the INT8 quantization pipeline reports about a 2.4× speedup and 4× smaller models with an output-deviation check; a reproducible Docker build pins the C++ and Python ORT versions and was tested with Docker Engine 29.x on the WSL2 backend, about 3 GB free for the image with models in models/. MIT.", "arch": "Native C++17 inference engine for Laya, optimized for low-latency CPU and robotics workloads: it runs the Laya decision model via ONNX Runtime 1.23.2 with no Python dependency at inference time, and ships a CLI runner with a built-in latency benchmark plus an INT8 dynamic-quantization pipeline.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "psyb0t-decidealot", "name": "Decidealot", "author": "psyb0t", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/psyb0t/decidealot", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "Docker-first: the quick start runs the CPU image on loopback, stores downloaded model files in one narrow host directory, and gives the container no capabilities and no writable root filesystem. Guides cover using the API and MCP, exposing MCP through a proxy, picking a model, configuration, CUDA, model storage and unloading, and agent integrations. CI with coverage badges, a Docker image on the Hub, and a versioned release pipeline.", "arch": "Local decision-model server for Laya and Von over TypeSafe-compatible HTTP or MCP on your own hardware: at startup it downloads and verifies both local model bundles, then loads only the model a request selects, unloads it after the configured idle period, and returns typed choice, score and noul answers with model probabilities, exposing the TypeSafe HTTP API and MCP Streamable HTTP from the same local container.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "deqio", "name": "Deqio", "author": "iluce", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/ILuce/deqio", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 6, "likes": "", "detail": "", "arch": "One local API and a lightweight browser UI for fast typed AI decision models with multiple swappable engines: SemIf on Qwen3.5 4B, Kev 0.8B 4B and 9B, Decider 0.8B 2B and 4B, Laya English 421M, Laya Multilingual 322M, Laya Typed Decisions 421M and Von, on MLX, MPS or CUDA backends; the public endpoints, POST /v1/noul, /v1/choice and /v1/shared, stay the same regardless of the active model, so application code never changes when the model does", "approach": "", "training": "", "data": "", "result": "", "notes": "deqio serve loads the chosen model on demand with a warmup and keeps the API on one address, http 127.0.0.1:8787; the UI shows the answer, probabilities, latency, token count and cache per decision; model setup and switching through deqio models setup, installed, use and status; created 2026-09-23", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "strangeloopcanon-dynajev", "name": "Dynajev", "author": "strangeloopcanon", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/strangeloopcanon/dynajev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 5, "likes": "", "detail": "approach: per-question compiled heads over frozen weights", "arch": "Dynamic Jev: turns any open-weight LLM into a Jev-style typed decision API by compiling a per-question output head at request time — which rows of the output matrix to read, where in the prompt, how many branches off a shared prefill, how many layers to run, and how to combine scores; weights stay frozen and closed questions generate nothing.", "approach": "head", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gitev", "name": "gitev", "author": "Mombrane", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/Mombrane/gitev", "base_model": "Jev-class decision model as the judgment layer", "params": "", "metric": "Classification, conflict detection and relevance reranking over git-stored memories", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Git as storage, decision model as judgment: an AI agent long-term memory system where a git repository is the single source of truth; a decision model layer does classification, conflict detection and relevance reranking; memories move through a state machine of active, superseded, patched, contradict, pending, expired and archived; nothing is ever physically deleted; silent by default, an empty output is the correct answer; evaluation is first-class", "approach": "", "training": "", "data": "", "result": "", "notes": "As of 2026-09-25 the repo is design documentation only, README plus docs/design.md, docs/evaluation.md and docs/research.md, no implementation code published", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "go-jev", "name": "go-jev", "author": "mattn", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/mattn/go-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 28, "likes": 0, "detail": "The client reads no environment variables: NewClient takes options, WithAPIKey sends the Authorization header only when a key is given, WithModel defaults to jev-latest, WithURL defaults to the api.typesafe.ai systemone endpoint, WithTimeout defaults to 60 s including retries, and retries default to 3 on 429 and 529. Option descriptions keep their declared order, and a nil description is sent as null. By Mattn Matsuoka, creator of mattn/go.", "arch": "Go SDK for TypeSafe Jev, a decision-only model that returns typed answers, yes-or-no probabilities, choice and score, instead of text, with jev-cli, a command-line tool built on it for UNIX pipelines. One question goes through Client.Ask; several questions in one request go through Client.Evaluate, where the state may be any JSON value.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "By mattn.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "janmejai2002-gutcheck", "name": "gutcheck", "author": "janmejai2002", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/janmejai2002/gutcheck", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Fast, calibrated System-1 decisions on the NPU or GPU you already own — Python 3.10 to 3.13, Apache-2.0, runs on Intel NPU — plus a lease layer that stops your AI agent from loading every skill and tool on every turn", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "artalis-io-jev-bush", "name": "Jev Bush", "author": "artalis-io", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/artalis-io/jev-bush", "base_model": "DiffusionGemma 26B-A4B", "params": "", "metric": "pinned public benchmark, 2,000 decisions", "license": "0BSD", "paper": "", "stars": 4, "likes": "", "detail": "", "arch": "CPU-first probabilistic decisions for DiffusionGemma: evaluates bounded OpenJev decisions directly from the model's answer-slot logits without autoregressive generation or structured-output parsing; tests whether the resulting distributions remain comparable to the reference GPU implementation; engine is one C11 file with no runtime dependency beyond the C standard library, OpenMP optional", "approach": "logits", "training": "No training; direct probabilistic readout of a diffusion language model", "data": "OpenJev bounded decision semantics and public evaluation data", "result": "On the pinned public benchmark the strongest run reaches 67.40% accuracy, 5.3 points behind Jev 1.13.0 published 72.70% on the same 2,000 decisions — comparable argmax decision quality on this benchmark, not model equivalence", "notes": "GPU backends deliberately out of scope; points to OpenJev and SemIf for production GPGPU inference; -ffast-math versus strict IEEE floating-point behavior reported separately", "domain": "general", "score": "accuracy 67.40%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yeyan00-jev-decision", "name": "Jev-Decision", "author": "yeyan00", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/yeyan00/Jev-Decision", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "A small, fast structured decision model built on Qwen3-VL-2B-Instruct. It accepts text-only or image-plus-text inputs and returns a calibrated probability distribution over a dynamic set of options without generating answer text", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-topk-logits", "name": "jev-native-agent-with-extended-options", "author": "6Mikao9", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/6Mikao9/jev-native-agent-with-extended-options", "base_model": "Jev agent design study", "params": "", "metric": "Bounded resident-set coverage versus the unbounded action space", "license": "Open", "paper": "", "stars": 23, "likes": "", "detail": "", "arch": "Open research runtime for bounded, non-generative decision models: the unbounded world is split into two manageable logical spaces and the decision interface sees only a small part of them on demand; a Core mode runs with a deterministic scripted chooser, no model download, no GPU and no API key, while an optional 0.8B helper and live Jev round it out", "approach": "", "training": "", "data": "", "result": "Decision-space virtualization turns the logically unbounded tool, action and parameter space into a bounded resident set, with PAGE and EXPAND widening coverage and REFINE raising candidate granularity; options carry stable IDs, page ownership, revision and dependencies, and OptionFault, ContextFault, PAGE, REFINE, INVALIDATE, CLARIFY and STOP are recordable, replayable state transitions; a virtual context space keeps Pinned, Working and Cold context ranked by utility", "notes": "20 stars; Chinese-first docs with an English README; research repo, weights kept outside the repo; repo renamed or transferred since the 2026-09 sweep, URL updated", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "greghavens-jev-no-bullshit", "name": "jev-no-bullshit", "author": "greghavens", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/greghavens/jev-no-bullshit", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "AI coding assistants sometimes finish with a summary that says more than they did: \"All tests pass\" when the tests never ran, \"should work now\", or an upbeat line that says nothing", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "h0j5bz0adh0-stack-jev-pilot", "name": "jev-pilot", "author": "h0j5bz0adh0-stack", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/h0j5bz0adh0-stack/jev-pilot", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 5, "likes": "", "detail": "", "arch": "System-1 decision, arbitration and safety engine for autonomous agents on top of TypeSafe's Jev: best-of-N candidate arbitration generates 2–3 LLM approaches and picks the winning strategy with mathematical confidence; pre-execution safety guardrail intercepts dangerous shell commands, SQL drops and destructive mutations before they run; on_error fail_closed blocks on timeout, fail_open allows", "approach": "sdk", "training": "No training; Jev API wrapper with arbitration and guard policy", "data": "Agent tool-call candidates and proposed actions", "result": "Sub-second answers around 0.3 s based on TypeSafe internal published benchmarks and live API tests; $42 per billion input tokens; exact 0.0–1.0 probabilities instead of text strings", "notes": "pip; MIT; English and Persian docs; targets Claude, GPT, Gemini, Llama, Hermes and custom agent runtimes", "domain": "agent", "score": "", "latency": "300 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chemany-jeva", "name": "jeva", "author": "chemany", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/chemany/jeva", "base_model": "MiniCPM5-2B", "params": "2B", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "Fine-tune of openbmb/MiniCPM5-2B from ModelScope, trained on trajectories collected by driving a real Chrome with a deterministic solver; the weights are merged into the base, so there is no adapter to pair and nothing to install beyond your usual inference stack. 2B parameters, 1.6 GB as a Q4_K_M GGUF, running under llama.cpp on a single V100. The data pipeline uses zero human labels, the eval suites are frozen, a model card doc is included, Apache-2.0, and the README is bilingual English and Chinese.", "arch": "A 2B browser-agent decision model you can train and run yourself: it turns a page observation plus a goal into exactly one action, a compact JSON object with no prose, from a typed action space of CLICK, TYPE_TEXT, SELECT, WAIT, DONE and BLOCKED targeting indices from the observation you supplied, so it never emits CSS, coordinates or invented selectors, and your executor resolves the index against the same snapshot.", "approach": "undisclosed", "training": "", "data": "Frozen suites", "result": "Claimed at field level: 100% on the frozen eval suites at roughly 230 ms per decision.", "notes": "Fields claim-level.", "domain": "computer-use", "score": "100%", "latency": "230 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jawzod3th-jevapi", "name": "JevApi", "author": "JawzoD3TH", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/JawzoD3TH/JevApi", "base_model": "", "params": "", "metric": "Typed NoulAnswer, ChoiceAnswer and ScoreAnswer with full probability distributions and confidence, AOT-safe .NET 10 client plus stdio MCP server", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": ".NET tooling for the TypeSafe AI Jev System One model. Jev evaluates typed questions — noul, yes or no, choice, pick an option, and score, rate on a rubric — against a state and returns structured answers with probability distributions and confidence values", "approach": "", "training": "", "data": "", "result": "Client retries with exponential backoff on 429 and 529 honoring Retry-After; MCP exposes evaluate, evaluate_batch up to 50 items, validate_questions dry-run and examples; results keep raw probabilities and confidence plus request_id, latency_ms and token usage", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ibrahimcesar-jevdev", "name": "jevdev", "author": "ibrahimcesar", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/ibrahimcesar/jevdev", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": "", "detail": "Six decision points with typed answers: context visibility, a choice of hide, short, long or full for a chunk against a query; cache, reuse the cached prefix or rebuild, noul plus probability; routing, can this subtask leave the frontier model, choice plus cost estimate; tools, which tool fits this intent, ranked choice top-k; permissions, should this command run, allow, ask or deny; and security, which files will this task touch, a sensitivity score. Rust, published on crates.io, Apache-2.0; jevdev init writes jevdev.toml, .jevdev/exec.cedar and AGENTS.md, and the terminal UI runs against a TypeSafe key plus an Anthropic key or ant auth login.", "arch": "Coding-agent harness built around TypeSafe Jev, the System One decision model: all agent state lives as explicit, typed, content-addressed chunks, and Jev answers the per-turn questions that current agents leave to defaults, on the premise that the leverage in a coding agent is not in the loop but in what the harness puts in front of the model on every turn. Jev is not the model that writes the code: frontier models, sub-agents, tools and deterministic code do the work, and Jev returns typed choices, scores and noul decisions with probabilities, so the harness validates, thresholds and branches on them without parsing prose.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevlish", "name": "Jevlish", "author": "jacobgoldfarb", "date": "2026-09-23", "category": "runtime", "url": "https://jacobgoldfarb.github.io/Jevlish/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: client-side library over the Jev API; arch: judge one value · filter or rank a collection; notes: HN; name: TypeScript decision DSL", "arch": "Jev judgments in ordinary TypeScript business logic: given / from build a lazy expression — building it sends nothing;.plan previews the state+questions that would leave the process; await/.run folds code predicates, compiles the remaining Noul/Score/Choice questions and asks Jev once; AcceptancePolicy turns model probabilities into decisions; cache, evidence and tracing; classes: Choice, Meaning, Scale, SenseError, Decision, Evidence, GradeReport…", "approach": "sdk", "training": "", "data": "", "result": "Docs cover choice/meaning/scale types, acceptance & partial policies, fixtures, running and planning; 'The official Jev SDK is shaped like an LLM SDK… In application logic, the response usually decides what code does with a value it already has'", "notes": "Type-safe DSL layer for in-process decision logic; surfaced 09-23/24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-next", "name": "JevNext", "author": "DejaAI2", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/DejaAI2/JevNext", "base_model": "Qwen3-0.6B", "params": "0.6B", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "base: Qwen3-0.6B, LoRA rank 16; arch: fused decision model and OpenAI-compatible generation server, zero-copy LoRA switching; result: 102-question holdout, MiniJev 58.8%, JevNext 86–91; notes: Apple Silicon MPS, learning project", "arch": "Decision model on a Qwen3-0.6B backbone with LoRA and a fused decision endpoint at /v1/decisions.", "approach": "", "training": "", "data": "", "result": "", "notes": "MiniJev minus the ceiling: a Jev-style decision model on a Qwen3-0.6B backbone with LoRA rank 16, fused with a full OpenAI-compatible generation server — one set of weights, two capabilities, zero-copy LoRA switching. Built and benchmarked end-to-end on Apple Silicon MPS. Learning project and the continuation of MiniJev, whose from-scratch 680K model scored 58.8% on a hand-written holdout. On the same 102-question holdout, JevNext reaches 86–91%.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zojeda-jevons-rs", "name": "jevons-rs", "author": "zojeda", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/zojeda/jevons-rs", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A Rust implementation of TypeSafe AI's System One API: typed, probabilistic answers, yes or no, choice, rubric scores, from diffusion language models, DiffusionGemma and NVIDIA Nemotron-Labs-Diffusion, both with image input, running on AMD GP", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevper", "name": "jevper", "author": "zhulinchng", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/zhulinchng/jevper", "base_model": "Any OpenAI-compatible model", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "base: hosted LLM or self-hosted llama.cpp; arch: noul/choice/score · anything exposing responses.create or chat.completions.create works · example: gpt-5.6-terra; name: Jev wire format on any OpenAI-compatible model", "arch": "Independent implementation of the documented System One wire format: state in, typed questions out, answers with probabilities and confidence; client is duck-typed; method='logprobs'", "approach": "logits", "training": "", "data": "", "result": "pip install jevper; 'point jevper at a hosted LLM or a self-hosted llama.cpp server and code written for Jev keeps working, unchanged'; no runtime dependency on typesafe-sdk or openai", "notes": "'An independent implementation of the documented System One wire format. Not affiliated with, endorsed by, or supported by TypeSafe AI.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "j1s4nn-jevXagent", "name": "jevXagent", "author": "j1s4nn", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/j1s4nn/jevXagent", "base_model": "Local LLM + Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "The mechanism is Line J, injecting Jev's roughly 120 ms decision directly into the agent's context: without the proxy the path is user to agent to classify, think, generate to response, about 2,800 ms and 250 tokens; with it, user to Jev to Line J to agent to generate only to response, about 950 ms and 50 tokens. v0.2, 19 tests passing, Python 3.11 or newer, MIT, with live weekly statistics from production use.", "arch": "Transparent local proxy and decision router for AI coding agents, Claude Code, Codex, Kilo and Cline: it routes every prompt through TypeSafe Jev's decision engine before the main LLM generates a response, so the agent sees what was already decided before it starts, targeting the repeated classification work, is this a DevOps question or a bug fix, should I search or generate, what is the user actually asking for, that otherwise burns 200–300 output tokens per prompt.", "approach": "router", "training": "", "data": "", "result": "Claimed in the README: 70% cut in AI agent response time and 80% token savings, with the example run going from about 2,800 ms and 250 tokens to about 950 ms and 50 tokens, Jev's decision costing about 120 ms.", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kime", "name": "kime", "author": "tamnd", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/tamnd/kime", "base_model": "Laya checkpoints, native kime models planned", "params": "", "metric": "Speed-cost scorecard against Laya and Jev baselines, with a 10× bar", "license": "Apache-2.0", "paper": "", "stars": 2, "likes": "", "detail": "name: Rust inference engine and server for typed decisions", "arch": "Rust inference engine plus server answering choice, score and yes/no questions with calibrated probabilities, no text generation. Runs the published Laya checkpoints on CPU, CUDA and Apple GPUs with the same answers as Laya, and serves the System One API over HTTP; Jev and Laya clients work unchanged. kime-v1 encodes the state once, caches its keys and values, and answers every question with a small cross-attention tower, so ten questions cost little more than one; options are permutation-equivariant by construction. Early: engine works, native models, router, caches and SDKs still to come", "approach": "", "training": "", "data": "", "result": "Stated bar: 10× faster and cheaper than Laya or Jev on every speed-cost row and matching or better accuracy. Scorecard shows baselines only — 32.8 ms per decision on T4 for Laya, $0.042 per million tokens for Jev, 0.766 typed-decisions accuracy, 0.366 MASSIVE multilingual — with targets 3.3 ms, $0.0042, 0.78 zero-shot, 0.55", "notes": "kime. Companion repos kime-bench and kime-compat announced; Python, TypeScript and Swift SDKs planned. 決め, 'the decision' · benchmark harness · TypeSafe API and SDK compatibility", "domain": "general", "score": "", "latency": "32.8 ms", "benchmark": "", "cost": "", "speedup": "10× faster"}
{"id": "lakun", "name": "LaKun", "author": "AI-Discussion-Room", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/AI-Discussion-Room/LaKun", "base_model": "mmBERT-base + SigLIP SO400M", "params": "756M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 6, "likes": "", "detail": "The text and vision encoders were jointly fine-tuned; no Laya fine-tuned checkpoint was loaded, but the decision head uses Laya-derived code with its notice preserved, and the README is explicit that LaKun's results are not Laya's. Output is per-question criteria, probabilities and predicted_index; probability calibration is not validated. Install with pip install lakun, weights on ModelScope, downloaded and cached on first use, with ms-hub login if access is restricted; the CLI prompts for the text state or image path and the question. Jev inspired the project, with optimization ideas from Laya's RLCD work. Apache-2.0, bilingual English and Chinese README, with a dataset profile and charts.", "arch": "Multimodal Jev-style typed decision model: given a text state or one image, it answers specified choice, rating-bin and binary questions and returns a softmax score for every option and the highest-scoring option, rather than generating free-form text. One call can mix all three question types, with up to 20 questions and one image; choice takes 2–20 options, score takes 3–20 bins, noul is false or true. The architecture is mmBERT-base plus SigLIP SO400M plus a visual-token bridge plus a typed decision head, 756,409,158 parameters, about 0.756B, across 627 checkpoint tensors, with a 512-token context that reserves 64 visual tokens for images.", "approach": "", "training": "", "data": "", "result": "", "notes": "pip install lakun; ModelScope weights", "domain": "multimodal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aaditagrawal-laya-inference-engine", "name": "Laya Inference Engine", "author": "aaditagrawal", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/aaditagrawal/laya-inference-engine", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "A GPU inference engine for Laya, tested on the NVIDIA RTX 5070 Ti. Choose the default balanced mode for simpler setup, or fast mode for compiled SM120 kernels and lower warm latency", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "afshinm-laya-mps", "name": "laya-mps", "author": "afshinm", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/afshinm/laya-mps", "base_model": "", "params": "", "metric": "32 ms median, 2.1 GiB RAM on M5 Pro", "license": "", "paper": "", "stars": 22, "likes": "", "detail": "", "arch": "Run Jev-style typed decisions locally on your Mac with low RAM usage and fast responses.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Run Laya on PyTorch MPS on Apple Silicon: about 32 ms median latency with about 2.1 GiB RAM on M5 Pro, and a slower about 0.74 GiB low-RAM mode; Pong demo with live response latency; model about 843 MB; local server at 127.0.0.1:8000 that works offline after the first run. Curated in 1 awesome-jev list.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "redwolf2019-laya-rs", "name": "laya-rs", "author": "redwolf2019", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/redwolf2019/laya-rs", "base_model": "Laya multilingual", "params": "421M", "metric": "", "license": "", "paper": "", "stars": 9, "likes": "", "detail": "arch: laya-multilingual default", "arch": "Rust inference runtime and laya-server HTTP service for Laya, Linux CPU-first and multilingual: typed choice, score, and boolean questions answered from model-resident weights shared across clients.", "approach": "head", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-server", "name": "laya-server", "author": "1Panel-dev", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/1Panel-dev/laya-server", "base_model": "Laya", "params": "421M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 66, "likes": 0, "detail": "base: convaiinnovations Laya; license: Apache-2.0; notes: published docker image 1panel/laya-server with web console at port 8080, API keys page and laya-data volume; model: auto and multilingual", "arch": "Self-hosted API + web interface for Laya's structured decision models, compatible with the TypeSafe Jev API format", "approach": "head", "training": "", "data": "", "result": "", "notes": "Self-hosted API and web interface for Laya's structured decision models, compatible with the TypeSafe Jev wire format; from the 1Panel team", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-system-one-webgpu", "name": "laya-system-one", "author": "italoalmeida0", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/italoalmeida0/laya-system-one", "base_model": "Laya multilingual", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "Claims about 15 ms on modern CPUs via native C++/SIMD and WebGPU, multilingual support for over 100 languages out of the box, and universal JavaScript support across Node.js 18 or newer, Bun, Deno and modern web browsers. Dual operation: an independent local HTTP daemon via the npx laya-system-one CLI, or an in-memory import into the application process for zero network overhead. Any existing Jev client or SDK connects by changing the base URL. Apache-2.0.", "arch": "Self-contained, ultra-fast System 1 decision engine with WebGPU and WASM SIMD acceleration, a drop-in, 100% wire-compatible replacement for the TypeSafe Jev API's POST /v1/systemone endpoint: it runs entirely on the local machine with zero Python, zero PyTorch, and zero external network requests at runtime, and the INT8 multilingual model of about 324 MB is embedded directly in the package, enabling secure, air-gapped corporate deployments with sub-20 ms inference latency.", "approach": "", "training": "", "data": "", "result": "", "notes": "Browser-first runtime", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mushroom-systems-lichen", "name": "Lichen", "author": "Mushroom Systems", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/Mushroom-Systems/lichen", "base_model": "Gemma 4 26B A4B", "params": "26B-A4B", "metric": "JevBench 231 public decisions", "license": "MIT", "paper": "", "stars": 10, "likes": "", "detail": "", "arch": "Drop-in API-compatible Jev replacement on local open-weight models: serves the same /v1/systemone endpoint and returns the same typed answers, choice, noul and score, with a probability for every option, so existing Jev clients work against it without changes", "approach": "head", "training": "No training; hosts open-weight models behind the System One wire format", "data": "Single laptop GPU", "result": "With Google gemma-4-26B-A4B answers 89.6% of the 231 public JevBench decisions correctly, against 86.6% for Jev 1.13, at 147 ms median latency per decision on a single laptop GPU", "notes": "MIT", "domain": "general", "score": "89.6%", "latency": "147 ms", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "mylaya", "name": "myLaya", "author": "RyanPeng1111", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/RyanPeng1111/myLaya", "base_model": "Laya multilingual 322M", "params": "322M", "metric": "Synthetic scenario match rate", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Windows local-CPU decision workbench with Chinese documentation: the full Laya multilingual 322M weights plus all 39 pinned Python wheels are stored as 45 MiB git blobs with no Git LFS; a SHA-256-verified Bootstrap.cmd restores and installs completely offline with no Nexus, PyPI or Hugging Face, then starts the workbench at 127.0.0.1:9622 on the existing Python 3.12 x64; Laya structured decisions plus an optional corporate OpenAI-compatible LLM; no Git, LFS, Node, Docker, CUDA or Hugging Face account needed; verified pinned checkpoint b4a904d", "approach": "", "training": "", "data": "8 synthetic scenarios", "result": "5 of 8 synthetic scenarios match human expectations; the sentiment and IT-versus-finance ticket classes succeed, two evidence judgments and one document-lookup route fail; explicitly a small-sample demo, not a business accuracy", "notes": "About 5 GB disk recommended; 8 GB RAM minimum, 16 GB tested", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ollaya", "name": "ollaya", "author": "ollaya-dev", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/ollaya-dev/ollaya", "base_model": "", "params": "", "metric": "laya:en answers 5 questions in 8–10 ms on an RTX 4090; decider 0.591 on typed-decisions", "license": "Apache-2.0", "paper": "", "stars": 157, "likes": "", "detail": "accuracy: fp32 exports match the PyTorch reference on 100% of 2,383 questions per checkpoint; notes: serves laya 421M en, laya 322M multilingual, decider 2B and 0.8B, kev 0.8B, qwen3guard 0.6B, NLI, gliclass and von; each model keeps its own license", "arch": "local daemon plus CLI, Ollama-style, for open decision models: pull, run and serve by model name; wire-identical POST /v1/systemone, /v1/decisions and /v1/models, so the TypeSafe SDK works against it with TYPESAFE_BASE_URL", "approach": "sdk", "training": "", "data": "ships only ~3 MB of ONNX graphs; weights are read from the author's HF repo, commit-pinned and sha256-verified, never re-hosted", "result": "routers for Laya, Decider, NLI and other open decision models; native /api/decide with routing info and timings", "notes": "port 11435; run open decision models locally the way Ollama runs LLMs", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "open-jev-laya", "name": "open-jev-laya", "author": "killkli", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/killkli/open-jev-laya", "base_model": "Laya multilingual checkpoints", "params": "421M", "metric": "About 647 MB fp16 ONNX weights, fully client-side inference", "license": "Open", "paper": "", "stars": 11, "likes": 0, "detail": "base: Transformers.js in-browser; name: browser, Transformers.js", "arch": "Laya multilingual running entirely in the browser tab via Transformers.js: Vite for development, static hosting on Cloudflare Pages in production; on first use the browser downloads the fp16 ONNX weights, about 647 MB, and falls back to WebAssembly when WebGPU is unavailable; the model revision is pinned", "approach": "head", "training": "", "data": "", "result": "Inference runs on the user's device with no server; the pinned ONNX checkpoint is killkli/open-jev-laya-multilingual-onnx; the dev flow needs Node 24 and pnpm 12", "notes": "7 stars; browser-only Laya demos", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "intikhab49-open-jev-typed-decision-engine", "name": "open-jev-typed-decision-engine", "author": "intikhab49", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/intikhab49/open-jev-typed-decision-engine", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 44, "likes": "", "detail": "arch: noul/choice/score in one non-autoregressive pass, calibrated confidence", "arch": "Open reproduction of TypeSafe Jev: a 150M typed decision engine. 0.697 vs Jev's 0.727, 2.5× better calibrated, 4× faster, free. Trains on a Colab T4 in 30 min.", "approach": "undisclosed", "training": "", "data": "", "result": "A quarter of all decisions at 91.5% accuracy is a shippable policy: autoroute that band, escalate the rest.", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "accuracy 91.5%", "latency": "", "benchmark": "", "cost": "", "speedup": "4× faster"}
{"id": "cequence-io-openai-scala-client", "name": "openai-scala-client", "author": "cequence-io", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/cequence-io/openai-scala-client", "base_model": "", "params": "", "metric": "Jev typed answers with calibrated probabilities in about 100 ms", "license": "MIT", "paper": "", "stars": 250, "likes": "", "detail": "jev: openai-scala-typesafe-client wraps POST /v1/systemone, model jev-latest; notes: no streaming, the API ignores a stream flag; the OpenAI-compatible Jev adapter serves json_schema structured output only and rides the calibrated probabilities in originalResponse", "arch": "No-nonsense async Scala client for OpenAI and many LLM providers with a TypeSafe Jev adapter: a native System One service for choice, score and noul with calibrated probabilities, plus an OpenAI-compatible closed-vocabulary JSON schema adapter", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "version 1.3.0, MIT; curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openjev-worker", "name": "openjev-worker", "author": "brandonbondig", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/brandonbondig/openjev-worker", "base_model": "vLLM workers", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Built for a RunPod load-balancer endpoint, published as ghcr.io/brandonbondig/openjev-worker. start.sh starts a readiness server, then vLLM, waits until /health returns 200, starts the helper and marks the worker ready once the helper answers /v1/version; exits status 1 as soon as any of the three processes stops, so the container restarts instead of half-serving. vLLM runs with SERVE.md flags: prefix caching, max-model-len 16384, gpu-memory-utilization 0.90, limit one image per prompt, max-num-seqs 256, max-logprobs 64, triton GDN prefill backend. Helper client libraries are pinned to SERVE.md versions, openai 3.16.2 and httpx 0.28.1, in /app/deps which only the helper sees.", "arch": "Docker image running OpenJev helper shim.py unmodified next to vLLM 0.29.0 on one GPU worker, answering POST /v1/systemone like the reference serving box in the model SERVE.md", "approach": "undisclosed", "training": "None", "data": "None", "result": "Two documented deviations from SERVE.md: the model is the FP8 checkpoint openjev/openjev-FP8 so the fp8 quantization flag is dropped; the build fails unless the helper sha256 is pinned. Readout knobs: T 0.85, noul T 1.829074, noul bias 0, targeted readout, pyrepr instruction style, stagger enabled", "notes": "Built for a RunPod load-balancer endpoint, published as ghcr.io/brandonbondig/openjev-worker; start.sh starts a readiness server, then vLLM, waits for /health 200, starts the helper, marks ready on /v1/version and restarts the container if any of the three processes stops", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "boozedog-pi-codemode", "name": "pi-codemode", "author": "boozedog", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/boozedog/pi-codemode", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 28, "likes": "", "detail": "jev: optional jev.ask global injected into the QuickJS sandbox calls TypeSafe System One for calibrated noul, choice and score answers inside the TypeScript program, key from TYPESAFE_API_KEY, codemode.json or a secrets file; guidance: slice state, ask one factor per question, never use Jev to choose tools; arch: sandbox is QuickJS with explicit tool globals, the code body is type-checked before execution", "arch": "Pi Codemode is a Pi extension that replaces many small tool calls with one typed execute_tools call. The model writes a TypeScript code body, Pi type-checks it, then runs it in a sandbox with explicit tool globals.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lateos-reflex", "name": "reflex", "author": "lateos-ai", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/lateos-ai/reflex", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 18, "likes": "", "detail": "arch: GGUF-native Rust & CUDA inference for System 1 loops; arch: generate loads a GGUF and generates · system1 runs a System 1 evaluation from a local GGUF path; notes: MLA", "arch": "GGUF-native Rust and CUDA inference engine optimized for cold-start latency and real-time System 1 agent decision loops - process launch to first token, not sustained server throughput; every CUDA kernel is compiled ahead-of-time by nvcc at build time and shipped inside the binary, never compiled at runtime; single binary with subcommands.", "approach": "", "training": "", "data": "", "result": "", "notes": "system1 currently supports dense/MoE Qwen3 only; the Qwen3.5 hybrid mixer and DeepSeek-V2/V3 are on the roadmap; example flow downloads Qwen3-0.6B-Q8_0 GGUF then runs the system1 test. Distinct from the Gowtham25 reflex-s1 model and the kshetrajna12 reflex fine-tune. GitHub new-repo sweep 2026-09-25.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rlcd-gateway", "name": "rlcd-gateway", "author": "JimmyWesley", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/JimmyWesley/rlcd-gateway", "base_model": "Go gateway fronting RLCD-style endpoints", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 2, "likes": "", "detail": "Apps that call Jev or open-rlcd System One decision models change only their base URL and get every decision audited and calibrated. Every request, client and cost shows in a live dashboard. One Go binary, no system proxy, no certificates; same prebuilt binary for macOS, Linux and Windows via npx, uvx, npm and PyPI packages.", "arch": "Self-hosted gateway for LLMs and decision models: Claude Code, Codex, OpenCode and any OpenAI or Anthropic SDK app route every call to the provider you choose, with context the model no longer needs pruned to cut token costs", "approach": "router", "training": "None", "data": "None", "result": "Proxy plus dashboard on http://127.0.0.1:4777/ui/; Claude Code keeps its own login, set ANTHROPIC_BASE_URL to the gateway", "notes": "", "domain": "routing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "clawdbot58-sentinel", "name": "Sentinel", "author": "clawdbot58-pixel", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/clawdbot58-pixel/sentinel", "base_model": "", "params": "undisclosed", "metric": "JevBench 87.9%", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Backend is llama.cpp Vulkan coopmat2 with Qwen3.8-27B UD-Q4_K_M on a Radeon AI PRO R9700 32 GB, where one decision is one forward pass with zero output tokens. The FastAPI proxy does letter-logit readout, temperature calibration at T 1.2, confidence and abstain fields in the TypeSafe wire format, so any Jev client works by swapping the endpoint. Latency p50 ~0.4 s, p95 ~3 s on a single slot, ~1000 tok/s prefill.", "arch": "Open self-hosted System One decision API wire-compatible with TypeSafe Jev: calibrated probabilities from Qwen3.8-27B, one forward pass per decision, running locally on consumer AMD hardware", "approach": "undisclosed", "training": "", "data": "", "result": "Public JevBench-231: Sentinel 87.9% accuracy and 77.0 score vs closed hosted Jev 86.6% and 75.4; calibrated ECE 0.049", "notes": "Includes an agent skill for Claude Code, pi and opencode, the /v1/systemone proxy plus start script, and an LLM plus oracle Snake demo, Hamiltonian fill-the-map, 36/36; requirements are Linux plus AMD GPU RDNA4 tested", "domain": "general", "score": "JevBench 87.9%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "sezika", "name": "Sezika", "author": "IoTSharp", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/IoTSharp/Sezika", "base_model": "mmBERT-base class multilingual encoder plus typed decision head", "params": "", "metric": "Choice, Score and Boolean typed decisions with full probability distributions", "license": "Apache-2.0", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Multilingual non-autoregressive System 1 decision engine in pure C# on .NET 10 with Native AOT: text or JSON state plus typed questions plus candidate criteria go through a multilingual encoder, a typed decision head and calibration into Choice, Score and Boolean outputs; the engine, CPU and GPU operators and scheduler are implemented in C# with no Python, PyTorch, ONNX Runtime, cuBLAS or cuDNN and no separate service process; GPU path runs C# kernels exported as PTX and ABI by ILGPU 1.5.3 at build time, executed through a static CUDA Driver binding under Native AOT; W8A32 quantized encoder and head path alongside scalar and SIMD FP32; tokenizer oracle and calibration evaluator included; intended for intent recognition, model and tool routing, RAG candidate reranking, content judgment and result verification; the engine returns a suggestion and the caller controls action and authority", "approach": "from-scratch", "training": "", "data": "Fixed real Laya/mmBERT development assets, safety-verified loader", "result": "S5-04 and S5-05 evidence: Windows win-x64 Native AOT smoke on all four backends covering 1, 8 and 32 questions each with 5 formal samplings per item; Ubuntu WSL2 linux-x64 Native AOT smoke on all four backends with 3 questions and 1 sampling each; the WSL smoke does not represent bare-metal Linux performance; per-language quality is not yet accepted; the W8A32 path is currently slower than SIMD and adds a quantization cache on top of the retained FP32 weights without reducing total resident memory; results are uncalibrated and model weights are not shipped with the repo", "notes": "Chinese-first documentation; reference-project analysis covers the auditable Laya implementation and the TypeSafe Jev public protocol", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "snap", "name": "SNAP", "author": "emnlmn", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/emnlmn/snap", "base_model": "Any local LLM", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 21, "likes": 0, "detail": "base: llama.cpp; arch: A–Z, one per option; training: bound to model+prompt version, ECE printed before/after; result: method repo; notes: __abstain__ · up to 256; name: Single-pass Neural Answer Probabilities", "arch": "Every question compiles to a prompt whose first answer token must be a letter; SNAP reads logits at that single position, pools letter variants, softmaxes — 'the jaw snaps shut on the answer'. One llama_decode per question; state prefix shared and evaluated once", "approach": "logits", "training": "No training; prompting + readout method; snap calibrate fits a temperature per question type on your labelled cases", "data": "Your own labelled cases for calibration", "result": "", "notes": "Prompting-only alternative to fine-tuning: noul/choice/score/numeric + abstention + out-of-range anchors; >26 options handled by per-candidate yes/no probes. 'Local, deterministic, drop-in Jev compatible'.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sparky-reflex", "name": "sparky-reflex", "author": "Applied-AI-Solutions-hub", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/Applied-AI-Solutions-hub/sparky-reflex", "base_model": "Laya open-weight encoder on a local GPU", "params": "Laya 421M", "metric": "Route selection latency per message", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Fast local routing for desktop AI apps on Windows: a loopback-only POST /route service at 127.0.0.1:18791 answers one question in about 50 ms on a consumer GPU, which part of my app should handle this message; apps skip a full LLM turn for requests they can handle themselves and send everything else to their model as usual; the model only picks from the routes given, it never writes text; dependency-free Node and Electron client; built for Foxsocket, usable by any app; NVIDIA GPU recommended, tested on an RTX 5060 Ti with driver 616, CPU works but is several times slower", "approach": "router", "training": "", "data": "", "result": "About 50 ms per question; 4 GB disk for PyTorch plus 1.7 GB model on first start; about 2.4 GB GPU memory while running; service ready in 10–40 seconds after GPU warmup", "notes": "", "domain": "routing", "score": "", "latency": "about 50 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "spring-ai-community-spring-ai-typesafe", "name": "spring-ai-typesafe", "author": "spring-ai-community", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/spring-ai-community/spring-ai-typesafe", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 37, "likes": "", "detail": "arch: typesafe-java-sdk, typesafe-spring-ai with JevJudge, JevGuardrailAdvisor, JevDocumentFilter, JevDocumentReranker and JevToolIndex, spring-ai-starter-typesafe and typesafe-bom; result: the default suite runs offline via MockRestServiceServer, live ITs opt-in twice behind a profile and an env var", "arch": "Java client for the TypeSafe System One API on Spring RestClient and Jackson 3, plus Spring AI integrations: Jev as model-as-a-judge, guardrail, RAG post-processor and tool index", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Apache-2.0, Maven Central, Java 17+, Spring AI 2.0.1+, version 0.3.0-SNAPSHOT; curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "stuntd", "name": "stuntd", "author": "bladedevoff", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/bladedevoff/stuntd", "base_model": "Laya head + your app's LLM", "params": "421M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 27, "likes": 0, "detail": "Three ways to run it: as local Jev, with no key, no provider and no training, pointing typesafe-sdk at stuntd so the base Laya checkpoint answers your typed questions; in front of OpenAI, a drop-in replacement for the provider base URL, where typed requests are recorded and everything else is relayed byte for byte; and in front of the paid Jev API, where stuntd relays, learns from the provider's own answers, and takes the decision over once its head is right often enough. A Hugging Face Space puts zero-shot Laya and the heads stuntd trained side by side on three demos, no install. First release v0.1, Apache-2.0, Python 3.10 or newer, on PyPI, with CI, and every number in the README traces to a demo in the repository or a source.", "arch": "Local self-hosted proxy that records the typed decisions your app already makes and learns to answer them itself: it speaks the Jev System One protocol, POST /v1/systemone with choice, score and noul questions, and the OpenAI Chat Completions API, distils each decision site into a small head on a frozen Laya encoder, serves that answer locally with calibrated confidence, and hands anything it is unsure about back to the provider.", "approach": "head", "training": "Online learning from observed app decisions", "data": "Your app's own decision traffic", "result": "", "notes": "Unusual angle: distill a running production LLM into a local Laya head automatically.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system1-reflexive", "name": "System 1 Reflexive", "author": "91wallace", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/91wallace/system1-reflexive", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Sub-millisecond non-autoregressive decision engine for LLM agents: a ModernBERT backbone calibrated with RLCD in the Laya spirit, organized in three tiers, a tier-1 fast-path memory with IDF pattern matching under 1 ms, a tier-2 Laya neural backbone at about 30 ms for non-autoregressive typed decisions with RLCD confidence calibration and risk evaluation, and a tier-3 active distillation loop that mines keywords from successful System 2 runs into persistent memory; calls below the confidence threshold fall back to the generative LLM, an MCP server ships in the box", "approach": "", "training": "", "data": "", "result": "Claims to resolve 70% or more of agent triage decisions in under 1.5 ms, with RLCD plus temperature scaling at T 1.15, against 35–50 ms for Jev and 400–2,500 ms for a standard LLM router; self-reported in the README", "notes": "The latency and calibration numbers are the project own; the sub-ms tier is local pattern matching, the calibrated path is the tier-2 encoder; domain self-mining scans codebases, SQL and APIs; created 2026-09-23", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "carloschiva-easy-open-jevs-instance", "name": "system-1", "author": "CarlosChiva", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/CarlosChiva/easy-open-jevs-instance", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "open-jev evaluates a state with typed noul, choice and score questions and returns probabilities; it does not generate text. The first open-jev start lasts 20+ minutes, downloading the ~18 GB Qwen-based checkpoint, so the healthcheck start_period is deliberately 20 minutes; checkpoint and HF cache live in the open-jev-models volume and should not be deleted. Both services bind 127.0.0.1 unless JEV_BIND=0.0.0.0 in the .env.", "arch": "Local docker-compose deployment of two inference services plus a test client: Open-Jev on port 8791 and laya-serve on 8792, both NVIDIA GPU", "approach": "", "training": "", "data": "", "result": "Test client supports two backends: the official jev.client.Client against POST /v1/systemone, or the TypeSafe SDK pointed at the local server, which requires an api_key but the local server does not validate one, so local works with model jev-latest", "notes": "Spanish README; the scripts directory uses uv and Python 3.14", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-atomic-semantic-firewall", "name": "system-one-atomic-semantic-firewall", "author": "Jackzhang1983", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/Jackzhang1983/system-one-atomic-semantic-firewall", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Motivated by a new cost regime: when narrow semantic judgments become sufficiently fast and cheap, it becomes practical to trade inference volume for long-context and retrieval dependence. System One models create a third layer between deterministic code for exact rules and large general-purpose models for semantic reasoning: high-volume semantic predicates. Tasks in scope include canon and character consistency, local contradiction detection, AI-style and prose pattern scanning, educational-content rule checking, agent and skill acceptance testing, product-metadata truthfulness, and translation consistency. The public technical note, first published 2026-09-23, version V0.1, by Zhang Liang, defines atomic rule banks, bounded local state windows, exhaustive or near-exhaustive rule-state verification, the computation-for-context principle, the System One and System Two division of labor, and false-positive control as a first-class requirement.", "arch": "Early System One Models architecture for exhaustive atomic rule-state semantic firewalling: the Atomic Semantic Firewall compiles large natural-language knowledge assets into atomic semantic rules, then compares bounded local state windows against a large or exhaustive set of those rules using high-throughput System One models, escalating only anomalies to System Two reasoning. Local state window times atomic rule bank, high-throughput semantic verification, escalate only anomalies.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mmornati-system-one-router", "name": "system-one-router", "author": "mmornati", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/mmornati/system-one-router", "base_model": "Jev or Laya as router over candidate LLMs", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 2, "likes": "", "detail": "The request path runs a pre-check for secrets, PII and size, then the System One questions, then scoring, then the System Two model, Qwen, DeepSeek, GPT, Sonnet, Opus or local, and if the answer is not good, one escalation. It pays for what the prompt needs: a hi goes to a flash model, a multi-region failover design goes to Opus. Go, Apache-2.0, CI, a live benchmark report, and docs including a Claude Code and MCP guide.", "arch": "OpenAI- and Anthropic-compatible gateway where a tiny, cheap System One decision model picks the right System Two LLM for every prompt: the cheapest one that is good enough. Send model: auto, and the decision model, Jev on OpenRouter or Laya running locally, answers four typed questions about the prompt in one call, topic, complexity, risk and private data, then plain arithmetic over config.yaml picks the cheapest model that clears the quality floor, with no LLM-as-router prompt and no guesswork.", "approach": "router", "training": "", "data": "", "result": "Model-selection routing via typed decisions", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "trivium", "name": "trivium", "author": "7ucardie", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/7ucardie/trivium", "base_model": "One local Qwen answering three typed questions", "params": "", "metric": "kind, difficulty and tools routed per task", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Local System-1 router: a single local Qwen call answers three typed questions about the task, kind, difficulty and tools, in about 258 ms, then routes to a local Qwen answer, Claude Code, or Codex; it launches the actual CLI; every decision is logged to decisions.jsonl", "approach": "router", "training": "", "data": "", "result": "About 258 ms routing latency locally", "notes": "", "domain": "routing", "score": "", "latency": "about 258 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "valen", "name": "Valen", "author": "Liuziyu77 / Valen-Team", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/Liuziyu77/Valen", "base_model": "Multimodal decision model", "params": "undisclosed", "metric": "General and game eval sets: 5k general eval, 100k training rows", "license": "Apache-2.0", "paper": "", "stars": 168, "likes": "", "detail": "base: text, images and video in; training: configs/train; data: all public on HF", "arch": "Multimodal System One model: text, images and video in, decision probabilities out, tagged 'System One Model, now with vision'; trained with SFT plus experimental RLCD", "approach": "undisclosed", "training": "SFT + experimental RLCD", "data": "Valen-Team/Valen-Training-General-100k, Valen-Eval-General-5k, Valen-Eval-Game", "result": "Training and eval datasets released on Hugging Face: Valen-Training-General-100k, Valen-Eval-General-5k and Valen-Eval-Game, with eval tables in the repo", "notes": "31 stars; bilingual docs; part of the multimodal decision-model wave", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yaad25-velto", "name": "velto", "author": "yaad25", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/yaad25/velto", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "sub-millisecond typed decision engine on CPU; velto-fast with velto-onnx fallback; speculative margin cascade escalates when the top-2 gap drops below 0.15 or confidence below 0.60; negation windowing and BM25 windowing", "approach": "", "training": "", "data": "", "result": "0.07 ms per decision on 500-token docs; typed-decisions accuracy 0.781 vs Laya 0.768, FLock 0.764 and Jev 0.727 in its comparison table", "notes": "compares against Jev 1.13, this-that-1.0 FLock 1.88B and Laya-MLX", "domain": "general", "score": "0.781 typed-decisions", "latency": "0.07 ms per decision on CPU", "benchmark": "typed-decisions", "cost": "", "speedup": ""}
{"id": "verdict-runtime", "name": "Verdict", "author": "TobyNoSkillSon", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/TobyNoSkillSon/Verdict", "base_model": "Laya English + Laya Multilingual + Gemma E2B RLCD, on MLX", "params": "", "metric": "zero-shot accuracy on AG News and DAIR Emotion plus ECE and median ms per item", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "name: local System One runtime for coding agents, macOS menu-bar app", "arch": "macOS menu-bar app that keeps System One models hot in memory so a coding agent can classify, filter, rank and gate a whole collection from a script: Python client plus verdict CLI, JSONL in and out, a triage skill the agent installs into its own harness, auto-routing, multilingual model for non-ASCII text and Gemma for image, audio and video, loopback worker with no telemetry or hosted fallback. Model catalog with per-model precision, 16, 8 and 4-bit for Laya, and a keep-hot memory policy", "approach": "", "training": "", "data": "", "result": "Laya English 76.4% accuracy, 0.096 ECE, 6 ms per item; Laya Multilingual 71.7%, 0.113, 4 ms; Gemma E2B RLCD 65.7%, 0.315, 37 ms text; hosted Jev reference 69.5%, 0.246, 256 ms. Flagged: Laya Typed-decisions checkpoint 0.252 ECE and Gemma 0.315 are poorly calibrated in these runs", "notes": "Context finding: the Laya encoder's real limit is 8,192 tokens while the shipped config says 512; on 300 long BBC articles with the decisive text after 800 tokens of filler, accuracy was 26% at 512 and 91% at 1,024 or more. Gemma audio scores are raw likelihoods that swing with the question set. 91 seconds from install to first answer. Jev listed for reference only, hosted and closed.", "domain": "general", "score": "76.4%", "latency": "6.0 ms", "benchmark": "AG News + DAIR Emotion zero-shot", "cost": "", "speedup": ""}
{"id": "dip-proto-zlaya", "name": "zlaya", "author": "dip-proto", "date": "2026-09-23", "category": "runtime", "url": "https://github.com/dip-proto/zlaya", "base_model": "Laya", "params": "804 MiB weights on disk", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 9, "likes": "", "detail": "", "arch": "CPU-only Zig inference engine for Laya: native Zig tokenization, safetensors loading, ModernBERT and the trained decision and action heads; needs no GPU and can run entirely in WebAssembly; answers choice, score and noul questions about text or structured JSON; inference needs no network after the weight files are downloaded", "approach": "head", "training": "No training; loads Laya decision and action heads", "data": "Text or structured JSON state", "result": "Weights about 804 MiB on disk, expanded to float32 in memory; loading and inference take about 1.7 GiB of RAM", "notes": "Zig nightly", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "blink", "name": "blink by sqliteai", "author": "sqliteai", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/sqliteai/blink", "base_model": "System One model", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 18, "likes": 0, "detail": "The runtime is C99 with no dependencies beyond libc and libm; weights are mapped read-only and never copied, and scoring performs zero allocations, a property the test suite checks mechanically, not by inspection. The same sources build to a 66 KB WebAssembly module that runs unchanged in browsers and in Node, with no file system and no server. The C API encodes the state once, encodes the option menu once, then scores questions cheaply. The README states its scope honestly: Blink recognises the form of a decision and does not read text the way a pretrained language model does, so where the answer is carried by form, which queue a ticket's wording points at, whether a claim's verb agrees with the state, blink-tiny is near perfect and well calibrated.", "arch": "Open-source System One decision model with a one-pass typed-decision interface and an embeddable C runtime that also builds to WebAssembly: it answers questions of the form given this state and this criterion, which of these options, in a single forward pass, with no token generation, no decoding loop, no JSON to parse and nothing to repair, so the output is one probability per option declared at call time. It puts the Jev-style interface behind a C library you can link into a service, a daemon or a device, or load as WebAssembly in a web page.", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "'sqliteai' branding — decisions as embeddable runtime, the embedded-DB play for System One.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "compass", "name": "Compass", "author": "adimyth", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/adimyth/compass", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: document plus typed question, probability per allowed answer, no generation; notes: /v1/systemone endpoint", "arch": "A decision model: give it a document and a typed question, get calibrated probabilities back.", "approach": "", "training": "", "data": "", "result": "", "notes": "Turns a small open language model into a decision model: give it a document and a typed question with a fixed set of allowed answers, it returns a probability for every answer. Never generates text, so nothing to parse or repair, and one call costs one read of the document. Exposes POST /v1/systemone.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cua-jev", "name": "CUA-JEV, Jev for computer use", "author": "ZJU-REAL Lab", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/ZJU-REAL/CUA-JEV", "base_model": "a text model plans, Jev selects the action and its execution route", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 7, "likes": "", "detail": "", "arch": "open-source reference framework for Jev-powered computer use: a task adapter turns structured state from the browser, desktop UI, office applications, terminal or filesystem into legal, executable, verifiable action candidates; Jev selects a concrete action and its execution route; the framework guards and executes that choice, verifies the resulting state and observes again; no task-specific router is trained; Jev's fast typed decisions are a promising fit for downstream systems that must choose actions repeatedly under latency constraints, including computer use, embodied agents and potentially autonomous driving", "approach": "", "training": "", "data": "", "result": "four roughly twenty-action browser-to-tool cases run end to end with a model and Jev, but arbitrary-task generalization is not claimed; the four recorded real window-only Windows case studies on zjureal.com/CUA-JEV: Python automation guide, Edge/Terminal/VS Code, 19 actions, 19 Jev calls, 19 model calls, channels DOM 8, MCP 8, CLI 2, file API 1; JavaScript study guide, Edge/Notepad, 18/18/18, DOM 8, MCP 8, CLI 1, file API 1; Git workflow guide, Edge/Terminal/VS Code, 21/21/21, DOM 10, MCP 8, CLI 2, file API 1; PowerShell learning guide, Edge/Terminal/Notepad, 19/19/19, DOM 8, MCP 8, CLI 2, file API 1; a text model reads live DOM and registered tool descriptions and proposes grounded next actions, Jev selects one concrete action and channel at every step; a same-origin MCP reader supplies source evidence, scoped CLI and file capabilities complete the cross-app handoff; no chapter URL or click sequence is prewritten; every run has an expandable step trace and an action-channel breakdown", "notes": "the earlier four application workflows use task-specific adapters; neither set establishes reliable operation on arbitrary applications or tasks; Jev selects among constrained options and does not generate arbitrary scripts or interpret screenshots itself; the typed decision loop, guards and traces are designed to be portable, CI runs unit tests on Linux and macOS as well as Windows; the published desktop workflows depend on Windows integrations such as UI Automation, Excel COM and Explorer; macOS and Linux desktop adapters are not implemented or validated; the experimental open-task paths in docs/OPEN_TASKS.md separate model planning from Jev's typed action selection over discovered DOM elements or UI Automation controls; related: RoboJEV already explores the embodied setting with Jev-controlled manipulation in a MuJoCo simulator, see its own row", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decidr", "name": "decidr", "author": "devanmolsharma", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/devanmolsharma/decidr", "base_model": "Any local LLM via Ollama", "params": "", "metric": "One forward pass per decision, per-option probabilities", "license": "Open", "paper": "", "stars": 7, "likes": 0, "detail": "name: + decidr-ts", "arch": "Python reference implementation of typed decisions from any LLM in one forward pass: give it a question and named options and get back which option was picked and a real probability for each, with the model generating no words; no generation loop, no JSON parsing", "approach": "logits", "training": "", "data": "", "result": "The live TypeScript-port playground at devanmolsharma.github.io/decidr-ts needs no signup: paste your own API key or point it at a local Ollama model; the port shares the mechanism and the numbers of the Python reference", "notes": "7 stars; TypeScript port published the same day", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decidr-ts", "name": "decidr-ts", "author": "devanmolsharma", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/devanmolsharma/decidr-ts", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 4, "likes": 0, "detail": "TypeScript port of decidr: structured LLM output without JSON parsing, retries or prompt engineering. decidr-ts reads a real probability straight off the model's own logits for a question you define: pick the best option from a list, Choice, grade something on a rubric, Score, or get a calibrated yes or no, Noun, in one forward pass.", "arch": "TypeScript library for real LLM classification, scoring and yes-no decisions: reads a real probability straight off the model's own logits for a question you define, pick the best option from a list as choice, grade something on a rubric as score, or a calibrated yes-no, in one forward pass with no generated text to parse, no JSON retries and no prompt engineering; ships a browser playground and works against Cerebras, Ollama or any compatible endpoint", "approach": "logits", "training": "", "data": "", "result": "", "notes": "TypeScript port of the decidr row, github.com/devanmolsharma/decidr.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kingfs-decis", "name": "Decis", "author": "kingfs", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/chaitin/Decis", "base_model": "Laya + Kev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 3, "likes": "", "detail": "Runs out of the box: three commands, no build and no model download, and the images carry their weights, so nothing is downloaded at startup and there is no volume to mount. A published multi-arch image per engine for Laya and kev, weights included. A playground with three games, Snake, dino and tetris, each deciding through a real /v1/systemone call. Status pre-1.0 at v0.3.0; the wire contract v1 is stable and only gains fields, and the running server reports its own version at /healthz. Apache-2.0, Python 3.11 or newer, bilingual English and Chinese README, Docker image on the Hub.", "arch": "Self-hosted, Jev-compatible decision-model API: one /v1/systemone endpoint and one Docker image per engine. Decis is a self-hostable inference server for open decision models that implements the TypeSafe System One API, the /v1/systemone contract the official typesafe-sdk already speaks, so pointing the SDK at your own host is the whole migration: one wire contract, several interchangeable engines, one container per engine, for models that return calibrated probabilities for typed questions in one forward pass, small enough to run next to your app.", "approach": "head", "training": "", "data": "", "result": "Drop-in local replacement for the TypeSafe Jev API", "notes": "Repo renamed or transferred since the 2026-09 sweep; URL updated to the current location", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decision-engine-js", "name": "decision-engine-js", "author": "p-rk", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/p-rk/decision-engine-js", "base_model": "Laya", "params": "421M", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "arch: Laya-compatible marker-based inference in TypeScript on ONNX Runtime", "arch": "Cross-platform TypeScript implementation of Laya-compatible marker-based decision inference for Node.js, Bun, and the web; targets ONNX Runtime rather than MLX, so deployment is not restricted to Apple Silicon; one local model serves choices, scoring, Noul conditions, and routing.", "approach": "", "training": "", "data": "", "result": "", "notes": "Fast local structured decisions in JS/TS. GitHub new-repo sweep 2026-09-25.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decision-infra", "name": "decision-infra", "author": "hufaei", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/hufaei/decision-infra", "base_model": "Hosted Jev + local Reflex, SemIf, Laya models", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "training: gateway; notes: cf. jevcompat, weigh; name: multi-model Jev gateway", "arch": "A Jev-compatible /v1/systemone contract, per the Chinese README, one contract that switches between hosted Jev and three local decision models; TypeScript 5.9, Node 22+, Python 3.12, Fastify, PyTorch for the local models, Docker Compose optional", "approach": "router", "training": "None", "data": "None", "result": "One gateway for mixed hosted and local decision fleets; the three local models are Reflex, SemIf and Laya", "notes": "Chinese project, README in Chinese; 'Decision gateway' layer.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dev-04b", "name": "Dev-0.4b", "author": "mpnikhil; vgi-autodev by lmangani", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/mpnikhil/dev-0.4b", "base_model": "ModernBERT-large + one universal choice head", "params": "399M", "metric": "", "license": "MIT", "paper": "", "stars": 39, "likes": "", "detail": "arch: noul etc.; data: Query-farm VGI/TypeSafe writeup; notes: query.farm/vgi; name: + VGI DuckDB worker", "arch": "Bidirectional encoder; one forward pass, no generated tokens; three readouts mapped to VGI functions", "approach": "head", "training": "", "data": "HackerNews taste-filter corpus for the autodev worker", "result": "~28 ms per decision on Apple Silicon MPS once loaded; SQL: SELECT … LATERAL autodev.main.noul WHERE noul > 0.50", "notes": "DuckDB/Query-farm 'Vector Gateway Interface' worker — Jev-style decisions embedded in SQL queries; local, no API keys.", "domain": "coding", "score": "", "latency": "28 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yzfly-edgejev", "name": "edgejev", "author": "yzfly", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/yzfly/edgejev", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 11, "likes": "", "detail": "The 15.6 ms per single question on a 4-core CPU runs laya's same weights, not a smaller model: the saving is the network round trip, plus ONNX Runtime graph optimization at 1.7×, and INT8 over AVX512-VNNI integer multiply-accumulate at 1.2×, with the model also shrinking from 1290 MB to 324 MB. The latency matters because it is small enough: a voice conversation leaves a 50–150 ms budget for the decision layer, a turn often asks several questions, and three questions batched into one request take 44.8 ms, still in budget. Build with uv tool install of edgejev with build, then edgejev build with backend laya; the Python Agent calls system_one on a Chinese ticket with a choice question for the team and a noul question for urgency. Apache 2.0, Python 3.9 or newer, on PyPI, Chinese README.", "arch": "Local, offline Jev and System One inference on CPU: EdgeJev unifies the open-source reproductions, laya, kev, NanoJev and PlayJev, into one convert, quantize, deploy path, exporting ONNX, INT8 quantizing, and starting a local service compatible with the official protocol, running on an ordinary CPU, fully offline, with no API key, no network, and data never leaving the machine; the runtime needs only onnxruntime, tokenizers and numpy, and does not install torch.", "approach": "undisclosed", "training": "", "data": "", "result": "Latency per grid: official Jev hosted API 314 ms; laya original project 200–500 ms on CPU and 32.8 ms on a T4; EdgeJev 15.6 ms on a 4-core CPU, and 44.8 ms for three questions in one request.", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "elbruno-jev", "name": "ElBruno.AI.Jev", "author": "elbruno", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/elbruno/ElBruno.AI.Jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 3, "likes": 0, "detail": "Explicitly not an official TypeSafe AI SDK: the provider is api.typesafe.ai, documented at docs.typesafe.ai, and the independent jevtypesafeai.com proxy, which has different credentials and endpoints, is not supported. Tentative 0.5.0 release: intended for early evaluation, not a claim of production readiness; offline tests, samples and package-consumer checks pass, but live Jev compatibility remains unverified and live testing is deferred, so validate the service and your application before production use, with APIs possibly changing before 1.0. MIT, on NuGet, with CI and a release pipeline.", "arch": "Community-maintained .NET 10 client for the official TypeSafe AI Jev API: evaluate Choice, Score and Noul questions, preserve uncertainty, discover models, and compose decisions with Microsoft.Extensions.AI, making Jev a first-class citizen in the Microsoft ecosystem.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Microsoft-ecosystem first-class citizen for Jev calls.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "evoke-build-evoke", "name": "evoke", "author": "evoke-build", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/evoke-build/evoke", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 17, "likes": "", "detail": "", "arch": "Software, by reflex. A sentence becomes a call of a small program, chosen by Jev, TypeSafe AI's classifier, and run only when it is sure enough. Reflexes are recipes anyone can write, share and improve. A CLI you talk to, a package manager for reflexes from git, and a TypeScript SDK.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "go-system-one", "name": "go-system-one", "author": "rcarmo", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/rcarmo/go-system-one", "base_model": "Gemma 4 12B", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 3, "likes": 0, "detail": "Started from a fixed Gemma baseline, built a llama.cpp prototype to define the expected results and speed, then replaced it with a native Go runtime. Go 1.26.2 or the version in go.mod is required; model files download separately under the Gemma license. A local playground on port 8080 answers Noul, Choice and Score, with batch decisions for the boolean and enum context interface. Docs cover the model and API, the TypeSafe question types, benchmarks with recorded workloads, revisions and precision trade-offs, model files and hashes, and tests and validation.", "arch": "Native Go runtime, billed as what happens when a gopher met Jev, for finite-choice decisions, ordered scores and yes-no probabilities on Gemma 4 12B: the current NVIDIA path loads hand-tuned PTX kernels directly from Go through the NVIDIA Driver API, needing no CGo, no llama.cpp and no CUDA toolkit at run time, and serves Noul, Choice and Score questions over HTTP with a batch interface for boolean and enum context.", "approach": "sdk", "training": "", "data": "", "result": "72.80 ms median on a pinned RTX 3060, down from about 521 ms in the first native version; automatic two-field batching reaches 12.01 entries per second at 100 entries; 84.85% public-subset accuracy, 196 of 231, on JevBench v1.4.0: easy 48/48, original 71/72, hard 77/111, with no failed requests, stated as public accuracy rather than the official sealed-set composite score or leaderboard rank.", "notes": "One-line repo; part of the Go/System One wave. with go-jev", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jeff", "name": "jeff", "author": "logan-markewich", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/logan-markewich/jeff", "base_model": "gliformer-large-v1", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 249, "likes": "", "detail": "base: GLiNER-lineage encoder, name-based inference; arch: per Decision Index entry; result: 21st of 31 open models · Jev 59.51; notes: data generated 2026-09-22", "arch": "Decision model on a GLiNER-family encoder base", "approach": "head", "training": "", "data": "", "result": "Jev Decision Index: 27.23 on the current panel, 22.33 frozen; 131,356 of 132,422 requests answered", "notes": "First tracked in the community Jev Decision Index.", "domain": "coding", "score": "27.23 current, 22.33 frozen", "latency": "", "benchmark": "Jev Decision Index", "cost": "", "speedup": ""}
{"id": "jev-compatible-server", "name": "jev-compatible-server", "author": "Hanno-Labs", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/Hanno-Labs/jev-compatible-server", "base_model": "Bundled registry of open decision models, first entry bosun-v3.1-0.6b", "params": "varies by model", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "arch: one typed request/response shape across model implementations; result: bosun-v3.1-0.6b bundled; notes: explicit unsupported-question reporting", "arch": "Runs open decision models behind a Jev-compatible API: uvx --from jev-compatible-server[transformers] with the Transformers backend, then POST /v1/systemone with the single typed request and response shape across different model implementations; the --model flag takes a bundled registry entry; questions the model cannot support are explicitly reported rather than guessed", "approach": "", "training": "", "data": "", "result": "Bosun v3.1 0.6B ships as the first bundled model", "notes": "Docs: docs/README.md, docs/API.md, docs/MODELS.md; issues tracked in the Hanno-Labs/decision-bench repo; released 2026-09-22 alongside DecisionBench and Bosun", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arc-jev-switch", "name": "jev-switch", "author": "ARCJ137442", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/ARCJ137442/jev-switch", "base_model": "Local Jev-protocol upstreams", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "Dual-state mode: local is token-free and install-and-use, in the CC Switch and LM Studio style, while cloud adds a Bearer call token plus an independent admin password and becomes a relay station, in the sub2api and newapi style. Eight HTTP endpoints, health, models, systemone plus five admin, all JSON with a unified error body of error, upstream and retryable. The Jev-native POST /v1/systemone keeps the TypeSafe and jev-life shape: criteria required, no confidence for the boolean family, noul and probability dual keys retained. A multi-upstream model routing DAG has exact and prefix routes, multi-candidate failover per model, priority, sticky and on_error, double-checked on load and PUT. A translation layer means callers always send type noul, and the Vercel dialect's boolean conversion happens inside the adapter, so switching the model switches the upstream. Upstream keys live only in the daemon, the API returns only a masked key with no plaintext read-back, and errors and logs are uniformly redacted. Six contract docs pin the shapes, and TS types are generated from Rust via ts-rs so the UI does not hand-write a second copy. Four-crate cargo workspace: jev-protocol with zero IO, jev-core with the router DAG and no HTTP dependencies, jev-adapters holding vendor dialects, and the jev-switch daemon, with the React console on port 5173 in three pages: Providers, Routing as a bipartite graph, and Playground. Chinese README.", "arch": "Rust multi-upstream router for the local Jev protocol, in Jev-native entry, multi-upstream switching and bridge mode: a Rust axum backend plus a React console, with a Tauri desktop app as the target form. One kernel, locally routable and cloud-releasable, aggregates official, Vercel and other upstreams into one Jev API, and the upstream topology is resolved by where the kernel sits, so a LAN-internal Laya connects directly.", "approach": "router", "training": "", "data": "", "result": "Multi-upstream Jev-protocol routing", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cogentica-ai-laya-goish", "name": "Laya Goish", "author": "cogentica-ai", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/centillex-labs/laya-goish", "base_model": "Laya", "params": "421M", "metric": "", "license": "", "paper": "", "stars": 16, "likes": "", "detail": "arch: laya-goish", "arch": "Rust HTTP server serving Laya GGUF inference for English and Thai text decisions: choose from options or score with calibrated probabilities, fully self-hosted.", "approach": "head", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24; repo renamed or transferred since the 2026-09 sweep, URL updated", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lesterppo-laya-agents", "name": "laya-agents", "author": "lesterppo", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/lesterppo/laya-agents", "base_model": "Laya multilingual", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "Two native integrations ship: plugin/, a native tool for Hermes Agent, the long-lived agent process with a warm router across calls, and muse/, a dependency-free native module for the Muse runtime, with a clean Python API plus a JSONL batch mode, because each Muse CLI call is a fresh subprocess and a fresh process would pay the checkpoint load every time. An LLM classification call costs 500–2,000-plus tokens; the same judgment here is about 100 tokens out, sub-second on CPU, and free, measured on a CPU-only box with no GPU. MIT, Python 3.10 or newer.", "arch": "Fast, local, non-autoregressive text classification and guardrails for AI agents, exposing Laya as native tools: jailbreak and prompt-injection detection, content moderation, support ticket triage and LLM model routing, without an LLM call, built on Laya, a multilingual System 1 decision engine that returns typed choice, score and noul decisions in a single forward pass, with no API key, no quota, and no network at inference time.", "approach": "head", "training": "", "data": "", "result": "Warm latency, one multilingual question: about 140 ms for Laya versus 3–30 s for an LLM-as-classifier; English with 3–5 questions: about 1.2–2.4 s versus 3–30 s; cost is zero and local versus per-token plus quota; output is about 300–400 characters of JSON versus free text you must parse; hallucination risk is none versus always present.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "trystan-sa-laya-candle", "name": "laya-candle, Trystan-SA", "author": "Trystan-SA", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/Trystan-SA/laya-candle", "base_model": "Laya", "params": "421M", "metric": "", "license": "", "paper": "", "stars": 9, "likes": "", "detail": "arch: Laya port", "arch": "Candle-based Rust crate for JEV-style local decisions: classify, score, or yes/no probability over a message, fully local, aimed at triaging support tickets, sorting mail, moderating posts, and guarding prompts before they reach an LLM.", "approach": "head", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "notjev", "name": "notjev", "author": "9pings", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/9pings/notjev", "base_model": "Any OpenAI-compatible endpoint", "params": "", "metric": "Single-token logprob as the decision probability", "license": "Open", "paper": "", "stars": 21, "likes": 0, "detail": "", "arch": "Node.js proxy that turns any OpenAI-compatible endpoint into a one-token decision server: the options are presented as A, B, C and the logprob of the model's chosen first token becomes the calibrated probability; 'one token, a real probability, a decision you can trust or refuse'", "approach": "undisclosed", "training": "", "data": "", "result": "Apache-2.0, Node 20 or newer, published on npm as notjev; the pitch is that closed questions do not need free-text generation at all", "notes": "18 stars; the name is a joke but the architecture is the standard Jev pattern", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ondevice-system1", "name": "OnDevice-System1", "author": "ahtsham0715", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/ahtsham0715/ondevice-system1", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Community model family for fast calibrated on-device typed decisions, derived from Laya and aimed at phones, laptop CPUs and a Raspberry Pi instead of datacenter GPUs: a bidirectional HF encoder, ModernBERT, BERT or mmBERT, plus a shallow transformer head where each answer option gets one MASK token in the input, a small MLP scores each option final hidden state, and softmax over those scores gives the choice label, ordinal score or calibrated noul probability, one forward pass answering every question about a state at once", "approach": "", "training": "", "data": "", "result": "Candidate family, none trained yet: OnDevice-Tiny 32.3M on bert_uncased_L-4_H-512, OnDevice-Small 48.1M on the L-8 variant, OnDevice-Base 164.0M on ModernBERT-base; all three build, status untrained", "notes": "Phase 1 done: docs/LAYA_ANALYSIS.md is a full write-up of the upstream Laya architecture, training loss and licensing, produced by reading the upstream source directly; phase 2 design plus first student in progress in ROADMAP.md, every experiment including failures in RESEARCH_LOG.md; the repo keeps a non-negotiable rule that no number is published without an actual run, so every benchmark figure is cited from upstream Laya or left blank; the tree covers RLCD loss, calibration, teacher-student distillation, INT8 and INT4 quantization, and ONNX, CoreML and TFLite export", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "fp-ooda", "name": "ooda", "author": "FuturePresentLabs", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/FuturePresentLabs/ooda", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "MIT or Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "complete() is deliberately never used to widen what decide() accepts, and never to let a model choose what to ask next inside a hand-written decision chain. Already running in production across EDA and PCB design, CAD/CAM, and browser-agent workflows, and the README invites you to copy the homework. Five-line usage: derive a Choice enum over the options, build the HttpClient from the environment, which points at typesafe.ai's Jev by default, and call decide_choice, whose answer is a typed value. 55 tests passing, status in production, dual licensed MIT or Apache-2.0.", "arch": "Rust client for Bifrost, Future Present Labs' inference gateway, in an observe, orient, decide, act shape: one credential and one retry story behind two clearly separate capabilities, where decide() takes bounded Choice, Score and Noul questions and returns typed, confidence-scored answers, with no free text in either direction, ever, strongly typed over typesafe.ai's Jev and Laya, its MIT-licensed FOSS twin, while complete() is genuinely open free text for the rare cases that actually need it, such as typing a value into a web form or drafting a planning note, and Act is always left to the caller's own concern, because the crate stops at the decision or completion.", "approach": "sdk", "training": "", "data": "", "result": "Rust-native Jev decisions", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bz-ai-openjev", "name": "openjev by BZ-AI", "author": "BZ-AI", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/BZ-AI/openjev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "approach: clean-room adaptive runtime", "arch": "Independent clean-room OpenJev for typed probabilistic decisions: v0.2 is an adaptive decision runtime where deterministic rules fire first and typed probabilistic decisions handle the rest; no TypeSafe model, weights, sampler, training code, or affiliation.", "approach": "rules", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-skills-openjev-multimodal", "name": "OpenJev Multimodal", "author": "jev-skills", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/jev-skills/openjev-multimodal", "base_model": "Qwen", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "OpenJev Multimodal: text and images to typed decisions, one output token per question, on your Mac via Qwen plus llama.cpp/Metal; Noul, Choice, and Score primitives with native PNG/JPEG/WebP vision, 255 Choice options with verified single-token labels, and a Jev-compatible SystemOne API; independent of TypeSafe, no paid inference required.", "approach": "logits", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "multimodal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "abhishekgahlot2-openjev-server", "name": "openjev-server", "author": "abhishekgahlot2", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/abhishekgahlot2/openjev-server", "base_model": "", "params": "27B", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "One-pass decisions from any open LLM, served as an API: send a situation and typed questions, get back one choice, a yes/no probability, or a score per question with a probability for every option, read off the first output position — the server behind OpenJev, an open 27B decision model.", "approach": "logits", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openvons", "name": "Openvons", "author": "genai-craft", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/genai-craft/openvons", "base_model": "Qwen3-4B-Instruct-2507", "params": "4B", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "arch: per Decision Index entry; result: 10th of 31 open models · Jev 59.51 · 132,422; notes: data generated 2026-09-22", "arch": "Open 'von' variant of the System One decision-model pattern", "approach": "fine-tune", "training": "", "data": "", "result": "Jev Decision Index: 45.59 on the current 19-benchmark panel; 36.41 on the frozen panel; full suite answered", "notes": "First tracked in the community Jev Decision Index.", "domain": "general", "score": "45.59 current, 36.41 frozen", "latency": "", "benchmark": "Jev Decision Index", "cost": "", "speedup": ""}
{"id": "qwen-jev-like", "name": "qwen-jev-like", "author": "sting8k", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/sting8k/qwen-jev-like", "base_model": "Qwen-series models", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 5, "likes": 0, "detail": "Nothing in the weights changes: the engine changes how the model is asked and how the answer is read. One prompt puts the catalogue of questions and allowed values first and the state last, and asks for JSON. The chat template runs with thinking off, then appends an open brace, so the next token is the first answer key. There is no generation: max_tokens is 1 and temperature is 0, and it reads the log-probability of each allowed value at its position, with multi-token values following their token path and shared prefixes sharing the work through the trie. Every allowed value must appear verbatim in the catalogue, so the model echoes what it was shown, and the distribution is renormalised over the allowed values, where in_set_mass says how much probability landed on them at all, and under 0.5 means the prompt is broken, not that the question is hard. One forward pass per state carries all its questions, reusing the catalogue's KV state. Most code and measurements came from AI coding agents, with the author setting scope and publishing, and every number states its configuration and the run it came from.", "arch": "A Jev-shaped contract run on open models on one consumer GPU: you hand the engine a state, a piece of text, and a list of typed questions, noul for yes and no, choice for pick one option from a list, score for a level on a rubric, and it returns one answer per question with a probability on every option, generating no text at all. It is a for-fun port of harshatheg's same-night 1B-Qwen logits scoring, with the decision tree replaced by a trie, plus the calibration protocol from vinnylarouge's jevlike, with Jev 1.13 itself as one column of the comparison tables, measured through OpenRouter like any other model.", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "Small converter in the any2jev/SNAP family.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "codenamev-ruby-laya", "name": "ruby-laya", "author": "codenamev", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/codenamev/ruby-laya", "base_model": "Laya", "params": "421M", "metric": "", "license": "", "paper": "", "stars": 20, "likes": "", "detail": "arch: text, email, ticket, JSON", "arch": "Ruby gem porting Laya: multilingual, non-autoregressive System-1 decision engine answering choice, score, and noul questions over any state in a single forward pass with calibrated probabilities and a router that picks the right checkpoint per request.", "approach": "head", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ticofab-scala-jev-sdk", "name": "scala-jev-sdk", "author": "ticofab", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/ticofab/scala-jev-sdk", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 7, "likes": "", "detail": "Scala 3.3 LTS or newer, JDK 17 or newer, and the only dependencies are sttp-client4-core and upickle. It is published against Scala 3.3 LTS on purpose: TASTy is forward-compatible but not backward, so an artifact built on a newer Scala would shut out every project still on LTS, while built on 3.3 it is readable from 3.3 through 3.9 and beyond. One request carries one state and any number of named questions, answered in a single round: noul is a calibrated probability in the unit interval, choice is the selected option with the full distribution and a confidence, and score is a probability-weighted position that can fall between levels. Published on Maven Central under io.github.ticofab.", "arch": "Scala client for Jev, TypeSafe AI's System One model, with no effect system bundled: you hand it an sttp backend and the client speaks whatever that backend speaks, so it runs on Future, on blocking Identity with no wrapper at all, or on cats-effect, ZIO, Monix and Pekko if that is what your codebase already runs on.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sys1-rust", "name": "sys1", "author": "alvarobartt", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/alvarobartt/sys1", "base_model": "Laya, ModernBERT", "params": "", "metric": "About 14 ms per query on an RTX Pro 6000", "license": "Open", "paper": "", "stars": 44, "likes": 0, "detail": "base: e.g. Laya", "arch": "Rust System One-compatible API for open decision models: tokio, axum and serde over candle, with dynamic token-based batching and ModernBERT running Laya's custom decision heads; CPU, CUDA and Metal backends", "approach": "head", "training": "", "data": "", "result": "Uses the tokenizers release candidate; Laya serves as the reference model, with ModernBERT for the small-head path", "notes": "22 stars; the Rust entry in the System One client ecosystem Derivative: github.com/mcembalest/sys1, 09-23, same Rust feature set, ~14 ms per query on an RTX Pro 6000.", "domain": "general", "score": "", "latency": "~14 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system1-server", "name": "System 1 Server", "author": "andrewcampi", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/andrewcampi/system1_server", "base_model": "Laya", "params": "", "metric": "1,773 decisions per second batch", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Private decision server: loads Laya multilingual once and answers Jev-shaped choice, score and noul questions, each with a probability, no text generation; the client is the official OpenAI Python package pointed at the process through OPENAI_BASE_URL, so the same code makes a live call, submits a batch, or both, and requests stay on the machine running the model", "approach": "", "training": "", "data": "", "result": "On an RTX 5080 a warm live call through that client returns in 9.5 ms; a batch of 10,000 different requests, 23,333 decisions, finished in 13.2 seconds, 1,773 decisions per second", "notes": "Created 2026-09-22", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "iamaamir-system-one", "name": "system-one", "author": "iamaamir", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/iamaamir/system-one", "base_model": "Provider-neutral", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 59, "likes": 0, "detail": "base: any System One backend; notes: Rust, Go, .NET, TS, Elixir/Nx, C/WASM; name: iamaamir, TypeScript/Pi runtime", "arch": "Provider-neutral TypeScript runtime for bounded AI decisions: write the decision once, run it on TypeSafe Jev, Reflex locally, any compatible /v1/systemone endpoint, or a custom SystemOneProvider, and the application keeps the same choice, noul and score code while the provider is an implementation detail; ships a Pi integration and the pi-bifrost companion", "approach": "router", "training": "", "data": "", "result": "The stable-boundary pitch: change the provider, keep the decision code", "notes": "Part of a 2026-09-22 wave of language-native System One runtimes.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-gemma", "name": "system-one-gemma", "author": "akash-kamat", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/akash-kamat/system-one-gemma", "base_model": "Gemma 3 270M", "params": "270M", "metric": "acc 0.361 / ECE 0.234 / Brier 0.769", "license": "", "paper": "", "stars": 6, "likes": "", "detail": "arch: 'LoRA + head'; result: frozen 132,422-request suite · Jev itself 52.49 on the frozen panel · 48,200 unsupported; notes: HF space multimodalart/jev-decision-index, index generated 2026-09-22T11:12Z", "arch": "LoRA + decision head, autoregressive technique per Jev Decision Index metadata", "approach": "head", "training": "LoRA + head", "data": "", "result": "Jev Decision Index: 15.22 — 31st of 31 open models; calibration stats acc 0.361 / ECE 0.234 / Brier 0.769 over 66,496 answers; only 84,222 of 132,422 requests answered", "notes": "First tracked in the community Jev Decision Index.", "domain": "general", "score": "15.22, 31st of 31", "latency": "", "benchmark": "Jev Decision Index", "cost": "", "speedup": ""}
{"id": "systemone-onnx", "name": "SystemOne-ONNX", "author": "Viratvishnu13", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/Viratvishnu13/SystemOne-ONNX", "base_model": "", "params": "22M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": 0, "detail": "A small, from-scratch alternative to System-1-style typed-decision models, TypeSafe's Jev and Convai's Laya: about 22M parameters, exported to ONNX, Python 3.10+. Non-generative typed decisions without an autoregressive backbone.", "arch": "A from-scratch alternative to System One typed-decision models like Jev and Laya: a roughly 22M-parameter shared encoder instead of their 300–400M parameter backbones, aimed at genuinely local, sub-second inference on an embedded desktop app, trained in PyTorch and exported straight to ONNX, for cases where you already have direct per-example calibrated confidence labels rather than hard classification labels", "approach": "from-scratch", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alanhuangyoo-wev", "name": "wev", "author": "alanhuangyoo", "date": "2026-09-22", "category": "runtime", "url": "https://github.com/alanhuangyoo/wev", "base_model": "", "params": "1.7B / 4B / 8B", "metric": "76% browser steps", "license": "Apache-2.0", "paper": "", "stars": 3, "likes": "", "detail": "Trained by interface distillation: a browser agent's typed requests are answered by a teacher LLM on live websites, an LLM judge keeps the verified episodes, and the wev decision model trained on them serves the same interface locally. Ships a public dataset, wev-data, a pip package, wev-ai, a project page on Hugging Face, and a paper on Zenodo with DOI 10.5281/zenodo.22941164, with a quickstart, results, how-it-works, and train-your-own sections. Apache 2.0.", "arch": "Local System One decision models for agents, in 1.7B, 4B and 8B sizes: they answer the POST /v1/systemone request shape, a free-form state plus any number of choice, yes-no and score questions, in one forward pass, with no text generation and no API key, covering general decisions such as triage, routing, policy checks and agent monitoring, and browser-agent steps, which operation and which element, running on your own GPU or on a laptop.", "approach": "", "training": "", "data": "", "result": "Browser steps on the Mind2Web test split, 873 requests on websites unseen in training, where a step counts when both the operation and the target element are right: wev-4b 75.9% step success with 91.2% operation accuracy, wev-8b 75.5 and 90.3, wev-1.7b 68.2 and 88.1, versus Kev-4B 21.2, Kev-8B 19.0, and Laya 0.7. On general typed decisions, wev-8b scores 82.4 on Kev decision-v7, 77.2 on Kev transfer-v4 and 79.1 on typed-decisions, against Kev-4B at 88.2, 82.1 and 65.1, all on held-out test splits.", "notes": "pip install wev-ai; Zenodo DOI", "domain": "computer-use", "score": "76% browser steps unseen sites", "latency": "10–37 ms general on RTX 5090", "benchmark": "", "cost": "", "speedup": ""}
{"id": "any2jev", "name": "any2jev", "author": "hwfengcs", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/hwfengcs/any2jev", "base_model": "Any Hugging Face causal LM", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 6, "likes": 0, "detail": "base: demos: Qwen3-0.6B; arch: LoRA + pointer head, ~1 GPU-hour; data: data format documented; result: vs GIF: same weights, same RTX 2060 SUPER; notes: bielik-minitron-jev, etc.; name: any open model → Jev-style decision model", "arch": "Converter: 'any2jev train' + 'any2jev serve' exposing POST /v1/systemone — the official TypeSafe SDK connects with one base_url change; one forward pass, typed answers, calibrated probabilities, zero tokens generated", "approach": "head", "training": "~1 GPU-hour per target model", "data": "A few thousand labelled decisions", "result": "Published benchmark: Qwen3-0.6B prompted-for-JSON 778 ms / 0.621 acc / no probabilities → any2jev one-pass", "notes": "The general-purpose converter behind several other entries. EN + 中文 docs.", "domain": "general", "score": "", "latency": "778 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "any-decision-model", "name": "AnyDecisionModel", "author": "mattt", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/mattt/AnyDecisionModel", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 10, "likes": "", "detail": "The API follows the model-and-session pattern of Apple's Foundation Models framework and of Hugging Face's AnyLanguageModel. The README example scores a ticket that describes both a billing problem and a technical problem, with a calibration of temperature 6.5 and binary bias 1.0, because raw probabilities from language models are often overconfident, and the resulting distribution is 0.75 for billing, 0.18 for technical and 0.07 for sales, so code can route the ticket to billing and also flag it for the technical team. A choice answer carries the value, a probability for each option, and a confidence value; a score carries the expected level, a probability for each level, and a confidence value. The example runs mlx-community's Qwen3-4B-Instruct-2507-4bit.", "arch": "Swift package for typed decisions, with yes-or-no probabilities, choices among options, and scores on ordinal scales, and two backends: MLXDecisionModel runs a small language model on Apple silicon and reads each decision from next-token probabilities in one forward pass, with no text generation, while JevDecisionModel calls TypeSafe's Jev through the System One API, or any server that implements the same API.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nokia-anyjev", "name": "AnyJev", "author": "nokia-applied-research", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/nokia-applied-research/AnyJev", "base_model": "any LLM", "params": "", "metric": "52.0% auto-decidable at 5% risk", "license": "Apache-2.0", "paper": "", "stars": 699, "likes": "", "detail": "author: Jiamu Zhang, Tianze Yang, Yucheng Shi of Tencent Hunyuan, Liang Wu; base: no training required; arch: L0/L1 · L0 answers identically when option order is reversed", "arch": "Level-based post-processing that turns an arbitrary LLM into a Jev-style decision model: typed decisions, real probabilities, order-invariant readout", "approach": "logits", "training": "None at L0; 100–500 labels for L1 gating", "data": "User's own labels for L1", "result": "L0 with zero labels: order-flip rate 0.230→0.073, calibration error 0.240→0.095; with 100–500 labels: auto-decidable fraction at 5% risk 7.7%→52.0%", "notes": "405 stars. CI + PyPI package + docs/levels.md contract.", "domain": "general", "score": "52.0%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "apofasi", "name": "Apofasi", "author": "A3S-Lab", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/A3S-Lab/Apofasi", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "arch: Rust typed-decision engine, lexical and neural paths; arch: απόφαση, \"decision\" · pure Rust, no weights · mlx is the fast path, links a prebuilt libmlx; result: MLX, f32 weights, english, one model load for all 300 rows", "arch": "Typed decisions in one forward pass - no generation, nothing to parse: a host gives Apofasi a state and a map of typed questions and gets back a map of typed answers; the Rust client speaks one schema to either engine - Client::default is the lexical engine and infer enables the neural checkpoint; metal/mlx on Apple Silicon, cuda on NVIDIA, mkl/ort for warm CPU.", "approach": "", "training": "", "data": "", "result": "release 0.1.2 benchmarked on an Apple M5 Max; compared against Jev and GLiNER figures from the nibzard/decision-model-benchmark package; group_scores uses 10 ECE bins with a 5% error budget", "notes": "Bilingual English/Chinese README; checkpoint weights not shipped with the repo. GitHub new-repo sweep 2026-09-25.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cpa-plugin-systemone", "name": "cpa-plugin-systemone", "author": "ahoo", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/ahoo/cpa-plugin-systemone", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "CLIProxyAPI native provider plugin for OpenCode Zen SystemOne, opencode.ai/zen/v1/systemone, the Jev family from TypeSafe AI, for fast structured decisions. Instead of generating text, Jev evaluates a state against typed questions, noul, choice, score, and returns values that code can use directly. The plugin exposes jev-1.13, alias jev, and jev-1.13-free, alias jev-free.", "arch": "CLIProxyAPI native provider plugin for OpenCode Zen's SystemOne endpoint: exposes jev-1.13, alias jev, and jev-1.13-free as ordinary chat-compatible models, where the last user message becomes the state and the typed answers come back as a JSON object in the assistant message content", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "deepopen", "name": "DeepOpen", "author": "deepopen-com", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/deepopen-com/deepopen", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 1020, "likes": "", "detail": "arch: Laya-based multilingual System 1 decision engine + whitepaper; base: improved Laya checkpoint; arch: <0.5 ms · 0% accuracy on a Khmer task while reporting 95.2% wrong confidence; data: fully reproducible training and evaluation pipelines, one-click; notes: English and Chinese", "arch": "Non-autoregressive System 1 decision engine built on Laya: one forward pass completes multi-dimensional classification across 100+ languages; a pure-Python language detector identifies script and language before forward inference and routes the request to the optimal checkpoint, which removes the English-only checkpoint failure mode.", "approach": "rl", "training": "RLCD reinforcement learning with a strictly-correct reward rule", "data": "banking77, clinc150", "result": "33 ms per single request and 7.2 ms per batch request on NVIDIA T4; state-of-the-art on the banking77 and clinc150 intent benchmarks per the whitepaper", "notes": "Open-source multilingual System 1 decision engine with a technical whitepaper; pip install deepopen; deployable as a local decision service; permissive open-source license. GitHub new-repo sweep 2026-09-25.", "domain": "general", "score": "", "latency": "33 ms single / 7.2 ms batch", "benchmark": "", "cost": "", "speedup": ""}
{"id": "towow-ai-jpp", "name": "J++", "author": "towow-ai", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/towow-ai/jpp", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 10, "likes": "", "detail": "The motivation is that a few simple operations, organized well, can accomplish something surprisingly complex, and JEV prompted the question of what happens when semantic judgment joins those operations alongside exact computation, search and feedback. The design intuition is that a program should be able to construct its next question, accept a method as an argument, and return a method that another program can use, with the resulting composition remaining a building block, so others can construct methods the authors did not anticipate. The project's first application question comes from Towow: can a fuzzy intent meet different participants' local contexts to produce new cooperation possibilities. Docs cover why J++, language design and grammar, an ecosystem reassessment, and progress, with a bilingual English and Simplified Chinese README.", "arch": "An experimental programming language, J++, that explores semantic judgment as a programmable operation, where questions are values and methods are values, and a composed method can become a building block in another method: you write standalone .jpp source, which a native Rust implementation parses, checks against the language rules, and executes through one shared kernel, with the earlier Python 3.12 embedded implementation kept as a behavior reference and experiment tool.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "joshlong145-jev-cli", "name": "jev-cli", "author": "joshLong145", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/joshLong145/jev-cli", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "CLI that analyzes JSON, NDJSON and JSONC system artifacts with Jev and returns typed, calibrated answers instead of prose; uv-run jcli analyze with question packs, sliding windows and a mark-uncertain confidence threshold, tabular output", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Show HN 2026-09-21", "domain": "data", "score": "2 points", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-rs", "name": "jev-rs", "author": "yijunyu", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/yijunyu/jev-rs", "base_model": "Any LLM", "params": "", "metric": "", "license": "", "paper": "", "stars": 11, "likes": "", "detail": "base: any GGUF behind llama-server; arch: yes/no · one of N · ordinal scale · Claude Code / Codex / Grok Build / OpenCode; result: macOS arm64/x86_64, Linux x86_64/arm64; notes: with jev-serve, jevper, Kev, openjev; name: Rust System One engine, one prefill", "arch": "'System One judgments from any LLM in one prefill': noul / choice / score with probabilities, never generated text; wire-compatible with TypeSafe POST /v1/systemone; exposed to coding agents as an MCP tool", "approach": "logits", "training": "", "data": "", "result": "Prebuilt binaries or cargo install jev-rs; example: Qwen3-4B-GGUF on llama-server; TYPESAFE_BASE_URL drop-in for the official SDKs", "notes": "Created 2026-09-21; first-week single-logit runtime.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-serve", "name": "jev-serve", "author": "rreinold", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/rreinold/jev-serve", "base_model": "Any MLX model or OpenAI-compatible API", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 5, "likes": "", "detail": "base: default · ollama / LM Studio / OpenAI via --api; result: 0.23 s vs 7.80 s per decision; notes: Jared Palmer; name: single-logit inference runtime", "arch": "Serves /v1/systemone via first-token logit readout — the same approach openjev.com uses in the browser, running server-side; choice / noul / score with full probability distributions, not a point estimate", "approach": "logits", "training": "", "data": "", "result": "Claims 34× faster than structured JSON generation; example model lmstudio-community/Qwen3.8-27B-MLX-6bit on Apple Silicon", "notes": "Python 3.11+; derived from Kev. Created 2026-09-21.", "domain": "general", "score": "", "latency": "230 ms", "benchmark": "", "cost": "", "speedup": "34× faster"}
{"id": "jev-nx", "name": "jev_nx", "author": "dannote", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/dannote/jev_nx", "base_model": "Laya", "params": "421M", "metric": "", "license": "Open", "paper": "", "stars": 8, "likes": 0, "detail": "Ships Laya, Apache 2.0, 421M parameters on a ModernBERT-large backbone, with sequences and probabilities verified against the reference. Jev.Nx.Model is the contract for adding more models: encode a question into a marked token sequence, batch, run, and read a distribution per question out of the outputs; Jev.Nx.Serving does the padding, batching and compilation for every model that implements it, and Jev.Nx.Defn covers models that run on Nx.Defn. Laya runs two ways: the bumblebee runtime, the default, builds the encoder as an Axon graph and the head in Nx.Defn, so it goes wherever Nx goes, while the onnx runtime loads the published export. The README shows a cascade: ask the local model first, and when its confidence is below 0.7, re-send the same request to Jev.", "arch": "Elixir backend that runs open decision models in-process on Nx, answering a Jev.Server exactly as TypeSafe Jev does, so the same handle_answer/3 clauses, thresholds and telemetry apply whichever model answered: the open models run where your Elixir code runs, with the Jev wire shape but no HTTP server of its own.", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "Nx-based in-process backend — Jev-shaped API without the API.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jqv", "name": "jqv, decision API on Qwen3", "author": "Octalab-Inc", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/Octalab-Inc/jqv", "base_model": "Qwen3, stock open LLM", "params": "Qwen3, size per deployment", "metric": "five inference structures times two readouts, measured on the same model and prompt", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Reproduces Jev's inference structure on a stock open LLM, following ArcherHume's unmasking: the state is prefilled once, every question runs as an isolated branch behind a block attention mask, and the answer is read directly from the option-letter logits in one forward pass with no decoding; a single fitted temperature turns the readout into calibrated probabilities; five switchable inference structures, the generate baseline, per-question naive forward, KV-cache replication, packed block-mask sequence, and a shared mask-free Hydragen-style attention with log-sum-exp combination, plus two readouts, LM-head option-letter rows versus next-token logits; the server speaks the TypeSafe /v1/systemone wire format as well as its own /decision endpoint; full experimental report in English and Japanese", "approach": "", "training": "No training: an inference-structure study on a frozen base", "data": "", "result": "JevBench v1.4.1 official table: rank 14 of 77 at 44.4, with calibration 72 the second-best of the top 15 behind Jev's 76; the packed and shared structures are the ones matching Jev's architecture", "notes": "docs/jevbench-serving.md documents the exact JevBench-measured configuration for reproduction", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "infinimindcreations-laya-rlcd-training", "name": "Laya RLCD training", "author": "InfinimindCreations", "date": "2026-09-21", "category": "runtime", "url": "https://huggingface.co/models/InfinimindCreations/laya-rlcd-training", "base_model": "Laya", "params": "undisclosed", "metric": "Per-question accuracy vs held-out gold", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "Everything public about System One models so far has rebuilt the inference path, scoring allowed answers instead of generating text, which is well covered; the part everyone leaves out is how to make the model good at your questions, and that is the point of this repository. It supports the three Laya question types, choice, score and bool. The bundled example is built from AG News, so it runs without private data, and it prints the majority-class baseline before training starts, the number a result has to beat before it means anything. The base model is fetched on first use, with the LAYA_SNAP environment variable pointing to a local copy or your own checkpoint. Depends on torch, transformers, safetensors, huggingface_hub and datasets.", "arch": "The first public training loop for Laya, the open-weights System One model, written from Laya's own method description, RLCD with Gaussian noise on the logits, a strictly proper scoring rule, and REINFORCE with a group-mean baseline, because Laya ships the building blocks but no training loop, and its repository contains no backward call and no optimizer, checked 2026-09-20: it takes labelled text, trains the encoder to answer several typed questions in one forward pass, and reports accuracy per question against a held-out gold set.", "approach": "fine-tune", "training": "RLCD-style loop; Laya's own repo ships no backward pass or optimizer, checked 2026-09-20", "data": "Labelled text corpora; example data.py and train.py included", "result": "Reports per-question accuracy against a held-out gold set", "notes": "First public Laya training loop; the Laya repository ships the inference ingredients only.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-for-web", "name": "layaForWeb", "author": "vishalmysore", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/vishalmysore/layaForWeb", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 20, "likes": "", "detail": "Built on the Laya English checkpoint convaiinnovations/laya, Apache-2.0, converted to ONNX and quantized for ONNX Runtime Web. No server, no API key, no data leaves the page; live demo at vishalmysore.github.io/layaForWeb. Choice returns a probability for each labeled option plus the top pick; Score returns a probability for each step of an ordered scale plus a continuous score; Yes/No returns the probability the statement is true. Every answer carries a confidence, 1 minus normalized entropy, and the model temperature is calibrated per question type, so the numbers can decide when to act automatically and when to hand off to a person. Unlike a chat model it does not generate text: it reads the input once and scores the options, which makes it deterministic and small enough to ship to a browser.", "arch": "Decision model that runs in the browser: give it text or a JSON object and up to ~20 options per question, it answers each with probabilities and a confidence in a single forward pass, entirely on the visitor device", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "juspay-neurolink", "name": "neurolink", "author": "juspay", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/juspay/neurolink", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 140, "likes": "", "detail": "", "arch": "Unified AI SDK whose TypeSafe decide API supplies typed judgments for model routing, compaction, tools, and retrieval planning. Provider abstraction: the pipe layer of an AI nervous system — Juspay's TypeScript interface connecting provider neurons to an application, with decide as a first-class inference type alongside generate and stream. The pipe layer of an AI nervous", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "grzegorznowak-noul", "name": "noul, grzegorznowak", "author": "grzegorznowak", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/grzegorznowak/noul", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "A Jev-shaped decision API built on the Voyage API, or any compatible embeddings plus rerank, plus a provenance audit for cited answers. Built for the chunkhound deep-research pipeline: the decision core gives the pipeline bounded choice, score and noul primitives with distribution statistics and semantics labels, and the audit layer checks whether the citations in a synthesized answer actually point at the sources they cite.", "arch": "A Jev-shaped decision API built on Voyage, or any compatible embeddings and rerank API, plus a provenance audit for cited answers: built for the chunkhound deep-research pipeline, it gives the pipeline bounded choice, score and noul primitives with distribution statistics and semantics labels, and the audit layer checks whether the citations in a synthesized answer actually point at the evidence the answer claims; not a model, trains nothing", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "noulgate", "name": "NoulGate", "author": "Manish-Anchan", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/Manish-Anchan/NoulGate", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "A high-speed System 1 gateway that prunes MCP tool bloat before it reaches your LLM. When you connect 5+ MCP servers to an AI agent, you can easily end up with 30–60 tool schemas injected into every single request, even when the user just says Thanks; that costs thousands of tokens, slows responses, makes models confuse similar tools and hallucinate tool calls. NoulGate filters the tool schema set with a fast System 1 decision pass before each request.", "arch": "A high-speed System One gateway that prunes MCP tool bloat before it reaches the LLM: when 5 or more MCP servers inject 30–60 tool schemas into every request, NoulGate sits between the agent and the LLM, evaluates each prompt with Jev in about 100 milliseconds, and prunes the tool list down to the 1 or 2 tools actually needed, or strips all tools entirely for conversational turns", "approach": "router", "training": "", "data": "", "result": "", "notes": "", "domain": "routing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "one-system", "name": "one-system", "author": "rawwerks", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/rawwerks/one-system", "base_model": "Multi-backend", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 8, "likes": "", "detail": "base: TypeSafe API + local", "arch": "Gateway between your application and compatible TypeSafe System One servers: send your data and questions once, then choose a backend directly or let a model decide where the request should run, across local and hosted decision models", "approach": "sdk", "training": "None", "data": "None", "result": "Keeps one client interface while trying different models or hosting arrangements; suitable tasks run locally while others go to hosted Jev, or the whole pipeline runs entirely local", "notes": "7 stars; multi-backend System One client", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kyegomez-openjev", "name": "open-jev from first principles", "author": "kyegomez", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/kyegomez/open-jev", "base_model": "", "params": "undisclosed", "metric": "", "license": "Open", "paper": "", "stars": 30, "likes": "", "detail": "base: random weights, hash tokenizer; arch: no causal mask · dict/array positions · true · distribution + expectation; data: architecture research code", "arch": "Bidirectional transformer state encoder + structural path embeddings + learned query slots that cross-attend to one shared cacheable state; Noul = p, Choice = softmax over runtime options, Score = ordinal thresholds; separate evidential head for epistemic confidence", "approach": "from-scratch", "training": "RLCDLoss: soft-target NLL + Brier + consistency + confidence + ECE terms", "data": "None", "result": "— explicitly 'not the production Jev model… no claim of matching published results'", "notes": "Reverse-engineering-by-architecture: encodes state once, answers every question through small typed readout heads in a single parallel pass; questions attend to state but never to each other.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "deepanwadhwa-opendecision", "name": "OpenDecision", "author": "deepanwadhwa", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/deepanwadhwa/opendecision", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 57, "likes": "", "detail": "", "arch": "Open semantic decision engine implementing Choice, Noul, and Score primitives with a TypeSafe comparison harness. Open alternative: a local semantic decision engine that describes itself as the open-source equivalent of Jev, answering Choice, Noul, and Score questions from structured state and documents without a hosted call.", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "privatemode-decisions", "name": "Privatemode Decisions", "author": "edgelesssys", "date": "2026-09-21", "category": "runtime", "url": "https://github.com/edgelesssys/privatemode-decisions", "base_model": "GLM-5.3-Flash on Privatemode AI", "params": "undisclosed", "metric": "~150 ms per decision, network included", "license": "Open", "paper": "", "stars": 7, "likes": "", "detail": "base: GLM-5.3-Flash in attested enclave; arch: one masked forward pass, one token out; metric: ~150 ms; result: 1M ctx, 191 options; notes: encrypted processing + attestation; approach: prompt", "arch": "Ask an LLM to choose from a fixed set of options and get its choice plus a probability for each option; each decision is one masked forward pass read out of a single logit row, one token produced, no fine-tuning, no free text to parse; works like TypeSafe Jev but runs on GLM-5.3-Flash; the Python library gets the token IDs it needs from the model tokenizer, so models can be switched without changing code; portable to any vLLM-backed OpenAI-compatible server, without the confidentiality guarantees", "approach": "logits", "training": "", "data": "", "result": "Up to 1M token context per decision; up to 191 options per question with optional descriptions; screenshots, scans and photos alongside the text; on Privatemode AI the data stays encrypted while it is processed and the deployment attestation is verified before anything is sent", "notes": "Companion comparison vs Jev and Laya in edgelesssys/privatemode-decisions-benchmark; blog post at privatemode.ai/blog/system-one-from-glm-flash; the sample web app shows the full probability distribution for each answer", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "taeold-djev-run", "name": "djev-run", "author": "taeold", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/taeold/djev-run", "base_model": "DiffusionGemma 26B A4B NVFP4", "params": "26B A4B", "metric": "JevBench v1.3.0, N=231 public suite: 81.4% overall, composite 73.4 at steps=1 samples=1", "license": "", "paper": "", "stars": 554, "likes": "", "detail": "throughput: about 100-123 req/s at concurrency 32; notes: cold start about 47.5 s vs 4 m 05 s baseline", "arch": "serves DiffusionGemma-Jev djev on Cloud Run with RTX PRO 6000 Blackwell GPUs via vLLM; pre-built ghcr.io/taeold/djev-run image with dev/shm staging and vllm serve flags", "approach": "", "training": "", "data": "", "result": "deploy with 20 CPU, 80 GiB RAM and concurrency 32; built-in snake, dino and tetris demo apps; GCS volume with buffered reads for weights", "notes": "built on mmastrac/djev; demos inspired by mizorewww/laya-coreml, virajbhartiya/laya-vs-jev and trungdq88/jev-tetris; RTX PRO 6000 regions us-central1, europe-west4, asia-southeast1, asia-south2", "domain": "general", "score": "JevBench v1.3.0 composite 73.4", "latency": "61 ms median on server, 117 ms end-to-end", "benchmark": "JevBench v1.3.0", "cost": "RTX PRO 6000 at $3.19 per hour active, $0 idle", "speedup": ""}
{"id": "prasanthj-duckdb-jev", "name": "duckdb-jev by prasanthj", "author": "prasanthj", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/prasanthj/duckdb-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 4, "likes": "", "detail": "Structured evidence: it evaluates text, JSON, STRUCT, LIST and ARRAY values without exporting columns through Python or pandas. Credentials are read from a scoped environment variable. Builds against DuckDB 1.4.5 and 1.5.5, for macOS and Linux on x86-64 and ARM64, with a native build and test pipeline and published releases. The README demonstrates nested account evidence, renewal-risk classification with confidence, and cached query reuse, with timings from one local run, reproducible with vhs. Apache 2.0.", "arch": "Native C++ DuckDB extension that evaluates semantic predicates, classification and rubric scoring through TypeSafe Jev directly from SQL, batching and streaming inference without Python UDF registration or a separate inference server: Noul predicates, finite Choice classification, ordered Score rubrics and mixed multi-question evaluation, with up to 1,000 independent judgments packed per request, bounded concurrent HTTP work, and streaming across DuckDB chunks.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-spring-boot", "name": "Jev Spring Boot starter", "author": "danvega", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/danvega/jev-spring-boot-starter", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 37, "likes": "", "detail": "An independent community project: version 0.1.0-SNAPSHOT is a local development version and is not published to Maven Central. No enable annotation or component-scanning configuration is needed: the starter activates in servlet applications, supplies one JevClient, backs off when you provide your own, and makes no HTTP calls during startup, with a missing API key producing an actionable startup error. The starter supplies spring-boot-starter-restclient, while your application supplies spring-boot-starter-webmvc. The example routes a Stripe outage message with a single choice question over billing, integrations and support, read back with response.choice(\"team\").choice().", "arch": "Spring Boot starter that adds TypeSafe Jev to a Spring MVC application: add one dependency, set the TYPESAFE_API_KEY, and inject the JevClient, which speaks the three Jev question types, noul, choice and score, in a single request, built on Spring MVC, RestClient and Jackson 3 for Spring Boot 4.0 or newer and Java 17 or newer.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shinpr-jev-reranker", "name": "jev-reranker by shinpr", "author": "shinpr", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/shinpr/jev-reranker", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 3, "likes": "", "detail": "It returns the same objects in best-first order, with a rerankScore from 0–1 added to each, where higher means Jev considers the result more relevant to the query, and IDs, source paths, retrieval scores, and other metadata pass through unchanged. You choose the field that contains the text. Install from npm, Node.js 14 or newer on macOS, Linux or Windows x64 or Arm64, globally or via npx, with the TYPESAFE_API_KEY from the TypeSafe dashboard. MIT, with CI.", "arch": "Jev reranker CLI for JSON search results: it reads a JSON array from stdin and writes a JSON array to stdout, choosing the search results worth passing to your LLM, and uses TypeSafe AI's Jev to rerank retrieved documents, remove candidates that contain no usable evidence, or extract query-specific passages, slotting in after BM25, vector search, or any command that emits a JSON array of candidate objects.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gopalanj-jevons", "name": "jevons", "author": "gopalanj", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/gopalanj/jevons", "base_model": "LFM2.5-1.2B-Instruct; LoRA seed biasing toward option-key / yes-no / score-digit tokens", "params": "1.2B", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "base: MLX 8-bit; training: rank 16, scale 2.0", "arch": "Local System One server: scores allowed outcomes from logits and assembles {choice, probabilities, confidence, noul, score, legend} in code", "approach": "logits", "training": "QLoRA seed on the MLX 8-bit checkpoint", "data": "", "result": "HF seed weights: gopalanj/jevons-lfm25-1.2b-systemone", "notes": "'This is not official Jev.' HF seed-weights row, gopalanj/jevons-lfm25-1.2b-systemone, lfm1.0 backbone license: rank 16 scale 2.0 on 16 layers, attn q/k/v/o + MLP w1/w2/w3, prompt tokens masked during training; served as mlx-community 8-bit.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-cpp", "name": "laya.cpp", "author": "lkarlslund", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/lkarlslund/laya.cpp", "base_model": "Laya + Laya multilingual + Laya typed-decisions", "params": "", "metric": "", "license": "", "paper": "", "stars": 90, "likes": "", "detail": "Binary releases cover Windows and Linux x64 CUDA and Vulkan executables plus a macOS arm64 Core ML executable, with runtime docs for drivers, Windows CUDA DLLs and compiled Core ML model buckets. The build needs a C++20 compiler, CMake 3.24 or newer, ICU and nlohmann-json, plus the chosen GPU backend's dependencies. Performance is measured as paired comparisons against matching-precision Python, 250 fixed questions across all three models at batch sizes 1, 2, 4 and 8: on an NVIDIA RTX PRO 6000 Blackwell 96 GB capped at 450 W, optimized FP32 CUDA is 1.35–2.48× Python and BF16 CUDA 1.11–2.71×; on an AMD Radeon 8060S, compensated FP32 Vulkan is 1.26–2.45×. All measured answer checks pass, with exact categories and numeric absolute error of at most 0.0001; timings include preprocessing, inference and formatting, excluding model loading and JSON transport.; early-version re-upload kyr0/laya.cpp had no Core ML backend, 0 stars, dropped", "arch": "Native C++ inference for Laya typed decisions, powered by ggml with CUDA, Vulkan and Apple Core ML backends, where tokenization, inference and JSON output run without Python, supporting the english, multilingual and typed-decisions models, plus a JEV-compatible HTTP server with automatic request batching.", "approach": "", "training": "", "data": "", "result": "Throughput relative to matching-precision Python: NVIDIA RTX PRO 6000 Blackwell, 96 GB, capped at 450 W, CUDA optimized FP32 1.35–2.48× and CUDA BF16 1.11–2.71×; AMD Radeon 8060S, Vulkan compensated FP32 1.26–2.45×. Answer checks: exact categories, numeric absolute error at most 0.0001.", "notes": "Native ggml port alongside the Python Laya runtimes tracked in the laya row notes", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-ex", "name": "laya_ex", "author": "ChristianAlexander", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/ChristianAlexander/laya_ex", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 15, "likes": "", "detail": "Laya.load() and then Laya.predict with typed questions, for example a choice over billing, technical and other with per-option criteria, plus a noul asking whether the customer explicitly requests a refund; answers come back as typed values with probabilities", "arch": "Native Elixir runtime for Laya on Nx and Bumblebee: downloads the official convaiinnovations/laya checkpoint on first load; choice criteria are ordered label-description tuples; noul, choice and score supported; a bench script is included; the package does not select an Nx backend, the host app configures one, e.g. EMLX on Apple Silicon or EXLA on CPU and NVIDIA", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "silverjinx-openjev", "name": "openjev own-data training", "author": "S1LV3RJ1NX", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/S1LV3RJ1NX/openjev", "base_model": "Open model + user data", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 12, "likes": "", "detail": "", "arch": "Small self-trainable model for typed decisions: Qwen3-1.7B backbone plus LoRA, state in and typed answers with calibrated probabilities out, no text generation; Python 3.10 or newer, Apache-2.0, research preview with a full report PDF in the repo", "approach": "undisclosed", "training": "User-data fine-tuning pipeline", "data": "User-provided", "result": "Self-trainable Jev-compatible endpoint", "notes": "9 stars; research preview of own-data Jev-compatible training", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gpt-agi-openjev", "name": "OpenJev, GPT-AGI", "author": "GPT-AGI", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/GPT-AGI/OpenJev", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 5, "likes": "", "detail": "", "arch": "Jev-compatible System One decision engine with a Claude Code style REPL; Choice, Score and Noul from open models in one forward pass, no JSON parsing, every answer with a probability", "approach": "", "training": "", "data": "", "result": "", "notes": "Phase 0 alpha", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "harnessrouter-systemone-harness", "name": "System One Harness", "author": "HarnessRouter", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/HarnessRouter/SystemOneHarness", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 163, "likes": "", "detail": "The first supported model is Jev by TypeSafe, available through OpenRouter or TypeSafe directly. A demo shows Jev playing a live browser game through the harness, one typed decision per step, with no generated control text. Version 0.3.1, Python 3.10 or newer, with UHP conformance at the Core level, and reports published in the repository. Show HN 2026-09-20.", "arch": "System One Harness, an Apache-2.0 harness for System One models, that turns a System One decision model into an agent loop: it observes an environment, compiles its finite action space into typed questions, gates each decision by confidence, executes the chosen action, and records the complete trace, with one model call per step, no generated actions, and a probability on every transition.", "approach": "", "training": "", "data": "", "result": "", "notes": "Show HN 2026-09-20", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "systemane", "name": "systemANE", "author": "kerryrm", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/kerryrm/systemANE", "base_model": "MiniLM-L6 tier 1, Apple Foundation Model tier 2", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "On CLINC150, 1,300 human-written utterances collected independently of the project, 300 in-scope plus 1,000 genuinely out-of-scope: 93.3% accuracy, ECE 0.041, and 0.994 AUROC at spotting out-of-scope input, with 94.6% of the graph executing on the ANE. The ANE claim is verified four ways, each by a script in the repo: where.py asks Core ML's own compute planner for the per-op device assignment rather than inferring it from timing, and 157 of 166 ops land on the MLNeuralEngineComputeDevice, the nine on CPU being five fp32 to fp16 casts, the embedding gather, and three mask ops, with every matmul, softmax and layernorm on the ANE including 21 of the 22 adds; drift.py runs the fp32 torch path against the fp16 Core ML path end to end, anchors and queries, at two label counts, showing fp16 costs almost nothing; plus measurement scripts for the rest. Tier 2 is served by fm serve, the Apple Foundation Models CLI built into macOS 27 at /usr/bin/fm, with nothing external to install, and fmserve.py talks to it over stdlib HTTP only. Explicitly positioned as a proof of concept: tier 1 is not smart, it is cheap enough to run on everything and, once calibrated, honest enough to say when it should not decide, and the accuracy lives in tier 2.", "arch": "Typed decisions on the Apple Neural Engine, a System-1 decision engine over a sentence encoder paired with a second tier that only runs when the first is not sure: tier 1 is MiniLM-L6, 22.6M parameters, on the ANE at 1.4 ms each, answering most inputs, and tier 2 is the Apple Foundation Model, about 3B, at 1212 ms, answering what tier 1 escalates, from a model with no fine-tuning.", "approach": "", "training": "", "data": "", "result": "CLINC150, 300 in-scope plus 1,000 out-of-scope: 93.3% accuracy, ECE 0.041, 0.994 AUROC at out-of-scope spotting; 1.4 ms per decision on the ANE, 94.6% of the graph on the ANE, from a 22.6M-parameter model with no fine-tuning.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "systemone-harness", "name": "systemone-harness, aubreyhayes47", "author": "aubreyhayes47", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/aubreyhayes47/systemone-harness", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "A deliberately small System One action harness plus a local inference runtime. The application side stays simple: state + goal + currently legal actions go to a System One model and come back as a selected action with probabilities. The harness uses TypeSafe's official Python SDK as the client contract; the local runtime implements the two SDK endpoints so the same harness runs against a local model.", "arch": "A deliberately small System One action harness plus a local inference runtime: state, goal and currently legal actions go in, a selected action with probabilities comes out; it uses TypeSafe's official Python SDK as the client contract, and the local runtime implements the two endpoints that contract needs, GET /v1/models and POST /v1/systemone; v0.1 supports choice only, with no planner, memory, executor, quantization or KV-cache management", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "systemoneprompts", "name": "systemoneprompts", "author": "Svilupp", "date": "2026-09-20", "category": "runtime", "url": "https://github.com/svilupp/systemoneprompts", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "Define TypeSafe questions, required state, and Boolean factors in TOML. Validate definitions and generate typed modules for TypeScript or Python, with npm and pip packages.", "arch": "Defines TypeSafe questions, required state and Boolean factors in TOML, validates the definitions and generates typed modules for TypeScript or Python; both packages ship the same ten examples, and the shared client talks to native TypeSafe, OpenRouter and Cloudflare Workers AI", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "taku-me-chakuho", "name": "chakuho", "author": "taku-me", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/taku-me/chakuho", "base_model": "Local LLM", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Named chakuho after the choosing-the-dharma faculty of the seven-recollections chapter: weighing the dharmas and selecting the correct one. It stands in for Jev's System One model using a general model you already have, with dependencies on the Python standard library only. Start with uv sync and uv run chakuho serve; the backend is any OpenAI-compatible /chat/completions, defaulting to localhost port 8006, and the default sampling estimator works with any backend supporting n sampling, such as vLLM, SGLang with speculative decoding, or Ollama, while the logprobs estimator needs logprobs and top_logprobs, measured on vLLM and Ollama 0.33. POST /v1/systemone accepts both Jev's noul spelling and the Vercel AI Gateway boolean spelling, and questions within one request are evaluated in parallel, without knowing each other's answers. Choice questions run one round up to 52 options; 53–2704 options are chunked at 52, the top k of each chunk advance to a final, stages 2, with preliminaries losers at probability 0; a __none__ option, when present, is always included in every chunk and the final so none applies can be answered explicitly; above 2705 options the request is a 400. The response carries the choice, per-option probabilities, coverage, and the stage count. Because OpenAI-compatible layers may not honor turning thinking off, thinking models such as Qwen3 can have their first token be the start of a thought rather than a judgment, so point it at an instruct model, or a backend where chat_template_kwargs.enable_thinking=false takes effect. Japanese README.", "arch": "A Jev-compatible, no-generation decision endpoint served on top of a local LLM: it takes a state plus questions declaring the shape of the answer, and either repeatedly samples a single token from a local LLM through an OpenAI-compatible API, the default, or reads one call's logprobs, then returns the probability distribution over the declared options, emitting no text, code or coordinates; the candidates are enumerated by the caller at run time, such as an AX tree, DOM, or task list.", "approach": "logits", "training": "None", "data": "", "result": "Jev-compatible API without Jev weights", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yohanargentina-oss-foq", "name": "Foq", "author": "yohanargentina-oss", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/yohanargentina-oss/Foq", "base_model": "Ternary 8B, Qwen3 architecture", "params": "8B ternary", "metric": "", "license": "", "paper": "", "stars": 7, "likes": "", "detail": "The README's comparison table: Jev is cloud-only, a closed LLM behind an early-access waitlist, with 4k to 8k context, usage-based pricing, and Pydantic schema extraction; Laya is local, a 320M to 421M BERT encoder, 512 or 1024 tokens, about 8.6 GB in a 3-model PyTorch router, 77.8% on the operational triage suite, failing on critical triage; Foq is 100% local, the 1.58-bit ternary 8B in C++, about 4.8 GB of RAM plus VRAM, 4,096 token context, 100% on the operational suite at 8B depth, and Pydantic schema extraction via extract(). The Python API takes a state and Boolean or Choice questions, and res.danger.answer with its confidence comes back in milliseconds. pip install foq.", "arch": "A free, open-source, 100% local alternative to Jev, billed as the decision engine for AI agents: an 8B ternary model in the Qwen3 architecture, quantized to PQ2_0, a 2.2 GB GGUF, run on a custom llama.cpp fork, built on the same idea as TypeSafe's System One Model, where one feed-forward pass over your context returns calibrated probabilities on typed questions, booleans, choices, scores and Pydantic objects, in about 25 ms, with no prose, no tokens generated, no waitlist, and no per-token bill.", "approach": "", "training": "", "data": "", "result": "About 25 ms per system_one call locally; 100% on the operational triage suite at 8B depth, versus 77.8% for Laya in the same table, which fails on critical triage; about 4.8 GB RAM plus VRAM; 4,096 token context.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alitrack-jev-clone", "name": "jev-clone: local contract-compatible System One decision server", "author": "alitrack", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/alitrack/jev-clone", "base_model": "Qwen3.5-4B + Qwen3.6-35B-A3B", "params": "4B / 35B-A3B", "metric": "classify 24/37 → 40/40", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Rust server, field-for-field compatible with TypeSafe's public HTTP contract; many questions share one prefill and run in parallel; 422 error semantics for readouts that do not hold; research artifact, M0-M3 landed", "approach": "logits", "training": "", "data": "zh-evidence-v3 frozen Chinese-first eval set, 195 items; one-click verify recomputes 572 checks with 0 mismatches", "result": "Same Qwen3.5-4B weights with only the readout channel swapped: classify 24/37 → 40/40 and split 0/8 ↔ 8/8 — the readout is model-dependent and wants a model trained for single-token slot readout; renaming all labels with a uniform prefix took classify 25/38 → 40/40, so the real variable is the interaction of slot layout and label text", "notes": "1 star; created 2026-09-19. Independent project, not affiliated with TypeSafe; source-level study of three reference implementations in docs/blueprints.md. llama.cpp M3 Ultra endpoint dialect measured 2026-09-21.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ismaelsoilet-jev-harness", "name": "jev-harness zero-dependency", "author": "ismaelsoilet", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/ismaelsoilet/jev-harness", "base_model": "TypeSafe Jev", "params": "", "metric": "per-gate P/R/F1 and ECE on a 160-case labelled corpus; offline gate latency", "license": "Open", "paper": "", "stars": 7, "likes": "", "detail": "CI green with 626 passing tests; Sigstore npm provenance; supports Python 3.9 through 3.13; Rust crate and npm package mirror the same harness.", "arch": "Polyglot decision harness for AI coding agents in Python, TypeScript and Rust with 626 passing tests: six System-1.5 gates between fast heuristics and frontier reasoning — test-failure triage, trajectory abort, model routing, step-completion verification, per-generation reasoning-effort modulation named Astra-Jev, and a nudge gate that judges whether an agent paused prematurely; Python uses only the standard library, zero external dependencies", "approach": "sdk", "training": "", "data": "", "result": "A missing-dependency failure that would cost ~50,000 frontier tokens, $0.50-2.50 and a 15 s delay becomes a deterministic pip or npm install at ~$0.00002 per call on ~470 input tokens with 0 LLM tokens; System 2 is invoked only when strictly necessary, cutting token spend by up to ~80-90%; a 160-case labelled corpus replays every gate with confusion matrices, P/R/F1 and ECE, and CI fails on regression against docs/REPLAY_REPORT.json", "notes": "v0.2.0, MIT. Offline heuristics make zero network calls; measured 2026-09-23 in-process mock: Rust triage p50 22.0 µs and p99 58.5 µs, abort p99 43.1 µs, effort p99 40.5 µs; TypeScript triage p99 340.3 µs; Python triage p99 295.5 µs; the p99 500 µs budget is asserted in CI for Rust. Live Jev runs ~80-100 ms offline CLI and 0.5-1.0 s on the free tier. Fail-open degrades to the deterministic engine and marks the answer with is_mock and degraded_reason; --fail-closed exits 2; shadow mode reports would-exit-N while always exiting 0. Redacts credential-shaped material from the state in all three runtimes and sends a focused slice plus causal context instead of raw logs. Host plugins for Claude Code and Codex/OpenCode; MCP tools with lease_steps. Semantic parity across 9 programming and 3 natural languages; red-team hardening in the verify, abort and effort gates. Astra-Jev saves up to $1.15 per mechanical generation on Western frontier pricing, detects non-reasoning models and preserves DeepSeek reasoning_content; inspired by the Astra-Ares framework", "domain": "agent", "score": "", "latency": "22 µs p50 and 58.5 µs p99 offline triage in Rust; 80-100 ms offline CLI; 0.5-1.0 s live on the free tier", "benchmark": "", "cost": "$0.042/1M input vs $10/1M frontier input, ~238x cheaper; $0 output; ~$0.00002 per triage call", "speedup": ""}
{"id": "xinyao27-jevonian", "name": "Jevonian", "author": "xinyao27", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/xinyao27/jevonian", "base_model": "", "params": "", "metric": "", "license": "AGPL-3.0", "paper": "", "stars": 4, "likes": "", "detail": "", "arch": "One local endpoint, the right model for every turn - enforced in code, not prompts: sits between your agent and providers, routes each turn to the most cost-effective capable model, manages quota and cache economics, and logs every decision", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "receptron-laya", "name": "Laya for Node.js", "author": "Receptron", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/receptron/laya", "base_model": "Laya", "params": "", "metric": "", "license": "MIT code, Apache-2.0 weights", "paper": "", "stars": 475, "likes": "", "detail": "data: options limited to 192 tokens, state truncated to 512 tokens on the English checkpoint", "arch": "Node.js and TypeScript runtime for Laya on ONNX Runtime: choice, score and noul answers with calibrated probabilities in one forward pass; no PyTorch or Python at runtime.", "approach": "sdk", "training": "", "data": "", "result": "Output matches the Python reference implementation and the TypeSafe system_one wire format to four decimal places; about 1.7 GB of fp32 ONNX weights downloaded once and cached; Node 20+.", "notes": "@receptron/laya on npm; same request and response shape as the Python RLAgent.system_one reference. 410 stars.", "domain": "coding", "score": "", "latency": "140 ms per 3-question call on an Apple-silicon CPU, warm", "benchmark": "", "cost": "", "speedup": ""}
{"approach": "", "arch": "Independent Laya fork adding image input: replaces Laya's ModernBERT text encoder with a small vision-language model and answers choice, score and noul questions on an image plus optional text in one forward pass with no text generation; the predict(state, questions) API, output schema, RLCD training objective and temperature calibration are unchanged; each option is cut to 48 tokens within a 256-token head budget, a truncated field reports dropped content and strict mode raises; the same model plays simple games from pixels, the screen as image and the buttons as options", "author": "r33drichards", "base_model": "SmolVLM-256M-Instruct + ModernVBERT", "benchmark": "", "category": "runtime", "cost": "", "data": "", "date": "2026-09-19", "detail": "", "domain": "", "id": "laya-vision-repo", "latency": "", "license": "Apache-2.0", "likes": "", "metric": "answer accuracy on official validation splits, ECE for calibration, autoresearch games benchmark 0 random to 1 expert", "name": "Laya Vision code", "notes": "Checkpoints: laya-vision recommended, 201M, same weights as row thaitea-laya-vision-201m; laya-vision-smolvlm-256m-score, 237M with the first trained score head, row laya-vision-smolvlm-score; laya-vision-modernvbert-250m, 250M bidirectional, A-OKVQA 65.2%, row laya-vision-modernvbert; original laya-vision-smolvlm-256m, 237M, trained on A-OKVQA, ScienceQA and VQAv2 yes/no, 61.8%, 86.6%, 73.4%. Games, greedy play, one forward pass per move: ViZDoom basic 0.99, Atari Freeway 0.81, Acrobot 0.76, MountainCar 0.64, a variant that also trains the vision tower 0.54, LunarLander -0.44. Docs at r33drichards.github.io/laya-vision; demo space thaitea/laya-vision-demo on free CPU, about 3 s per image; family rows laya-vision and laya-vision-microduck", "paper": "", "params": "", "result": "Full suite of 59,427 questions over 34 validation sets: recommended 201M checkpoint 69.1% correct vs 69.3% for the previous 237M checkpoint, within a point on 23 sets, better calibration ECE 0.041 vs 0.064 and 15% smaller; autoresearch games benchmark 0.35 vs -0.04; per-question predictions committed in results/raw and recomputed by benchmarks/verify_published.py against the README table and each checkpoint metrics JSON", "score": "", "speedup": "", "stars": 57, "training": "", "url": "https://github.com/r33drichards/laya-vision"}
{"id": "apiplant-laya-rs", "name": "laya-rs, from-scratch Rust reimplementation", "author": "apiplant", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/apiplant/laya-rs", "base_model": "Laya", "params": "421M", "metric": "", "license": "", "paper": "", "stars": 8, "likes": "", "detail": "arch: from-scratch Rust reimplementation of Laya; arch: text, email, ticket, or JSON · choice / score / noul; notes: Rust runtime + laya-server HTTP service", "arch": "From-scratch Rust reimplementation of Laya, Convai Innovations' sub-35 ms non-autoregressive System 1 decision engine: give it a state and typed questions and it returns typed answers with calibrated probabilities in a single forward pass.", "approach": "", "training": "", "data": "", "result": "", "notes": "Distinct from redwolf2019/laya-rs. GitHub new-repo sweep 2026-09-25.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openjev-p300x2", "name": "openjev-p300x2", "author": "changh95", "date": "2026-09-19", "category": "runtime", "url": "https://huggingface.co/models/changh95/openjev-p300x2", "base_model": "openjev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": "", "likes": 4, "detail": "result: entailment", "arch": "Deployment of AlexWortega/openjev, the Qwen3.5-4B fine-tuned 3-way NLI cross-encoder, on a Tenstorrent P300x2: two p300 boards as four Blackhole chips in 4-way tensor parallel behind vLLM as a pooling model; POST /classify returns contradiction, entailment and neutral probabilities matching the fp32 CPU model", "approach": "", "training": "", "data": "", "result": "Flappy Bird played zero-shot on the four chips with the game state as the premise and flap or do-nothing as the two hypotheses, the bird following the higher P: 28/28 pipes at about 77 ms per decision on two inputs; demo videos and an interactive replay viewer ship with the repo", "notes": "Code-only repo: deployment code, tt-metal and vllm-tt-plugin patches, setup scripts and validation results; the openjev weights are pulled from the upstream AlexWortega/openjev repo", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "iambusy-openjev-vision", "name": "OpenJev-Vision", "author": "IamBusy", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/IamBusy/OpenJev-Vision", "base_model": "Qwen3-0.6B", "params": "0.6B", "metric": "reference subset 45/60 for v0.3 vs 39/60 for v0.2; DeepSeek 59/60 on the same subset", "license": "Apache-2.0", "paper": "", "stars": 37, "likes": "", "detail": "paper: IamBusy/OpenJev-Vision; IamBusy/OpenJev-Vision-Research-v0.1; metric: candidate reversal consistency 100% in v0.3 vs 68.75% in v0.2 on 16 fixed questions; notes: v0.3 is Qwen3-0.6B plus LoRA plus a scalar head, v0.2 a letter-logit head with up to 26 candidates", "arch": "encode an image once and answer several questions from a shared probability distribution; synthetic scenes use a small CNN plus a supplied prior, public images use frozen DINOv2 features with trained readouts", "approach": "fine-tune", "training": "Supervised cross-entropy plus held-out temperature calibration", "data": "8,192 original synthetic images, 2,960 Oxford-IIIT Pet, 1,680 CLEVR-4; 725 training, 176 dev, 167 calibration, 405 fresh test text judgments", "result": "", "notes": "open research toolkit; original OpenJev text-decision experiments retained in the repo", "domain": "multimodal", "score": "45/60 on the fixed 60-question reference subset", "latency": "0.70 s warm median for a long state with 8 questions, 104 ms for a short state on an M3 Pro", "benchmark": "", "cost": "", "speedup": ""}
{"id": "opensourcejev", "name": "OpenSourceJev", "author": "sabeel111", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/sabeel111/OpenSourceJev", "base_model": "Qwen3-1.7B and other open-weight models", "params": "1.7B on llama.cpp, CUDA", "metric": "structured-judgment latency on consumer hardware", "license": "MIT", "paper": "", "stars": 37, "likes": "", "detail": "", "arch": "Local System One decision engine: a llama.cpp backend under a FastAPI server turns open-weight models into ultra-fast deterministic strictly-typed decision engines via inference-time logits projection instead of generating verbose text or streaming fragile JSON that needs retry loops and regex parsing; Python 3.11 plus; ships a ViZDoom agent demo; carries an explicit research-experiment notice stating no TypeSafe affiliation", "approach": "", "training": "Not a trained model: an inference-time serving stack over open weights", "data": "", "result": "Sub-100 ms structured judgments on an RTX 3050 laptop GPU with 4 GB VRAM; JevBench v1.4.1 official table: rank 18 of 77 at 40.9, estimated $0.016 per 1k decisions", "notes": "The framing the authors use: agents usually need fast probabilistic branching, smart if-statements, not conversational prose", "domain": "agent", "score": "", "latency": "100 ms", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "tangerg-typesafe-sdk-go", "name": "typesafe-sdk-go", "author": "tangerg", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/tangerg/typesafe-sdk-go", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 9, "likes": "", "detail": "Go 1.25 or newer, no third-party dependencies. Install with go get; the package is named typesafe, so an import needs no alias in most editors but reads better with one. Set TYPESAFE_API_KEY in the environment; the quickstart sends a choice question over a billing, technical, other category and reads back the Choice and Confidence. A runnable demo ships in examples/demo. MIT, with a pkg.go.dev reference badge.", "arch": "Go SDK for the TypeSafe AI API: typed questions in, probability distributions out, with nothing to parse and no format to coax out of a model, and three question types mixable in one call, each evaluated in parallel.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "saibimajdi-typesafeai-dotnet-sdk", "name": "typesafeai-dotnet-sdk", "author": "saibimajdi", "date": "2026-09-19", "category": "runtime", "url": "https://github.com/saibimajdi/typesafeai-dotnet-sdk", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 8, "likes": "", "detail": "", "arch": "Community.NET SDK for the TypeSafe AI System One API — typed noul, choice, and score questions with structured, confidence-scored answers. Not affiliated with TypeSafe AI.NET ecosystem: community.NET SDK for the TypeSafe AI System One API with strongly-typed Noul, Choice, and Score questions and structured answers.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-routing", "name": "jev-routing", "author": "nekowasabi", "date": "2026-09-18", "category": "runtime", "url": "https://github.com/nekowasabi/jev-routing", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Local Go proxy between a coding-agent CLI, Claude Code, Codex, Grok Build or Devin CLI, and the upstream LLM API that reshapes each outgoing request: it drops or truncates conversation tool results with the fast-jev-compaction judgment without ever summarizing prose, asks Jev for the next tool as a Choice and for done as a Noul in the same call, shrinks that step tools array to a single schema, zero when the answer is a plain response, strips thinking and reasoning blocks the next decision does not need, and matches model and effort to the difficulty so a big model is not paid for on a small job", "approach": "", "training": "", "data": "", "result": "", "notes": "Single Go binary, Go 1.22+, no Node, one environment variable on the agent side, existing grok, claude, codex and devin logins keep working; fail-safe design: when uncertain it does not narrow the candidates, unsupported history shapes pass through unrewritten, and real tool execution and approval stay on the host; a loopback-only read-only dashboard shows what was rewritten and what was not applied and why; jev-routing bench runs chess tasks ported from the jev-ga suite; Japanese and English READMEs; created 2026-09-18", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevcache", "name": "JevCache", "author": "hyperspaceai", "date": "2026-09-18", "category": "runtime", "url": "https://github.com/hyperspaceai/jevcache", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 72, "likes": "", "detail": "Local-first and offline: an embedded, zero-dependency store, an in-memory map plus an append-only log, where a point lookup is a map access. Private by construction: state is redacted and canonicalized before it is hashed, so the fingerprint is over decision-relevant content only, with PII such as emails, phones, and long digit runs, and volatile fields such as ids and timestamps, never entering the key, and a per-schema salt for sensitive schemas, keeping outsiders from enumerating decided states. Deterministic CI: publish fixtures, replay them, catch model drift. This is the distribution repo, the jevcache CLI binary and docs: a single static binary, 2–3 MB, no runtime, for macOS and Linux, arm64 or x64, installed with a curl one-liner that verifies a SHA-256 checksum, or from Releases. recall() checks the local ledger only, never a backend; decide() recalls, then on a miss routes to the configured backend, local, jev, or mock, and caches the result. The CLI is written in Rust, and its fingerprint is byte-identical to the TypeScript core in this repo, so a Rust ledger, the TS internals, and the hosted commons all share the same keys.", "arch": "A decision cache for TypeSafe Jev-class models, in decision-ledger form: a Jev decision is approximately a pure function of model, schema and state, so it is memoizable, and jevcache caches those decisions locally, for the same decisions at fewer bills, and deterministic replay in CI, staying backend-agnostic, pointed at any local decision endpoint, a hosted Jev provider, or a mock, and never proxying or reselling inference, so your key stays on your machine.", "approach": "", "training": "", "data": "", "result": "", "notes": "Distribution repo: Rust CLI binary, about 2–3 MB static, via Releases v0.1.0 for darwin and linux, arm64 and x64, with SHA-256 sums, install via jevcache.sh; source code is not in the repo", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wiktorb2004-llama-index-jev", "name": "llama-index-jev", "author": "wiktorb2004", "date": "2026-09-18", "category": "runtime", "url": "https://github.com/wiktorb2004/llama-index-jev", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 7, "likes": "", "detail": "Two PyPI packages: llama-index-postprocessor-jev, providing JevRerank, which scores each retrieved passage and keeps the top n, and llama-index-selectors-jev, providing JevSingleSelector and JevMultiSelector, which pick which query engine or tool handles the query. Python 3.10 or newer; credentials via TYPESAFE_API_KEY, or OPENROUTER_API_KEY with the openrouter provider. An independent community project, not affiliated with TypeSafe or LlamaIndex. MIT, with docs on GitHub Pages and CI.", "arch": "LlamaIndex reranker and router powered by TypeSafe Jev, using typed Score and Choice answers for retrieval, cheap compared to LLM-as-judge, and not a Cohere or FlagEmbedding cross-encoder.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 7 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "razorback-openjev", "name": "openjev DiffusionGemma server", "author": "razorback16", "date": "2026-09-18", "category": "runtime", "url": "https://github.com/razorback16/openjev", "base_model": "DiffusionGemma 26B-A4B", "params": "26B", "metric": "", "license": "Open", "paper": "", "stars": 426, "likes": "", "detail": "base: NVFP4, Apache-2.0; size: 4B active; arch: no token streaming for answers; result: 09-24 · public 67.9% / sealed 5.9%, gap +62.0 pp; arch: open-source System One decision server: state + typed questions → probability + confidence for every answer in tens of ms · answers are read straight off model probabilities — no text generation, no parsing, so an answer cannot go off-schema · image questions supported; data: the same wire API as TypeSafe Jev, so the official SDKs work unchanged · vLLM on an NVIDIA GPU or in-process MLX on Apple silicon · also serves text generation on an OpenAI-compatible /v1/chat/completions; notes: hosted for free at Codiv, api.codiv.ai/v1/systemone · an independent project, not affiliated with TypeSafe AI", "arch": "Full local Jev-API-compatible server on DiffusionGemma — the model is diffused in, then 'solved' by a deterministic post-processor", "approach": "diffusion", "training": "None", "data": "None", "result": "Self-hosted Jev-API compatibility; ~374 stars, 'top open reproduction' per gallery. JevBench v1.4.1: open-jev q8_0 #23 at 36.9", "notes": "Fast, calibrated, typed decisions from an open diffusion model; 374 stars Forked with a \"why not just use an LLM for Jev?\" explainer at SteFletcher/openjev; also the GPU backend behind the zoedsy/openJEV local Mac playground.", "domain": "general", "score": "67.9%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "arnabgho-rlcd-lite", "name": "RLCD-Lite", "author": "arnabgho", "date": "2026-09-18", "category": "runtime", "url": "https://github.com/arnabgho/rlcd-lite", "base_model": "Gemma 3 1B / 4B", "params": "1B / 4B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 5, "likes": "", "detail": "arch: prefill once, then one masked step per JSON field, all fields batched; training: GRPO with Brier proper-scoring-rule reward; result: central calibration claim testable on a laptop; license: Apache-2.0", "arch": "Minimal RLCD inference recipe for Gemma 3, 1B and 4B; no weights shipped", "approach": "", "training": "Inference recipe only", "data": "", "result": "", "notes": "Small reproducible reimplementation of RLCD: parallel constrained JSON decoding on a causal LM, trained with GRPO and a Brier proper-scoring-rule reward, evaluated with real calibration metrics. Adds the missing training stage to the inference-only harshatheg/Qwen-2.5-1B-RLCD reproduction so the central claim — RL with a proper scoring rule yields calibrated decisions, binary reward does not — can be tested on a laptop. Explicitly not TypeSafe's algorithm and unrelated to Yang et al. 2023 RLCD contrastive distillation.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "simple-jev", "name": "Simple Jev", "author": "featherless-ai", "date": "2026-09-18", "category": "runtime", "url": "https://github.com/featherless-ai/simple-jev", "base_model": "Any Hugging Face model", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 534, "likes": "", "detail": "author: Lorenzo Piretti; base: demo: gemma-4-26B-A4B-ituperagents-lab/jev-search, 440★ · wy-coliney/jev-browser-use, 441★", "arch": "Runs compatible open Hugging Face models for structured classification and scoring without training a separate classifier head, no LoRA, no fine-tuning, no extra training: the model never generates a JSON completion, the server reads the next-token logits for each question and builds the JSON response from those scores; shared request validation, versioned prompt instructions and response scoring live in a plain-Python common folder so other inference backends can reuse the same rules; public demo API with no login, a 2k-token context limit and 2 requests per second, serving featherless-ai/gemma-4-26B-A4B-classifier", "approach": "logits", "training": "None", "data": "None", "result": "Turns ANY model into a Jev-shaped decider; demo on Gemma 4 26B MoE; the evaluation framework includes native JevBench scoring, text, decision and vision suites, a 477-decision quick preset, resumable HTTP execution and raw-response audits", "notes": "502 stars; 'zero-training' Jev adapter. Also by Piretti: Jev Search and Jev Browser Use.", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "compozy-yoshi", "name": "yoshi", "author": "compozy", "date": "2026-09-18", "category": "runtime", "url": "https://github.com/compozy/yoshi", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 26, "likes": "", "detail": "Coding agents resend their whole conversation on every request, and most of that history is completed tool input, tool output and teammate reports the model no longer needs. Yoshi sits between the client and the provider: it judges new spans once, completed Bash, Write and Edit arguments, tool results and teammate reports, with the default gate starting above 50,000 estimated input tokens, while previously forwarded content, human instructions, assistant notes, errors and opaque blocks stay protected, and cached omissions are replayed without rejudging, asking Jev one span per request. A proof-of-concept warning up front: the real version is heading into CompozyOS, an open-source operating system for AI agents, where context pruning becomes automatic for every durable session, the session goal and plan feed the judge, and judging runs between turns instead of on the request path, removing the latency this POC adds. Benchmarks are measured, not claimed, in docs/BENCHMARKS.md. MIT.", "arch": "A local context-pruning proxy for Claude Code and Codex: Jev judges new context once, above a configurable size gate, and Yoshi applies the validated omissions while keeping the protocol intact, then forwards the request to Anthropic or OpenAI and streams the response back unchanged, running on Bun with strict TypeScript.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "0xnatoshi-jev-codex-router", "name": "Jev Codex Router, 0xnatoshi", "author": "0xNatoshi", "date": "2026-09-17", "category": "runtime", "url": "https://github.com/0xnatoshi/jev-codex-router", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 273, "likes": "", "detail": "A self-contained monorepo: it embeds the maintained Codex Router fork under router/ and connects Jev through the generic-provider and curated-model extension points, so no second Git checkout, submodule, or hidden source clone is required, with the imported fork and its provenance documented in ROUTER_FORK.md. The request path: Codex talks to the Codex Router on port 4202, where native models go straight to the ChatGPT backend on your plan, and jev/auto goes through LiteLLM to an API forwarder to the local jev_server on 127.0.0.1 port 4319, which builds a compact decision state, asks Jev for a model and an effort, and logs a canonical Codex replay plus the decision; the local caller edge shares a native session across the luna, terra, sol and astra models. An AGENTS.md is provided for installing with an AI agent, and contributing has a SECURITY.md and a release checklist. A historical simulation shows roughly minus 60% versus full Astra over 237 turns under the old policy, and the README states plainly that this is not measured Codex quota saved, nor evidence for the current policy, with the protocol and limitations in BACKTEST.md. MIT.", "arch": "Per-call model routing for Codex, driven by Jev, the TypeSafe System One model: Jev chooses a model and a thinking effort together for each model call, including continuations after tools, every route uses standard speed, and the objective is sufficient capability for the next decision with no unnecessary quota consumption.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24; identical re-upload ericwanderlust/jev-codex-router, 0 stars, dropped; repo archived, read-only, as of 09-26.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "typesafe-router", "name": "Jev Tool & Model Router", "author": "TypeSafeAI org", "date": "2026-09-17", "category": "runtime", "url": "https://github.com/TypeSafeAI/typesafe-router", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "base: TypeSafe API", "arch": "Jev tool & model router: request + allowed options → Jev choice → validation + fallback → effective option; one choice question per call over the supplied option ids", "approach": "router", "training": "", "data": "", "result": "3 stars", "notes": "TypeScript routing library + Next.js demo; Jev selects, the application authorizes and executes; independent community project, not an official SDK", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jkudish-jev-mcp", "name": "jev-mcp", "author": "jkudish", "date": "2026-09-17", "category": "runtime", "url": "https://github.com/jkudish/jev-mcp", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 353, "likes": "", "detail": "Each judgment comes back typed: probabilities, and for most tools a confidence score, in roughly 150–500 ms, for a fraction of a cent, covering the cheap mechanical checks agents otherwise skip because a frontier model is too slow to run on every page, claim, or candidate list. Example uses: fact-checking a report, PR description, or agent brief against the sources it cites, claim by claim; screening a fetched page for injected instructions before it enters context, and skipping pages with nothing to say; finding which document, file, or note answers a question across hundreds of candidates, with no embeddings and no index to maintain; reranking retrieval results, triaging near-duplicates, or ordering a feed by relevance; routing support messages, labeling issues, or sorting an inbox against your own label set, in batches. MIT, with CI.", "arch": "MCP server that exposes TypeSafe's Jev model as eleven typed judgment tools for any MCP client: jev_verify, which checks claims against evidence, jev_screen, which judges content before it enters context, jev_noul, which returns a calibrated probability for a stated proposition, jev_find, which picks the best candidate by meaning, jev_rerank, which scores and sorts every candidate, jev_classify, which batch-assigns items to classes, jev_decide, which settles bounded alternatives, jev_compare, which judges how two passages relate, jev_extract, which pulls field values with regex plus judgment, jev_review, which scores a proposed diff before the task is called done, and jev_gate, which reviews a patch and verifies completion claims in one call.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 8 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevmlx", "name": "JevMLX", "author": "bnsd55", "date": "2026-09-17", "category": "runtime", "url": "https://github.com/bnsd55/jevmlx", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 64, "likes": "", "detail": "Quickstart: clone, run setup.sh, then jevmlx decide with the support_triage preset for one CLI decision, or jevmlx serve for an HTTP server on port 8000 that takes a schema and a context over curl. Requires an Apple Silicon Mac, M1 or newer, and Python 3.12 or newer; first use of a model alias downloads the weights, about 2 GB for fast, about 4.5 GB for quality. Model aliases: quality resolves to mlx-community's Qwen2.5-7B-Instruct-4bit, the default, best accuracy; fast is Qwen2.5-3B-Instruct-4bit, lower latency; test is Qwen2.5-1.5B-Instruct-4bit, tests only, too small for production; a full Hugging Face Hub id also works, such as mlx-community's Llama-3.2-3B-Instruct-4bit. Install as a library with pip from git, or as CLI only with uv tool. MIT.", "arch": "Jev-style parallel constrained decisions for any MLX model on Apple Silicon, with no generation: it turns a schema of fields, booleans, enums and multi-selects, plus a context string, into a single batched forward pass on a local Apple Silicon model, where every allowed option for every field is scored from logits in one prefill, and the JSON is assembled from the winners, with a probability per field.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "butochnikov-laravel-typesafe-jev", "name": "laravel-typesafe-jev", "author": "butochnikov", "date": "2026-09-17", "category": "runtime", "url": "https://github.com/butochnikov/laravel-typesafe-jev", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 4, "likes": "", "detail": "Not affiliated with Laravel or TypeSafe. Requirements: Laravel 12 with PHP 8.2–8.5, Laravel 13 with PHP 8.3–8.5, and the TypeSafe SDK at 0.1.x. Install with composer require and vendor:publish of the jev-config tag; the provider is auto-discovered, manual registration is also supported, and the package registers no global Jev alias. Configuration lives in .env: TYPESAFE_API_KEY, TYPESAFE_DEFAULT_MODEL, defaulting to jev-latest, TYPESAFE_BASE_URL, defaulting to api.typesafe.ai, and TYPESAFE_TIMEOUT, defaulting to 10. The key is validated only when a real SDK client is first used, so package discovery, artisan list and config:cache work without a key; config values are the source of truth after caching, and the adapter passes the connection settings explicitly to the SDK; numeric env values are validated and normalized when the lazy client is resolved, and invalid configuration fails then with a descriptive exception, not during provider registration. Use the facade or DI with Noul, Choice and Score question objects. A Russian README is included. MIT.", "arch": "Unofficial, community-maintained Laravel integration for butochnikov's typesafe-sdk-php: it keeps the SDK's typed DTOs, promises and exceptions, and adds Laravel package discovery, scoped lazy DI, configuration, a facade, and a recording fake. Jev Noul and Score are probability and score questions; this package is not a text-generation or chat client.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openjev-verdict-2-0", "name": "OpenJev / Verdict", "author": "Heman10x-NGU", "date": "2026-09-17", "category": "runtime", "url": "https://github.com/Heman10x-NGU/openJev-verdict-2.0", "base_model": "ModernBERT-base gliclass-modern-base-v2.0, 151,378,177 params, plus GLiClass; Verdict 2.0 is a separate 149.6M specialized architecture", "params": "151M / 149.6M", "metric": "77.10% top-1 on 2,000 held-out enterprise decisions, Verdict 2.0", "license": "Apache-2.0", "paper": "", "stars": 286, "likes": 31, "detail": "base: gliclass-modern-base-v2.0, 151,378,177 params, plus GLiClass; arch: non-autoregressive, zero token generation, one forward; metric: LocalLLaMA/typed-decisions, 2,000 held-out; result: 77.10% top-1, 0.0636 Brier, 1.44% ECE, 20–25 ms; training: 8.8 hours on a budget consumer laptop GPU; license: Apache-2.0; notes: two models in one repo, v1 at heman10x/rlcd-modernbert-151m v1.4, v1 line at Verdict-open-jev; approach: GLiClass on ModernBERT-base with marker-pointer distribution logits and an out-of-fold confidence MLP; paper: Heman10x-NGU/Verdict-open-jev", "arch": "non-autoregressive single forward pass over Choice, Score and Noul; NLI hypothesis framing of candidate labels aligns inputs with pretrained representations; dual-channel calibration with a distribution head and a dedicated confidence head", "approach": "head", "training": "fine-tuned in 8.8 hours on a single GTX 1660 Ti with 6 GB VRAM", "data": "2,000 held-out enterprise decisions from LocalLLaMA/typed-decisions", "result": "Verdict 2.0, 149.6M, on LocalLLaMA/typed-decisions: 77.10% on 2,000 held-out enterprise decisions vs Laya 76.60% and Jev 72.70%, 0.0636 Brier best overall, 1.44% ECE on the dedicated confidence head, option flip rate 4.76%, about 20–25 ms per decision, in-browser WebGPU demo under 600 MB, 2.8× fewer parameters, fine-tuned in 8.8 hours on a budget consumer laptop GPU. Verdict v1, 151M, public checkpoint heman10x/rlcd-modernbert-151m, 18.9k+ downloads, 31 likes: Banking77 top-1 95.00%, CI 93.60–96.20; v1.4 inference-engine fix on byte-identical weights — calibrator auto-loading, NLI hypothesis templating, 1024-to-512 context budget — lifts JevBench public 231 tasks: Easy 87.5%, Standard 69.4%, Hard 36.9%, hard-tier ECE 0.298–0.118, probability fidelity 62.8–72.8.", "notes": "Two distinct models in one project, Apache-2.0; Verdict 2.0 weights tracked via Git LFS in artifacts/verdict2-base/model.pt. Verdict, 151M, now v1.4, is a general-purpose post-trained decision model on ModernBERT-base and GLiClass: calibrated discrete choices, ordinal scores and binary probabilities in a single forward pass with zero generated tokens; public checkpoint heman10x/rlcd-modernbert-151m. Verdict 2.0, 149.6M, is a specialized architecture for typed software workflows. The v1 line and the in-browser WebGPU playground live at Heman10x-NGU/Verdict-open-jev, 97 stars. Port: FluidInference/verdict-coreml, Core ML for Apple Silicon, Apache-2.0. Wrapped by h004888/openJev-bro, a Vietnamese web-automation project with Chrome DevTools MCP, headful mode and anti-detection stealth. 09-23 guardrail red-team: martocd1709/openjev-guardrail-redteam built the jailbreak gate the Verdict docs recommend and the 151M model caught 0 of 26 categorized jailbreaks, 18 actively mislabeled benign at up to 62.5% confidence, below an 18-word keyword list at 10 of 26; full write-up in the openjev-guardrail-redteam row", "domain": "general", "score": "77.10% top-1, Brier 0.0636", "latency": "20–25 ms per decision", "benchmark": "typed-decisions", "cost": "", "speedup": ""}
{"id": "sgoedecke-system-one", "name": "System One, sgoedecke", "author": "sgoedecke", "date": "2026-09-17", "category": "runtime", "url": "https://github.com/sgoedecke/system-one", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 34, "likes": "", "detail": "pip install -e ., then SystemOne.from_pretrained loads a model such as Qwen2.5-0.5B-Instruct, and system_one takes a state and a map of Choice questions, returning the choice and the probabilities. The author reimplemented the Doom and Wikiracing demos from the Jev release post using Qwen3-8B: on Doom, playing Freedoom MAP0, the run starts with two shotgun shells to force a switch to the pistol, and the model chooses to pick up armor and health later in the level, with the MP4s in docs/demos. For comparison, with the same model and prompt, ordinary tool calls were 3.5× slower between actions than a newer System One run, 600 ms versus 172 ms median, and tended to do one input per turn, rather than the System One version, which routinely entered many simultaneous inputs, such as strafing plus turning plus firing. The README notes the code is vibe-coded but the README was written by hand.", "arch": "Turns any LLM into a System One model like Jev, a fast general classifier that you supply a set of questions to and get an answer in a single forward pass: batched single-token choice inference for open language models, compatible with the TypeSafe System One API.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-open", "name": "system-one-open", "author": "mithalouni", "date": "2026-09-17", "category": "runtime", "url": "https://github.com/mithalouni/system-one-open", "base_model": "Gemma 4 E2B / Gemma 3 270M", "params": "270M–2B", "metric": "Strict common subset of 343 pairs from TypeSafe's public eval, ms per launch-demo ticket, emails per second", "license": "Open", "paper": "", "stars": 36, "likes": "", "detail": "Head-to-head against published Jev on the TypeSafe public eval, strict common subset of 343 pairs: 76.7% versus 86.9%, with the same eval on stock Qwen 7B from the jev-on-a-laptop study at 73.8%. The 27-question launch-demo ticket, one call, takes 97 ms on an H100, versus 114 ms for Jev. One thousand real emails, four decisions each, take 13.4 seconds, 74.6 emails per second, 95.4% spam accuracy. Demo task families, Doom, smart home, support, security, invoice, agent trace, catalog, score 98.8% with ECE 0.003, and held-out task types, never trained, score 74.8%. Weights are open, merged safetensors on the Modal volume, Hugging Face upload pending, versus closed, API-only Jev, with price in GPU seconds on your own L4 or H100, or CPU, against $0.042 per million input tokens. Eight demos, all real recordings of the live model with no task-specific fine-tuning: TypeSafe preset scenarios, 28 typed fields each in one call; a launch-video terminal race; an Enron inbox classifier on 1,500 emails; a viral-post analyzer that judges 500 ms after you stop typing; ViZDoom freedoom, where the model picks every action from labels, depth and game variables; a real Google Flights browser agent, where the model picks element and operation each step; and a self-driving sim with lidar sectors and code reflexes, where the model makes the tactical call at about 3 Hz. The full report with baselines and caveats is on a claude.ai artifact.", "arch": "An open replica of Jev, in the sense of state in, typed calibrated decisions out, one forward pass, no decoding: built on Gemma 4 E2B with attention LoRA, and on Gemma 3 270M, trained and served on Modal, MIT licensed.", "approach": "undisclosed", "training": "Open replica training", "data": "", "result": "TypeSafe public eval, strict common subset, 343 pairs: 76.7% versus 86.9% for Jev, and 73.8% for stock Qwen 7B; 27-question launch ticket, one call, 97 ms on an H100; 1,000 real emails, 4 decisions each: 74.6 emails/s, 95.4% spam accuracy; demo task families 98.8% with ECE 0.003; held-out task types 74.8%.", "notes": "35 stars; 'System One, open': the named Gemma replicas in the dataset", "domain": "general", "score": "em 95.4%", "latency": "114 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "typesafe-ui", "name": "TypeSafe UI components", "author": "TypeSafeAI org", "date": "2026-09-17", "category": "runtime", "url": "https://github.com/TypeSafeAI/typesafe-ui", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 6, "likes": "", "detail": "", "arch": "Reusable React components and interface patterns for TypeSafe AI projects: shadcn base-nova style, Base UI primitives, Tailwind v4, RTL-aware; Turborepo + pnpm with a Next.js component browser and interactive Lab", "approach": "sdk", "training": "", "data": "169 local examples in the Jev Labs guide; all outputs are explicitly local fixtures", "result": "5 stars", "notes": "Independent community project under the TypeSafeAI org; a private workspace package, not published on npm", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ellipsis-dev-blink", "name": "blink", "author": "ellipsis-dev", "date": "2026-09-16", "category": "runtime", "url": "https://github.com/ellipsis-dev/blink", "base_model": "", "params": "", "metric": "auth query ends at src/services/auth/login.ts for 74% of 100 walkers; invoices 82%, reusable button 100%", "license": "", "paper": "", "stars": 79, "likes": "", "detail": "notes: Jev scores file and folder names, more likely paths get more walkers, each result shows the percentage of starting walkers that ended there", "arch": "Codebase search powered by Jev: an ensemble of walkers walks the file system to find the file that answers a natural-language query; Bun-based, recursive search with N walkers", "approach": "rules", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists; cross-list sweep 2026-09-24; Bun 1.3.14+ and a TYPESAFE_API_KEY, bun test with fake API responses runs offline", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "Jev API 0.042 USD per million input tokens, output free", "speedup": ""}
{"id": "djev", "name": "djev + vLLM DiffusionGemma patch", "author": "mmastrac", "date": "2026-09-16", "category": "runtime", "url": "https://github.com/mmastrac/djev", "base_model": "DiffusionGemma 26B-A4B", "params": "26B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 85, "likes": "", "detail": "author: Mike MacCana, vLLM core dev; size: 4B active; arch: merged 2026-09-22; training: stock DiffusionGemma; license: vLLM; result: span battery of 49 span fields, five lists and a mixed schema against a live server; notes: span answers carry confidence, coverage and a read count; the model never writes the value", "arch": "vLLM PR 57250 structured generation on DiffusionGemma: diffuses answers directly into JSON schema slots instead of streaming tokens; the constrained flag reads the unembedding over the labels only, the same argmax for about a quarter less GPU time. djev: minimal local Jev-API-compatible service with choice, score, noul, ordered levels and span/spans extraction as character offsets by construction", "approach": "diffusion", "training": "None", "data": "None", "result": "Local Jev-shaped API endpoint; structured decisions filled into schema slots in one diffusion pass", "notes": "'Official-adjacent' open runtime: from a vLLM core developer, with an upstream PR. Jev's structured answers via parallel denoising rather than token streaming. djev-spark, github.com/mmastrac/djev-spark, 09-22: DiffusionGemma NVFP4 structured-decision container recipe for a DGX Spark; no repository-level license file was found during review.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "blakestone-x-jev-mcp", "name": "jev-mcp by blakestone-x", "author": "blakestone-x", "date": "2026-09-16", "category": "runtime", "url": "https://github.com/blakestone-x/jev-mcp", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 23, "likes": "", "detail": "Set TYPESAFE_API_KEY in the environment used by your MCP client, then run with uvx from the git tag v0.2.1, with install snippets for Claude Code, Codex config.toml, and Cursor or any other MCP client; passing the key through the client environment keeps it out of tool arguments and request state. The scripts/register.sh helper requires Bash 4 or newer, reads the key from the environment, and refuses to change client configuration when it is unset; --apply writes the plaintext key into the Codex config and the Claude Code user config, and the key is briefly visible in the process list while claude mcp add runs, because that command receives it as an argument; on Windows, scripts/register.ps1 restricts the resulting configuration files to the current user and keeps a .bak copy before rewriting an existing Codex configuration. Install examples pin the public v0.2.1 tag; omitting the tag tracks the default branch. A worked example classifies one state containing five support-ticket texts against one label map, returning one label for the combined state; send one request per ticket when you need an individual label per ticket. MIT.", "arch": "MCP server for TypeSafe Jev, providing typed classify, score, check, match and select tools: Jev provides typed judgments over a closed answer set in about 130 ms, with no text generation, through TypeSafe.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 7 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevlike", "name": "Jevlike", "author": "vinnylarouge", "date": "2026-09-16", "category": "runtime", "url": "https://github.com/vinnylarouge/jevlike", "base_model": "", "params": "undisclosed", "metric": "98% top-1 on synthetic menus; Wikispeedia next-click 26% vs 8% shuffled and random-encoder controls", "license": "MIT", "paper": "", "stars": 1299, "likes": "", "detail": "arch: Doom + chess demos, joint imitation checkpoint · runs on CPU; training: joint imitation for game control; result: 10-second film in docs; result: demo windows selected for activity, not competence claims; notes: research starter, not a copy of Jev; from-scratch model trained on 40,000 clicks reached 29% on Wikispeedia", "arch": "Jev's exact shape: text + N options in, one probability per option out, in one pass; option-attention head that can also score controller buttons from image patches", "approach": "from-scratch", "training": "Option-attention scorer trained from scratch; byte-embedding encoder by default or a frozen pretrained encoder", "data": "Text decision JSONL, Wikispeedia SNAP next-click, Doom 640×480 traces, chess positions", "result": "Doom deadly_corridor combat on seven buttons and a chess controller from the same checkpoint; joint checkpoint 0.60 kills and -97.50 reward over 10 episodes; chess-only checkpoint 4 wins 46 draws 0 losses vs a random mover, 0 wins 2 draws 48 losses vs Stockfish level 0 in 50 games", "notes": "1,200+ stars. 'Train a small model that chooses among a changing list of text options.' Named in AINews' two-day roundup.", "domain": "general", "score": "", "latency": "", "benchmark": "synthetic menus, Wikispeedia next-click, Doom deadly_corridor, chess", "cost": "", "speedup": "100× faster than a small decoder at 400 tokens"}
{"id": "kw2828-openjev", "name": "OpenJev browser and Doom", "author": "kw2828", "date": "2026-09-16", "category": "runtime", "url": "https://github.com/kw2828/OpenJev", "base_model": "Pinned Qwen3-4B locally; Qwen3-0.6B in the browser", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "Use POST /api/decide with the X-OpenJev: 1 header, or openjev decide request.json; a complete example, support.json, and the API reference cover request limits, errors, model metadata and execution boundaries; architecture and calibration notes explain what public Jev-inspired implementations establish and which claims still need evidence. Run locally on Apple Silicon macOS, Python 3.11–3.13, with uv; setup downloads the pinned Qwen3-4B weights, and inference then uses the local cache, with no paid API key needed. A free browser demo runs Qwen3-0.6B through WebGPU. Interactive pages: a text decision playground, a chess candidate replay, and recorded chess, Doom and robot policies, with an experiment archive. Open license.", "arch": "OpenJev, a local decision interface and a collection of controlled learning experiments: supply English context, your questions, and candidate IDs with descriptions, and it returns each chosen ID and the relative probability of every supplied answer, while your application decides what to do next, and the API itself executes no external action. The local scorer uses a pinned Qwen model, maps candidates to single-token labels, and scores those labels without generating an explanation; questions are independent, and stable IDs constrain the response format, not whether the choice is correct.", "approach": "undisclosed", "training": "None", "data": "None", "result": "Browser Jev playground + Doom", "notes": "Interactive demo in the awesome-jev gallery.", "domain": "gaming", "score": "", "latency": "", "benchmark": "Doom", "cost": "", "speedup": ""}
{"id": "qwen25-1b-rlcd", "name": "Qwen-2.5-1B-RLCD", "author": "harshatheg", "date": "2026-09-16", "category": "runtime", "url": "https://huggingface.co/models/harshatheg/Qwen-2.5-1B-RLCD", "base_model": "Qwen2.5-1.5B-Instruct", "params": "1.5B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 566, "detail": "author: harshagundal; base: LoRA fine-tune; arch: KV-cache broadcast, logit slicing per candidate; training: RLCD-style · live demo Space: parallel-constrained-decoding; data: see Space/repo; result: M4 Max benchmarks in repo; notes: MLX · tree-attention, MPS, M4", "arch": "Structured generation via PARALLEL CONSTRAINED DECODING: all schema fields scored in one broadcast prefill — 100% schema validity by construction", "approach": "constrained", "training": "Fine-tune + parallel constrained decoding", "data": "JSON schema decision data", "result": "5.6×–7× faster than autoregressive on Apple Silicon; side-by-side web interface + CLI benchmark runner included", "notes": "559 likes; named in AINews' '6 clones in 2 days.' Variants by others: botp/Qwen-2.5-1B-RLCD, epsilon3/Qwen-2.5-1B-RLCD-Fast, shreyansh26/Qwen-2.5-1B-RLCD. Chinese derivative: github.com/vicksiyi/qwen-2.5-1b-rlcd, local offline launch script, pinned to HF commit 2af86848.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": "5.6–7× faster"}
{"id": "semif", "name": "SemIf", "author": "TheoLeeCJ", "date": "2026-09-16", "category": "runtime", "url": "https://github.com/TheoLeeCJ/SemIf-OpenJev", "base_model": "Frozen open models; a ladder from Qwen3-0.6B up to Qwen3.8-27B; no weights of its own", "params": "0.6B–27B", "metric": "JevBench 73.1%", "license": "MIT", "paper": "", "stars": 4322, "likes": "", "detail": "base: EXL3 bridge; arch: masked/option-token logit readout; training: inference-time readout · 'reproduces the interface pattern, not Jev's undisclosed model or training'; result: Meanblock · Qwen3-0.6B, 40,960-token context · 09-24 · 74.4; notes: openjev.com is now the SemIf site · dp-IED #15 · jkyamog #9 · samarthpatel24 #19 · 09-19→24 · 18★, SDK-first SemIf fork · 18★, CPU readout · Rust+candle · Gemma 4 E4B + visual snake · NicolaiMTLassen 'bonzi' · HF, 16 KB card; name: formerly OpenJev", "arch": "Inference-time 'semantic if': reads typed option probabilities directly from the frozen model. No answer sentence, JSON repair, or decoding loop. Runs in browser via WebGPU demo.", "approach": "logits", "training": "None", "data": "Auditable: owned fixture, exact runners, row-level outputs, revisions, prompts and known failures committed", "result": "'Accuracy scales with the model you point it at' — JEV-CPU 0.6B variant shows the accuracy floor on CPU. JevBench v1.4.1 board: #2 at 73.1, behind Jev 1.13.0 of 82 systems", "notes": "4,100+ stars. Renamed from OpenJev after TypeSafe; independent, not affiliated. 09-18→09-22: MiniCPM5 2B + Qwen3.5 4B in browser demo, WebGPU, 'Unsloppify' conventional-UI switch, PyTorch/MPS scoring for Apple Silicon, Qwen3.8-27B EXL3 bridge, per-workload temperature calibration + calibrated prediction outputs. Ecosystem: chengyongru/fastjev, leesk212/JEV-CPU, wnzn/semif-go, apiplant/semif-rs, zsdx233/semif-gguf, dddanielliu/semif-serve, ILuce/ColtsFan07/semif-local, zushicat/semif-api-wrapper, vinci00/semif-qwen3.5-4b-mlx-4bit; SemIf's mechanism was also ported to 1-bit Bonsai weights and served by wweir/weigh. Meanblock/JEV-CPU is the CPU-runnable 0.6B cut with SemIf ladder benchmarks.", "domain": "general", "score": "JevBench 73.1%", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "specter-decision-engine", "name": "Specter Decision Engine", "author": "aigodsend9-boop", "date": "2026-09-16", "category": "runtime", "url": "https://github.com/aigodsend9-boop/specter-decision-engine", "base_model": "None, no trained model bundled", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Python 3.11+ framework with zero runtime dependencies for bounded, independent decision evaluation inside software and agents; implements Jev's three primitives, noul, choice and score, with typed confidence event definitions, probability of the event being above, not entropy and not raw max probability; 126 unit tests", "approach": "", "training": "No trained model: SystemOneLocalBackend is a deterministic lexical rule scorer, an auditable baseline rather than a semantic model", "data": "", "result": "Explicitly does not reproduce Jev's weights or training", "notes": "Portuguese README; original design by Guilherme Peralta Novaes; v0.3.0 refined with AI assistance while preserving the public contract; surfaced via r/SideProject 2026-09-16", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-lite", "name": "System One Lite", "author": "snellingio", "date": "2026-09-16", "category": "runtime", "url": "https://github.com/snellingio/system-one", "base_model": "Qwen3-1.7B-4bit", "params": "1.7B", "metric": "", "license": "", "paper": "", "stars": 45, "likes": "", "detail": "limits: 64 questions per request, 578 choice options, Apple silicon only on MLX; base: larger profile mlx-community/Qwen3-4B-Instruct-2507-4bit", "arch": "Proof-of-concept HTTP service for constrained decisions with a local language model on MLX: reads the model's scores at a fixed answer position, returns a validated response; no text generation, no model-written JSON parsing; choice, score and noul.", "approach": "head", "training": "", "data": "", "result": "", "notes": "Independent experiment on stock open-weight models, intended for narrow tasks whose possible answers are known in advance.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "joshmn-typesafe-sdk", "name": "typesafe-sdk", "author": "joshmn", "date": "2026-09-16", "category": "runtime", "url": "https://github.com/joshmn/typesafe-sdk", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 8, "likes": "", "detail": "client.system_one takes a state and a questions hash of Choice, Score and Noul objects; every question takes optional instructions, and instructions and criteria can be a string, a hash or an array if you need more structure than a sentence. The response exposes choices, scores and nouls: the picked choice, the score, and the noul probability, 0–1. Uses jev-latest unless told otherwise; API key from the TypeSafe console.", "arch": "Ruby gem typesafe-sdk for the System One API, Ruby 3.1+: client.system_one takes a state plus Noul, Choice or Score questions and defaults to jev-latest; the response exposes .choices, .scores and .nouls for direct program use", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alterhq-typesafe-sdk-swift", "name": "typesafe-sdk-swift", "author": "alterhq", "date": "2026-09-15", "category": "runtime", "url": "https://github.com/alterhq/typesafe-sdk-swift", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 5, "likes": "", "detail": "The recommended production architecture on every platform is to route SDK requests through your own authenticated backend, keeping the TypeSafe API key in server-side secret storage; direct API-key mode in this Swift SDK is intended only for quick local development and debugging. In the authenticated-backend proxy pattern, the application authenticates with its normal session credentials, and only the backend holds and uses the TypeSafe API key; the application's access token is passed through an async provider that the SDK resolves again for every retry, so the provider can refresh an expired session; TypeSafeClient takes a bearerToken authentication, a base URL such as api.example.com/typesafe, and default headers such as X-App-Version. systemOne takes a SystemOneRequest. MIT, from version 0.6.0.", "arch": "Unofficial Swift library for the TypeSafe API: a dependency-free Swift 6 client, ported from the official JavaScript and Python SDKs, using strict Swift 6 concurrency, URLSession, Codable and native task cancellation, supporting macOS 13 and newer, iOS and iPadOS 16 and newer, tvOS 16 and newer, and watchOS 9 and newer.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "circuit", "name": "circuit", "author": "Barneyjm", "date": "2026-09", "category": "runtime", "url": "https://github.com/Barneyjm/circuit", "base_model": "Open-weights System One models across text, images, audio + the harness that trains and measures them", "params": "undisclosed", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "arch: POST /v1/systemone contract; training: per-model recipes in repo", "arch": "Each option wrapped in delimiter tokens; sequence ends with a decide token; the head scores every option's closing delimiter — 'anything written for Jev runs against them with a URL change'", "approach": "head", "training": "Harness trains and measures", "data": "Per-model datasets in repo", "result": "Jev-API-compatible open models with a unified eval harness", "notes": "Multi-modal open weights + training/eval harness under one contract.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-use", "name": "jev-use", "author": "shitianfang", "date": "2026-09", "category": "runtime", "url": "https://github.com/shitianfang/jev-use", "base_model": "", "params": "", "metric": "p50 274 ms per decision in the directions demo; 86 Jev decisions in 20 s for Pong vs 6 for haiku and 3 for gemini", "license": "MIT", "paper": "", "stars": 28, "likes": "", "detail": "When content needs to be written the LLM takes over; when a step just needs a fast decision Jev executes it, making the LLM and Jev true collaborators. Demos are real runs at 1× speed. Directions task: Jev clicks, the LLM types; 10 decisions at p50 274 ms plus 4 writes; Jev rejects a wrong 1809 km geocode and the LLM repairs it, ending on the real 3.7 km walking route.", "arch": "Makes Claude Code, Codex and pi work with Jev: hand the tasks that need no text output to Jev for faster steps, fewer tokens and tasks done sooner", "approach": "", "training": "", "data": "", "result": "Context compaction: 200 messages judged in 7 calls, one LLM paragraph replaces the dropped block, context window from 94–44%, recall 3/3. Pong, ball speed equals decision latency: 86 Jev decisions in 20 s vs 6 for haiku and 3 for gemini called the usual way; enum-constrained both and the gap is 3×. Gate every shell command: dangerous ones held", "notes": "MIT; bilingual README, English and Chinese", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevbetter", "name": "jevbetter", "author": "olanotolu", "date": "2026-09", "category": "runtime", "url": "https://github.com/olanotolu/jevbetter", "base_model": "Custom one-pass scorer", "params": "undisclosed", "metric": "", "license": "Open", "paper": "", "stars": 15, "likes": "", "detail": "Trains a small model that chooses among a changing list of text options, done better, and uses the same JSONL data format as the open-source jevlike scorer, so any jevlike dataset trains here unchanged, and the benchmark compares the two head-to-head on identical data. Against jevlike: raw byte embeddings, weak on meaning by their own words, versus hashed character n-grams; byte embeddings plus positions, versus the 2-layer transformer; no option interaction, each option scored alone, versus options attending to each other; a single dot-product head, versus the gated 2-layer MLP; fixed epochs and a flat learning rate, versus the cosine schedule with warmup, early stopping and label smoothing; raw softmax, versus temperature scaling; trivial exact-match synthetic menus, versus a hard mode with near-miss negatives, distractor sentences, case noise and varied templates; and top-1 and top-3 plus ECE, versus top-1/3/5, MRR, ECE, per-menu-size accuracy, throughput and a shuffled control. A Jev-like model takes a piece of text and a list of N text options and returns one probability per option in one pass instead of writing an answer word by word, which makes it a natural fit for routing, ranking, classification with open label sets, and game controllers. An independent project, not affiliated with TypeSafe or the Jev model.", "arch": "A one-pass option scorer, in the sense of text in, one probability per option out, in a single forward pass, with no token-by-token decoding: built from scratch with a hashed character n-gram text encoder, fastText-style subword features, case-insensitive and still CPU-tiny, a 2-layer transformer over n-gram embeddings for the context, a rival-aware scoring design where options attend to each other first, so near-miss rivals sharpen the call, a gated 2-layer MLP scoring head on the option to context interaction, and a sharper training loop with a cosine schedule and warmup, early stopping, and label smoothing, plus temperature scaling fit on validation for calibration.", "approach": "head", "training": "Trained from scratch", "data": "", "result": "One-pass scoring with rival-aware attention", "notes": "One-pass scoring with rival-aware attention; head-to-head benchmark against jevlike on identical data.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "litjev", "name": "LitJev", "author": "zhengxuyu", "date": "2026-09", "category": "runtime", "url": "https://github.com/zhengxuyu/LitJev", "base_model": "Any Qwen decoder, default Qwen3.8-27B", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 45, "likes": "", "detail": "An independent research project, not affiliated with or endorsed by TypeSafe AI: a hypothesis-based reproduction from public information, where the request and response JSON follows the public Jev schema, but the internals, confidence values and performance are not identical, and probabilities are not calibrated by default. It supports the full Qwen family, Qwen3.x text and vision checkpoints, any size; the default and most-tested checkpoint is Qwen3.8-27B on one H100 80 GB, vision checkpoints are required for screenshot decisions, and other model families are not guaranteed to work. The API uses exactly the schema defined in the Jev documentation: the same POST /v1/systemone path, the same model, state, questions request body, and the same choice, score and noul question types. Run with uv from the git checkout, serving a local browser frontend on 127.0.0.1:8000; the first request downloads and loads the model, which can take several minutes; a local checkpoint path and --device-map pin a GPU. Not yet published to PyPI. Bilingual Chinese and English README.", "arch": "A Jev reproduction that turns any Qwen model into a fast decision model, in prefill plus readout form: define questions and options, load an off-the-shelf Hugging Face Qwen checkpoint, and get typed choices with probability distributions from a single API call, with no training and no generated answer text, because the scores are read directly from the model's output head.", "approach": "logits", "training": "None", "data": "None", "result": "Jev-shaped decisions from any Qwen model's prefill logits", "notes": "Same logit-readout family as Simple Jev / SemIf, tuned to Qwen.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "llm2jev", "name": "LLM2Jev", "author": "Yinsongxu", "date": "2026-09", "category": "runtime", "url": "https://github.com/Yinsongxu/LLM2Jev", "base_model": "Any LLM", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 334, "likes": "", "detail": "An independent open-source project, not affiliated with or endorsed by Jev or TypeSafe. Python 3.12 or newer, Apache-2.0, bilingual English and Chinese. The news log of September 2026: on September 20, the SGLang scoring backend and the compatible /v1/systemone endpoint; on September 21, prefix reuse on cold requests, with staged candidate submission that reuses SGLang's Radix Cache, plus architecture, usage and benchmark documentation, and interactive web and Snake demos for composing mixed questions and model-driven decisions; on September 22, multimodal inputs, text-and-image requests for SGLang, Transformers, and the System One HTTP API; on September 23, the MLX backend with text and image scoring, candidate batching, bounded prefix reuse, and a compatible System One HTTP service.", "arch": "Turns local text and vision language models into Jev-style structured decision models, wrapping any LLM as a Jev-compatible decision endpoint: it gets results from text and images with prefill alone, with no token-by-token decoding required, and runs on SGLang, Transformers, or MLX on Apple Silicon, serving a compatible POST /v1/systemone HTTP endpoint.", "approach": "undisclosed", "training": "None", "data": "None", "result": "Jev-compatible endpoint from a generic LLM", "notes": "'Turning any LLM into Jev.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "localjev", "name": "LocalJev", "author": "githubnext", "date": "2026-09", "category": "runtime", "url": "https://github.com/githubnext/localjev", "base_model": "DiffusionGemma 26B-A4B", "params": "26B", "metric": "", "license": "MIT", "paper": "", "stars": 776, "likes": "", "detail": "From githubnext, the Next.js team. It exists because OpenJev implements the Jev wire protocol by obtaining probabilities with a special one-step DiffusionGemma structured read, whose backend depends on unmerged vLLM request extensions such as diffusion_seed_canvas, diffusion_read_only, and requested token logprobs, and the normal oMLX API does not expose those primitives, so LocalJev takes the portable prompt-based approach instead. It is wire-compatible but explicitly not mathematically equivalent to OpenJev's logit read: the probabilities are generated or self-reported by the model rather than read directly from its logits, and the README says to evaluate their calibration on your own workload before relying on them for consequential decisions. Defaults: the inference server at 127.0.0.1:8000, the model diffusiongemma-26B-A4B-it-4bit, and the LocalJev API at 127.0.0.1:8080. Requires Bun 1.2 or newer and a running oMLX server, with the upstream key set via .env or the shell. MIT.", "arch": "A local, Jev-compatible POST /v1/systemone API written in TypeScript for Bun, backed by DiffusionGemma through an OpenAI-compatible Chat Completions endpoint: it translates the state and typed Jev questions into a classification prompt, asks DiffusionGemma for a JSON probability scalar or vector, validates the complete result and retries malformed output, normalizes the vectors, and returns the normal Jev response shape, with Jev-compatible choices, expected scores, and entropy-based confidence.", "approach": "diffusion", "training": "None", "data": "None", "result": "Local Jev-compatible endpoint from the Next.js team", "notes": "Listed in the awesome-jev gallery under the Jev API/SDK tier.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "medjev", "name": "MEDJEV PAI-CUHK", "author": "PAI-CUHK", "date": "2026-09", "category": "runtime", "url": "https://github.com/PAI-CUHK/MEDJEV", "base_model": "", "params": "undisclosed", "metric": "", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "Evidence-grounded decisions over clinical text, biomedical literature and sleep signals, with auditable benchmarks, including a PubMedQA dev evaluation with calibration metadata, and an interactive arc. It is a research prototype: not a medical device, diagnostic system, or source of clinical advice, and it must not be used with identifiable patient data or in patient care. It is not the official TypeSafe AI Jev API, SDK, hosted service, or a reproduction of proprietary Jev weights. MIT, Python 3.10, 3.11 or 3.12, alpha status, with CI and releases.", "arch": "An independent JEV-inspired, System One-style research implementation for typed decisions over clinical evidence and biomedical text: it treats a clinical record, a proposed statement, and an explicit candidate meaning set as a programmable evidence-relationship query, with runtime-defined candidate semantics, Choice, Score and Noul-style outputs, and calibration metadata, where the default semantic space is supported, contradicted, unresolved, and custom candidate sets are accepted but remain unvalidated unless matching calibration metadata exists.", "approach": "undisclosed", "training": "Independent implementation; weights/datasets excluded from git, bring a compatible checkpoint", "data": "PubMedQA development evaluation + auditable benchmarks", "result": "PubMedQA dev eval with calibration metadata; interactive architecture demo", "notes": "Same lab as SLEEPJEV; biomedical-evidence decisions with auditable benchmarks.", "domain": "health", "score": "", "latency": "", "benchmark": "PubMedQA", "cost": "", "speedup": ""}
{"id": "joshuasp-openjev", "name": "open-jev DiffusionGemma typed JSON", "author": "JoshuaSP", "date": "2026-09", "category": "runtime", "url": "https://github.com/JoshuaSP/open-jev", "base_model": "DiffusionGemma 26B-A4B", "params": "26B", "metric": "", "license": "Open", "paper": "", "stars": 41, "likes": "", "detail": "Measured September 16, 2026, on one H100 80 GB in BF16, with google/diffusiongemma-26B-A4B-it; every result below uses one denoising step, and public-eval comparisons label each step budget. On Every's TypeSafe lab bundle: code retrieval, 8 documents by 6 questions, hit 48/48 relevance labels and 6/6 top-1 retrieval; customer voice, 24 messages by 6 questions, hit 138/144, 95.8%, agreement with saved TypeSafe decisions, which is agreement with a saved model, not human-labeled accuracy, and a four-step run reached 139/144 support agreement with the same retrieval result. With six boolean fields on each canvas, batch 16 achieved 30.41 documents per second, 182.44 judgments per second, and an estimated $0.0361 per 1,000 documents in warm GPU time, while batches 64 and 32 OOMed; that throughput workload repeated the 32 unique documents into 384 evaluations, not 384 independent examples, and prompt KV caches were not reused. It also replayed all 408 available questions from Jev's saved execution paths on the 20 public cases, with a grouped runner that puts questions sharing a document into one JSON canvas, splitting only when the 256-token output limit requires it, and batching different canvases together.", "arch": "An experimental inference harness for typed JSON decisions with DiffusionGemma: it denoises freely, then chooses the most likely allowed tokens from the final logits, with no training or fine-tuning, exploring how close an open diffusion model can get to TypeSafe AI's Jev on structured judgments, as an independent experiment, not a reproduction of Jev's architecture, training, calibrated probabilities, or published workflow scores.", "approach": "diffusion", "training": "None", "data": "None", "result": "Code retrieval: 48/48 relevance labels, 6/6 top-1; customer voice: 138/144, 95.8%, agreement with saved TypeSafe decisions, and 139/144 at four denoising steps; batch 16 on an H100: 30.41 documents/s, 182.44 judgments/s, about $0.0361 per 1,000 documents warm.", "notes": "Minimal DiffusionGemma + typed-JSON bridge.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "daseinlabs-openjev", "name": "open-jev Gemma 3 4B KV expansion", "author": "daseinlabs", "date": "2026-09", "category": "runtime", "url": "https://github.com/daseinlabs/open-jev", "base_model": "Gemma 3 4B", "params": "4B", "metric": "", "license": "Open", "paper": "", "stars": 112, "likes": "", "detail": "Design notes cover one-pass option scoring and per-task fine-tuning with Gemma. A terminal Doom demo runs ViZDoom headless, describes each frame in a line of text, and lets the server rank the action menu with one /score call, or one System One choice question, with a full-resolution recording in docs/media. Setup on Windows and Linux needs Python 3.12 or newer and uv: authenticate with Hugging Face, download google/gemma-3-4b-it after accepting the Gemma license, then serve with --backend auto, which selects MLX on Apple silicon and PyTorch elsewhere, and --device auto, which selects CUDA when available, then MPS, then the rest. No Make, Xcode, shell activation, or .venv/bin paths are needed. Open license.", "arch": "One-pass option scoring with a local Gemma 3 4B, on MLX for Apple silicon or PyTorch on Windows and Linux, CPU or GPU: given a context and a list of pre-written options, the model prefills the context once, expands that KV cache across the option batch, and scores every option in a single padded forward pass, with no decoding, where the score is the log-probability of the option tokens given the context, and a softmax over the option scores gives a probability per option.", "approach": "logits", "training": "None", "data": "None", "result": "Jev-shaped decisions on a 2-bit Gemma 3 on Apple Silicon", "notes": "KV-expansion approach: extend the cache so option positions can be read without re-prefill.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nicomartin-openjev", "name": "open-jev Transformers.js browser", "author": "nico-martin", "date": "2026-09", "category": "runtime", "url": "https://github.com/nico-martin/open-jev", "base_model": "Kev-0.6B, Kev-4B and DeBERTa-v3-large ONNX", "params": "0.6B to 4B", "metric": "", "license": "MIT library, Apache-2.0 weights", "paper": "", "stars": 36, "likes": "", "detail": "author: Spring I/O / Spring AI; base: Transformer.js · browser; training: uses jaredpalmer/kev checkpoints; result: demo: nico-martin.github.io/open-jev; weights: kev-0.6b 0.34 GB q4f16 with an 8192-token context, open-jev DeBERTa 0.35 GB with 512; data: open-jev trained on banking support, movie reviews and Wikipedia yes-no, the kev models on ten domains", "arch": "Open-source Jev in the browser: Kev-0.6B + DeBERTa via Transformers.js — typed decisions with no server", "approach": "head", "training": "None", "data": "None", "result": "In-browser Jev-compatible decisions", "notes": "Spring I/O talk + project; browser-portability focus.", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zhihz-openjev", "name": "openjev bilingual", "author": "zhihz", "date": "2026-09", "category": "runtime", "url": "https://github.com/zhihz/openjev", "base_model": "Qwen3-4B-Instruct-2507 frozen", "params": "", "metric": "212/236 on the fixed development challenge and 97.7% on the reading subset, FP16 and MLX 8-bit alike", "license": "Apache-2.0", "paper": "", "stars": 34, "likes": "", "detail": "Not affiliated with, sponsored by or endorsed by TypeSafe; contains no Jev proprietary code, weights or RLCD implementation, and the name does not imply an official open-source release of Jev. Current backend is frozen Qwen3-4B-Instruct-2507, not a newly trained Open JEV foundation model, with no demonstrated performance lead over Jev. Accepts English or Chinese context, free-form questions and your own candidate descriptions, with no intent registration or question-specific training; returns a choice and relative probabilities only over the supplied candidates, with no additional answer or abstention option inserted. Choice and binary decisions, multiple questions per request, editable examples, JSON export; inference runs locally without a paid API.", "arch": "Independent open-source alternative inspired by TypeSafe Jev: local, bilingual probability decisions from your context, questions and candidate answers", "approach": "logits", "training": "None; frozen Qwen3-4B-Instruct-2507 backend", "data": "", "result": "Research preview; model identity, scoring protocol, evaluation limitations and a frozen results snapshot are published", "notes": "Bilingual UI, English and Chinese, switch without changing inputs or results", "domain": "general", "score": "", "latency": "533 ms median of 8 requests on an Apple M3, MLX 8-bit", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lookski-openjev", "name": "openjev masked logit softmax", "author": "lookski", "date": "2026-09", "category": "runtime", "url": "https://github.com/lookski/openjev", "base_model": "Qwen3-0.6B", "params": "0.6B", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "The default model is openjev/models/Qwen3-0.6B, and a measured demo on CPU-only fp32 shows a three-day Stripe connection problem in state: department choice technical at 0.9990 with confidence 0.9985, frustration score 0.1298 with confidence 0.8562, and is_urgent yes at 0.8733, using 3 forward passes and 338 input tokens at about 1.2 seconds of latency, with your numbers differing slightly by version and hardware. The argument for why it works: LLMs already know the answer, and the problem is the decoding, which is slow, sampled and untyped, so OpenJev never decodes and reads the raw logits at the first answer position. MIT, Python 3.9 or newer, CI, no API key, bilingual English and Chinese; a companion openJev-0.6B-1-0-1-1-39214 artifact exists on Hugging Face.", "arch": "Turns any local LLM into a Jev, the viral System One decision model, running 100% on your machine, via masked-logit softmax: one forward pass, mask the answer-token logits, and a softmax gives raw probabilities, with zero API cost, zero data leaving the machine, and type errors mathematically impossible.", "approach": "logits", "training": "None", "data": "None", "result": "Jev-shaped decisions from a 0.6B model", "notes": "Tiny MIT runtime; also openJev-0.6B-1-0-1-1-39214.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ekzhang-openjev-sglang", "name": "openjev-sglang", "author": "ekzhang", "date": "2026-09", "category": "runtime", "url": "https://github.com/ekzhang/openjev-sglang", "base_model": "Qwen3.6-35B-A3B", "params": "35B A3B", "metric": "smoke test covers 3 answer types, a 64-answer question accepted and a 65-answer one rejected", "license": "Open", "paper": "", "stars": 325, "likes": "", "detail": "SGLang 0.5.19 Rust frontend with radix caching and breakable prefill CUDA graphs; a separate Python API process uses FastAPI, uvloop, the Rust-backed HF tokenizer and pooled asynchronous HTTP connections to SGLang on localhost. CUDA dependencies stay in SGLang container, uv sync on your laptop installs only the API, deployment tools and tests. Deploys to Modal with autoscaling that scales to zero after five idle minutes; if SGLang exits unexpectedly the API exits too, so Modal replaces the container instead of leaving a live HTTP process with a dead inference backend. Cache warmups request one unused token probability to avoid SGLang mixed-logprob batch crash.; limits: 64 questions, 2 to 64 answers per Choice and Score, 2 MiB JSON, 32,768 tokens per branch, 262,144 total input, 16 concurrent evaluations; arch: N+1 one-token calls for N questions with radix-cache warmup, answer labels A to Z plus letter pairs, logprobs renormalized with stable softmax", "arch": "Server implementing the TypeSafe Jev HTTP API with Qwen3.6-35B-A3B on SGLang, one B200 per container", "approach": "logits", "training": "None", "data": "None", "result": "", "notes": "One B200 per container with SGLang 0.5.19 Rust frontend, radix caching and breakable prefill CUDA graphs; a separate FastAPI and uvloop API process with a Rust-backed HF tokenizer and pooled asynchronous HTTP connections to SGLang on localhost; deploys via Modal", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zhangcy-openjevpro", "name": "OpenJevPro", "author": "zhangcy122", "date": "2026-09", "category": "runtime", "url": "https://github.com/zhangcy122/OpenJevPro", "base_model": "Qwen3 / DeepSeek-V4.1 / Gemma4 / gpt-oss", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "base: multi-backend; training: routing + readout", "arch": "Production-grade open alternative to Jev that turns open-weight LLMs such as Qwen3, DeepSeek-V4.1, Gemma 4 and gpt-oss into high-throughput typed probabilistic decision services; non-autoregressive Choice, Noul and Score primitives with mathematically calibrated posterior probabilities", "approach": "logits", "training": "None", "data": "None", "result": "Multi-model Jev-API-compatible service", "notes": "25 stars; openjev.pro site; positions itself as the production counterpart of the research Jev replicas", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "qwen-rlcd", "name": "qwen-rlcd", "author": "shamazharikh", "date": "2026-09", "category": "runtime", "url": "https://github.com/shamazharikh/qwen-rlcd", "base_model": "Qwen3.5-0.8B", "params": "0.8B", "metric": "", "license": "Open", "paper": "", "stars": 5, "likes": "", "detail": "A prototype brief for text and multimodal, inspired by TypeSafe AI's Jev, with an explicit status of claims: anything under public facts comes from TypeSafe's docs and launch coverage, everything else is the author's hypothesis of how such a model could work, because TypeSafe has not published Jev's architecture. As of 2026-09-24 the scope is inference only, with training and fine-tuning on hold. Milestone M0, the backbone spike, is done: the prefix fork is a full forward on real weights, on CPU or CUDA with fla, in fp32 or fp16, 8–19 times faster than one forward per branch, with chunked branches bounding memory. M1, templates, heads, typed API, zero-shot baselines, has the inference slice done: predict() with typed answers, letter, likelihood, PMI, and read-head scorers, order and fan-out invariance tests, and zero-shot numbers on three datasets at CPU, n = 100, with GPU numbers at larger n and a two-level fork remaining. M2, LoRA plus heads, is on hold; M3 has metrics in place, accuracy, NLL, Brier, ECE-15 and MAE, with temperature fitting and the stress suite not started; M4 serving and M5 multimodal are not started. The dev box is two RTX 2080 Ti, 11 GB, sm75, and GPU 0 fell off the PCIe bus during a dual-GPU benchmark on 2026-09-24, so one GPU job at a time on that box.", "arch": "The smallest decoder-based open reproduction of a System One decision model: Choice, Score and Noul on one small decoder, Qwen3.5-0.8B-Base, where the model takes a state plus typed questions and returns calibrated probability distributions over a fixed set of answers in a single forward pass, with no text generation, and the key design change is a prefix fork instead of a tree mask, exploiting that Qwen3.5-0.8B is a hybrid model.", "approach": "fine-tune", "training": "RLCD-style training", "data": "", "result": "All three primitives on a 0.8B decoder", "notes": "Smallest full-decoder open reproduction.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rizzo-flow", "name": "Rizzo Flow", "author": "Rizzo-AI-Academy", "date": "2026-09", "category": "runtime", "url": "https://github.com/Rizzo-AI-Academy/rizzo-flow", "base_model": "Spark-X2.5 via llama.cpp", "params": "4B / 1.7B", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 491, "likes": "", "detail": "About 50 ms per decision at Q8_0 on an RTX 5060 Ti, with about 5.6 GiB of GPU memory at Q8_0, and the probabilities are uncalibrated. Apache-2.0, from Rizzo AI Academy, with a website and detailed documentation in Italian.", "arch": "An open-source, local-first implementation of the idea behind Jev, TypeSafe's System One model: typed decisions from an LLM, without generating a single token, where unstructured state goes in and typed, probabilistic decisions come out, on your own machine, running Spark-X2.5 4B and 1.7B through llama.cpp on Metal, CUDA, Vulkan, ROCm, SYCL or CPU, with a 1M-token native context and a Jev-compatible API.", "approach": "undisclosed", "training": "Fine-tune", "data": "", "result": "49 ms p50 for short Q8_0 decisions on an RTX 5060 Ti, about 5.6 GiB GPU memory at Q8_0; uncalibrated.", "notes": "First local Jev-compatible flow on the Spark/X2.5 stack.", "domain": "general", "score": "", "latency": "p50 49 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sleepjev", "name": "SLEEPJEV", "author": "PAI-CUHK", "date": "2026-09", "category": "runtime", "url": "https://github.com/PAI-CUHK/SLEEPJEV", "base_model": "", "params": "undisclosed", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "base: PyTorch research framework; arch: PSG; training: incl. a real SHHS-derived replaytaging, apnea events; name: Sleep-JEV", "arch": "One reusable overnight representation + sparse temporal retrieval + option-conditioned inference: runtime Choice/Noul/Score probabilities over fixed candidate sets for polysomnography sleep analysis", "approach": "from-scratch", "training": "Research framework: reusable overnight representations, sparse retrieval, option-conditioned inference", "data": "SHHS-derived replay + PSG datasets", "result": "Runtime semantic sleep analysis over long-horizon PSG without per-question re-encoding", "notes": "Clinical-domain System One: ask many explicit sleep questions against one overnight encoding.", "domain": "sleep", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-lists-wave", "name": "09-24/26 awesome-jev list long tail, grouped", "author": "GitHub community", "date": "2026-09-26", "category": "app", "url": "https://github.com/search?q=created%3A%3E2026-09-01+awesome-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped row: the generic low-star awesome-jev list repos created around 09-24/26, individually too thin for a full entry; full member list in the notes field", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; generic awesome-jev list repos, individually too thin for a full entry: dakotac1994/awesome-jev, 0 stars, curated list of resources for the Jev decision-only System One model; onlyoasis/awesome-jev-cases, 0 stars, source-linked catalog of 12 use cases and 27 project entries; aliaihub/awesome-jev-usecases, 20 stars; anandi1989/awesome-jev-usecases, 19 stars; whyashthakker/awesome-jev-use-cases, 13 stars, 50 interactive Jev vs GPT-4o-mini use-case demos; mrjev/awesome-jev, 12 stars, CC0 curated list; robokrunch/awesome-jev, 2 stars. The differentiated and high-star lists keep their own rows, see the 27 remaining awesome-list entries.  Also tracked: kraayenjon/awesome-jev, a curated list of Jev use cases, projects, SDKs and resources; fatwang2/awesome-jev, a source-backed Jev project directory with a reusable Jev-only GitHub review workflow; AnotiaWang/awesome-jev, Jev and System One applications, libraries and tools.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gh-jev-wave-3", "name": "GitHub Jev/Laya app wave, 09-25", "author": "GitHub search sweep", "date": "2026-09-26", "category": "app", "url": "https://github.com/search?q=created%3A%3E2026-09-24+jev+OR+laya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped row: Jev and Laya GitHub repos created 09-25 that are individually too thin for a full entry; full member list in the notes field", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; Jev/Laya repos created 09-25, too thin for a full entry: charlesdove977/claude-x-jev, st 5; shalevamin/jevii, st 4; rayanweragala/jev-call-router, st 4; Jaluus/JevPertus, st 3; xafold/jev-router, st 3; robertogallea/laravel-judgment, st 3; illumi-ai/sentimento-em-tempo-real, st 3; ssd1051/hearmemory, st 3; velocitatem/jevcodereview, st 2; jinlh060109-cyber/laya-clipper, st 1; AkshayKulkarni1904/laya-voice-browser, st 1; harshavarma02/system1-jev-laya-agent, st 1; pranav6266/omarchy-agent, st 1; Omniaeye/omnia-trading, st 1; Omniaeye/omnia-news, st 1; Muhammad-Mirab/dungeonmaster, st 1; byrash/laya, st 0; bunkerlab-net/laya-shim, st 0; vizakan10/jev-vs-laya, st 0; Charanraj-24/laya-reliability-, st 0. Higher-star items from the same sweep got their own rows: christianmat/jev-pokemon 45, UditAkhourii/quicksilver 36, YUTA-fywoo/jev-gui-delegate 35, kieranklaassen/truffler 25, imikerussell/beebots 23, Emenowicz/jev-sap-commerce 7.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gh-mobile-jev-wave", "name": "09-17/25 mobile-Jev companion wave, thin repos", "author": "GitHub search sweep", "date": "2026-09-25", "category": "app", "url": "https://github.com/search?q=created%3A%3E2026-09-16+jev+mobile", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped row: the small mobile and GUI-agent Jev repos found at the 09-25 companion sweep, individually too thin for a full entry; full member list in the notes field", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; mobile and GUI-agent Jev projects found at the 09-25 companion sweep for the Jev-Mobile paper, row arxiv-jev-mobile, and the droidrun mobile-jev app, row mobile-jev, individually too thin for a full entry. Members: Programalyst/realtime-vision-decision-agent, st 2, 09-17, YOLO plus Jev models playing a mobile game; Friedjof/jev-mobile, st 6, 09-17, fast structured Android control loops with TypeSafe Jev and Mobile MCP; raul1934/open-jev-mobile, st 0, 09-23, Open-Jev-2B running locally on a 4 GB Android phone via Termux and llama.cpp with a GGUF quant; Capitalofgeorgiapolitician1569/mobile-jev, st 0, 09-25, real Android tasks on live phones with Jev, Mobilerun and TypeSafe, no ADB MiaoWuNYA/rikkahub-sillytavern-android was promoted to a full row.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "andy-harness", "name": "andy-harness", "author": "andyqiuqiubo", "date": "2026-09-25", "category": "app", "url": "https://github.com/andyqiuqiubo/andy-harness", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Plugin-based agent harness, open source: conversation GUI, multi-model access for DeepSeek, Qwen, Doubao and custom endpoints, session management, context management, sandbox management and system settings; every feature is a plugin with fully decoupled modules, aimed at developer learning and secondary development; Python 3.11+, MIT", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-system-one-models", "name": "Awesome System One Models", "author": "pozapas", "date": "2026-09-25", "category": "app", "url": "https://github.com/pozapas/awesome-system-one-models", "base_model": "", "params": "", "metric": "", "license": "CC-BY-4.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Curated, evidence-graded map of typed probabilistic decision models: the decision contract, a state and a typed question through a decision head in one forward pass to a distribution over the options, which a policy threshold turns into act, review or escalate, plus the models and the failures; CC BY 4.0", "approach": "", "training": "", "data": "", "result": "", "notes": "Companion repo: pozapas/system-one-models-survey, census, evidence ledger, harness and results.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "beebots", "name": "beebots", "author": "imikerussell", "date": "2026-09-25", "category": "app", "url": "https://github.com/imikerussell/beebots", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 23, "likes": "", "detail": "", "arch": "Three AI trading bees race each other on OKX perpetual futures; every decision comes from Jev, TypeSafe AI's decision model, and every order goes through a risk layer written in plain code; a live dashboard shows each decision, order, fee and funding payment as it happens; it runs on paper by default, real market prices with simulated money, and nothing touches an exchange account unless you change the settings yourself on purpose", "approach": "", "training": "", "data": "", "result": "One-click deployment on Hostinger, a KVM 2 is plenty, Docker preinstalled, code MAGIC10 for 10% off; explicitly not financial advice, an experiment and open-source software with no warranty", "notes": "Leveraged crypto trading can lose everything; the disclaimer is in the repo.", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "clm-codex-runner", "name": "clm-codex-runner", "author": "jonsmit9165", "date": "2026-09-25", "category": "app", "url": "https://github.com/jonsmit9165/clm-codex-runner", "base_model": "CLM 8B, a contrastive language model", "params": "8B", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "One-click Windows launcher for a local CLM decision router at 127.0.0.1 port 8700 for Codex and OCX: start_clm_windows.bat creates the virtual environment and downloads the lightweight packages, then Codex and OCX use CLM for instant tool selection and step validation; the OCX or Codex config sets decision_router with provider typesafe, model clm-8b, timeout 150 ms and fallback_to_main; closing the console window frees the RAM completely. Russian-language documentation", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "150 ms router timeout", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jangtrinh-design-os-generative-ui", "name": "design-os-generative-ui", "author": "jangtrinh", "date": "2026-09-25", "category": "app", "url": "https://github.com/jangtrinh/design-os-generative-ui", "base_model": "Laya-MLX + TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": "", "detail": "Claimed comparison versus traditional LLM generative UI from v0 or Claude: 6.5–25 ms forward pass against 3,000–8,000 ms streaming latency; Zod-validated correctness against hallucinated tags, broken CSS and missing props; purely structural JSON spec output with zero injection surface against high XSS and prompt-injection attack surface; zero marginal cost on local MLX unified memory against 0.01–0.05 USD per prompt; design-system-bound Stark Zinc aesthetics against random clashing styles. TypeScript 5.7 or newer, 15 of 15 tests passing.", "arch": "Sub-50ms real-time generative UI engine powered by Laya-MLX on local Apple Silicon and TypeSafe Jev through the cloud API in a dual-tier decision cascade: the user prompt goes to DesignOSComposer, Laya-MLX handles local-edge decisions in about 7 ms at zero cost, and cloud handles the rest. Outputs are validated against Zod schemas under the Catalog Pattern, emitting structural JSON specs rather than streamed JSX or HTML tokens, so the output is type-safe and injection-free.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cslawyer1985-dsh-jev-router", "name": "dsh-jev-router", "author": "CSlawyer1985", "date": "2026-09-25", "category": "app", "url": "https://github.com/CSlawyer1985/dsh-jev-router", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "DSH plugin that uses Jev, the TypeSafe System One decision model, to judge inference intensity", "approach": "router", "training": "", "data": "", "result": "", "notes": "Default behavior only switches thinking strength at zero cache cost; automatic model routing is off out of the box. v0.1.0.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "markelphoenix-gettowork", "name": "Edutainment game, where goal is to 'get to work'", "author": "markelphoenix", "date": "2026-09-25", "category": "app", "url": "https://github.com/markelphoenix/GetToWork", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A silly text adventure that runs a real AI model on your own computer and teaches how it works along the way: it is 8:45 and work starts at 9:00, and between you and your desk are squirrels with a toll booth, a bicycle that joined a jazz band, and a polite dragon sunbathing on the zebra crossing; optional Jev integration", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "github-long-tail-wave", "name": "GitHub long tail, Jev and Laya thin repos, 09-15–09-25 sweeps, grouped", "author": "sweeps", "date": "2026-09-25", "category": "app", "url": "https://github.com/search?q=created%3A%3E2026-09-15+jev+OR+laya+OR+systemone&sort=stars", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped catalog of Jev and Laya GitHub repos individually too thin for a full entry across the 09-15–09-25 sweeps: 2221 repos created 09-19–09-24, 83 created 09-24, 17 and 216 more from the 09-23–09-25 sweeps, plus the language SDK and client tail; false-positive, spam and test repos excluded. Full member lists with dates and star counts in the notes field below; members promoted to full rows are omitted from the counts.", "approach": "", "training": "", "data": "", "result": "", "notes": "Sweep 09-15 long tail: Grouped row; individually too thin for a full entry at 2026-09-25 sweep time.\nLanguage SDKs and clients: Go, devbackend/jevgo, Stumble/jev-go, olivere/systemone, mheers/typesafeai-systemone-jev-go, dwisiswant0/typesafe-sdk-go, zhirschtritt/typesafe-go, robertjndw/gosys1, mhmdkzr/jev, justintout/systemone, codesoda/systemone; Rust, lu-zero/systemone, 2commits/typesafe-systemone, aovestdipaperino/laya-rust, Fanaperana/laya-rs, b0xtch/laya-candle; .NET, JabbaKadabra/SystemOneDotNet, timdinh/typesafe-ai-lib; C#, egarim/systemone-deciders; Java, Premo-Cloud/typesafe-sdk-java, githubMJ/Laya4j; R, mountainMath/JevR; Nushell, cablehead/jev.nu; Zig, li-ming1/laya-zig; Jolt, jlt-commons/lev; TypeScript, SC0d3r/jev-systemone; other local engines, bradAGI/ruling, iksnerd/SystemOne, condeadriankeith/SystemOneEngine, anusornc/jev-decision-engine, getclients4u-lab/systemone, fritzprix/systemone-lite, Lancer59/SystemOneModels, tensor-goat/ModernBERT-systemone, systemonemodels/systemonemodels-sdk, stevexin2018/typesafe-systemone, tangwut/systemone, Franzferdinan51/SystemOne, oozsen/systemone-tr, beratcmn/qwen3.5-0.8b-systemone, XingQiPan/rwkv-jev.\nJev-compatible local servers and proxies: TOSUKUi/jev-bridge, dashbi1/jev-sim, Djancyp/oido-systemone, Bruna-ltd/oido-systemone, infinitylogesh/systemone, NakliTechie/verdict, ReallyArtificial/stuntdouble, us/jev-local, amithgc/local-jev, katya4oyu/jev-systemone-local, jagsan-cyber/reflex-gate, ziozzang/hearim, smallnest/laya-jev-api, exfly/laya-jev-compatible-server, stiermid/laya-serve, khimaros/verdict, temitayocharles/systemone-decision-service, lenML/deep-jev-seek, ZJemYoung/jev-chat-windows-laya, arjun988/Kev, a TypeScript System One engine that self-hosts over Ollama or any OpenAI-compatible model.\nLaya ports and runtimes: gqgs/laya-onnx, MstyAI/laya-onnx, r4ai/laya-web, MatteoGauthier/laya-portable, tc3oliver/laya-apple, AXERA-TECH/laya.axera, ASNB-Smart-Solutions/laya-ara, ryuzcorp/laya-sdk, HQarroum/laymbda, shpati/laya.cpp, DreamBlooms/dohnuts.cpp, bvolpato/kevala, mizorewww/pastewhat-ranker-v1, jinlio/mjbrain, aovestdipaperino/laya-pong, zxrneu/laya-snake, AmirTahaMim/LayaSnakeGame, Xuancuongdoo/laya-ultrafast, pjt3591oo/laya-server, ljw98/Laya, he-jev/laya, lmangani/vgi-laya, vishalmysore/layaForWorkflows, myxamediyar/chunklaya.\nAgent plugins, MCP and harnesses: Obrais-cloud/typesafe-mcp, NVentimiglia/laya-mcp, F0Rextasy/omp-laya-judge, kevinbadi/laya-decision-brain, d0nj/opencode-smart-reasoning, tomstiani/pi-systemone-guardrails, Pidbid/laya-deploy, CubicMaldo/n8n-nodes-systemone, AtHeartEngineer/HA-SystemOne, ahoo/cpa-plugin-zen, mmyddd/astrbot_plugin_systemone_keyreply, Zyw052/astrbot_plugin_jev_radar, HuXioAn/jev-telegram-channel-router, Excalibur9527/dsh-jev, zhangxaochen/dsh-jev, haowenyang188-arch/dsh-jev-judge, PerryLink/dsh-laya, ranjan2829/AskJev, himomohi/aside-jev, aryanbhujade/laya-mlx-voice-browser, ChenneyZhuang/laya-browser-agent, paulsmith/computer-use-jev, GhostDroid-Dev/ghost-droid, shreyashmhop/shreyashm, llm-learner/headroom, ruban-24/switchboard, docxology/daf-jev, kaiyes/fast-jev-compaction-laya, lhemerly/mcts-agent, vishalpalepu/ActionItemExtractor, diwakersurya/mac-dnd-categoriser, fr3akX/systemone-mail-filter, Macasacker/design-review, aarora79/jev-samples, api-evangelist/typesafe-ai, AIAnytime/jev-crash-course, haseeb-heaven/jev-system-one, kenhuangus/jev-usecases, AzPepoze/openthai-systemone-server.\nGames, demos and evals: ShivaniKumar1/Jev-Chess, NatBrian/pokemon-showdown-jev-agent, comoc/jev-minesweeper, caijinchun/nanojev-arena, joshhu/jevtest, cv/laya-plays-smb3, dhruvmehra/jevbench, wondertwins/jev-benchmark, umstek/zero-shot-ie-bench, zhengbangbo/structured-decision-bench, harrymunro/jev-laya-benchmark, actuallyrizzn/decision-systems-bakeoff, theyashwanthsai/jevals, ItisShikhar/gg-friggin-ez, jiayylu/jev-as-quant, Shashank-H/jev-trader, psychodissection-ship-it/aithyrion-director, rupeshs/flappybird-jev-test.\nSweep 09-24, 83 repos: Grouped row; 83 repos created 2026-09-24 that are individually too thin for a full entry at sweep time, false-positive and test repos excluded; repos since promoted to full rows omitted. 190ibrahim/jev-graphify; Andris73/openjev-mc; BP602/ntfy-hermes-jev-bridge; IAmNo1Special/typesafe-sdk-godot; Kamio-Misuzu/Jev-as-a-Reward-Model; Layasri2005/Layasri2005; Shrishkd/JEV-MODEL; SuperInstance/quilt-jev-toolkit; Taylor-eOS/jev-deberta-decision; Upendhar111/jev; airen3339/laya; amansahani/jev-laya-openai-comparison; briankim1027/typesafe_jev; bwsbws149-source/Layananpusatbantuan; damian87x/jev-pi-skills; deskengineai/typesafe-watcher; eSaadster/jev-effort-router; frquintero/jev-typesafe-spike; goodbyedavid/LayaOnAWS; guhan-tofu/System-One-plus-Two-Ops-Agent; guozihang/awesome-jev-for-sequence-modeling; gutoportelaa/estudo-system-one; himanshu231204/jev_model_routers; hoangvuhuong/jev-model-router; itsadrianxv/jev-quant; jersonboydmilan/Laya; justao/quarkus-langchain4j-typesafe; kknd4eva/jevdotnet; lnuxe/typesafe-docs-zh; mrgonzales-dev/rlcd_x_1b-4b_instruct; naturalmoods/zajszuro; nicothollot/system_one_model_testing; shmcsensei/easy-jev-fine-tune; superdanacompeny-byte/Layananbantuan.-Muamalat-; wenli03/laya; xxxx00000008-sketch/skills; yoru0/laya; zahariivanov87/jev-classifier; zchee/typesafe-sdk-go\nSweep 09-19–09-24, 2221 repos: Grouped row; 2221 repos created 2026-09-19–2026-09-24 that are individually too thin for a full entry at sweep time, false-positive, spam and test repos excluded; 3 members since promoted to full rows and removed from this list; GitHub mirrors of the chaoliangUNSW Jev-Style and MacJev models omitted, the models already have rows; repos promoted to full rows are omitted, e.g. mattheworiordan/jev-landscape. Cap recovery 2026-09-24: the original sweep missed repos because each GitHub search query returns at most 1000 results; a per-star-slice re-fetch recovered them and merged them in here. 020909/KyrosLabs-Kepler-1, st:1; 07anishu12/SEO-Agent-using-LAYA; 0x440-1me/laya-unity, st:1; 0xjba/jev-swap; 0xlau/jev-rps, st:1; 0xSarnavo/laya-coding-router; 0xshikhar/jev-fuse, st:1; 0xwhrari/grok-jev-guard, st:2; 0xzynex/zynex-signal-core; 1104480426-hash/jev-wingman, st:4; 1105623876/qwenpaw-jev-memory-gate, st:1; 173787247/dsh-wsl-jev, st:1; 1chimaruGin/lumina-wealth; 1cyberlangke1/minicpm-jev-like, st:1; 1cyberlangke1/rwkv-jev-like, st:2; 1jehuang/jev-pr-labeler, st:4; 1wu-davy-2/jev-chat-windows, st:1; 2023Anita/codex-jev-assistant, st:1; 202wh/loving-artist-practice, st:2; 207studio/jev-codex-tools, st:1; 2456868764/jevguide, st:5; 2nugu/laya-ko-decision-onnx, st:1; 419vive/jev-crm-decision-board; 42tr/laya-service; 455-dIAO/jev-codex-router-skill, st:2; 455-dIAO/windows-save-token-jev-setup, st:4; 47vigen/catherd; 48Nauts-Operator/skill-dash, st:1; 4nt0ineB/jev-from-java; 4rays/profanity-checker, st:1; 50bbx/laya-needle, st:1; 549w/mujoco-env-check; 54yyyu/rev, st:1; 7starsseeker/dsh-fact-check; 8endit/CasaJev, st:1; 979569650/dsh-typesafe, st:1; a-chris/pi-ask-jeff; a-Fig/jev-score, st:1; a3165458/ai-trading, st:12; AAA611/stardew-jev; AABBAASS1/jev-router, st:2; aadhar-build/claude-code-jev-router, st:1; aadhil-kh/jevx, st:2; aadilghani1/qc-use, st:1; aadithya1996/enterprise-jev; aarontaycheehsien/jev-systematicreview; aasimjaved/Real-Time-Fraud-Detection-System; abdallahshaban557/jev_test, st:1; AbdelaliSaadali/MacVault; AbdelStark/abdelstark.github.io, st:1; AbdulHannan-o1/laya_mcp; Aben25/jev-sim, st:2; abenojardev/laravel-jev-memory-ai; abhaybhargav/juardrails, st:1; abhishek085/JevControl; abhishekbasu/decmodels; abhishekmamdapure/jev-information-extraction, st:2; abhishekswe/agent-fastpath, st:3; Abhyodaya1/Intel_Engine; ABilenduke/jev-agent-tools, st:1; AbsoluteGeist/code-geist, st:1; abubakarsiddik31/wild-jev-builds, st:3; acharyaanusha/magic-jev, st:1; achenachena/jev-trader; acorn181/semantic-bookmark, st:2; actions-marketplace-validations/DhanushNehru_jev-sec-audit; adamjen/laya-cuda, st:1; adamkaegi/jev-flappy-bird; adelaserna82/jev-model-net-sdk; adetbekov/pixel; adhishthite/laya-agent; Adi0073/Employee_management_RestAPI; adigulalkari/Jev_GC, st:4; Adityakhalkar/JevEye; AdityaMogare/Jev-CI-Triage; Adkid-Zephyr/chinese-workflow-decision-bench, st:1; Adkid-Zephyr/work-with-jev, st:4; adlternative/tally, st:2; adnan2307110/advance-algorithm-assignment; adnanalicic/jev-document-classifier; adorosario/jev-rag-claim-verification; Adrian-lzr/jev-spire-brain, st:1; adventists-ai/duplexjev, st:1; ady95/jev_tutorial, st:3; Adzic/system-one-playground; aehsan275/jev-monopoly; AercherC/jev-multi-agent-system, st:1; AetherZZ2/codex-laya-mcp; afcodehub/Jev-AutoPilot-3D, st:2; afcodehub/jev-dev-kit; afcodehub/Jev-Subway-Runner-3D, st:1; afurm/typesafe-sdk-ruby, st:1; agencyenterprise/jev-recipes, st:10; Agnuxo1/TartanIMU-Cognitive-Stack, st:2; Agnuxo1/Universal-Cognitive-Architecture-JEV-v2, st:2; agruvewov/noulk; ahmadfreijeh/laya-agent; ahmadghoniem/jev-lf2; ahmeddeyaa04/Noise-filtering-Project-; ai-suifeng/jev-job-web, st:4; Ai-trainee/awesome-jev, st:9; AIagentPRO78/cybersec-10000, st:1; aibotjock/JEV-ECC; AIGNLAI/ReflexRoute, st:7; Aimark-dai/jev-chat-windows-deepseek-jev, st:6; AiPersonacademy/Awesome-jev-use, st:4; AiPersonacademy/jev-resume-disqualifier, st:3; AISidesKicks/yesmom-selectia-dev; Aitejiu/jev-harness-lab, st:1; Aitherium/awdecide; ajaman190/lumen, st:1; ajanm007/jevrag, st:3; Ajasra/jev-hooks; ajayk/jev-go-sdk, st:1; ajensenwaud/hermes-jev-plugin, st:5; ajmeese7/hdd-analyzer, st:1; ajmeese7/jev-chess, st:1; akanksha-rajhans-ai/diffguard, st:1; akarsh-k/jev-form-filler-extension, st:1; akash-kamat/jev-craft, st:2; akksh-afk/securedocs-platform; Akramovic1/jev-pilot, st:1; Akshat-Rai0/COMP_USE_X_JEV; alejandro-torrero/laya-101; alekseitylindus/s1gate, st:1; Alevsk/laya-lab, st:1; alexadark/jev-openrouter-skill, st:1; alexanderatallah/jevslist, st:1; Alexandre-Borghi/jev-ncr-demo, st:1; alexandru/opencode-shell-safety, st:1; alexforman1/postgres-search, st:1; alexj11324/open-jev-approvals, st:3; alexmeckes/jev-the-spire, st:1; alexrudloff/tuidraw, st:2; alexwestco/llm-to-jev, st:5; ali-master/usejev; aliasfoxkde/OpenCodifier; Alien0218/Jev_GUI_agent; alikendir0/jev-pilot, st:1; alina-yur/jev-graalvm, st:2; alisadiq-ai/worth-my-scroll, st:1; Alisson-P/ioc-pua-hunting-jev; Alisson-P/vulnerability-management-jev; allahjuriobhanbhro881-spec/Allah_Jurio_26K3087_Post_LAB_05; allenporter/home-assistant-laya, st:2; almcc/slop-linter, st:3; almeidasrenato/clean-mac-jev; almodover/laya-demo, st:2; alpaslan-exe/hd5-antigravity-jev, st:1; alpbahadur/jevrouter, st:1; alperenerol/jev-1.13-mini-benchmark, st:1; Alpha-Harper-Franklin/jev-drive, st:1; alpha-tales/alphaoptimizer, st:3; alsoleg89/decide, st:1; alyssaagard/defense-budget-lab, st:1; alystria-ai/chessbuddy, st:2; amanadhav/traderai, st:4; amansingh619/save-token-with-jev, st:1; ameeetgaikwad/laya-vs-jev-traffic; amidabuddha/jev-decision-mcp, st:1; Amine-LG/jev-creature-forge, st:1; amishah1998/said-done, st:1; amkob868/tagging-agent, st:1; ammar-genai/decisis; amr05008/jev-sandbox, st:1; AmRitJain0442/Tern, st:1; amulyagavankar20-glitch/Local_Email_Communication_System; ananakmal/ngam; anasbekheit/typesafe-jev-mcp, st:2; anchorshell/relay, st:4; anderson188/layaaimodel.com; AndiMajore/laya-kev-docker; andragon3110/laya-mcp, st:1; andrei10k/claude-jev-model-router, st:1; AndreuVM/jev-reasoning-navigator, st:1; andrewsilber/JevsBistro, st:1; AndrewZuo01/whatsapp-jev-guard; andromdaa/kev; andrueandersoncs/jev-cli, st:1; andrueandersoncs/visual-jev, st:3; andyhorn/jev, st:1; andyrewlee/awesome-system-one, st:1; AniruddhaK07/jev-airlock; ankepoipoi/wechat-response-jev-agent, st:4; ankit-realfast/snake-jev; anpicasso/hermes-jev-curator, st:2; anqorithm/jev-alert-poc; AnshEzhava/jeval; ansidium/jev-codex-bridge, st:4; Anson-gzy/jev-paste, st:1; AnthusAI/Biased-Decisions; AnthusAI/Jev-Flywheel, st:1; antigravitysoham-eng/enhance-cx-with-jev; antigravitysoham-eng/esg-brsr-solar-desk; antigravitysoham-eng/esg-disclosure-desk; antlobach/clojev, st:1; antonBy77/moex-wall-trader; antoniofulg/jev-parser, st:1; antTing/jev-accounts-hub, st:3; anyforge/ruhui, st:1; apidance/JEVX, st:2; apixly-ai/jev-filter, st:1; Apothic-AI/jeb, st:2; AppChainAI/Jevatar, st:1; Applied-Cybernetic-Systems/layanow; apscot/jev-auto-router-skill; Araiseimitsu/jev-use; arbazsiddiqui/kev-browser-use, st:1; ARCJ137442/jev-2048, st:5; argaumartinez-png/jev-and-ai-sdk; ariqhadibinmohdalif-star/think-keyboard; ArmanJR/Jev-Persian-Benchmark, st:3; arnab621/typesafe-jev-plugin, st:1; Arnav1771/snap-judge; arnodjiang/Vision-JEV, st:2; arshiaez/system-one-memory, st:1; arslanr-com/perfectrecall, st:2; arthurcolle/jev-reasoning-lab; aryanchauhanoffical/no-hallucination, st:3; Ashadeepa/typesafe-jev-model-use-cases, st:1; Ashadeepa/typesafe-showcase, st:1; ashafizullah/jev-triage, st:1; Ashfaqbs/jev-mcp-spring, st:2; Ashfaqbs/laya-adk-toolkit, st:1; ashleyotooligan/jevbrain, st:1; AshwaniKottapalli/brain-speeddial-eeg-jev, st:1; AskTheWay/dsh-jev-interceptor, st:1; asmirrr/DriftLab; asopitech/laya-multilingual-qnn; assadiandre/jev-search, st:2; assembledadam/typesafe-jev-benchmarking; AstonyCat/jev-tab-grouper, st:3; Astro-Han/jev-harness, st:5; asynq-io/system-one, st:1; ATANU0023/agent_pilot_with_JEV, st:1; atarikcaliskan/jevball, st:2; AtelierArith/JevClient.jl, st:2; AtelierArith/Laya.jl; atmaneayoubdev/jev-ar; atom63/atom63-design-system; AttackJev/AttackJev; atulify/omp-plugin-jev-router, st:1; Ausar12371/Zhiyan; authrain-cloud-abdullahformuli/jev-ultrafast, st:1; autohandai/weka-samples; avendesta/Rekord; avgon/jev-seo-geo, st:1; awesamarth/jev-experiments; Awlaursen/pi-lookout; awoaCrim/pi-smart-subagents, st:2; axuanyaa/jev-paper-figures; aydinozturk/LayaMCP; AyushChauhan9389/kev; Ayyankhan101/Autonomous-Decision-Intelligence-Platform-ADIP-; az9713/jev-model-router, st:2; azalio/doomLaya, st:2; azterizm/jev-vs-sovereign-benchmark, st:2; b0bleet/syn; babybear-labs/system-one; bacoco/Omni-JEV; baggottsbots/superstackos; baibizhe/jev-decision-benchmarks, st:1; BaiqingL/jev-plays-pokemon, st:1; baize7815/jev-mcp-open-source, st:1; balazsorban44/nvim-jev-plugin, st:1; Bald0Wang/jev-docs-zh, st:5; balevine/jev-demo, st:1; banksoalgere/jev_drone; Barneyjm/decision-circuits, st:1; basmilius/homey-jev; bbecht/cold-take-skills; bcharleson/jev-gtm-cookbook, st:1; beamnxw/minelog, st:3; BeBetterCoder/jev-decision-mcp, st:1; benjamincanac/tia, st:3; BenjaminPolge/jev-architect, st:2; benkya/nlaut; Bentlybro/siftr, st:2; BetterZflyee/dsh-jev-adapter, st:2; BevisHan/jev-copilot; BeyondModels/requirements-deep-agent; bgokden/assay; bhcbhc/Jev-Rubik-Cube-Restoration, st:1; bill9924/laya-main, st:1; BingChanCN/omp-jev-toolkit; bingjianzhuoo-ui/jev-demo; BipinRajC/Jev-api-experiments; bkarak/jev-mac; bl888m/jev-bot, st:2; Blackphi6/laya-mlx-mcp; blacksinisterx/jev-bench; blacksinisterx/jev-guard; blacksinisterx/jev-langgraph; blacksinisterx/jev-router; blazejkustra/softlint, st:6; blingdivinity/jevseek, st:4; Blue-And-White/Grill-Jev; BlueTakato/InventorySystemDemo; bnskaggs/jev-vs-polymarket, st:1; Bnymn1306/jev-github-quality-gate, st:1; Bodila51/Jev-chooses-a-LLM, st:2; Bodila51/jev-hft-model-router, st:1; bojansandhaus/jev-decisions, st:3; bojansandhaus/jev-home-assistant-sentinel, st:1; bokuweb/omg, st:3; boldbug1/jev-triage, st:3; Bonzokoles/36_chambers; Borr1/gtos, st:1; bothuany/jev-browser-mcp, st:1; bradsec/mynameisjev; Braedennn/OpenJev, st:1; brainstormity/Jev-For-Dummies, st:5; brandonrc/jev-bench; Brandsma/semif-conlang-lora; BrantWladichuk/jev-stealth-demo; BraveRam/jev-slop-detector; bravo01-labs/DIM-1-Decision-Intelligence-Model; brian-w-zhang/askjev; brightshore/jev-net, st:2; Bring-AI/jev-photo-control, st:1; Bring-AI/jev-rl, st:3; BruceZhao0728/Mini-Agent-System; brunogpj/jarvis-ai-os; bspiritxp/jev-cli; buckmoon/jev-issue-router, st:1; budityw23/fhir_jev; BuilderIO/jev-computer-use-tests, st:2; buluoray/JevOnly, st:5; burgerwdev/what-is-jev, st:1; bvolpato/bruv; bystreamzhang/NBC_JEV, st:1; c4bbage/laya-serve; Cab14bacc/jev-sheets, st:1; cadeos/jev-grounds, st:1; cahaseler/jevlint, st:1; caio0452/jev_search, st:7; Cairn-ink/cairn-jev-lab, st:4; CameronEzraJones/mille-bornes-jev-bot; cannacre8ive/switchboard-ai, st:1; cao8011158/imdb-sentiment-analysis; caohaolin556/jev-vibe-coding-pm, st:1; CaptainPhantasy/JevCoreBuilder; carlaiau/can-jev-play, st:1; carlaiau/judge-jev; carlmatthewcastro/lubosmart-platform; carlosbasto/joule-studio-jev-invoice-triage; carter1111/jevcore, st:1; ccai40359-wq/jev-triage, st:1; cdepuy/hermes-skill-router, st:1; cebor/cerno; Ceciile/jev-use-cases-typefast; cephalization/jev-oxlint, st:1; cernst11/graphql-classifier, st:3; cesaregarza/moon-council; cexll/train-your-first-jev, st:1; cgasgarth/nerqova; chahero/trade-jev; chalkychalk42/jev; chand45/JetDesk, st:1; Chandler-Sun/chat2jev, st:2; chaqchase/jev-cache, st:1; charetterat/awesome-jev-essentials, st:1; CharlieChenyuZhang/jev-trading, st:1; charliepgarcia/jev-mcp, st:1; Charlyhno-eng/jev-codex-pilot, st:3; chaseai-yt/jev-audit, st:1; chasemc67/Jevis, st:1; cheeaun/jevmoji, st:5; CHENG-LIANG1/laya-cli; chenmingtang830/jevarena, st:4; chenrui333/jev-docs, st:2; ChenYCL/jev-browser-skill, st:2; chilligerchief/simple-jev; chinna250801/agent-decision-firewall, st:1; chneau/docker-laya, st:2; chongchong59699/isaac-jev; chottokun/logit-gate-rag; choxos/jevchess, st:5; choxos/LetJevDecide, st:1; chriscoveries/jevalaya, st:1; chrishan17/claude-jev-mod, st:1; chrisns/laya-mac-serve, st:1; chrisstarr7/jev-kit, st:1; ChristianAlexander/effect-jev-cwe, st:3; chuloontop4-code/jev-instance-review; chy4pro/chat-nojev, st:12; chy4pro/jev-dev-kit, st:1; chy4pro/jev-in-mcp, st:1; chyiiiiiiiiiiii/flutter-jev; cindehaa/jev-cut, st:1; claudfuen/jev-genui, st:1; clchrf/jev-course-demo; clduab11/jev-test, st:1; clydechen0228/SmartMom, st:1; CMaintz/jev-guard; CMaintz/jev-triage; cmoro-deusto/laya-server; cnwelee/cev, st:1; coasty-ai/JevCode, st:1; cobusgreyling/Jev, st:3; coco-research/jev-use, st:2; cocodedk/jev-bench, st:1; codaaiteam/jev-ai, st:4; codaaiteam/jev-chat-ai; codaaiteam/jev-companion-arena; codaaiteam/jev-loop-detector; codaaiteam/jev-semantic-racer; codaaiteam/jev-skill-router; codaaiteam/jev-typesafe-ai, st:1; codealitle/jev-agents; CodeIA-Academy/jev-mcp, st:1; codejunkie99/codex-model-routing, st:5; codejunkie99/jev-engineering, st:5; codenamev/ruby_decision_model-providers-laya; Codercise/jev-in-practice, st:1; CoderInPajamas/JEV-MLX, st:4; coding-hermes/auger; Cognition-Forge/snake-laya; collapseindex/jev-builder, st:3; collapseindex/jev-ultralightspeed, st:12; Colvin0315/MiniSystemOne, st:4; CompleteDotTech/jev-fle030-benchmark; CompleteTech-LLC-AI-Research/jev-311-heatmap, st:2; CompleteTech-LLC-AI-Research/jev-context-fabric, st:1; CompleteTech-LLC-AI-Research/jev-prune-kit, st:1; CompleteTech-LLC/jev-learning-lab, st:1; composio-community/jev-orchestrator, st:8; composio-community/jev-router, st:2; CondorCommodore/jev-git-graph; ConfusedSky/jev-tests; connectedGraph/claude-jev-warden, st:1; coo-quack/jev-pii-checker, st:2; CooLguNxDD/Nekomimi-Waifu-Seeker; copyleftdev/jev-labs, st:1; CraigFreedomCom/Va_gov_Data_Jev_Test; craigh33/adk-go-typesafe, st:1; craigm26/duckbatch; crieck2010/trade-suite; CristianAlCubo/FindmeAJob, st:1; cristianoliveira/jeq, st:8; crownpku/FunctionGemma-Jev, st:2; ctaxnagomi/dgui-hypermem, st:2; ctaxnagomi/instruct-jev, st:1; cuba1511/jev-engineering, st:1; cvsgireesh/jev-usher, st:1; cw-jlu/Drug-Repurposing-Agent; cyberofficial/dsh-plugin-jev, st:9; Cyvid7-Darus10/docfriction, st:1; d-callan/bionym, st:1; D9-cell/laya-snake; da-vinci-noob/pi-jev-model-router, st:5; dabaicai001/jeves-desk, st:1; dagfinndybvig/Fight, st:1; daidr/browser-jev, st:1; dainlucas/jev-tg-classifier; daniel-farina/nitro, st:5; danielamitay/swev, st:1; DanielMulec/jevpaste, st:1; danmaps/jevmap; dannyowelch/jev-skill-suggester; dansya-arsana/jev-harness; dante01yoon/laya-jev-arena, st:1; dark-hxx/jev-safety-gateway; DarkFalc0n/play-with-jev; darrenli6/jev-recruitment, st:2; darthblanc/tictacjev, st:1; Darthwares/jev-style; dashidhy/GemmaJev, st:1; DaStru/jevtrick; DataGobes/jev-demos; datbiggie/listaJev; davertor/jev-slop-guard, st:3; David-Lolly/Jev-Compatible, st:5; davyjones7321/jev-state-engine; day253/microjev, st:1; ddbatista/jev-lab; ddh4r4m/zed-shades-of-stoicism; ddlaws0n/jevportfolio, st:1; DDnim/jev-tweet-radar, st:1; Dearest/plotveil, st:1; deepansh-saxena/jevcode, st:1; Deepender25/chess-with-jev-, st:1; deifos/decision-derby; DelvisorLabs/Pyro, st:4; denser-org/rerank-bench-jev, st:1; dereknguyen269/jev-harness, st:5; devanmolsharma/spacebarai; devjtv/jev-router, st:1; Devonance/DoomSat, st:2; Devonance/rover-claude-jev-demo, st:2; devsangho/jev-robotics-example, st:1; devsoniclk/jev-crypto-decisions; devsoniclk/jev-polymarket-executor; dfinke/Jev, st:1; dfinke/jev-experiments, st:2; dgarci4/autorestic-jev-lab, st:1; dglazkov/jev2ui, st:1; dgu0323/xcrystal; dguzman1012/jev-langchain-harness; DhanushNehru/jev-sec-audit, st:1; DhanushPrince/jev-web-agent; Dhruv123-123/jevvingaround; dhruv15598/RAG-Financebench-Jev; diachoofley/noul; diamitani/jev-backend-qa, st:1; diegocp01/living-folders, st:2; Dielldev/rrufe-support-agent; Dililianxice/jev-inner-speech-bci, st:1; Dililianxice/jev-robotic-arm-benchmark, st:1; Dimesio/typesafe-chess, st:2; DimisCodes/tavli; dingw530/playwright-jev, st:1; dipanwitasarkar/Pattern-Paradox; diptihumraskar/jev_demo; direwolfiy/JevPi, st:1; DiscreteTom/jev-sts2, st:3; distil-labs/invoice-processing-pipeline, st:1; distractdiverge/laya-ai-poc; divyanshi0604/Skillswap_platform; diyorbeknematov/inventory-management-system; Dj-Shortcut/rekordbox-jev, st:2; Djancyp/oido-typesafeai-mcp; DJLougen/laya-fast, st:5; DL-Software/godel-trading-jev; DM010727/jev-superpowers-review, st:1; dmirkov/Onescrin_PlanningSystem; dmitrysobolev/jev-spring-boot-bookstore, st:1; dobladov/jevroni, st:1; dockndevai/laya-models; dog-last/awesome-jev, st:1; DolphinMiner/jev-rss, st:1; domini-code/-demo-jev-youtube; DonaldMurillo/system-one-playground, st:1; dougsong/jev-android, st:4; Dowwie/validator, st:2; dperezcabrera/ai-chess-battle; dperezcabrera/system-one-chess, st:3; dperussina/jev-agent-sdk; dqiu01/risk-intelligence-lab; dr-dimitru/claude-jev-plugin, st:2; draeder/Vej; dragonlin-ai/llmbridge, st:1; DragosTana/JEV-FC, st:3; dre-pin/jev-treasure-hunt; drycool/jev-router;drdxy449tc/noulvt, st:1; dsaad68/fuzzy-jev, st:1; dtduc-git/jevnav, st:1; dtheofr/typesafe-jev-ruby, st:1; dttfrancesco/lossless-rewrite, st:1; dtunai/cu-Jev, st:4; duc-minh-droid/jevbox; duckegg0623-create/jev-wechat-live, st:4; DylanSchell/s1; dymzz/lgtm-dsh, st:1; E-FL/typesafe-as-a-judge, st:2; eachann1024/pi-jev-reply, st:3; eadst/polyjev; early-signal-tech/jev-duckdb-analytics-cli, st:2; eclecticv/jev-adcp-decision-economics, st:1; edamame-labs/tab-jev, st:4; EdgeForgeLab/jev-kol-mcp, st:1; EduardoGHdez/laya; edwardyen724-g/jev-compactor, st:1; Ege-BULUT/jev-play-games, st:1; Ege-BULUT/jevcraft, st:1; egma-ai/jev-code-reviewer, st:2; eijiaraki/toxic-filter, st:5; ekkyarmandi/jev-upwork-job-classification, st:2; elcronos/jev-vs-open-decision-models, st:1; EldanRing/winnow-inference, st:1; Elegant-IT-Limited/saleque-assistant; elliothux/tweet-911, st:3; ElshinQ/jevaluate, st:1; Elue-dev/jev_elixir, st:7; Embodied-AI-System/Qwen3.5-OneForward, st:4; Emenowicz/jev-sap-commerce; eminetto/typesafe-poc, st:5; Enapiuz/jev-client-swift; EnesDemir143/jev-laya-benchmark, st:1; EnesYilmazcode/JevMinesweeper, st:1; EnesYilmazcode/JevRubiksCube, st:1; Eniip/jev-game-tools, st:14; ennsharma/scrollpatrol, st:1; enoyola/jev-grand-prix, st:7; epergaboni/jevseo, st:4; ERA-Software/Intelliwind_Training; eralabs-ai/jev-dom; eran-broder/jev-skills, st:2; erboland/jev-fund, st:3; erendikmenn/jev-rag-benchmark, st:14; Eric-Zhou-0302/jev-A-share-trader, st:2; ericdelorefice/unattended-agent-security; erik-dunteman/ChatJev, st:7; erkamyaman/jev-enforce, st:6; esse/laya-mlx-advisor; EthanAlgoX/jev-trading, st:3; etsabary/jev-deterministic-benchmark, st:1; etweisberg/jev-ui, st:3; eugeniughelbur/jev-engineering, st:2; evan87863/awesome-jev-examples, st:1; Ex8-ca/jev-router; exoticknight/dsh-system1; eyesofish/jev-lab; fabiobraganet/laya-dataset-manager; fabricioctelles/modelsystem, st:1; fabricioism/jev-expirements, st:1; FailproofAI/jev-policies; Faiziahmad/jev-projects, st:1; fajarhide/askgrep, st:4; fajarnuha/klassify; fallow-rs/fallow-verdict, st:1; fancyboi999/ockev, st:1; fanly/Jev-awesome, st:1; fanweiya/gomoku-jev; FarmerTian/Jev-MACE, st:1; fatelei/jev-compact, st:8; fatelei/yueli, st:3; FazalAAli/jev-robotics-demo, st:4; fazlerocks/jev-adblock, st:2; FBddcz/Beat-Jev, st:1; fbettag/elixir-jev, st:2; fellowship-dev/jev-second-brain, st:1; felvasquez/poc_jev; fengliner/jev-tank-battle, st:1; fidecastro/jevify, st:39; filedcom/playjev, st:8; finalfantasy224/jev-chat-linux, st:1; finetuningsingh/intelliprompter, st:2; fitchgc/jev-ccrouter-extension, st:1; Flam1ngFir3ball/jev-claude-router, st:1; flazouh/ego-jev, st:1; florian-hoenicke/jev-gpt, st:3; flpetho/docket-public; flxbl-io/sf-autopilot, st:2; flymywife/jev-n; FogMoe/necro, st:4; fol2/jev-playground; fontlaborg/ornotto; fortemate/dicechess-bot-jev; Fox-Islam/jevlint; fpcMotif/colab-lab; franciscojunqueira/jev-tiss; franckverrot/lev, st:4; FrancyJGLisboa/decision-system-forge, st:1; Frank-ZY-Dou/awesome-jev, st:16; freakynit/jev-test-runner, st:1; fredppm/jev-wf; freepik-company/jev-mcp, st:2; frolleks/soter, st:4; frontierlabai/JevHub, st:6; frozencity/jawgyi, st:2; fuleinist/laya_mcp; Fuwn/typesafe-mcp, st:1; g0runmezadam/jev-architecture-research, st:2; g0runmezadam/what-is-jev, st:1; gaborishka/jev-canvas, st:7; gaborishka/jev-wrapped, st:1; Gabriel382/BioJev; GabrielBigardi/TibiaJevBot, st:1; Gackson/jev-olympics, st:1; galarzafrancisco/jevster; galitianu/jev4j, st:1; Gamma-Software/jev-signals-lab, st:1; garry-schuette/browser-use-with-jev, st:3; GarvitBansal45/cafe-management-system; garygentry/system1; gauravjain14/kernel-lens-jev, st:2; gavansmyth-arch/jev-chrome-extension, st:1; gbesse/jev-lifecycle; gdamiani1/sieve; gdrpaul3-byte/jev-agent-router; gecm0/jev-judge-mcp, st:1; geekailab/ad-zhaoyaojing, st:1; GeekLinkDev/jev-subtitle-translator, st:1; gemini0ani/ew22i1jev; generallymatthew/factlabel, st:1; Georgy-hook/rimworld-autopilot, st:1; Gerry9000/awesome-jev, st:2; Gestalt-Lab/jeff, st:1; getvoicify/jev-reviewer, st:1; ghzcool/kev; ghzcool/xev; gi-dellav/jevdo, st:1; gignac-cha/jevcrypto; GiladR1979/jev-dino; Gilbert09/jev-cli, st:1; Giobebbe/jev-voice-agent, st:1; GiskardB/jev-agentbridge; git-pratap-shrey/gmail-classifier; GitHub30/OpenJev, st:1; Gitmaxd/agent-seek, st:2; GitNimay/-run-jev-run, st:1; GitNimay/jev-plays-tetris, st:1; glamboyosa/docket, st:2; glud123/jev-assist, st:4; GMR714/jev-decision-lab; GoddyB/ask-jevs; GodModeAI2025/JevCoreML, st:2; goldfisheep/jev-data-guideline; goncy/quiet-replies, st:3; goodrahstar/pdf-race, st:10; goodruizhan/pi-jev-control, st:1; gopaljigaur/decide, st:1; gopinav/jev-demo, st:9; gordan-code/jev-metrics, st:1; goredn07-crypto/AcademiX-ToolKit; GoSpinUp/jev-reviews; govindup63/skillpick, st:2; Gowtham-R-2002/gyra; goya4140/jev-reward-model-evaluation, st:4; GracieZGC/jev-chat-analyzer, st:1; grandamenium/jev-anything; grayrepo-byte/jev_filter_for_x, st:1; greghavens/jev-graph-builder; grgy078033/grill-jev, st:1; grvd5678/b_tech_final_year_project_heart_disease, st:1; GTC6244/Laya-Decision; gualican/jev-model-router, st:2; gudcks0305/jev-java, st:3; guilhem/codex-subagent-router; guilhem/jev-ci-selector, st:2; gurkannz/pi-jev-compaction, st:1; GWQLike/Jev-Multiple-choice-tool, st:1; GxZzzzz/WeChat_Galgame, st:1; h1code2/jev-x-blocker, st:1; hajime-goshima/jev-lab; hajime-goshima/jev-playground; halfspin-qc/jev-reviews, st:2; hamidfarmani/jev-resume-match, st:5; HamsterPark/Jev-Huarongdao, st:2; HamsterPark/Jev-Xiangqi, st:2; HamzaShaikh17/jev-experiments; HanboyLee/typesafe-decision-skill, st:1; Hand-In/openjev-multimodal, st:2; Hangzhi/diffusion-jev-sglang, st:2; HaoxiangXia/fork-cast, st:3; HappyMonkeyAI/kev-decision-mcp; HAR5HA-7663/hunch, st:1; HAR5HA-7663/jev-compact; Harlowwinston17/CS2-FPS-Optimizer-for-Windows-10-11-Boost-Performance-2026-; harrisonmuskat/jev-codenames, st:1; HarryReidx/jev-laya-tetris; harshwasan/jev-sentinel, st:9; harveybc/news-signal; hassankhan2608/sysone; Hatuw/jev-chat-feishu, st:1; havietkok-sys/BizzJev, st:1; hazlema/jev-connect4, st:1; hcl-z/pi-jev-gate, st:1; HcodeQ/Langchain-Jev, st:2; HCTDIP/jev-calib; hdkiller/jev-panerelay, st:2; Hduc/synapse; hejuntt1014/mario-jev-lab, st:1; hemanth/hfjev, st:1; hemanth/jev-chess, st:2; hemanth/jevql, st:1; HengyuLi-Ozaki-lab/kite_population_simulator, st:1; henriquekieckbusch/henriquekieckbusch-module-jev, st:1; henrykey/jev-mcp; herakles-dev/typesafe-claude-kit, st:2; HermannAI/kev-jev-seo, st:1; heswy/Jev-Benchmark, st:2; heswy/jev-console, st:1; hewenyu/jev-card-agent; HexyeDEV/JevPR, st:9; heyman333/jev-router, st:1; heyman333/jev-skill, st:1; hfnissum-byte/Hunkpick, st:1; hgqimo/JevRanker, st:5; hhdfh782/Jev-MiniMax-H3; hhdfh782/jev-patch-pam; hichem300/bruv, st:1; HiGal/parallel-constrained-decoding; Himanshutadse114/honeytrap; Himanshutadse114/reverse-honeypot; himesh-07/SmartEducation; himomohi/jev-skill-router, st:3; HiroakiIchimaru/ad-sentinel; hiroki-abe-58/sokudan, st:1; hiroyannnn/yuru-kit; HisuiKoh/jev-vtuber-ime-core, st:1; HiteshS08/laya-system-use; hj01857655/jev-router, st:1; Hldwsd/minesweeper-jev, st:1; hndrr/ComfyUI-Jev, st:5; hoangnb24/paseo-supervision, st:5; hoangngochuong24947-gif/jev-figure-router, st:1; hoangngochuong24947-gif/jev-patent-disclosure, st:2; hoangtrietdev/jevguard; holiq/jevlaya; HomayoonAlimohammadi/jev-sdk-go, st:1; HopLee6/Qwev, st:2; HorusJiang/dsh-jev-tools, st:6; hoshinodis/opencode-context-pruner, st:2; hoshinodis/opencode-intent-gate, st:1; hosseintoussi/jev-flappy-bird, st:3; hotdata-dev/datafusion-jev, st:3; hqbvqv/jev-for-codex; hr276620-cpu/CARE-HEALTH-PHARMACY; HsiangNianian/jev-turtle-soup, st:2; Hu-xiao-max/jev_robot, st:1; huangserva/hipporoute-jev-codex, st:4; huanxueshengmou/panpan-jd-lens, st:6; Hugo-DDT/JevTape, st:1; Human-Cognome-Project/code-decision-model; HusDev/LinguaTrace, st:2; HusDev/pisocheck, st:2; hyeoki0831-stack/jev_disaster_tweets; hypnguyen1209/jev-paseo; hyprstream/hyprstream-synthesis; i-priyanshuverma/laravel-jev, st:8; iamomiid/jev-inbox, st:1; iamyou-uoymai/EDR-System-1-model; icesixgod/x-human-ai-jury-extension; idCntrue/laya-service; ieedan/label, st:1; ieee0824/jev-mcp, st:1; ifoster01/jev-effort, st:1; igloomatics/jev-ai-detector, st:1; igrejaborabora/lus222-jev-challenge, st:1; iheeb1/lx; iikareem/skillfeed, st:2; iikawa-games/lark-mbti-jev, st:1; ikashana/jev-dingtalk, st:2; imaddde867/jev-position-test, st:3; imendezval/JevResearch; imMamdouhaboammar/fable-jev, st:9; Imrank26/3d-Game-with-jev, st:1; imrishit98/jev.aitools.fyi, st:2; imrozkh/jev-poc; indiejoseph/opencode-jev-pruner, st:1; InfamousCube/JevPilot, st:1; inhabitants/laya-invaders, st:1; inhabitants/laya-snake-cuda; initrd/himalaya-jev-mail-classify, st:1; intelliDean/reflexgate; inversionkinescope38/T58-QUANT-ALGO-BACKTESTER-PUBLIC; IPECTER/jev-runway, st:2; Iron-Mark/appcon2026-lumiere-linawai, st:1; Ironieser/banjev, st:1; IshaanLabs/laya-support-ticket-triage; ishibashi-futos/fm-jev; island99/jev-decision-graph, st:1; Itachi-1824/simplotel-guest-concierge; ItBayMax/typesafe-ai-jev-example, st:2; ItIsCuthNotCup/MutuallyAssuredDestructionSim, st:1; ItIsCuthNotCup/SameThing, st:1; ItisNoMatter/kojev, st:2; itsamejoshab/jev-explorer; itscloud0/codex-jev-native-router, st:1; itsflownium/Kestrel-Agent, st:1; ItzSupra13/jev-is-not-odd, st:1; j341nono/jev-prompt-optimization, st:1; jackbarunz/jev-tool-router, st:6; jackie-cqz/dsh-jev-plugin, st:6; jacks3tr/Jev-Desktop, st:4; jackygu2006/reasonix-jev-compaction, st:1; jacobgoldfarb/Jevlish, st:2; jacobjerryarackal/Jev-vs-Human-Deep-Space-Interceptor; JacopoBonanno/jev_benchmark; JacquesGariepy/ORIGIN-CIVILIZATION, st:3; JaelChen/obsidian-jev-archiver, st:1; jaewgwon/jevis, st:1; jai2010/Jev-Meets-the-Market; jaibhasin/jev-flappy-bird, st:2; jaibhasin/jev-yt-time-saver, st:6; jakenbear/the-jev-enator; jamescazzetta/five-lines, st:2; jamilxt/typesafe-ai-java, st:4; jammaru/jev-affected, st:1; janarashed04-cmd/EVORE; JanDalhuysen/jev-clash-royale-test, st:1; jaredwerba/xword, st:1; Jason-Doyle/jev-parallel-dispatch, st:1; jasonjeske/hermes-jev-context-engine, st:1; javimp2003/claude-code-jev-guardrails, st:1; JayDoubleu/cc-mod-jev, st:1; jayluxferro/laya-mlx-benchmarks; jb-akp/jev-avatar; jbt95/jev-toolkit, st:1; jcezardasilva/jev-n-laya; jclumingkit/jev-poc; jclumingkit/jev-poc-nextjs; jcressler/jev-codex-token-saver, st:6; jeanjeanloic/seo-aeo-jev-skills; jedimasterseamus/ninjarmm-dispatch; jeffloo886/jev-notion, st:3; jeffonelson/jev-bigquery-cloudrun, st:7; jeffscottward/omarchy-jev; jeffyuysw/jev-xianhui, st:1; jeffyuysw/jev-xianhui-windows, st:1; jemsbhai/pollard-jev; jerelvelarde/jev-copilotkit, st:1; jeremy-clerk/is-a-jemail, st:2; JeronimoRepetto/local-issue-classifier; jerryshadow/jev-chat-jarvis; Jessie-jzn/jev-ai-organizer, st:1; jev-ai/jev-api, st:1; jev-chat/.github, st:1; jev-ids/jev-ids, st:3; jev-ids/jev-ids.github.io, st:1; Jev-trading/Jev-trading; jevAgentDev/jev-polymarket-trading; jevaidev/jev-ai-radar, st:2; JevForge/jev-ci-pathfinder; JevForge/jev-cloud-cost-guardian; JevForge/jev-flaky-detective; JevForge/jev-pr-profiler; JevForge/jev-release-forge; JevForge/jev-release-oracle; JevForge/jev-resource-rightsizer; JevForge/jev-reviewer-navigator; JevForge/jev-security-sentinel; JevForge/jev-test-intelligence; jevkit/jev-java, st:3; jevlinai/jevlin-go; jevnewsdev/jev-news, st:1; JevResearch/Jev-Research; jevrl/jevrl, st:1; jexp/classify-plugin, st:1; jexp/watfile, st:2; JGalego/Jevs-Garage, st:1; JH3lou/GridCue, st:1; jharshada332-wq/school-erp-; Jhonnyr97/JevGuard, st:1; jiangkoumo/ego-jev, st:4; jiangxiluning/Visual-Jev, st:8; jiawei686/jev-av-analysis-mcp, st:1; jiawei686/jev-legal-clause-mcp, st:1; jiawei686/jev-paper-review-mcp, st:1; jiawei686/jev-review-mcp, st:1; jiawei686/jev-screen-mcp, st:1; jiayao/jev-grep, st:2; jimbobbennett/typesafe-guardrails, st:1; jimmckerchar/raya; jimmyliao/jev-storyboard-lab, st:3; JingHao-Leon/awesome-jev-apps; jjd-lab/jev-synthetic-survey, st:1; jkaloger/spec-judge, st:1; JKasteele/jev-mail-safety-lab, st:1; jkf87/jev-ultrafast-naver; JkRheezy/win-cu-router, st:1; jkrup/jeveryword, st:4; jkudish/jev-agent-tools; JLegends/opencode-jev-compaction, st:1; jmelahman/typesafe-sdk-go, st:1; jnhzzzk/flux-jev-microgrid; JNT-h04/janit-portfolio; joaovaleri/jevkenpo, st:1; joemaddalone/jev-transcript-analyzer-demo, st:1; JoeSun-421/robotic_agent_use_jev, st:1; johnhenry/laya-js; jon-devlapaz/burrito-frontier, st:1; jon-devlapaz/tink-route, st:4; jonahuche12/oneyard_outfitters; jonascheng/jev-lab; JonRoosevelt/stf-sim; jonymusky/jev-browser-qa, st:2; JordanDalton/jev-mermaid, st:3; jorgemdnt/jev-flow; jpol34/trellis; jpwinans/claude-herdr-jev-compaction; jqueryscript/awesome-jev, st:1; jsherman999/claude_jev_experiment; jstdlee/jev-spaceshooter-demo, st:1; jtave111/hyprism; Jua12n/jev-dungeon-runner; jukkatupamaki/better-call-jev, st:4; Jul1en-Lin/pi-jev-compaction, st:1; jumboly/pkmn-shdwn-jev; justhalfbit/dsh-plugin-jev-effort-selector, st:3; justin1233-crypto/What-is-Student-Assessment-Platform-; jxxfdgd/jev-ai-slop-detector, st:1; jyatesdotdev/jev-logtriage, st:1; jyothivelpula/jev-ai-article; jzhg6/jev-embodied-media-agent, st:2; K0stIa/pi-laya; Kadihx/jev-x-kit, st:2; kai-feinberg/jev-theme-designer; kairugakuo2/jev-arena, st:1; kallurayaankit/jev-ticket-router; kamisaberi/sentinel-stack; Kanaricc/dejavu, st:2; kang915-deep/Jev4agent, st:1; kangshifu1/jev-computer-use, st:2; Kaos599/jev-writer, st:1; karishnu/flagship-jev, st:1; KarlTaylorKnight/unstable-government, st:3; karozi/awesome-jev-resources, st:1; Karthick-Ramachandran/jevfilter, st:2; karthika-u07/jev-starter; kashyapmadhavapeddy/jev-understanding; kashyaprparmar/100-days-of-jev-and-laya; kashyaprparmar/jev-rankkit, st:1; kataras/jev, st:2; KathanModh259/system-one, st:1; KCsaba75/trading-analyzer; kdandu001-arch/leanroute; kdubois/quarkus-langchain4j-jev-laya; keduseworku/jev-transcript-screener; KeeganFargher/auto-jev; keepwonder/jev-hub; keiffff/jev-kit, st:1; KeithHanson/jev-email-classifier; keltokhy/jcol, st:1; keltokhy/jevkit-core; kemalkilicaslan/Vehicle-Speed-Estimation-System; KEN3pei/jev-tools; kenmori/jev-dojo; kentaro/whistle, st:2; kentilaok/Jev-Orchestrated-Decision-Mesh, st:1; KenWuqianghao/jev-fighting-games; kero-zxk/jev-capability-eval; kerwin2046/jev-autosuggest, st:1; kevinlupera/jev-strands-router; kevinwindisch/trex-jev-bot; kevnk/jev-claude-statusline; kez4q6agxc/jevt; khaledsAlshibani/jev-ci-classifier, st:1; khmuhtadin/n8n-nodes-jev-classification, st:3; kidzik/jiffy, st:9; kierandotai/jev-client, st:1; kieranklaassen/compound-cli, st:1; killerz3/jevalyzer, st:2; Kiln-AI/jev_jsonschema, st:7; kindintelligence/jev-rust-review, st:2; kinfey/jevHarnessAgent, st:2; kingd2925-beep/handoff; kingdsa/jev-chinese-grader, st:1; kingsword09/jev-dati; KiritoKing/midscene-jev-runner, st:7; kisshan13/typesafe-ai-go, st:4; kitoutou999/firefox-jev-mcp, st:1; kknd4eva/jevdotnet-empty-backup; Klikwork/feedlens, st:1; KNambiarDJsc/Jev-Reactor, st:1; KNambiarDJsc/second-thought, st:1; kodexArg/laya-mcp; kofanlabs/typesafe-computer-use-windows, st:1; koladev32/jev-classify, st:1; kolawong/fast-compaction-dsh, st:3; kolibril13/jev-in-blender-experiment, st:6; konaito/jev-fetch, st:1; kongbai006/jev-wa-mimo; KonghaYao/fast-memory, st:2; KonghaYao/laya-jev, st:1; kongyo2/similarity-ts-jev, st:1; koosbcom/jev-delegate; Koushik890/jev-firewall, st:1; krisitown/jev-loop-control; krisitown/jev-router, st:2; ksalk/laya-docker-api; KsanaDock/verdict-lab, st:2; kuhung/ask-jev, st:8; Kunyanli230/jev-clean, st:1; kurihada/pi-jev-permit, st:1; kurotaky/jev-music; kushals256/jevcache, st:9; KuzanJ/awesome-jev, st:1; kyle-chalmers/typesafe-jev-incident-router, st:6; kyledickey/jev-go, st:1; kylehovance-ai/jev-the-janitor; kyr0/typed-decision-bench; kzkhykw/jev-auto-ime, st:11; kzkhykw/jev-or-not, st:1; LabGuy94/jevtok, st:3; lacemou/handraw-style-galaxy-jev; lacemou/handraw-stylepop-jev; Lagnajit09/sgrep, st:1; laguagu/jev-rerank-bench; laguagu/jev-skills, st:1; lakshyadangwal/Regret-Forecast; lalitsonawane/jev-snake, st:1; LankiYang/jev-got-doupo; lanqi677/jev-inspired-fast-slow-agent, st:1; laosu888/anshangjian-chat-copilot, st:1; larguesa/jev-search, st:6; Larkspur-Wang/Jev_steer_or_queue, st:2; latent-inpainter/comfyui-laya; lauralin-lab/agent-50-jev, st:1; laurentkempe/TypeSafeJevPlayground, st:3; lautottt/MESA-showcase; lazniak/jevskill, st:1; lbildzinkas/jobfit-jev; lbildzinkas/quiet-review-axi; LeddoEngano/jev-eyes, st:1; leepokai/llm-prompt-techniques-on-jev, st:2; legendarysamw3-droid/jev-the-Agent; Lehtien/kicho-bot; leitongda/jev-minesweeper-driver; leiyikang-tech/jev-cache-rules; leo1394/oh-my-laya, st:2; leojacinto/my-name-jev, st:1; leomfu/gmail-classifier, st:1; leonelfame/jev-with-codex; LexerPatty/JEV_Testorial; lexingtonhibiki/judgekit, st:2; lexmount/jev-browser-bridge, st:7; lgy1027/jevshield, st:2; lhviet/jev-bridge, st:1; Li-Evan/awesome-jev, st:5; LiamSherline/jev-lead-scorer, st:1; lianghsun/jev-tmmluplus-eval, st:2; liangruibupt/jev-interest-cases, st:1; liaoyuhua/jev-trip, st:4; liatrio/jev-demo-jburns24, st:1; lihua2003419-cmyk/yanban, st:3; limboinf/semantic-live-caption, st:8; Lindagr78/openeval; Ling1304/jev-bezos; LingyeNBird/codesafe, st:3; linlin131236/jev-chat-jarvis, st:2; lious68/autoffer; lirantal/discoprint, st:3; litshing/jevcore, st:1; littlewindy123/jev-bili-filter, st:3; littlewindy123/jev-weekend-shopping-chrome, st:7; liudejua27-blip/jev-huamn, st:1; LiuHao-1443/jev-table-tennis, st:1; liuhongrui087-art/jev-routed-agent, st:1; liuhongzhang-create/comment-opportunity-radar, st:1; LiuRJ99/dsh-decision-engine; LivingG0D/laya-trade; liyifan2004/obsidian-jev-inbox-router; ljwwwiop/JEV-mini, st:6; llt22/jev-lab, st:1; lmangani/vgi-autodev, st:8; LMDHQ-0420/Vision-Jev; LocoLoboZ/jev-seatbelts; logicrw/ask-jev, st:1; loktar00/llm-lan-party, st:1; LonelyFellas/jev-codex-cua, st:2; lookymlive/Jev-TypeSafe-Lookym, st:1; loongWoong/jev-zen; lorenzejay/convo-flow-example-jev, st:2; louis-szeto/open-jev-bridge; LouisUltra/jev-deep-dive, st:1; Love-Neko/Rime-Capsule-AI, st:1; Lu1sR/jev-forms; lucianfialho/jev-model-router, st:2; lucioamor/jevify, st:1; luckyluucc/laya-mini-proposal; luhayes/jev-agent-router, st:1; luigivis/jev-sdk-java, st:3; lutiatosu/laya; LuticaCANARD/L2S1, st:2; luw2007/omp-jev-extensions, st:1; LuXZ1z/Jev-Living-Melody, st:3; lvzhaobo/jev-assayer, st:1; lvzhaobo/jev-loop, st:1; lwf225-source/jev-codex-router; LXBWOW/dsh-completion-supervisor, st:2; LXBWOW/dsh-context-curator, st:2; LXBWOW/dsh-teacher-consult, st:2; LYchoon/paper-radar-jev, st:1; m8g1y5-art/Jev-Gripper, st:1; maayanlevy/mysql-ailike, st:4; mabodx/awesome-jev, st:1; maceip/jevalin-web-demo, st:1; machaomc/jev-checkpoint, st:1; MadCodeTX/synthetic-ehr-decision-evals; madeye/homebrew-tap; MadhavBahl/jev-guide, st:1; Madheshvivekanandan/jev-vs-cosine; Madikhan33/jev_codex, st:1; Madniel/jev-benchmark, st:1; maeandrew/laya-laravel; MaekawaAo0604/jev-ux-poc; MagicLex/jevworks; Mahad-007/jev-why; mahmut-gundogdu/bes-kelime-jev, st:2; MainaCherif/Car-Selling-Price-Regression; makenaford/liferay-illustration-system; mallahyari/system-one-benchmark, st:3; ManankumarThakkar/jev-escalation-gate; manas95826/-Semantic-Firewall-laya; manish-9245/Wayfinder; MANISH007700/tab-bouncer, st:4; manukj/laya_dart; manutej/jev-tape-wiki-review; MaojieXu/typed-mini-guard; marc2332/findme, st:9; marceloatoledo/JEV-MCP-REST, st:1; MarchLiu/deltajev; marcorisi/jevalcode; MardonBa/jev-calendar-agent; marinatrajk/tiny-world, st:3; marioalexandreantunes/light-rag-jev; marioquer/jev-mdr, st:1; markkkkkkkkkkkkkkkkkkkkkkkkk/ai-restart; markusbug/jevymarket, st:9; marszhongx/pi-jev-score, st:1; MartianInGreen/NoYouDoNot, st:1; Martin20213/booking-template; MartinesEmanuel/athena-jev, st:1; martinvilu/jev-accelerator, st:1; masa-whitestone/jev-find, st:1; MASAKIOKUDA-eng/jev-testapp; masiarek/javascript-typescript-learning-library; MasihTak/Pausetta, st:2; mastwet/dsh-fast-jev-compaction, st:2; mathixu/jev-dotnet-sdk; matiasbinagora/tetris-jev; matsuikentaro1/jev-title-abstract-screening; matthew004-web/heyreach-jev-bot, st:2; matthewman/jev-snake, st:1; MattiooFR/mcp-server-jev, st:1; MattiTynka/JEV-RTS, st:1; Mauro-AGalvezT/Jev-demo; maurorosero/hermes-jev-helper; Mawfyy/jev-router.nvim, st:2; Mawfyy/jevflow, st:9; Maxi91f/jev_testing, st:1; MaxIvanyshen/jev-review, st:1; maxlibin/jev-rubiks, st:1; maxlibin/jev-toto, st:1; maxlibin/moomoo-jev-trader, st:6; maxsumrall/jev4j, st:2; Mcbeer/laya-code-review-action; mcftira/jev-route, st:3; mchamoudadev/jev-home, st:1; mdabydeen/stopline; meetr1912/jev-counterspeech, st:1; mehdihadeli/typesafe-jev-dotnet-sdk, st:1; meksof/spam-detector; Menox27/Jev-Postfach; MersivMedia/jermes; MertSoylu/noble, st:1; metalagman/laya-go; metalbear-co/jev-auto-approve, st:8; metask-ai/metask-jev, st:2; mgarlabx/Jev-Enem, st:4; miaopj0325-collab/jev_mcp, st:1; micahchoo/qualitative-query, st:1; michaelpersonal/jev-trade-cc, st:2; microchipgnu/jev-hooks, st:1; microlineindia-lang/Microline-India-Unified-Portal; MidasMulli/kev-ane, st:1; miguelgarcia/better-email-filter, st:1; mikecann/magic-jev-ball; mikegc-aws/jev-strands-video, st:3; mikegomezg/deslop; mikekelly/s1m, st:1; MiladBahariQaragoz/HighwayEnv-But-Jev-is-Driving, st:2; milimomweetwa06-glitch/BAZO-1;mikeyacobian/jev-stub, st:1; mingo-liu/vela, st:1; minhnghia2k3/jev-k8s-awareness, st:4; minhquan23102000/agent-kit, st:1; minkhant1996/system-one-playground, st:1; minorun365/jev-cloud-quiz, st:2; mionax/decisionops, st:1; mishakgg/jev-governor; Mist-wu/qqbot, st:1; Mistertelecom/NEXUS-AI-Gateway-with-JEV, st:1; mit37/BEMA; MIt9/building-with-laya-skill, st:1; MIt9/laya-cli, st:2; miyabiver39/VisionLocalJev; mizchi/jev-lexer, st:3; mja00/cmdc-auto-mode, st:1; mjdileep/OpenJev, st:2; mjvmsteixeira/jev-browser-mcp; mk20mm/jev-compaction, st:1; mkeco/Cerebellum-2B, st:1; mkrupo/som_rst_parsing; MM-sheng/jevspeak, st:2; MO7YW4NG/JEVPapers, st:2; MobAI-Inc/Cynosure, st:2; MoeclubM/PlayJev, st:8; moecly/laya-docker; moecly/laya-rust; mohamedMok/ambre; MohandL3G/homelab-scripts; mohanpaineti/interlock; Mohit12112004/APEX, st:1; mohit67890/imajev, st:1; moisesfilho/typesafe-jev-opencode, st:2; MominaButt24/jev-real-estate-test-demo; monster000777/jev-playground, st:1; montaguegabe/answerfit, st:1; moonstar-x-bot/jev-issue-triage; mooooorty/jev-openworld, st:1; moritalous/jev-browser-use-demo, st:1; moritzkremb/jev-sales-copilot, st:1; MoRohn/flowaid; morre95/Laya-GuardRails-Harness; mr-sujay-patil/ecomdemo; MrChessT/JevState-; mrkpatchaa/github-star-classifier-jev, st:1; Mrlyk/jev-browser, st:4; mrmt/elevator-three, st:1; mstf-svndk/jev-windows-voice, st:4; muhammedilyasy/jev-mail, st:4; muhandis525/jev-code-reviewer, st:2; muhandis525/jev-esp32s3-gateway, st:1; mukiwu/jev-search-mcp, st:1; mukiwu/vault-tag-system, st:7; muneeb-pulsegen/JEV-ARENA; muqi2004/jev-api; muratcakmak/jev-guard, st:7; musman550/musfira-ai-jev-isnt-new-tech-its-marketing-targets-people-who-think-ai; mustafaakin/jev-cpu, st:1; Mvstnz/jev-n8n-b2b-lead-qualification; mwoodward-rh/request-pre-processing-playground; mychaelangelo/tempo-jev-demo, st:2; mywwave/cursor-jev, st:1; n0thingNoob/dataflow-architecture-jev-mapper; n4ze3m/hmm, st:2; nabendu82/jev-reflex, st:2; Nabsku/pi-follow-through, st:1; nadeem4/jev-demo; nadeemcite/jev-crash-course, st:1; nafi-ullah/laya-fine-tune-bn-eco-voice; naiersaidane/jev-demos, st:1; NakliTechie/llamacpp-jev; nalantishantha/Jev_Snake_Game; Nanako0129/stingray, st:5; nanami-0713/dsh-jev-decide, st:2; nanami-0713/jev-resume-screening, st:1; Nancy-Chauhan/hearth-jev-rental-search, st:9; narayann08/Jev.ai-Phone-Review-Analyzer; narekgevorgyan/ultrabrowse, st:5; narphorium/nl-logic-interpreter, st:7; NatersGonnaN8/talk-to-jev, st:1; nautahakk/jev-codex-router, st:1; naveenreddy61/jev-experiments, st:1; navidkashani/jev-guard, st:1; nedzen/jev-terminal-browser-driver; neko233-com/laya-go, st:5; NemanjaManic/ci-gatekeeper-bot-jev, st:1; nemke82/jev-ops, st:1; neo4j-field/jev-graphrag, st:1; NewRudy/Jev_paper; newuser7171/jev-gamepilot, st:3; newuser7171/jev-vpn, st:1; nexibeo/jev-browser-control, st:3; nexibeo/jev-organize, st:1; nexscope-ai/jev-amazon-keyword-checker, st:1; nextflow-io/nf-jev, st:2; ngallodev-software/agent-workflow-typesafe-ai; ngallodev-software/typesafe-ai-implementation-skill, st:1; ngocvychu38-web/cube-lab; ngpool/jev-codex; Nguyen-Dinh-Long-GMO-Z-VN/DrissionPage-Jev-Browser-Harness-Skill, st:1; nguyenhuuluan434/l_jev; NicolaiLassen/open-bonsai-jev, st:5; NicolasMahn/VotingAid; NicoSKOOL/jev-cannibalization; NicoSKOOL/jev-chatgpt-visibility; nighthawk6389/Jev-credit-agreement-parser; Nik-1019/nextwork-jev-inbox-autopilot; nikhil1raghav/jev-playground, st:1; Nikhil383/JEV-Demo; niksargent/pulse-cx-with-jev; NilsWidal/glassbox; ninjasweb/jev-mute-words, st:1; nirmallama/jev-triage; nishit1617/jev-traffic-control, st:1; nisten/assist; nocoo/falcon; noelserdna/colonias-felinas-san-roman; nogataka/uibolt; Nolane-x/JEV-language, st:1; NomaDamas/kojev, st:1; Nomothings/Jetau, st:1; noripto/pigeonhole, st:2; nothans/sieve, st:1; notsointresting/shouldiapply, st:1; nozomi-koborinai/jev-spec, st:7; Nplace-su/jev-father, st:2; nqobile-x/jevdj; nrdz-labs/fast-jev-opencode, st:6; nshkrdotcom/system_one_sdk, st:2; nshkrdotcom/typesafe_api_sdk, st:1; nssmd/jev-bot, st:4; NSStudent/JevSwiftSDK, st:8; Nubet/jev-chess-benchmark; nullean/layar; nun/jev-emoji; nunezb/jevlint-tests; Nutlope/jev-fraud, st:6; Nuu-maan/pastewise, st:4; Nuu-maan/undertone, st:8; O-Midey/switchboard; O-taxi/jev-samples; obetomuniz/auto-mode-for-paseo, st:2; ohernandezdev/jev-pr-review, st:1; OhMyKing/JevTuringMachine, st:1; ojas-mohbansi/system1-gamebot, st:1; Okbatti/SnakeGame_Laya, st:1; okohlbacher/consent-check; Okubo-1/Project-SentinelQHSE; Okura66/kahn1; olivier-motium/jev-doom, st:1; oluies/jev-vs-spacy, st:1; OmarMujahid/jev-decision-bench, st:3; omkarghugarkar007/actiongate-jev, st:2; Omniaeye/laya-jev; omribenami/jev-operated-drone, st:1; Omrigotlieb/awesome-jev, st:1; ONLY-yours/Jev-Components, st:1; ONLY-yours/Jev-Texas-Hold-em, st:1; onlyjq04/jev-agent-hooks, st:2; onmyway133/awesome-jev, st:6; open-factoryai/myJEV; openJiuwen-ai/model-router, st:6; orcawhisperer/lathe; ORiONx888/JevSentinel; osamabinIaggin/laya-micro, st:1; oskarscot/typesafe4j, st:1; osrim/readwise-jev-classifier, st:1; osuki-dev/opencode-osuki-agent, st:1; overfit-lab/OpenJev, st:2; owainlewis/jev-examples, st:1; oxwen11/pi-jev-skill-suggestion, st:1; ozkayas/jev-backgammon-simulator, st:1; p47t/hello-jev; pabloirracional/jev-evaluation; Pachaboo/Bryant-ZonePerfectPlus-ESP32; padmanabhan-r/Tangent-JEV; pambrose/jev4k, st:6; pambrose/laya-server, st:1; pangpang778/what-to-eat, st:2; pangwenfeng/Jev-Blackjack, st:1; parkavenue9639/jevloop, st:4; Parth1811/JevFlow; Pasblinn/jev-lab, st:1; patchy631/jev-as-judge, st:7; patriceckhart/zot-jev; Patrick-SCH03/jev-issue-radar, st:1; pattersonbl2/sre-reflex; pattoor/JEV-agent-opencv, st:1; PauloBTX/exemplo-hev-roteamento; pawel-debik/author-tools; pawelmamcarz/linkedin-ai-slop; pblab-dev/claude-multiagent-coding, st:2; pblittle/fish-career, st:1; pedroknigge/mcp_jev, st:1; PenglongHuang/jev-demo, st:1; PengyiZhang/jev-vlm-decisions; pepedesigner/Sandbase-jev-trader, st:1; Peu77/JevFind, st:5; pewriebontal/typesafe-sdk-cpp, st:3; phiat/typesafe-elixir-sdk, st:1; phiat/typesafe-rust-sdk, st:2; PhiDung-hub/jev-context-compaction; PhiDung-hub/jev-input-standardizer; PhiDung-hub/jev-router; phunterlau/Jev-plus-reasoning, st:1; PhysicsCrave/Claude-AI-Council; PICDEV-collap/binance-jev-prediction-bot; pickle-debug/jev-chat-jarvis-ios, st:1; pietrushka/jev-youtube-filter; pilotspace/homebrew-tap; pilotspace/laya-codex, st:2; pininkara/Jev-Trades, st:1; Pioneer113/jev-chrome-mcp, st:1; piratchai/Jevonian-complete-guide-to-jev-router; piraveena26/ENTERPRISE-BILLING-ERP-SaaS-PLATFORM; PIXELZX0/XERON; PiyushAnand2006/Jevora-IDE; pjdurden/jevkit-js, st:1; pjdurden/jevkit-py, st:1; pjmenon45/Jev-IOT, st:1; pksw4u/jev-research-agent, st:1; plugpogpag/jev-for-cursor; pmarcanol/beef; pmarcanol/ungrift; pmerlin1/sf-early-learning-mcp; Pnawei/Jev-chat, st:1; pniessen/jev-test; poiuyjie/jev_project_context, st:9; Postman-Devrel/JevPong, st:1; poupar-ai/musaranho-cli, st:1; PPRAMANIK62/yc-jev-bench, st:1; PranavV205/jev-gateway-study; prerak1603/jev-vs-claude-benchmark; priyankark/jev-state, st:4; Priyansh1401/DevCost_CLI_Using_JEV; Priyanshu-5257/laya-medical-finetune; PromptEngineer48/langchain-jev-tutorial; proshunsuke/jev-tab-order, st:1; Protocol-Lattice/Predict-With-Jev, st:1; pruthvinathsudini/sudoku-jev; prx0r/content, st:1; psadventure/jev-model-router; psathi9988-max/-jev-chatbox; pulkitxm/jev-chess-agent, st:2; pulkitxm/jev-form-filler, st:1; pulkitxm/jev-reader, st:1; punk2898/awesome-jev-verified, st:4; purankuton2001/jev-vts-adapter; purplesmoke05/opencode-plugin-jev-auto-model-router, st:1; pyoclaw/ragdoll-jev; q93304989-bit/jev-lab, st:1; QCJLchina/Jev-chat-assistant, st:2; qiaohaojie/Jev-MongoDB, st:1; QingHua27/WeChat-Jev; qingshungLI/everything-about-jev, st:3; qinpei-dev/permit-mcp, st:1; qiulingzhu809-sudo/jev-draft-advisor; qs-lll/twitter-jev-guard, st:9; quaeast/vllm2jev, st:1; qualixar/jev-codex-workbench, st:1; QuBiit0/ohmylaya; QuicqDev/Jev-vs-ML, st:16; Quietcatalpa/reckon, st:1; r33drichards/decision-query, st:1; ra2web/jev-helper, st:5; RadRebelSam/awesome-jev, st:1; raffaellocantatelli/R3-sistema-riflesso; rafid29mehda/fairness-testing-of-llm-generated-code; rahulbhaicomtel-commits/Rahul_Admin_Tools_Windows.ps1, st:1; rahulthakore16/n8n-nodes-jev, st:2; rajasekharponakala/jev-mcp, st:2; Rajatgdev/Jev_Scrapper; rajdhakad9826/jev-router, st:8; Rajmeet/jev-phone, st:1; rakeshlanjewar/jev-email-classifier; ranand12/jev-model-router, st:1; rashedInt32/jev-lens.nvim, st:1; rashedInt32/jury.nvim, st:1; Rasukarusan/chrome-extension-jev-x, st:1; ratatoskr-labs/tab-jev-overview; ratheesh-aot/laya-local-http-server; rautsaurabh9-sketch/Claude-Jev-Test; RavenValentin/TypeSafe.Jev, st:1; ravinsingh15/jev-factory-gate; RazeDen/grok-jev-reflex, st:1; reachjalil/system-one-bench; Readyaddy/open_system_one, st:2; RealHacker/jeveval, st:1; reallygood83/jev-router, st:8; realZachi/jevtest, st:3; Reamd7/omp-fast-jev-compaction, st:1; redreamality/jev-skill-selection, st:2; refaldyrk/typesafeai-go, st:1; Reindeer-AI/pi-jev-guard, st:5; reiswaffel78/jev-agent-toolkit, st:2; reliable-era/andrej-karpathy-skills-jev; religa/jfind, st:2; relliex/self_jev, st:1; RemiDuvoux/firejev, st:1; remotehostai/jg, st:1; renjithwarrier94/pi-model-router; Resadan-dev/jev-zork, st:1; RestCode72/dsh-laya; Reverie0123/crush-monitor-universal; RevocGG/typesafe-jev-bridge, st:3; rexballester1201/genericpos-hospital; rexbuilds/jev-triage, st:2; RickDevopsSecure/the-verification-gate; RickT34/dsh-just-enough-tools, st:1; RikaiDev/mesen; Rikinshah787/evalos, st:2; rikkooo/jev-trade, st:2; RileyCarney/JevTools, st:1; rimusz/localjev-mlx, st:1; ringzerosec/jev-runtime-security, st:4; rizafahmi/pi-jev-task-router, st:3; rmosleydb/jev-smart-router, st:4; robebots/kb-jev, st:1; robert-schmidt/interview-cheatsheet; robertn702/opencode-jev-router, st:1; robinwintertaylor/Prompt-Router; robokrunch/jev-physical-ai, st:2; RobotKitAI/piper-astra-jev, st:7; rockomatthews/jev-dashboard; rodrigopsasaki/jev-patterns; rohanarun/dynamic-context-engine, st:1; rohit9mehta/dwim, st:3; roj4s/jev-resume-scoring-report-site; Romain-Jochum/typesafe-jev-decision-studio; romanmeclazcke/codex-sift, st:8; romiluz13/jevmory, st:11; rominap22/strandsharness-langchain-jev; romulloqueiroz/OpenCode-JEV, st:1; roprgm/tierjev, st:1; rosdebbu/laya---jev; RossHanson/jevario; RostyslavDanyliv/jev-playground, st:1; rottenpen/agent-stardew, st:9; royalpinto007/jev-msw, st:1; Roylaffman/jevGIS, st:1; royosherove/graphlin, st:1; rsdkrasen/hermes-jev-router, st:3; rtuszik/salt; RubyBrewsday/jevcumber, st:2; ruffood/jev-reality-check, st:1; ruidpm/triage-bench; ruslanlap/jev-gate, st:1; russfranky/jev-crawlers, st:1; ruyianry/JevGym, st:2; rwjdk/jev-dotnet, st:1; ryacub/jev-android-mcp; RYANFFY/crush-monitor-pack, st:1; ryantsai/jev-llm-router, st:1; ryanzen9/XFlow, st:1; Rybens92/typed-gguf; Ryden-kai/intelligent_customer; rykhalskyi/laya-api; ryota-nakazawa/ontology_jev_research; rythmn1111/doom-war, st:1; S-O-A-TECH/Jev-search-Kor, st:1; sable-inc/jev-linter-action, st:1; Saeedabdf/jev-screening-benchmark, st:1; saembit/jeff, st:1; saembit/jeff-bot, st:1; sah1l/jev-battleship, st:1; sahajamit/jev-lens, st:1; SaiAbhijyan/Wastegate; saif27217/jev-hermes; saksham-malhotra-27/jev-relevance, st:1; saksham-malhotra-27/jev-relevance-evals, st:1; sallout/laya-coreml-vs-jev-benchmark; salsabielmesl/Medilab; Salvius10/Kural-Automation; SamanPandey-in/jevrail, st:1; samat2003/mini-Jev, st:3; samC3/nanoJev; samdoom-coder/Snapjudge, st:1; sameerkhan24/decidekit, st:2; Samge0/jev-arena;samiwolf/laya-noul, st:1; sandroandric/JevGram, st:1; SANGRAMLEMBE/CMU-Deep_Learning_Systems-notes; SanHsien/jev-chat-jarvis; sanjayr-12/jev-gen; sanohiro/jev-opt; Sanoy24/jevpolicy, st:1; santmun/radar, st:1; sarang-pratham/jev-computer-use, st:1; sarathi-aiml/jevsql, st:4; sathariels/jevcheck, st:1; sathariels/jevtriage, st:1; sathik11/decision-model-arena; sathish1945-dotcom/-jev-chatbox; satiricalguru/Fast-Jev-Agents, st:2; satiricalguru/Hinge-Jev, st:1; satkur/laya-rogue; satviksinha/jev-model-router, st:4; satyawikananda/gits, st:2; Sauhard74/mem-jev, st:1; saurabhkumar8112/jev-gpt5-routing-study, st:1; sauravsingla/DeciShift; savka777/jev-search, st:5; sawzhang/jev-demo, st:1; scottvannucci-sys/laya-linux; Seabass-up/jev-workflow-patterns, st:1; seanebones-lang/evidencelens, st:1; seanmphelps-ai/jev-ux-eve; SebastianMusic/smart-blocker-jev, st:1; sedthh/xjevboost, st:1; seedxdream/jev-inbox-decision; sekranstmon/noul; selcukusta/jev-mailroom, st:3; Selmar/typesafe-jev-calibrate-for-code-review, st:3; SENZO-NCEKANA/jev-triage-benchmark; seosangwon/jev-sr; serejkaaa512/jev-content-guard-ext, st:1; SergeiGolos/ask-jev, st:1; SEUOldCptn/AI-Roadmap; Sezaltyagi/Dashboard-Live-Tracker-; ShahriarBijoy/eslint-plugin-jev, st:2; Shahrukh-cyber/jev-pr-reviewer; Shakibuzzaman3104/claude-jev-funnel; SharmaTushar1/jev-search, st:2; shawon159613/laya-redirect;sergiecode/jev-test-si-o-no, st:1; sheepice/Jev-WBC, st:1; shekar-maker/Pricing, st:1; shengzing/local-vl-jev, st:1; shentonyan/pol2-laya-studio; shikaizhong-design/ego-jev-ultrafast, st:3; shima78/jev-chess, st:1; shimo4228/jev-research-pipeline, st:1; shimoverse/inbox-triage, st:1; ShinyDataTech/fast-laya-compaction, st:2; shirenchuang/awsomejev, st:2; shivam1608/nptel-solver, st:1; shivdeepnv/llm-vs-jev-email-triage; Shivp1413/real-JEV; shivpratapsinghpanwar/edgefront_JEV, st:3; shkumbinhasani/typedecide, st:1; Shoaib175/Jev-test; shraddha-shinde04/BloodConnect; shreya0204/jev-test; shrivardhan232006/jev-dynamic-web; shubhamshinde245/jev-image-gen; shubhangi013/prompt-oscilloscope, st:1; shubhangi013/prune-review, st:1; ShuhanSun/jev-oas-sentinel, st:4; shunshunNi/redmine_jev, st:2; ShunsukeTamura06/jev-local; sidney-tio/jev-ued; siiick/pi-pignon, st:2; Siim/jev-claim-vs-measured, st:1; silky-x0/Postmark, st:2; silkyland/use-jev, st:1; simkeyur/OmniSnap; simodepth96/cannibalization_classifier_jev, st:4; simonholm/jev-lab; simonmesmith/jev-bbq-experiment, st:1; sinemorzambak/koltuk; siren2345/jev-gateway, st:1; sirkirby/routr, st:1; sjungwon03/jev-langgraph-example; skhanzad/RILL; skhlo/rlcd-brwsr, st:1; slayerfx/windows-disk-cleaner; sliday/jev-chess-algo, st:1; Sm0k367/epic-jev; smartaces/jev-plays-streetfighter-2, st:6; smarthi/assembly-jev-router, st:2; smellship/Jev-browseruse-autoui; Smit417263/Gridiron-Jev; smithclay/dbt_jev, st:3; smixs/code-quality, st:9; smlayero/jev-debtgate, st:1; softpudding/jev-frontier-100, st:1; Solido/jev_dart, st:1; SomeshSampat2/jev-android-super, st:2; songjie19892027-boop/-JEV-, st:1; Songokou1983/jev-mcp, st:2; sonson0910/jev-router, st:1; Sorenskr/laya-player-generator; SoundBlaster/SwiftDecision; SoundBlaster/SwiftDecision-Examples; Sourav19o7/jev-examples; souravC01/jev-vs-jev; southpolesteve/probably, st:11; soyezequiel/filtro-inteligente-jev; sperictao/dsh-client-ui-permission-presets-jev, st:1; spoonnotfound/soupbase, st:5; SqaaSSL/openclaw-jev-compaction, st:1; sriharsha8991/JEV-use_cases, st:1; srinath1505/Sent1nel; srivastavnik97-lgtm/jev-ai; sshh12/nanojev, st:1; st1ne/jev-gem-scan, st:2; st1ne/jev-pumpfun-mvp, st:1; stacklok/typesafe-go, st:1; stardeckai/lgtm, st:3; stas4000/jev-clerk, st:7; stas4000/jev-geo-audit, st:2; steven-shoemaker/hunch, st:1; steven-shoemaker/hunch-js, st:1; Steven04hub/qq_clinet, st:2; StevenJPx2/jev-distill; StevenJPx2/laya; stolinski/gpui-agent, st:6; stperic/jev-medhallu-benchmark, st:2; strombolini/Armada, st:2; studee/jev-games, st:1; studioigor/jev-rts-assets, st:10; styles01/deci-serv, st:1; suarify/jev-kev-laya-selfhost; suenot/codex-jev-router, st:2; suenot/codex-jev-router-benchmarks; suffro/decport; sugarforever/tryjev, st:7; suidouble/let-jev-speak, st:2; sujalgawas/Neurox, st:1; sumleo/prompt2jev, st:4; sungatetop/Jev-robot, st:1; sunil-sadasivan/jevernetes, st:10; Sunny-commit/OpenDecision, st:1; SuparvaCode/naturalcodz; SuperInstance/api-orchestra; SuperInstance/jev-diffusion, st:1; SuperInstance/jev-quilt; SuperInstance/quilt-jev-toolkit-push; SuperInstance/quilt-readme-expansions; SuperInstance/quilt-research-canons; SuperInstance/quilt-spreadsheet-inference; supermcpco/supermcp; SupratikB23/JevCanvas, st:1; SupratimSircar05/jev-zig-cli, st:1; surajvs2710-stack/god-llm-decisions, st:1; sureshmanem/typesafe_jev_poc; swang666/polymarket-jev, st:1; Swastikbhat-lab/jev-sentinel; Sweet-Butters/korea-ai-contest-tracker; SwiftFaze/Jev-Studio; swisnl/DecisionEngine, st:1; sxeptical/larp-detector, st:1; syabdulr/responsible-ai-harness, st:1; syedsohailhussain1/gavel; SyedZawwarAhmed/jev-chess, st:1; symfony/ai-type-safe-platform, st:2; szafar-7101/reclaim; szybkie-ai/jeb, st:1; taigrr/gojev, st:1; taiheqi718-art/laya-cpu-benchmark, st:2; taiki510/JevPaste, st:1; takimdigital/noul, st:1; takumi-golf/jev-fill-pdf, st:1; taman-spirit/guardrail-chatbot-jev, st:1; taman-spirit/product-skill-jev, st:1; tangyuan1129/chat-aside, st:1; Tanimal19/jev-test; Tanmay-say/JEVelric; taodav/jev_deep_rl, st:8; tashfeenahmed/jev-deepresearch, st:2; taupirho/jev-test, st:2; tavily-ai/tavily-jev-public, st:1; Taylor-eOS/laya-decision; taylorbayouth/jev-infinite-ctx; tbrandenburg/classifier-model-test; TeaShaman-cyber/theseus-typed-decision-lab; tech-girlll/worth-my-attention, st:1; teempai/jev-in-codex, st:4; teknium1/hermes-and-jev-play-minecraft, st:8; tenfingerseddy/voicebind, st:1; TennousuAthena/Mailbox-Boy-With-Jev, st:1; tenuo-ai/safe-upgrade, st:1; TenzinDhonyoe/bev, st:2; TeoMastro/jev-vs-llm-guardrails-intent-router, st:1; terryds/jevplayground, st:1; Teylersf/WindowsJev, st:1; Thanh-Mathieu95/jev-model-tokengate, st:4; TheAdaply/jev-apply, st:5; TheCoder30ec4/model_router_python, st:4; TheEleventhAvatar/triage-bot, st:1; thehan-co/jevriel, st:5; thejoeejoee/git-judge-commits, st:1; thelau/jev-tetris, st:1; TheMarco/token-saver, st:1; Theo-Gkisis/jevops; ThePikey/AOS_GLM_language, st:2; TheWayWithin/jev-bench, st:1; thezem/jev-one, st:1; ThiagaoBR/typesafe_agent_gates, st:1; thiagoadril/typesafe-docs, st:2; ThinkyMiner/Winnow, st:2; Thneoly/r2r-jev, st:1; thomasbrueggemann/jeffrey, st:1; tiandee/codex-jev-router, st:5; tibuchivn/jev-complete.nvim, st:1; tic-top/llm2jev, st:2; timpratim/jevbrow, st:1; tin-xai/multimodal-jev-grounding, st:2; tinghualiu29-jpg/cash-recode; tinystruct/tinystruct-typesafe-sdk, st:1; Tirth-1999/email-automation-jev; Titanium-Devops/jde, st:1; tjkimcloud/jev-site-auditor; tjppppppppp/ground-truth; tkymx/jev-review; Todmy/jevqa, st:4; Tokeii0/Xiaoke-qqbot, st:11; Tom-R-Main/Footwork, st:1; Tomatio13/typesafe-usage; tomerglick57/Jevstiller; tomfrazier/slopmop, st:1; TomRichner/can-jev-bayes, st:1; tomsen02/jev-bombergirl, st:1; toneeraj/taxonomy-bench; Tongyun1/Jev-in-the-Loop, st:4; tonysprite/system-one-poc; tostechbr/partway, st:2; ToufiqQureshi/maayaa; tphakala/jev-mcp, st:1; trajectoire-ai/hermes-structured-aux-models, st:1; truongxxxx/jev-btc-test; TrustifAI/typed_evals, st:8; tryAGI/TypeSafeAI, st:1; trycatchkamal/typesafe-jev-traffic-demo, st:1; trytotest13/Jev-AnityG-Mode-Antigravity-Custom-Model; tsu-ld/chamuy0, st:1; tswawa/WechatVibe, st:2; ttlequals0/MinusPodJev, st:2; tubone24/jev-practice-speed, st:1; turenlabs/jast, st:1; turlockmike/decide-lab; tusharck/jev-inbox-queue, st:1; TuYv/laya-chat; twilso24/jev_router; Txy02/jev-harness, st:2; TYC-000/jev-dashboard; tycoding/jev-java-sdk, st:3; typakon4/jev-layer, st:2; tzt-company/decision-making, st:1; uberspaceguru/tmux-jev, st:2; uberto/jev-test, st:2; uchiha-vivek/Jev-Mathematical-Analysis; UditJain2622004/Jev-Trading, st:1; UgurcanAkkok/yks-bench; uhhfeef/jev-lm, st:3; umatter/jevtools; UMBR-A/Apache; Umbylicus/umby-jev-stack, st:1; undeemed/Jcyber, st:2; unimtx/typesafe-sdk-go, st:1; universam1/jevselector, st:1; Unni-Krishnan-M/jev-intelligence; usail-hkust/JevLight, st:4; uspraveen/Jevify, st:1; uzuw/laya-cli-gate; v4fs/awesome-jev-security, st:1; Vamo-Test/jev-visionary; vamsi243/FLOTER, st:1; Vancrown/laya-from-scratch; vandenbogart/jev-accessibility-tree, st:1; Vankleben/jev-arm-lab, st:1; Vann4799/Laya-Navigator; vansh-attri/Jev_findshorts, st:3; varadfromeast/form-studio; vasanthsreeram/myNameisJevToo; vasuchettyphd/jev-tutorial; vaynealtapascine/Chorus; vbcherepanov/jev-symfony-bundle, st:1; vcjdeboer/jev-reliability, st:1; vdqvinh2004/cam-laya-mcp; venumadhav7484/jev-bot, st:1; vercel-labs/jev-ai-sdk-form-router, st:6; vflores-io/jev-agent-router, st:1; VGabriel45/polymarket-btc5m-jev-trading, st:14; vhicktour/JevAuto; vibe-with-me-tools/n8n-nodes-jev, st:3; vij-sameerb5/JevX, st:3; VIKASHAR/Laya-demo; ViniCarvalhoDados/migration-laya; viralcodex/jelight, st:1; virolea/jev, st:3; virtualramblas/gromacs_jev_agent; vishxrad/clashroyale-jev, st:4; vit-cerny/jev-browser-kit, st:1; VitaDAO/open-jev-tinfoil; VIVAAN-DHAWAN/laya-voice-browser; vk-alto-none/laya; vladzima/jev-x, st:2; VoidLight00/jev-judge-aside, st:1; voidning/jev-figma-design-plugin; voxmenthe/code-search-jev, st:1; vpicone/jev-lab; vstrofago/vigia; vyacheslav-startsev/jev-support-router, st:1; vynnlee/jev-mail, st:6; wafaa-alhayek/masroufi; wahahaorg/jev-mcp, st:1; wairimukanene/boma; wakamenod/flymake-jev.el, st:1; wakamenod/jev-first-error.el, st:1; wakamenod/jev.el, st:1; walidboulanouar/jev-agent-kit, st:1; wangBoYi972/typesafe-mcp, st:1; wanghai673/jev-browser-skill, st:2; wangkuangkuang/jev-mcp-server, st:2; WanLanglin/jev-skills, st:1; Waxmell114514/jev-compaction, st:4; Wayne-Francis/BatchBase; waynesutton/ask-jev-ai, st:3; web3w/jev-trader, st:3; weemiles/commentlab, st:1; WeiS49/message-tone-checker; wellkilo/codex-jev-preflight, st:1; welosu06/MacDPI-OneClick; wenchenxi/jev-console, st:1; wendyeq/dsh-jev-router; WesleySmits/spark-jev-email-triage; Wh0rigin/pi-thinking-router-jev, st:3; what-my-names/operit-jev-bundle; Wickypolineni/gemma-jev, st:1; win4r/jev-humanize-writing, st:5; win4r/jev-security-scan, st:10; winkelmeyer/sc2-astra-jev, st:1; Wionerlol/wechat-jev-hud, st:1; wisalkhanmv/jevllm, st:1; wistrand/llav; withzombies/pelican-jev; WizdenOrg/wizden-moonlander; wizicer/jev_info_site, st:2; wjc1207/jev_dst_agent, st:1; wjw66/deepseek-harness-jev-pre-compaction, st:5; wn-mitch/audle; workszop/jev-dev, st:1; wudilyy999/jev-langgraph, st:3; WufeiHalf/pi-jev-agent-browser, st:1; wufuju2023-cell/jev-alpha-proof-analysis, st:1; Wujiaowang/plain-language-gate; WuZihang-bit/qq-persona-agent, st:3; WXK-AI/jev-opus, st:1; wzsxb233/gizzai-sense, st:1; X0EF/jev_projects, st:1; Xe/Xev, st:1; xergioalex/jev-lab, st:1; xhongc/jev-music-tag, st:5; xianggelila177/VideoAdGuard-Jev, st:2; xiaobai1017/laya-service; xiaol/Gut-RWKV-Jev-laya, st:1; xiaom8413-afk/jev-everyday-kit, st:1; XieChengYuan/jev-observatory, st:1; XieChengYuan/jev-playground, st:1; xienda/dsh-jev-verify, st:1; xingtu1228/Jev-Mobile; xinwang-nwpu/jev-mobile, st:2; xixi3548942758-design/laya-ternary-lite, st:2; xnuonux/jev-reflex; XSirch/laya-trader-binance; xuan7zhang/jev-toolspace, st:1; xuboboo/ashare-trader, st:2; xuebai2812/jev-travel-packing, st:4; xunziheng/jev-tank-arena, st:1; XYenon/ajevt-browser, st:1; xz-dev/pi-jev-todo-audit; y9Finsi/jev-mcp, st:2; yagocanton21/jev-extension; Yaklede/jev-harness; YanfLIZi56/jev-starter, st:3; yanglei070-ux/bili-hardcore-jev, st:2; yangyu666/dsh-jev-prune, st:3; yanqiangmiffy/chinese-laya, st:1; yAntPower/jev-decision-lab; Yanwei-0808/jev-driving-agent-demo, st:1; yaohao159123/paper-triage, st:1; Yarub/jev-trader; yasdelayu/jev-crypto-scout; yask123/ai-dj; ybagheri/jev-fm-indicator; Yeadon8888/jev-hypit-commerce, st:1; YehuiTang0316/jev-nlgrep, st:4; yibie/pi-jev-browser, st:1; Yii-Jing/Jev-Cowork, st:1; YiLight0/paperfocus, st:8; YIRC99/dsh-jev-context, st:1; YJGGZHK/midnight-soup, st:1; yldm-tech/loom, st:1; ymarux/tetris-jev; Ynchnnnn/jevwatch, st:1; yoanbernabeu/demo-symfony-typesafe, st:2; youdotcom-oss/risk-analysis-server, st:1; youzizzz1028/Awesome-Jev, st:1; yuan-phd/jevmark; yufeiyufei888/jev-quant, st:1; yukihirop/jany, st:1; yukurash/jev-playwright-repair-lab; yusukebe/my-jev-router-app; yusupsupriyadi/jev-skill, st:1; YUUM321/jev-klondike-benchmark; ywchiu/jev_benchmark, st:4; yzbcs/Should-I-Jev, st:1; yzxoi/lime-jev, st:1; yzxoi/RSI-Jev-Slay-the-Spire-2, st:3; yzyialy/crush-monitor, st:2; Zaceface/Jev-LLM, st:1; zach-ct/jev-and-ai-sdk; zachary-walters/laya-server; Zafer-Liu/jev-demo-guardrails, st:1; Zafer-Liu/jev-demo-moderator, st:1; Zafer-Liu/jev-demo-rag, st:1; Zafer-Liu/jev-demo-router, st:1; Zafer-Liu/jev-demo-screener, st:1; Zafer-Liu/jev-xiangqi, st:1; zaferayan/laya-vs-jev, st:2; zamax14/Laya-Showcase, st:3; zandy700/Jev-Assistant, st:1; zaydmulani09/jevgrep, st:1; Zboubkiller/jev-plays-sts2, st:1; zcoder-run/rust-sysone, st:4; zdenham/jev-lint, st:2; zeeshan8281/slo-router, st:9; zerodegress/jevinf, st:1; zerodegress/laya-rust, st:1; zexuanw958-svg/yanwai-mac; zfrqbl-CW/jev-support-copilot; ZhangYiqun018/jev-dimabsa, st:6; zhangyunting123/von-laya-jev-paint-compare; Zhao-Tian-yi/awesome-jev, st:1; zhentong98/jev-mahjong, st:1; zhiyaoli0221/fin-one, st:1; zhongpei/jev-chat-jarvis, st:1; ZHUBoer/ego-jev, st:2; zhulinchng/jevper-skill; ZHYsfl/learn-jev, st:1; ZihuaEvan/GUI_JEV, st:2; ZIJIAN004/jev-switchboard, st:1; Zilpione/typesafe-jev-dotnet; zixiang0623/jev-mail_cloudflare; zixiang0623/jev-mail_vercel; ziyuw-Plank/Jev_chat_overlay, st:1; zjuneverknow/test_jev; zkjoie/jevbus, st:2; zm2231/skill-router, st:1; zponk-meta/proof-engine; Zrc007/agent_dev; zsj-a11y/jev-in-jev, st:1; Zuhair-01/laya-windows, st:2; zun-tools/jev-demo; zurfyx/jev-browser-skill, st:2; zurk/hekajev, st:3; zytong523-bot/stock-lite; zzsong1023/jev-market-reflex, st:2; zzz1YAO/DataJev, st:1; zzzzzec/jevsort, st:1; 09-22/24 additions: rupeshs/laya-openvino, an OpenVINO backend fork of Laya with the weights at rupeshs/laya-ov-int8; brennanmke/system1arcade, Tetris, Frogger and Space Invaders as a Go and Wails desktop app where a System 1 agent plays through the same virtual controller as a person; dorkitude/decision-model-testing, daily experiments on where Jev already beats LLM and retrieval baselines; markylaredo/openjev-mcp, an MCP server in front of the public OpenJEV API with four tools, built for DeepSeek Harness; kevin-zhouu/jevify, a /jevify skill that audits agent code for AI calls that are really decisions and migrates them to Jev in shadow mode, plus its sibling kevin-zhouu/jevclaw, an OpenClaw assistant with Jev-powered memory decisions and an LLM fallback; lucioamor/jevify, the /jevify audit plus /jevify migrate skill pair, with the Lovable import at lucioamor/lovable-skill-jevify; ericjuta/omp-jevify, an Oh My Pi plugin doing rubric-first bulk classification with judge_batch, a generalization of the Jevict thread; miniongk/laya-mcp-mimo-desktop, a Laya bridge into MiMo Desktop via stdio MCP with four tools; rajasekharponakala/awesome-system1-decision-models, a curated System 1 decision-model list; nguyendkn/openjev, OpenJev-RS, a Phase-0 Rust port of the OpenJev/SemIf CPU-only benchmark comparing three scoring methods; cloudn1ne/system1tools, a Laya analysis webapp behind a LiteLLM proxy; codebooker/jevify, a stub\nSweep 09-25, 17 repos: Grouped row; 17 repos found at the 2026-09-25 sweep that are individually too thin for a full entry, with repos promoted to full rows omitted. Members: HunterXing/typesafe-ai-jev-skill, a portable agent skill for TypeSafe System One / Jev through any compatible provider, with typed uncertainty-aware Choice, Score and Noul questions; JACK-COM/augur, know why you choose, one piece of the Panoply, asks a decision model typed questions about a text and returns calibrated probabilities with no generated text; Luminousyyh/laya-decide, a DeepSeek Harness skill, Chinese docs plus PowerShell code, that hands the should-I-act judgment to a local ~30 ms Laya decision model; Sidneeuncharged29/jev-visual, an educational visual-inference experiment, see a model look at a picture in real time, no programming required; ericmjl/seems-laya, a variant of kavehmz/seems-lang with Laya answering judgment questions locally instead of TypeSafe Jev; holasoymalva/jev-test-impact, run only the tests that matter, using git diff, repository relationships and Jev to select the test files most likely affected; hypen-code/laya-testing, a Laya CPU classification example, structured decisions instead of generated prose; muck-stump/laya-scripts, curl test scripts for Jev-like endpoints, starting with auth; muck-stump/laya-image, Docker image for deploying Laya on AMD-accelerated OCP; agusbyna/omp-laya, thin omp plugin wrapping receptron/laya for fast System-1 choice, score and noul decisions inside omp; mt4110/system-one-router-lab, a Japanese validation project on whether typed-decision pre-filtering of exploration and classification in large codebases cuts cost, latency and rework of high-end models; janiojandson/nexus-decisor-laya, Laya as the System 1 of Nexus, thin Portuguese demo, no README at sweep time; petershifi123-wq/solve-lite, Solve Lite, local System-One decisions on an ordinary CPU, sanitized public rebuild in progress, no public release yet, closed local core; LHLLHL-hailong/jev_enhance_harness, Pi agent harness plus Jev structured decision model integration with a Pi extension review and gating plan, Chinese docs; sgnowicki-gif/DynamicDecisionModels, no README at sweep time; mostlind/vision-decision-model, no README at sweep time; VasyaDetective-cyber/decision-making-model, no README at sweep time; 09-25 additions: anentrypoint/sembrowse, Jev Ultrafast browser decisions run through a local SemIf-OpenJev scorer with no hosted API key; trysingh/classification, a System-1/System-2 hybrid classifier web app with editable taxonomy and a dependency-free keyword baseline; lucast4049/openjev, a Windows decision assistant running open System One models behind a Jev-compatible API; sokapil/jev-vs-llm-banking-demo, a synthetic 4.2M London-to-Singapore payment triage comparing TypeSafe JEV with GPT-5.6 Terra on latency, cost and tokens\nSweep 09-23–09-25 authenticated, 216 of 958 candidates: Grouped row; 216 repos created 2026-09-23–2026-09-25 from the 2026-09-25 authenticated GitHub sweep of 958 candidates that are individually too thin for a full entry at sweep time. False-positive, spam and test repos excluded; mirrors of rows already in the corpus omitted; repos promoted to full rows are omitted. Members: v1shay/jevtronics, st:3, voice-controlled computer use agent using Jev; chensheng43/jevtest, st:1; zalo/BayesianStudio, st:1, Visual studio for hierarchical Bayesian decision models: influence diagrams with Monte Carlo unce...; 5dive-ai/5dive-laya, st:0, 5dive Laya add-on: reflex decisions on a local Laya model, no API key. Installs in one command: 5...; APRESIA/laya-tri-commentaires, st:0; Ajaykvishwakarma/jev-usecases, st:0, This repo will contain the applications of JEV model. Software needs System 1 thinking, but we ke...; BhavyaFattania/laya-powered-interviewer, st:0; CaptainPhantasy/JEVRuntime, st:0; Codeenk/sev, st:0, Sev-X: Kev-descended decision models; Cokefish9527/laya_demo, st:0; Danu28/pi-browser-laya, st:0, General Playwright browser extension for pi — laya + jev ideas, 2–4 LLM calls for any site; Dephilia/pi-laya, st:0; Dipeshtripathi13/jevmatch, st:0, Human-in-the-loop resume and job-description matching powered by TypeSafe Jev; EndeavorYen/JevPilot-Vision, st:0; FedericoGabrielCastro/JevSpot, st:0; GitSujal/duck-jev, st:0, Jev Extension for Duckdb; Hantlowt/laya-steering-lab, st:0; HariharanS/jevpoc, st:0; Hjysir/jevpaper, st:0; HuanLi0311/jev, st:0; HuangQiuYu192/Jev4Rec, st:0, Jev4Rec; JVMoreiraD/laya-test, st:0; JoonHyuk0331/JevTest, st:0, llm task를 jev로 개선; Juniebentonitic2834/juniebentonitic2834.github.io, st:0, Build nano-scale 0.6B parallel decision models: input states/questions, output complete probabili...; Kesin11/my-jev-playground, st:0, My jev playground; LeeeeeeM/laya-decision-web, st:0; Leongwind/jev-boss, st:0, jev-boss; LukasMosser/jevthon, st:0, Experiments with typesafe.ai Jev; MaeTor/laya-detector, st:0, A stronger one-pass scorer that detects lies and false statements via a vocal voice input, decisi...; MatiasIac/layaSample, st:0; MayukhCars24/poc-laya, st:0; Mr-Blossom-1/system1-decision-engine, st:0, Full-stack website for a non-autoregressive System 1 decision engine with typed-choice, scoring,...; MrBug1024/jev-decision-maker, st:0, jev-decision-maker; NachiketKandari/jev-laya-explore, st:0; Nitish-kumar30/jev, st:0; PCTEJA/JEVTRENDS, st:0; Papajamesleonardfarmer1833/jevmlx, st:0, Batch-score structured JSON decisions from logits in one Apple Silicon forward pass, no text gene...; Per0x1de-1337/jevimage, st:0; Rau1CS/jevswarm, st:0; SGUPT174/Laya_testing, st:0; SaiVenkat01/spam_detection_with_jev, st:0, spam_detection_with_jev; Sajjat4/Trchx, st:0, Control a real browser by voice. Jev decides intent + target in ~300 ms per spoken word; Sathish-30/laya_playground, st:0; SaturnAura/LayaGUIDemo, st:0; Shadowsweep/jevastating, st:0; Sharjeelbaig/laya-retrieval, st:0; Sharjeelbaig/laya-tetris-ai, st:0; Slityak/rajzolj-mircinek, st:0, Draw it before the cat gets it: a browser/PWA drawing game judged by the Jev decision model on Cl...; Sorenskr/laya-search-ui, st:0; Sudhanshu3011/career-lens-ai, st:0, AI-powered career intelligence platform that analyzes resumes, evaluates resume–JD alignment, det...; TalalAhmed311/laya-multimodal, st:0; TangVarie/JevforCoentent, st:0; XelaNull/JevLander, st:0, A live Jev-powered Moon Lander; Yassineaur/jevtest, st:0; ZnOw01/JevBrow, st:0, Local MCP browser tools for Codex over Brave and Chrome CDP; aarush-dhingra/Jev-vs-Laya, st:0; actions-marketplace-validations/JevForge_jev-ci-pathfinder, st:0; actions-marketplace-validations/JevForge_jev-cloud-cost-guardian, st:0; actions-marketplace-validations/JevForge_jev-model-navigator, st:0; actions-marketplace-validations/JevForge_jev-security-sentinel, st:0; adhikasp/JevTTD, st:0, A fun-first OpenTTD company AI: plays for a good story with the player, not maximum profit.; adityasudhakar/Jev, st:0; adrian-purnama/laya-server, st:0; akshatnerella/jevedin, st:0; alex-power/hermes-jev-tool-gate, st:0, Gate Hermes Agent tool calls with TypeSafe Jev over OpenRouter — observe, escalate to the native...; alexanderpulido2098-gif/Layaoutuser, st:0; allebee/jevk5-decision-api, st:0; alperunlu07/Laya-Experiments, st:0; anirudhs001/opencode-jevk5-router, st:0; aniyababy/jev_private, st:0, 个人用jev; ankitaa20tr/Jev, st:0; anliang0306/narde, st:0, narde - a 1:1 clean-room re-implementation of the laya non-autoregressive System-1 decision engin...; anthony-maio/hn-oracle, st:0, Can a typed decision model find every prediction ever made on Hacker News, with calibrated probab...; apscot/The-Duel, st:0, Two fighters, one deadline: a typed-decision model races a general LLM in real time. Same state,...; area51b/laya, st:0; arshadakl/laya-local, st:0, A local-first Malayalam/English assistant for Windows that turns voice or text commands into safe...; ashp15205/decision-guard, st:0, Security & calibration middleware for System 1 AI models like jev & laya; atozuser0224/JevIs, st:0; bariskisir/JevTalks, st:0, A decision-driven chat experience, one character or common word at a time.; bharath-ui1027/laya-ui, st:0, Laya UI - Fast, non-autoregressive System 1 decision engine with multilingual support and UI comp...; bonsai/jeva, st:0, jeva — Jev analytics for GA4. GA4 イベントを Jev で組織打分・分析する Go PoC; calelamb/jevskillz, st:0, Calibrated TypeSafe Jev confidence checks for every step of coding with Claude Code — brainstorm,...; chihyu-wang-tsmc/Jevk5_study, st:0; cl2raul66/LayaDecisionMcp, st:0; clarencechien/jevlike, st:0; clawdreyhepburn/identity-jev, st:0, Type an identity concept in plain language; codesoda/kev-rs, st:0, Independent Rust runtime for Kev decision models : MLX on Apple Silicon and Candle CPU, parity-ga...; cvranjith/laya-first-look, st:0, Initial local testing of Laya on Apple M4, base checkpoints, no tuning; damienen/slop-alarm, st:0, Free, bring-your-own-key Chrome extension that estimates whether text was written by AI, using Ty...; danielmeppiel/apm-laya-triage, st:0, Read-only, reproducible Laya classification benchmark against the full microsoft/apm issue corpus; danna-zhou/jev-mcp, st:0, MCP server that lets Claude Code call a Jev-compatible System One API for classification/scoring/...; derickson/derickson-local-decision-model, st:0; dharm1287/laya-playground, st:0, A plain HTML and FastAPI playground for Laya survey predictions and Snake.; dongjune8931/JevOps-Lab, st:0, Jev랑 친해질거에요; doufuchuan/jev, st:0; dylanbstorey/laya-doom, st:0, An open-weights System 1 decision model playing Doom in a real-time control loop on Apple Silicon...; g31322543-crypto/poi-governance, st:0, POI data governance decision platform: Laya System-1 content gate + LLM System-2 dedup with expla...; ghobs91/vibe-check, st:0, Real-Time Ragebait & Spam Detection using Laya ONNX; ginvgo/jev01, st:0; gmassello/jev, st:0; griteshs6-boop/jev-model-router, st:0, Claude Code skill that uses Jev to route tasks to the best Claude model — Opus, Sonnet, Haiku, or...; gujiachun-rainbow/jev, st:0, jev; hakantapanyigit/jevascript, st:0, Semantic values for deterministic TypeScript: is, score and choose as ordinary values, backed by...; htpu/laya-tetris-ai, st:0, A Chrome Extension that plays chvin/react-tetris with local Laya decisions and a local heuristic...; hugomes14/laya-api, st:0; i1mT/jev-perf, st:0, Let jev play game.; iamarketings/jevttrade, st:0; iamtanay/heimdall, st:0, Self-hosted LLM gateway: prompt firewall + model router, powered by an open-source System-1 model...; insogao/jevdata, st:0; itsatgupta/Jev, st:0, JevDemo to compare which llm can do the job better; izhimu/pi-laya, st:0, Laya System 1 integration for pi and oh-my-pi — millisecond-scale safety guardrails, intent class...; jayeshvpatil/jev-agent-risk-gate, st:0, Can a non-generative 'System One' model safely gate an AI agent's shell commands? Four experiment...; jevaidev/jev-project-classifier, st:0, Classify and tag Jev projects, System One models, and real-world use cases with Jev. GitHub Stars...; jevido/jevidocs, st:0, I like fumadocs, but get physically nauseous when looking at react.; joaomottin/laya-test, st:0; joldibaev/laya, st:0; julianhintermann-cmd/JevControl, st:0; k-nishant09/Jevmodeldeployment, st:0, Jevmodeldeployment; karankessy/jevStudioPublic, st:0; karankulshrestha/jevpilot, st:0, This is the browser agent for automating the task on website in a fast and cheap manner.; kartikanand73/jev-decision-gateway, st:0, Governed decision-model gateway: TypeSafe Jev vs GPT-6 Sol vs rules on 100 withdrawals; kavehmz/mina-terrarium, st:0, Mina, an artificial person who feels time pass. TypeSafe Jev watches her body and senses every se...; kchaisor/jev, st:0; kenpeter/mario-laya, st:0, Mario RL: Laya VLM brain + Go-Explore chain + PPO on SuperMarioBros-Nes-turbo; kikutadev/openjev-avatar-preview, st:0, OpenJEV avatar motion preview; korbinjoe/laya-chrome, st:0; korra-pickell/JevRAG, st:0; krisvandecruys/JevRPS, st:0, Rock Paper Scissors against TypeSafe Jev in a terminal UI; kunalquantum/jev, st:0; kylejschultz/jevclaw, st:0, Plugin for enabling the use of Jev with Openclaw; lawrence3699/jevstyle.com, st:0, JevStyle — small, calibrated decision models; li-clement/Openjevtest, st:0; likhijogi/laya-issue-triage, st:0; ljxlck-source/jev-demo.com, st:0, jev-demo.com website; llrhino/jev-ui, st:0, Jev をフォームとJSONで分かりやすく試せるローカル用プレイグラウンド; lucasnpinheiro/laya, st:0; lytang63/OmniMed-Jev, st:0, Code for \"OmniMed-Jev: Calibrating LVLM Confidence for Trustworthy Medical Multimodal Decisions v...; mahabodi/mahabodi, st:0, Memory driven system-1 engine for AI agents. MahaBodi gives an agent two fast, non-generative fac...; mainhusharm/jevvybotte, st:0; manishhnnegi/laya-decision-engine-tutorial, st:0, Hands on Multilingual, non-autoregressive System 1 decision engine. Typed decisions over 100+ lan...; manul-audio/laya-service, st:0, FastAPI wrapper around Laya for Paperclip agent decisions; marlonthynk/jev-playground, st:0, experimentation with Jev; martinbowling/kev-voice, st:0, Local voice and text control for macOS: floating command bar, Accessibility action loop, and a lo...; marvinjude/ResumeFit, st:0, Jev Demo: Score a resume against a job description with LLM & Jev.; mateusgl8/laya-windows, st:0; matwate/super-research, st:0, Jev + Needle Research Agent; maxbergqv1st/JevDev, st:0; meddle-connect/jev-fault-diagnosis-poc, st:0, Fault Diagnosis PoC replays real compressor telemetry through an emulated Modbus device, spots su...; mikeboe/laya-ts, st:0; minicom365/laya-multilingual-dml, st:0, Laya Multilingual을 AMD RX 5700 XT + DirectML에서 구동·측정한 하니스. 밴드 어텐션·분할 reduction으로 긴 컨텍스트 4.3배 가속,...; msrshahrukh100/jev-resume-screener, st:0, Jev Resume Screener: open-source AI resume screening with TypeSafe's Jev decision model. YES/NO v...; mukishitsuu-png/awesome-jev-ja, st:0, TypeSafe Jev, System One モデルで作られたツール・事例の日本語まとめ。実測メモつき | Japanese curated list of Jev projects; mukti-sys/laya-, st:0; murongg/JevRepoTriage, st:0, A self-hosted GitHub issue and PR triage assistant powered by Jev; mwh0cker/jevjkxl, st:0; netologist/turbo-jev, st:0, Turbo JEV; nmnmcc/JevTrader, st:0, BTC 5-minute Polymarket trading worker powered by Jev; oboroge0/laya-mlx-windows-cuda, st:0, Run laya-mlx on Windows with an NVIDIA GPU via Windows-native MLX CUDA. Unofficial.; ooo-hq/fez, st:0, A small decision model improved through a Bittensor training competition.; open-ep/laya-neutron, st:0, jev for open source version using NXP IMX95 Neutron NPU; pawarbi/jev-bias-audit, st:0, Does Jev discriminate? A pre-registered counterfactual bias audit of the Jev decision model: code...; peter8015/FastAPI-JEV-demo, st:0, fastapi + jev test; piersonmarks/gamebot, st:0, A modular gameplayer that combines fast System 1 decisions with on-demand tactical and strategic...; poisson-labs/jev-replay, st:0, Measures whether TypeSafe's Jev decision model flips actions on identical replays near shipped th...; pradverma94/jev-bdd-automation, st:0, jev-bdd-automation; pranay-tecnomi/jev-2048, st:0, Watch the Jev decision model play 2048 live in your terminal; pumpkinfadly/laya-multilingual-playground, st:0; qingyu6688/jev-chat-jarvis, st:0, jev-chat-jarvis; r3sbarra/ec-dt, st:0, Universal Energy-Calibrated Decision Transformer: Provably Safe, Sub-Millisecond System 1 Decisions; r4topunk/jev-trading-study, st:0, Pre-registered test of the Jev AI decision model as a crypto trader on Base: 40,228 decisions, wo...; rabi/pi-laya-router, st:0; rai-wtnb/lab-jev, st:0, Go samples for trying TypeSafe's Jev API with the standard library only; razaowais6112/laya-turbo, st:0; resentful-hammerheadshark2276/reflex, st:0, Run AI classification and structured decision models locally on your GPU with confidence scores.; ringozzt/jev-research, st:0, Jev 技术趋势调研：GitHub 生态、工程应用与 Agent Harness 落地建议; risa-labs-inc/boss-plugin-jev, st:0, Ask a decision model structured yes/no, choice, and score questions from a BOSS sidebar panel or...; rithsila/Laya-Engineering, st:0; roryyu/laya-node, st:0; saapai/jev, st:0, Smart paste powered by LLM decomposition + Jev routing — document-aware form filling; santarosalia/laya, st:0; shaik-afridi-29/JevLab, st:0; shaul1991/laya-http, st:0, Self-hostable authenticated HTTP API for the Laya decision engine; shaunthebuilder/laya-snake-lab, st:0, Local Laya MLX Snake game, reproducible training, and visual paper; shettysaaproductions/Mark-LIII, st:0, J.A.R.V.I.S. Mark-LIII — Autonomous desktop AI with Laya local decision model & multi-agent system; shwetankg07/laya-playground, st:0, Three browser games that grade Laya, the open-source System-1 decision model, against exact groun...; shyamsridhar123/LAYA-RLCD, st:0, 🎮 Play. Learn. Respawn. Turn LAYA & ModernBERT Decoder into tactical AI pilots in Cinder Station,...; sirogarasu/codex_laya_dev, st:0; sitarelli/jev-ehr, st:0, Testing Jev speed; stefanus-ai-tech/wireshark-laya, st:0; synistr/JevBlock, st:0; tans/jevt, st:0, test tool base on jev; tehtommeh/laya-finetuning, st:0; that-daniel/laya-dino, st:0; thearpankumar/JevBrowserAutomation-poc, st:0; thuanlt/jev, st:0; tingyuansen/tetrisjev, st:0, A local language model plays Tetris: AnyJev on Qwen3 judges every placement, GPT when unsure; tjameswilliams/laya-r-mlx, st:0, Rust inference engine for Laya decision models on Apple silicon; torresnicolas0/laya-local-lab, st:0, Prueba local reproducible de Laya en Apple Silicon: 30 entradas, métricas, fallos y receta verifi...; tristankenney/laya-compaction, st:0, Verbatim context compaction for Claude Code, scored by a local Laya model; trysingh/Classification_Engine, st:0, Classify any dataset into N level using System 1 and System 2 models; ulevitsky/laya-infra, st:0, Configs to run Laya in Docker Compose or Kubernetes; userasg/LayaBasedAssistant, st:0; v3moreno/local-laya, st:0; v3moreno/omarchy-local-laya, st:0; vampirethoran/laya-decision-engine, st:0; vk-alto-none/laya-vs-dgpl-arena, st:0, AI vs AI Decision Arena: Laya vs DGPL System-1 & Jev; wantosure/JevHomeAssistant, st:0; wdlctc/jev-recommend, st:0, Jev-style typed decision models as recommenders: pointwise vs isolated-mask vs listwise LLM scori...; what-my-names/operit-jev-skills, st:0, Jev decision skills for agents that support skills . Skills give methodology and scenario indexes...; wiltonn/jevParser, st:0; xiangxinai/xiangxin-sdk-js, st:0, 象信 AI 官方 JavaScript / TypeScript SDK — The official TypeScript library for the 象信 AI System One API; xiangxinai/xiangxin-sdk-python, st:0, 象信 AI 官方 Python SDK — The official Python library for the 象信 AI System One API; xingchuan0105/JevWiKi, st:0; xrhuang10/jevolution, st:0, jev hackathon - san francisco 2026; xujialiang/laya-mario, st:0, laya-mario; zty2004/jev-xiangqi-lab, st:0, Self-trained Jev-style Xiangqi decision model with legal-move probabilities search opening data a.....\nMembers later promoted to full rows are also listed in some sweep lists above.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "groundingjev", "name": "GroundingJev", "author": "xyzzzh", "date": "2026-09-25", "category": "app", "url": "https://github.com/xyzzzh/GroundingJev", "base_model": "Qwen3.5-0.8B", "params": "0.8B", "metric": "", "license": "", "paper": "", "stars": 4, "likes": "", "detail": "", "arch": "Jev-inspired non-autoregressive visual grounding model, trained in-repo, on a Qwen3.5-0.8B multimodal backbone: continuous bounding-box regression replaces autoregressive coordinate decoding; a lightweight MLP head maps the last valid token's hidden state to normalized cxcywh coordinates in a single forward pass; training minimizes a weighted L1 and GIoU loss; head adaptation is followed by joint optimization of the language backbone, visual merger and regression head while the visual encoder stays frozen; trained with ms-swift, evaluated with EvalScope, Docker environment, optional SwanLab logging", "approach": "", "training": "", "data": "", "result": "Full test-set mIoU over the base Qwen3.5-0.8B: RefCOCO 72.83 to 78.26, +5.43 pp; RefCOCO+ 64.84 to 73.18, +8.33 pp; RefCOCOg 71.52 to 74.97, +3.45 pp; IoU@0.5 in the full report; online demo and local inference in the repo", "notes": "", "domain": "general", "score": "RefCOCO mIoU 78.26, +5.43 pp", "latency": "", "benchmark": "RefCOCO / RefCOCO+ / RefCOCOg test sets", "cost": "", "speedup": ""}
{"id": "infradi-demo-services", "name": "Infrai typed-decision demo services", "author": "Infrai, various authors", "date": "2026-09-25", "category": "app", "url": "https://github.com/KendrickBerg5327/fintech-usage-metering", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Eight small demo services showing typed decisions on Infrai's single-key REST billing, one key covering every capability: fintech usage metering, fintech usage metering in TypeScript, fintech FAQ suggest, fintech DNS risk gateway, game-event SMS cutover, gaming SMS suppression in TypeScript, gaming bounce suppression and a field-service agent failure ledger; zod validation, local suppression sets, moderation decisions and migration checklists", "approach": "", "training": "", "data": "", "result": "", "notes": "Members: KendrickBerg5327/fintech-usage-metering, WadeSterling7641/fintech-usage-metering-typescript, ZaneSterling2064/fintech-faq-suggest, elibennett278/fintech-dns-risk-gateway, ChrysostomHayes8537/game-event-sms-cutover, FinnOakley52946/gaming-sms-suppression-typescript, Lyle-00112/gaming-bounce-suppression-service, JensenCole4735/field-service-agent-failure-ledger", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jvlax-is-even-decision-model", "name": "is-even-decision", "author": "jvlax", "date": "2026-09-25", "category": "app", "url": "https://github.com/jvlax/is-even-decision-model", "base_model": "", "params": "421M", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Drop-in replacement for the is-even-ai npm package, same six functions, no API key, no cloud, no text generation", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "One forward pass of a 421M open-weights decision model on your own CPU, 1.7 GB, returning a calibrated probability that a number is even. A lineage table traces the is-even joke packages year by year.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "je-candidate-scorer", "name": "JE candidate scorer experiment", "author": "moX1-2", "date": "2026-09-25", "category": "app", "url": "https://github.com/moX1-2/qwen3.5-2b-text-candidate-scorer", "base_model": "Qwen3.5", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Bilingual candidate-scoring experiment inspired by TypeSafe Jev's typed-decision interface, explicitly independent of TypeSafe: where official Jev answers Choice, Score and Noul, this project implements single-correct-option candidate selection only, giving every candidate a score within the same question; supervised training with LoRA on a Qwen3.5 text backbone, public training pipeline, evaluation and four checkpoints", "approach": "fine-tune", "training": "", "data": "", "result": "", "notes": "Repo renamed or transferred since the 2026-09 sweep; URL updated to the current location", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-computer-use-jas", "name": "Jev Computer Use", "author": "JASGENIUS", "date": "2026-09-25", "category": "app", "url": "https://github.com/JASGENIUS/jev-computer-use", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Control a Windows PC by talking to it: press Right Ctrl plus Right Shift and speak multi-step instructions; it does each step in the order given and starts the first step before you have finished the sentence; includes JevFlow, a dictation tool that types what you say into the current window with um, restarts and no-wait corrections removed; both share one speech model in one process", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "codaaiteam-jev-games", "name": "Jev Pac-Man and Jev Wikiracer", "author": "codaaiteam", "date": "2026-09-25", "category": "app", "url": "https://github.com/codaaiteam/jev-pacman", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Pair of browser games played against live TypeSafe Jev: Pac-Man, you drive the keyboard while Jev drives the ghosts, one API call per turn picks both directions with the confidence behind each; Wikiracer, you race Jev across Wikipedia from the same start article, Jev makes one real call per hop picking the link closest to the target out of dozens; every decision is a typed, calibrated choice in about 100–300 ms; single dependency-free index.html, no build; playable live at jevtypesafeai.com/games", "approach": "", "training": "", "data": "", "result": "", "notes": "Second repo: https://github.com/codaaiteam/jev-wikiracer", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-pokemon", "name": "Jev plays Pokemon Red, christianmat", "author": "christianmat", "date": "2026-09-25", "category": "app", "url": "https://github.com/christianmat/jev-pokemon", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 45, "likes": "", "detail": "", "arch": "Jev, TypeSafe AI's decision model, plays Pokemon Red with no scripts or cheats: a Node Game Boy emulator reads RAM into a game state, map, party, battle and on-screen text; the harness lists the legal options with some facts about each, type matchups, damage estimates, distance toward the objective, and Jev picks one; Jev decides every menu answer, names, starter, yes/no, shop, heal, learn/forget moves, what to focus on, progress, heal, train, catch, shop, explore, team, which Pokemon to catch and which to swap in and out at the PC, where to go and who to talk to, and every battle action, move, switch, item, ball, run; the harness never decides: it only reads memory and presses buttons, A* pathfinding and menus, and never writes to game memory", "approach": "", "training": "", "data": "", "result": "The Jev Plays Pokemon Red live stream hit Hacker News on 09-25; you need your own legally obtained copy of Pokemon Red, no ROM is included or distributed", "notes": "Landing page in site/ of the repo. Separate from valentynkit/jev-plays-pokemon-red, row valentynkit-jev-plays-pokemon-red, which runs the same idea on PyBoy with code owning the route.", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dgr8akki-jev-voice", "name": "Jev Voice, dgr8akki", "author": "dgr8akki", "date": "2026-09-25", "category": "app", "url": "https://github.com/dgr8akki/jev-voice", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "Say it the way you would say it: Open the Britannica one, type hello in the email bar, surname Pahuja. Commands such as go back or scroll down act before you finish, running as soon as they are unambiguous, often mid-sentence. It fills in forms, picking the right field from its label, typing the right words, and submitting when you say and press enter; it takes corrections, such as No, that's the first name, not the surname, which moves what it just typed; and it shows what it is doing, with a cursor gliding to the element and outlining it before clicking or typing. The pipeline is speech word by word, a snapshot of the visible links, buttons and fields, then one Jev call choosing among the candidates. Private by default: on-device speech recognition where Chrome supports it, with no accounts and no analytics. MIT, with CI.", "arch": "A Chrome side-panel app, Manifest V3, for Chrome 116 or newer, that controls Chrome by voice: open sites, search, click links and fill in forms by saying what you want, built on TypeSafe's System One model Jev, where Jev never writes a URL, a CSS selector, or the text to type, because it does not generate text: code lists the candidates, and Jev picks one, answering typed questions with probabilities in a single call.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-cua", "name": "jev-cua, edgeteamio", "author": "edgeteamio", "date": "2026-09-25", "category": "app", "url": "https://github.com/edgeteamio/jev-cua", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Voice-driven Mac computer use on TypeSafe's Jev: speak a command and the app fires the action while you are still talking; macOS 26 plus Xcode 27 toolchain, TYPESAFE_API_KEY in a .env; build, test and doctor scripts for permissions, speech model and key presence", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-gui-delegate", "name": "jev-gui-delegate", "author": "YUTA-fywoo", "date": "2026-09-25", "category": "app", "url": "https://github.com/YUTA-fywoo/jev-gui-delegate", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 35, "likes": "", "detail": "", "arch": "AI-assisted Windows and Chrome GUI delegation for Codex: the main model delivers a task contract, goal, allowed actions, scope, inputs, steps and success conditions; a local controller executes deterministic steps, Chrome through the host-provided official browser session and Windows through UI Automation, and calls TypeSafe Jev only for semantic choices, then checks the actual result at every step; progress is saved with recovery, pause, cancel and user takeover; identity and scope are checked before an action and the real value or page state after; usage and failure causes are recorded separately; zero-model-call paths are recorded as zero, not counted as Jev successes", "approach": "", "training": "", "data": "", "result": "Two synthetic runs of 4 tasks and 24 actions each on 09-23: the main model Astra's tokens drop from 2,266,693 to 236,295, -89.6%, in run one and from 2,256,091 to 192,752, -91.5%, in run two, both runs 4/4 completed, end-to-end task rate 3.05x the baseline", "notes": "Personal design project, AI-assisted implementation; Jev, the TypeSafe SDK, Codex and the browser extension are upstream products.", "domain": "computer-use", "score": "-89.6% / -91.5% main-model tokens", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aj604-jev-chain", "name": "jev-jev-jev-jev", "author": "aj604", "date": "2026-09-25", "category": "app", "url": "https://github.com/aj604/jev-chain", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Chains of thought, minus the thought: Jev does not write prose, you give it state and typed questions, pick one of these, rate this 0–4, is this true, and it returns calibrated probabilities in tens of milliseconds, so a chain of Jev calls is a graph of decisions where each node asks something and the answers pick the path. Typed end to end: choice over a label set answers with the union type, routes must handle every label checked at compile time, and chain of a and b only compiles if a output fits b input.", "arch": "Typed TypeScript framework for composing Jev calls into decision graphs, plus a studio for building the graphs and watching them decide", "approach": "", "training": "", "data": "", "result": "", "notes": "Jev does not write prose: you give it state and typed questions and it returns calibrated probabilities in tens of milliseconds, so a chain of Jev calls is a decision graph, not a pipeline of prompts", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ztrura-jev-medqa", "name": "Jev-MedQA", "author": "Ztrura", "date": "2026-09-25", "category": "app", "url": "https://github.com/Ztrura/Jev-MedQA", "base_model": "Qwen3.5-4B", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "Medical exams and image-based questions often ask for one answer from a short list, which diagnosis best fits this case, or which finding appears in this image, and a generative model may produce several paragraphs before giving its choice, adding time to every question. For a question with options A through D, Jev-MedQA compares the four option scores and returns the highest-scoring letter, and also provides the scores for the other candidates. The same method handles written medical questions, clinical cases, and questions with one or more images, with faster answers across all nine benchmark sets. Bilingual Chinese and English, with evaluation and reproduction documentation.", "arch": "Answers medical questions without generating a full response: it reads the model's scores for the candidate options in one forward pass, one evaluation of the question and any accompanying images, without repeatedly generating new tokens, selecting the answer directly and bringing the Jev paradigm to medical question answering, where you supply evidence, a question, and the allowed answers, and receive a choice from that set, using Qwen3.5-4B without additional training and the same model for clinical text and medical images.", "approach": "", "training": "", "data": "", "result": "Across 153,889 questions from nine medical QA benchmark sets: 69.42% accuracy, versus 67.41% when the same Qwen3.5-4B generates its answer in the usual way; average time per question falls from 1.891 s to 0.182 s, a 10.37× speedup; on MedXpertQA-MM it takes 1/69.87 of the generation time.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hazlema-jev-riffs", "name": "jev-riffs", "author": "hazlema", "date": "2026-09-25", "category": "app", "url": "https://github.com/hazlema/jev-riffs", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Music pattern ripper: tokenize a MIDI file into an interval string, mine it in code for repeating units, then Jev grades each candidate motif for musical significance", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Web UI draws the piano roll, highlights where each motif occurs and ranks motif cards by significance. Demo rips the Swan Lake theme.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "adams-jev-search", "name": "jev-search, decision-only", "author": "AnthonyDavidAdams", "date": "2026-09-25", "category": "app", "url": "https://github.com/AnthonyDavidAdams/jev-search", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Agentic search where the model only makes decisions: fetch and parse deterministically, free; extract candidates, every link as anchor text plus URL and every email, phone and matching line; score all of them with one calibrated question per candidate in a single request; follow everything above the bar; the decision is a few tokens while the page is tens of thousands, so nearly everything paid for is reading, and this inverts that", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevcompass", "name": "JevCompass", "author": "acidkill", "date": "2026-09-25", "category": "app", "url": "https://github.com/acidkill/JevCompass", "base_model": "optional Jev ranking through OpenRouter", "params": "", "metric": "Advisory hook delivery, install and doctor checks in isolated environments", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Gives Codex a map of the tools and skills it already has: discovers installed candidates locally, narrows them with a reviewed catalog, and offers a short suggestion when a task has a clear match; works with Codex Desktop and CLI hooks without replacing the agent or gating everyday commands. pipx install jevcompass, version 0.1.15 on PyPI; install backs up the existing hooks.json and registers two advisory hooks with no PreToolUse gate, then doctor confirms local registration; suggestions never run tools, grant permissions or override required checks; local advice works without a key and the optional Jev ranking sees only coarse candidate metadata through OpenRouter. Source-only experiment: two small local skills for focused tests and regression review, copied into the Codex skill directory, never changing hooks, executing scripts or calling Jev; identical repeats are no-ops", "approach": "", "training": "", "data": "", "result": "Verification is deliberately limited to the environments tested: a fresh isolated Codex CLI 0.155.1 smoke with the published v0.1.12 wheel received openai-docs advice before its first tool; the initial four blinded synthetic CLI pairs tied on task quality and later focused pairs had mixed results including one baseline win and one treatment win; one native Desktop explorer child reported an advice ID before its first tool in a correlated smoke; the v0.1.15 package installed from the registry in an isolated Python 3.11 profile registered two hooks and doctor passed; the v0.1.14 source passed an isolated agent-spawn source check and a clean synthetic delivery check. No repeatable speed or quality improvement has been demonstrated; macOS runtime behavior and broad usefulness remain unverified; the checks do not establish that recommendations improve outcomes", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jeverifier", "name": "JeVerifier", "author": "silvariasereneblossom", "date": "2026-09-25", "category": "app", "url": "https://github.com/silvariasereneblossom/jeverifier", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Coding harness that puts Jev, TypeSafe's fast classifier, under a Claude coding session so the session can check more for little cost and point its reading at what a task needs: reading lists instead of reading every doc at session start, session digests instead of carrying a whole conversation into the next session, and doc-contradiction plus code-rule checks that, after one full review, only look at what changed; Jev only selects, ranks and labels, never writes summaries or code, so it can leave something out but cannot invent anything; every review is recorded so it is not paid for twice", "approach": "", "training": "", "data": "", "result": "First measured milestone: reading lists did not reduce what agents read, since agents told to read everything already pick out about a fifth of the docs and all doc reading is 6–12% of the token bill; the measured value so far is in the checks", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevguard-tecanmol", "name": "JevGuard", "author": "tecanmol", "date": "2026-09-25", "category": "app", "url": "https://github.com/tecanmol/JevGaurd", "base_model": "TypeSafe Jev", "params": "", "metric": "Ad probability per page element against a configurable threshold", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Browser extension that detects and removes ads using Jev semantic classification instead of only fixed ad-blocking rules: a content script scans the page for elements that look like ads by class names, ad-network links, iframes, data attributes and layout patterns, turns each candidate into a compact description rather than sending the whole page, and batches up to 30 candidates per request; Jev returns a probability that each one is an ad, and anything at or above the configured threshold is removed or highlighted depending on the mode; it picks up ads that load dynamically or appear while scrolling, avoids re-classifying the same element twice, retries temporary API failures with backoff, and shows per-page and total counts of blocked ads; manual unpacked install from source on Chrome, Edge or another Chromium-based browser", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-sap-commerce", "name": "jevintegration for SAP Commerce", "author": "Emenowicz", "date": "2026-09-25", "category": "app", "url": "https://github.com/Emenowicz/jev-sap-commerce", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 7, "likes": "", "detail": "", "arch": "jevintegration, an SAP Commerce extension that lets Jev, TypeSafe's typed-decision model, make narrow decisions about shop text: it moderates product reviews, suggests product categories and suggests classification attribute values; Jev does not write text, it answers typed questions, yes/no, pick one, score, with probabilities, and the extension's Java code decides what to do with them; anything Jev is not sure about is left for a person, and you measure it on your own data before it changes anything", "approach": "", "training": "", "data": "", "result": "7 stars at sweep time", "notes": "Independent project, not made by or affiliated with TypeSafe or SAP.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevlin-designmon", "name": "jevlin", "author": "designmon", "date": "2026-09-25", "category": "app", "url": "https://github.com/designmon/jevlin", "base_model": "Jev via OpenRouter, bring your own key", "params": "", "metric": "recall@10 and precision@10 on 33 real find-the-code tasks; ask accuracy on build output; cost per 400 questions", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Fast judgment calls for coding agents: jevlin filter narrows hundreds of candidates to the few that matter, jevlin ask gives a yes or no over a blob of text where the exit code is the answer, jevlin raw passes state plus questions straight through, and jevlin check resolves and verifies the key; git ls-files piped to filter, rg TODO piped to filter with a minimum probability, or the tail of a build piped to ask are the headline use cases. It is built on Jev, which returns calibrated probabilities and cannot write text, so it replaces none of the agent work, only the judging. init walks you to an OpenRouter key, checks it works before writing anything, saves it to dot config jevlin env at chmod 600 and names the key source on every receipt; there is exactly one network call in the whole package, to openrouter.ai, no analytics, no telemetry; npm install -g jevlin", "approach": "", "training": "", "data": "33 genuine find-the-relevant-code tasks from a developer own agent sessions", "result": "400 questions in about 1.5 seconds for $0.0004. Measured on real work, it is weak: recall at 10 0.15, precision at 10 0.14, at least one correct file in the top 10 on 58%, ground truth being the files the work went on to edit; the repo publishes that an earlier synthetic benchmark using commit messages as queries reported 0.61, which overstates the tool four times, and keeps both numbers because the gap is the most useful thing in the repository. ask holds up on the same measurements: 0.98 on a passing build, 0.01 on a failing one, in under half a second. Stated rules: if grep can express the question, use grep; below about 30 candidates, just read them", "notes": "", "domain": "coding", "score": "", "latency": "about 1.5 s for 400 questions", "benchmark": "33 real find-the-code tasks", "cost": "about $0.0004 per 400 questions", "speedup": ""}
{"id": "jevscout-mcp", "name": "JevScout, MCP result condenser", "author": "yctimlin", "date": "2026-09-25", "category": "app", "url": "https://github.com/yctimlin/JevScout", "base_model": "lexical selection; optional Jev ranking, off by default", "params": "", "metric": "Pre-registered evaluation rounds for the Claude Code hook and the Codex proxy", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Opt-in Claude Code PostToolUse hook for mcp tools: when an MCP tool such as GitHub, Notion, Linear, Slack or a fetch server returns a large text result over 8000 bytes, the hook replaces it with a compact packet of at most 6000 bytes, the most relevant parts verbatim with gaps marked and a command to recover any omitted text exactly; small results pass through untouched; when a result exceeds the Claude Code token limit, the hook condenses the saved copy instead, reading it only from the current session tool-results directory; segments by shape, JSON records first; installs into project or user settings with a backup, uninstalls only its own entries; Node 24 and above, tested with Claude Code 2.1.281; independent open-source software, not an official TypeSafe product", "approach": "", "training": "", "data": "", "result": "Pre-registered evaluation: the Claude Code MCP hook passed two pre-registered rounds and is recommended as an opt-in; Jev ranking in the hook showed no measurable gain over lexical selection in their tests; the Codex MCP proxy failed the pre-registered rule in both rounds and is not recommended; the shell hook that rewrites gh and curl through the filter is experimental, spike-tested only, and installs only with the with-shell flag; local search, github and check commands had mixed pilot results", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-web-app", "name": "Laya Decision Engine Web", "author": "meossistant", "date": "2026-09-25", "category": "app", "url": "https://github.com/meossistant/laya-web-app", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "An interactive Vietnamese web runtime for the Laya model, convaiinnovations/laya, the non-autoregressive System 1 decision model family; interface styled after Jev, TypeSafe AI; supports 100% offline and air-gapped deployment.", "arch": "A premium Vietnamese web runtime for Convai's Laya decision model, a non-autoregressive System One model: Jev-styled interface, 100% offline and air-gapped deployment with no internet needed, where the model takes a state, JSON, email, document or logs, and returns structured decisions in the standard three types instead of generating text token by token", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-studio-jiaheng", "name": "Laya Studio, Windows client", "author": "jiaheng6", "date": "2026-09-25", "category": "app", "url": "https://github.com/jiaheng6/laya-studio", "base_model": "Laya", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Windows desktop client for deploying and using Laya: Tauri 2 plus Rust plus React plus TypeScript plus shadcn/ui, the model runs in a separate Python process, no client account; workbench for classification, scoring, yes-no and multi-question editing with JSON mode, probabilities, latency, templates and export; model management for multilingual, English and specialty builds with fixed snapshots, separate CPU and NVIDIA GPU environments, download, import, verify, repair and uninstall; local API service, POST /v1/systemone, not a chat-completions endpoint", "approach": "", "training": "", "data": "", "result": "", "notes": "Independent client, not an official Laya product; Windows x64 only at this stage.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-tetris-nickyqqq", "name": "Laya Tetris Decision Lab", "author": "nickyqqq", "date": "2026-09-25", "category": "app", "url": "https://github.com/nickyqqq/laya-tetris", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Watch a local AI choose where every Tetris piece lands: live candidate previews, model probabilities, board metrics, inference timing and the exact request and response behind each move; Laya plus FastAPI plus vanilla JavaScript; play automatically or step through individual decisions and compare four candidate placements", "approach": "", "training": "", "data": "", "result": "", "notes": "Distinct from the rehman-ali laya-tetris entry.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-vision-stitch", "name": "Laya Vision Stitch", "author": "DanielTea", "date": "2026-09-25", "category": "app", "url": "https://github.com/DanielTea/laya-vision-stitch", "base_model": "pretrained vision plus Laya with small LoRA adapters", "params": "", "metric": "Recorded-action NLL on held-out D2E games, click-position agreement, screenshot-to-input latency in live Hordes trials", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "One local screenshot-and-goal model combining pretrained vision, a learned visual connector, Laya with small LoRA adapters, and action outputs; it exports as one checkpoint and runs without Qwen language decoding, generated captions or a reference-image bank; the original pretrained backbone weights stay frozen; current status and all research findings live in docs/STATUS.md, with general-model notes in docs/GENERAL_MODEL.md, experiments in docs/IMPROVEMENT_EXPERIMENTS.md and production readiness in docs/PRODUCTION_READINESS.md", "approach": "fine-tune", "training": "LoRA on 24 public D2E games; action decoder fine-tune on 58k frames of public D2E gameplay", "data": "24 public D2E training games, 5 fully held-out games, Hordes as unseen test only", "result": "LoRA trained on 24 public D2E games lowers recorded-action NLL on all five fully held-out games by 3–12%; a RADIO click-position head beats clicking the screen center on four of those five games; a goal-conditioned pointer trained on Molmo points doubles agreement on unseen games. The dual system runs Molmo-7B as a slow planner at 3–5 seconds per answer beside the 20 ms controller with a RADIO feature tracker keeping its target up to date, and in a 60-second live Hordes trial the dual system selected a monster by clicking it with a median of 36 ms from screenshot to input, never attacking because no ability key was pressed. Conditioning the controller on hindsight click targets did not teach it to act on a target; instead the planner now approaches with WASD and clicks the skill-bar icon Molmo points to, and in two live runs Molmo found no monsters, so those actions are not yet tested in play. On held-out D2E games not in the Open-P2P training data, the pretrained policy alone does not beat repeating the previous action; fine-tuning its action decoder on 58k frames of public D2E gameplay cuts held-out-session NLL by 24% and reaches the persistence baseline, while flow-matching chunks, real-time chunking, goal guidance, a slow planner, elapsed-time memory, foveation, latent actions, a world model, outcome weighting and the 300M checkpoint gave no qualifying gain", "notes": "The planner stack has only been tested on Hordes", "domain": "gaming", "score": "", "latency": "20 ms controller, 3–5 s slow planner, 36 ms median screenshot to input in the live trial", "benchmark": "held-out D2E games + live Hordes", "cost": "", "speedup": ""}
{"id": "laya-ignite", "name": "laya-ignite", "author": "sirgio03", "date": "2026-09-25", "category": "app", "url": "https://github.com/sirgio03/laya-ignite", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "From zero-shot criteria to specialized, calibrated sub-35ms Laya decision heads in 30 seconds; the philosophy: do not force a slow 70B generative model to make reflex decisions, let System-2 bootstrap System-1 once and deploy pure speed; it targets Laya's two practical problems, the cold-start dilemma where zero-shot base models hover near chance on complex domain routing without hundreds of labeled examples, and the negation blindspot, Laya issue #377, where standard zero-shot classification misfires on Do NOT cancel my account", "approach": "", "training": "", "data": "", "result": "Official Discussion #495 on NandhaKishorM/laya; the author was building Laya's high-throughput batch inference endpoint in PR #387; Discussion #347 showed Laya trained on custom tasks beating commercial alternatives at lower latency", "notes": "35 ms is Laya's zero-data-egress forward-pass figure from the Laya project.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-mc-bot", "name": "laya-mc-bot", "author": "LixiaoLeo123", "date": "2026-09-25", "category": "app", "url": "https://github.com/LixiaoLeo123/laya-mc-bot", "base_model": "Laya on a GPU box", "params": "Laya 421M", "metric": "Strategy decisions for managed Minecraft players", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Admin-managed AI players for Paper 26.2 Minecraft: mineflayer protocol clients; Laya on a GPU box runs the strategy through a prompt plus typed output_format plus conversion_rule; an external LLM harness over MCP writes the strategies and plans, handles chat addressed to the bot, and investigates report bits; a thermal gate keeps the box at or below 65 degrees C", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gliner2-demo-space", "name": "open-jev gliner2 demo", "author": "shreyask", "date": "2026-09-25", "category": "app", "url": "https://huggingface.co/spaces/shreyask/open-jev-gliner2-demo", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Typed decisions in the browser on WebGPU: the open-jev demo extended with GLiNER2 models, fastino/GLiNER2.5-Decide and onnx-community/GLiNER2.5-Decide-ONNX, alongside open-jev-deberta-v3-large-ONNX, kev-0.6b-ONNX and kev-4b-ONNX", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pi-laya-skill-router", "name": "pi-laya-skill-router", "author": "ericmjl", "date": "2026-09-25", "category": "app", "url": "https://github.com/ericmjl/pi-laya-skill-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Laya-powered skill autorouter for the pi coding agent: laya, 421M parameters, ~35 ms per forward pass, calibrated probabilities, runs locally on Apple MPS, scores all skills on every turn and injects the winners verbatim into the context, replacing pi's default of listing every skill's name and description in the system prompt and trusting the main model to read the SKILL.md when relevant; components: a FastAPI sidecar that loads the checkpoint once and scores skill batches in one forward pass, a pi extension that routes the prompt through the sidecar on every before_agent_start, a scanner that collects name and description from all SKILL.md frontmatter into skills.json, and a session miner that builds an eval dataset from pi session logs using which skills the model actually read via read as ground truth", "approach": "", "training": "", "data": "", "result": "Optional nightly fine-tuning loop on your own sessions; your data stays on your machine", "notes": "Directly usable with pi; the router is a pi extension plus a local sidecar.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "quicksilver", "name": "Quicksilver", "author": "UditAkhourii", "date": "2026-09-25", "category": "app", "url": "https://github.com/UditAkhourii/quicksilver", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 36, "likes": "", "detail": "", "arch": "Claude Code skill that hands bulk judgment calls to Jev: a big share of a session is spent reading things only to decide whether they matter, which of these 187 files handle auth, which of these 3,000 log lines are real failures, which of these 200 tickets are refund requests; Jev returns typed verdicts, yes/no, a label, a score, in about a second at $0.042 per million tokens, and Claude gets back a shortlist and spends its tokens on the thinking only it can do; one-line install, npx github:UditAkhourii/quicksilver, the installer copies the skill into ~/.claude/skills and asks for the Jev key once; Claude uses the skill on its own whenever a task looks like read a lot to decide a little", "approach": "", "training": "", "data": "", "result": "86% fewer Claude tokens on a 12-task benchmark and up to 20x faster; the 187-file auth example costs ~2.4k tokens instead of ~26k", "notes": "", "domain": "general", "score": "-86% Claude tokens, 12-task benchmark", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "superinstance-quilt-tools", "name": "quilt-tools", "author": "SuperInstance", "date": "2026-09-25", "category": "app", "url": "https://github.com/SuperInstance/quilt-tools", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Status Phase 1, scaffold, complete: fleet-pager, SRE paging with hysteresis band discipline, 7/7 checks; ledger-seal, a tamper-evident append-only witness ledger, 6/6; ocean-recall, vector-note recall with honest forget, 7/7; triagedesk, support triage routing, 8/8; budget-tide, cash-flow envelopes with dry-envelope fences, 8/8; home-ecos, home ecosystem automation, 6/6; driftwatch, config drift detection, 7/7; approvals, spend approvals with tier-based routing, 9/9; habit-atlas, habit streaks with momentum physics, 8/8; and pipeline-guard, pipeline row validation plus dead-letter replay, 9/9. The layout: tools/ holds the ten self-contained prototypes, each ending in a check harness; src/toolkit.mjs is the shared harness with sheet, check, done, SysOne, a deterministic embedder, and witness-chain helpers; vendor/quilt-core is a vendored @quilt/core build so the tools run hermetically, and the springboard lab can mutate a copy without touching the upstream repo; cards/ holds the per-tool claim, role, origin cards, the ten working tools proof deck; and site/index.html is the tool-picker UI. The springboard lab mandate for Phase 2 and beyond: these prototypes plus the vendored engine are the substrate for JEV/MothQuantum experiments, with instance-graph mutations as sheet diffs, midden harvesting as cell provenance, and ZPP certification runs.", "arch": "Ten working tool prototypes built on the Quilt reactive spreadsheet engine, plus a springboard lab for JEV/MothQuantum experiments: all ten tools run offline, on SysOne heuristics with no network, and self-check.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "f1-rb21-driver", "name": "RB21 self-learning driver", "author": "agcodin", "date": "2026-09-25", "category": "app", "url": "https://github.com/agcodin/f1-self-driving", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A neural driver that teaches itself to drive a 2025-spec Red Bull RB21 around scale-accurate, elevation-aware reconstructions of Monza, Spa-Francorchamps and the Circuit of the Americas, in the browser with the car visible on track while it learns; the driver policy is Jev-shaped, a typed-decision policy trained by evolutionary search; tools/serve.py sends no-store so a cached app.js against fresh weights cannot silently drive with an untrained policy", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dgr8akki-recipe-mode", "name": "Recipe Mode", "author": "dgr8akki", "date": "2026-09-25", "category": "app", "url": "https://github.com/dgr8akki/recipe-mode", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "Works on any recipe site: it reads the schema.org recipe data most sites publish for search engines, with a fallback for simple pages. It talks naturally, answering What was that and go to the step where I pour it into the tin, and ignores honey, pass the salt, because that was not for you. It answers out loud and highlights the current step on the page, so you rarely need to look. Smart timers: set a timer uses the time in the current step, and when a step mentions two times, it picks the one you meant. Simple commands like next act before you finish the sentence. Private by default: on-device speech recognition where Chrome supports it, with no accounts and no analytics. MIT, with CI.", "arch": "A Chrome extension, Manifest V3 for Chrome 116 or newer, that cooks hands-free on any recipe page: say next, how much butter, or set a timer while your hands are covered in flour, built on TypeSafe's System One model Jev, where Jev does not generate text, it answers typed questions with probabilities, so it never makes anything up, and every step, ingredient and time the assistant reads to you comes straight from the recipe.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "deepansh-saxena-jev-voice-guardrail", "name": "Relay Guardrail Lab", "author": "deepansh-saxena", "date": "2026-09-25", "category": "app", "url": "https://github.com/deepansh-saxena/jev-voice-guardrail", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A local, synthetic subscription-support lab comparing TypeSafe Jev with a separately configured structured-output LLM judge. React + TypeScript + Vite, Node + TypeScript. Native Azure OpenAI Realtime WebRTC audio; backend sideband o", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rhythmforge", "name": "RhythmForge", "author": "huzjie", "date": "2026-09-25", "category": "app", "url": "https://github.com/huzjie/rhythmforge", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": 0, "detail": "A realtime decision engine and lookahead safety planner framework, inspired by the open-sourced NeoHorse-Jev-4B realtime decision model from tokenrhythm.ai. Python 3.9+, Apache-2.0, zero dependencies.", "arch": "A realtime decision engine plus lookahead safety planner framework, in Chinese, inspired by the open NeoHorse-Jev-4B realtime decision model: for continuous long-horizon scenarios such as snake, realtime navigation, game AI, trading matching and robot obstacle avoidance, it lets a lightweight decision model score each candidate action at every step while a lookahead planner keeps it from closing off its own future; three independently replaceable parts, a decision-model backend, the realtime engine and the lookahead planner", "approach": "", "training": "", "data": "", "result": "", "notes": "Built around the neohorse-jev-4b row, TokenRhythm/NeoHorse-Jev-4B.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "scs", "name": "SCS", "author": "leonardoventurini", "date": "2026-09-25", "category": "app", "url": "https://github.com/leonardoventurini/scs", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Headless code-intelligence service: indexes source repositories and lets coding agents investigate them through a local MCP tool; an agent asks query_code a question and SCS returns bounded evidence from its structural and semantic index; starts with an empty index and enrolls a repository only after an explicit CLI, MCP or client request, never modifying repository source; stable releases for Apple Silicon macOS and x86-64 Linux on CPython 3.14; indexing needs an embedding provider, default the OpenAI embeddings API", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "skimmer", "name": "skimmer", "author": "arczhi", "date": "2026-09-25", "category": "app", "url": "https://github.com/arczhi/skimmer", "base_model": "Local decision model, onnx 150M int8 through mlx 1.7B variants", "params": "", "metric": "Per-sentence importance bands, document percentiles", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Local-first reading tool: a decision model scores every sentence of a document into four importance bands, core, important, minor and skippable; the reader can ask questions at read time such as key numbers, action items and risks; per-sentence scores aggregate to chunk scores and then to document percentiles; supports PDF, docx, doc, txt and md; fully local", "approach": "", "training": "", "data": "", "result": "Measured on the shipped backends: onnx 150M int8 at 153 MB runs 131 ms per sentence on CPU, CUDA or DirectML; mlx 1.7B at 117 ms; batch mode at 75 ms; batch 4-bit at 934 MB and 83 ms", "notes": "", "domain": "general", "score": "", "latency": "75–131 ms per sentence by backend", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dgr8akki-slop-radar", "name": "Slop Radar", "author": "dgr8akki", "date": "2026-09-25", "category": "app", "url": "https://github.com/dgr8akki/slop-radar", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "See which LinkedIn posts read like AI slop before you read them. Each post gets a quiet label as it scrolls into view: human, unclear or AI slop, with the reasons one hover away", "approach": "", "training": "", "data": "", "result": "", "notes": "Manifest V3 Chrome extension, MIT, CI on GitHub Actions", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "social-credit-cayman", "name": "Social Credit", "author": "cayman2142", "date": "2026-09-25", "category": "app", "url": "https://github.com/cayman2142/social-credit", "base_model": "Jev via OpenRouter or TypeSafe directly", "params": "", "metric": "Jev exam: accuracy, confidence and latency on 30 labelled scenarios", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Joke desktop app and a real field test of Jev: every few seconds the app looks at your screen and your camera, turns what it sees into a few lines of text on your computer, and asks Jev one question, is this comrade working; work earns social credits and praise, Shorts, TikTok and doomscrolling cost you, and hitting zero expels you from the Party. Sensors: the active window name and title, seconds since the last key or mouse event with keys never read, OCR of the active window at up to 1500 characters plus how much changed and video-like motion, and camera face presence and gaze; text only, nothing leaves the computer before Jev. Windows setup and portable exe, macOS arm64 and x64 dmg, unsigned builds; OpenRouter key recommended or TypeSafe directly. The Jev exam tab sends 30 labelled scenarios covering Figma, VS Code, Shorts, TikTok, phone in hand and nobody at the desk, and shows accuracy, confidence and latency at a fraction of a cent; every check in the log shows exactly what was sent to Jev and every probability it returned; thumbs up and thumbs down save your correction to labels.jsonl", "approach": "", "training": "", "data": "30 labelled exam scenarios", "result": "npm run eval reproduces the 30-scenario Jev exam from a terminal with your key", "notes": "Bilingual English and Russian documentation", "domain": "social", "score": "", "latency": "", "benchmark": "30-scenario Jev exam", "cost": "", "speedup": ""}
{"id": "tenjin-agent", "name": "Tenjin agent", "author": "BackTrackCo", "date": "2026-09-25", "category": "app", "url": "https://github.com/BackTrackCo/tenjin-agent", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "A tool router for coding agents: the Tenjin router watches the moments where a tool could help, your prompt, your agent's web searches and page fetches, and the tasks it hands to subagents; when a curated tool beats what the agent was about to do, the router suggests it and the agent calls it; the wallet pays for each call through x402, USDC on Base, so no API keys or accounts; Jev provides the routing judgment, deciding when a tool call helps", "approach": "", "training": "", "data": "", "result": "Show HN on 09-25: Tenjin, a Jev-based x402 tool router for Claude Code", "notes": "No API keys, no pile of MCP servers, no rules to write.", "domain": "routing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "truco-laya", "name": "Truco Laya", "author": "guillermojmontenegro-hub", "date": "2026-09-25", "category": "app", "url": "https://github.com/guillermojmontenegro-hub/TrucoLaya", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Web game of Argentine truco for 2, 4 or 6 players: a human takes the first seat and the rest are AI players controlled by Laya; teams alternate around the table, even seats against odd; Python 3.10+ and Node 20.9+; Spanish docs", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "truffler", "name": "Truffler", "author": "kieranklaassen", "date": "2026-09-25", "category": "app", "url": "https://github.com/kieranklaassen/truffler", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 25, "likes": "", "detail": "", "arch": "Ruby/Rails Jev integration for intent and exact-text query labeling: the benchmark loads fixtures into its own SQLite database and labels them through the real pipeline, then prints a JSON report with recall and precision for intent and exact-text queries, keystroke p50 and p95 latency, labeling throughput, cost per labeled record and per query, packed-batch agreement and the adopted batch size, prompt-injection checks for labels and rerank, and rerank bucket counts; it exits non-zero when a check fails; bench/params.yml holds the tunable thresholds, boosts and blend weights", "approach": "", "training": "", "data": "", "result": "CI replays committed cassettes, bundle exec rake truffler:bench; MODE=record with a TYPESAFE_API_KEY records live Jev runs; 25 stars at sweep time", "notes": "Rails gem style; the benchmark is the centerpiece.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "xiangxinai-skills", "name": "Xiangxin AI agent skills", "author": "xiangxinai", "date": "2026-09-25", "category": "app", "url": "https://github.com/xiangxinai/skills", "base_model": "Xiangxin One, a hosted System One decision API", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Agent skills that teach coding agents such as Claude Code, Codex and Cursor to route classification, scoring, yes-no judgment and routing decisions to the Xiangxin System One API and get a structured answer with calibrated probabilities in one call; install as a Claude Code plugin from the marketplace with claude plugin marketplace add, or for other agents with npx skills add, and the SKILL.md can also be read directly into any agent skill directory. Companion Python SDK via pip install xiangxin-sdk and JavaScript SDK via npm install at xiangxinai sdk, sources at xiangxinai/xiangxin-sdk-python and xiangxinai/xiangxin-sdk-js; docs at docs.xiangxinai.cn, console at console.xiangxinai.cn with a 5 yuan per month free credit", "approach": "", "training": "", "data": "", "result": "", "notes": "Chinese vendor offering a System One style decision API; Chinese-first documentation", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zapaia-que-modelo-uso", "name": "¿Qué modelo uso?", "author": "Zapaia", "date": "2026-09-25", "category": "app", "url": "https://github.com/Zapaia/que-modelo-uso", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Type what you want to build; from a catalog of 243 AI models in three layers — 48 general LLMs on OpenRouter, 36 of them with Artificial Analysis quality and price numbers, and 138 specialized Hugging Face models — the ones that fit rise and line up in a row, classified in a couple of seconds by Jev; Spanish interface, built for the Webflow x Nerdearla App Showcase, September 2026", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rickyfer22-laya-local", "name": "🏓 Laya AI Pong Agent,  Motor de Decisión Refleja en Tiempo Real", "author": "RickyFer22", "date": "2026-09-25", "category": "app", "url": "https://github.com/RickyFer22/Laya_local", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Sistema de Inteligencia Artificial para Pong basado en Redes Neuronales ModernBERT 421M, Anticipación Física y Control Híbrido en Tiempo Real. Desarrollado y optimizado por Ricardo Fernandez — Data Science", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hf-spaces-wave-2", "name": "09-21/24 HF spaces, grouped", "author": "Hugging Face community", "date": "2026-09-24", "category": "app", "url": "https://huggingface.co/spaces?search=jev+OR+laya+OR+openjev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Grouped row: 59 Hugging Face spaces created 09-19/24, playgrounds, demos and fine-tune trackers; full member list in the notes field", "approach": "", "training": "", "data": "", "result": "", "notes": "Grouped row; 59 spaces created 09-19/24, playgrounds, demos, fine-tune trackers: biswapm/jev-tool-model-router, 09-23; ait-hf/certus-jev-like-playground, 09-22; belk01/open-jev-2b-demo, 09-22; Demondad/open-jev-deberta-v3-large, 09-22; hugging-apps/agent-jev-demo, 09-22; BS24BS/JEV, 09-22; PerrinT/open-jev-typed-decisions, 09-22; henrybit/jev-vs-deepseek, 09-22; abidlabs/jev-typed-decisions-demo, 09-21; suryatmodulus/open-jev-demo, 09-21; abidlabs/jev-typed-decisions-causal-0-6b-trackio, 09-21; fgdsgfdsg/jev-response-reader, 09-21; jevai/jev-pricing, 09-21; jevai/jev-ai-llm, 09-21; jevai/jev-llm, 09-21; ti3x-m/laya-playground, 09-23; ti3x-m/laya-json-render, 09-23; ti3x-m/laya-webgpu, 09-23; khushali678/viralpilot-laya, 09-23; DennisRadix58/laya-multilingual-demo, 09-23; nickfury6023/laya-demo, 09-23; Parvathy-2025/laya-prompt-guardrail, 09-23; aired/laya-demo, 09-23; hadeas/laya-decision-playground, 09-23; wuyouxiaobai/laya-demo, 09-23; henrybit/laya, 09-23; siloh12/laya-demo, 09-22; SGK86/laya-multilingual-typed-decisions-trackio, 09-22; Anuragggggggg/laya_api, 09-22; nccyber/laya-school-email-triage, 09-22; Mezahir2025/laya-triage, 09-22; nile1801/laya-demo, 09-22; 2045max/laya-demo, 09-22; KGFCode/laya-decision-lab, 09-22; yasserrmd/laya-lab, 09-21; Skylarcc/Laya-Online, 09-21; Eloiselatourdepise/qwen-rlcd-demo, 09-18; hugging-apps/eikos-4b-typed-decisions-demo, 09-24; Kryyto/ss14-npc-decisions, 09-23; Prototype-Platform/AACRI-Integrated-Decision-Ecosystem, 09-23; omertugrulbayram/asteroid-decision-support, 09-22; MahmoudGhraibeh/kev-decisions, 09-22; sayshara/capability-decision-lab, 09-22; nolancassidy/s1v-decision-explorer, 09-21; jaredpalmer/kev-4b-decision-demo, 09-20; quaky-duck/kev-8b-decision-model, 09-20; cclemonExp/Waifu-Decision, 09-20; hugging-apps/kev-decision-model-demo, 09-20; anilvarmakav/ai-decision-room, 09-20; bojansandhaus/jev-agent-decision-playground, 09-19; Vishwasaidev/decision-lab, 09-18; pngwn/nanodiff-typed-decisions-v2-retrain-trackio, 09-16; pngwn/typed-decisions-causal-scorer-trackio, 09-16; pngwn/typed-decisions-scorer, 09-16; pngwn/nanodiff-typed-decisions-trackio, 09-16; dsif2012/Qwen3-Decision-v1, 09-16; rorshopping/parallel-constrained-decisions, 09-16; Wayne-Chang/jevk5-demo, 09-23; ZefanCai/Open-Jev-Workbench, 09-23 ; xauberer93/laya-fp16-parity, 09-24, throwaway ZeroGPU fp16 parity-check space, no documentation; zizodev2003/my-laya-api, 09-24, personal Laya API space, no documentation; AlenJoby/halo-laya, 09-24, Laya inside the Halo app, no documentation; Btkdv/mi-filtro-jev, 09-23, personal Jev-based filter in Spanish, no documentation", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ai-ecoverse-jev-omni-js", "name": "@ai-ecoverse/jev-omni.js", "author": "ai-ecoverse", "date": "2026-09-24", "category": "app", "url": "https://github.com/ai-ecoverse/jev-omni.js", "base_model": "Gemma 4 12B it, per the model card", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Jev-Omni decision classifier running in the browser on WebGPU, in the style of kev.js. Supply a state, a question and 2–256 options, and get one probability per option from a single forward pass", "approach": "", "training": "", "data": "", "result": "", "notes": "Open weights and exports on Hugging Face: huggingface.co/models/ai-ecoverse/jev-omni.js, image-text-to-text, ONNX, ONNXRuntime-Web, WebGPU and INT8 exports; tags jev-omni, gemma4, decision-model, classifier", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "actual-jev", "name": "actual-jev: budget categories from Jev", "author": "acifani", "date": "2026-09-24", "category": "app", "url": "https://github.com/acifani/actual-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Suggests categories for uncategorized Actual Budget transactions using the TypeSafe Jev JavaScript SDK; dry-run preview, per-transaction approval, or automatic application above a confidence threshold that defaults to 0.9 and can be set from 0–1; only uncategorized transactions are considered, with filters by account and date range; the package also exports a classifyTransaction function for custom scripts that supply their own allowed categories", "approach": "", "training": "", "data": "", "result": "", "notes": "Node 24 and pnpm 12", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pengchujin-ad-radar", "name": "ad-radar", "author": "pengchujin", "date": "2026-09-24", "category": "app", "url": "https://github.com/pengchujin/ad-radar", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 15, "likes": "", "detail": "Keyword filtering matches content, title, body, reposts, topics, and posters, the person, the original poster of a repost, and people in the repost chain, folding hits; matching is fuzzy, case-insensitive, full-width and half-width, ignoring spaces and punctuation inside Chinese, and whole-word for English, so AI does not hit Raider; an optional semantic match lets Jev also recognize aliases and rephrasings, such as weight loss as losing weight on the scale. Smart identification: multi-select the categories, and with ads selected every post gets a label, non-ad, or soft ad at 87%, while topics only label content that hits; all selected categories are judged in the same request. Custom categories take a name plus an optional description. Display as labels only or folded; sensitivity low, medium, high, with ad thresholds of 0.8, 0.6, 0.4 and topic thresholds of 0.9, 0.8, 0.7. The AI-generation share shows about 70% AI on the label when above half, and content under 60 characters after removing topics and links is not judged. Cost: one Jev call, jev-latest, per newly judged piece of content, about 1,200–1,700 input tokens, with a local cache so the same content is never called twice; at Jev's published prices, judging 10,000 posts is a few RMB. Privacy: the extension connects to no servers of the author's, the post text goes from your browser directly to Jev, and images, video, accounts and browsing history are never sent; with keyword filtering alone it makes no network requests. Chinese README.", "arch": "An open-source browser extension for Chrome and Edge that, on the web versions of Xiaohongshu, Weibo, X and Zhihu, identifies and folds ads and selected topics with Jev: keyword filtering is fully local matching, needing no key at all, while smart identification uses your own Jev API key to classify ads, hard ads, soft ads, engagement bait, and your chosen topics, and to estimate the share of AI-generated content; the extension calls the Jev official API directly, through no third-party server, and the cost is billed to your own Jev account.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ai-launch-radar", "name": "AI Launch Radar", "author": "BoringAlgos", "date": "2026-09-24", "category": "app", "url": "https://github.com/BoringAlgos/ai-launch-radar", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Live dashboard of new AI launches: what shipped, GitHub repo plus star count, implementation idea per launch, build status, and a JEV popularity score on a 0–1 scale produced by JEV through the workspace's TypeSafe skill; data lives in launches.json and a single static index.html with a raw.githubusercontent.com fallback; Cloudflare Pages", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "piyusinha-tab-organizer", "name": "AI Tab Organizer", "author": "Piyusinha", "date": "2026-09-24", "category": "app", "url": "https://github.com/Piyusinha/ai-tab-organizer", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: Chrome extension that sorts your open tabs into named, colored tab groups with one click, powered by Jev; notes: provider OpenRouter or TypeSafe", "arch": "Chrome extension that sorts open tabs into named, colored tab groups in one click; Jev classifies each tab through the provider you choose — OpenRouter, Vercel AI Gateway, Cloudflare Workers AI or TypeSafe directly; bring your own key, the extension has no backend", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ethanalgox-aistock", "name": "AIStock", "author": "ethanalgox", "date": "2026-09-24", "category": "app", "url": "https://github.com/ethanalgox/aistock", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 338, "likes": "", "detail": "Ask about a stock, read the evidence and risks, compare different investing perspectives, and test a strategy through paper trading. Research supports mainland China, Hong Kong, the US, Taiwan, Japan, Korea, the UK, Canada, Australia, India, Germany and France, with paper simulation supporting the first six markets; data availability and bounded screening coverage depend on the provider and configuration, with a market guide in docs. The easiest way to try it is the hosted website, sign up with an invitation code, no local deployment needed. Python 3.10 or newer, React 19, FastAPI, with CI, and docs in English, Simplified Chinese, Traditional Chinese, Japanese and Korean. MIT.", "arch": "An AI workspace for researching stocks, comparing ideas, and testing strategies with simulated money, billed as empowering every stock researcher with AI, so one person can research with the capabilities of a team: research assistant, expert roundtable, stock research, strategy screening and trade simulation, bringing market data, news, calculations and AI analysis into one website.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "walidboulanouar-jev-use-cases", "name": "awesome-jev-use-cases", "author": "walidboulanouar", "date": "2026-09-24", "category": "app", "url": "https://github.com/walidboulanouar/awesome-jev-use-cases", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "CC0-1.0", "paper": "", "stars": 215, "likes": "", "detail": "Every entry links to the original post or repository, and ideas that nobody has shipped are in their own section, marked as ideas. A top-30 popular demos section ranks the 30 most-liked demos, each card showing the demo's rank, area, author, likes, reposts, and reach, likes divided by the author's followers, with preview frames being low-resolution stills from the builders' own videos, and full metrics for every demo in docs. It also tracks Jev-related YouTube videos, with data/youtube.csv recording date, view count, channel and title. PRs welcome.", "arch": "A community-curated list of things built with Jev, TypeSafe's model for typed decisions, with the numbers behind them: who posted each demo, how many followers they have, how many likes it got, and what the limits of the model are; open source, CC0, free to copy and reuse, sponsored by AY Automate, and unofficial, not affiliated with TypeSafe.", "approach": "", "training": "", "data": "", "result": "Sample row at capture: 2026-09-18, 4,150 views, The Fintech Builder, \"I Tested Jev on 300 AI Trading Decisions\"", "notes": "Community index of the YouTube coverage wave", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesomejev-app", "name": "awesomejev.app", "author": "community", "date": "2026-09-24", "category": "app", "url": "https://awesomejev.app", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: 329 entries, 13 categories; result: Agents 70, Workflows 54, Research 40tar counts 09-24", "arch": "Ranked leaderboard of public GitHub projects and tools related to TypeSafe Jev: 329 entries across 13 categories, Agents 70, Workflows 54, Research 40, SDKs and tools 38, Evaluation 23, Browser 18, Frameworks 17, Interactive 17, Directories 16, Context 14, Creative 11, Guides 7, Official 4; provenance split 270 Community and 49 Independent; star counts as of 2026-09-24", "approach": "", "training": "", "data": "", "result": "Per-repo detail views with related-list cross links; top entries include NandhaKishorM/laya and langchain-ai/langchain", "notes": "Parsed from the live site 2026-09-24; this dataset covers the majority of its entries", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "harlley-basic-agentic-ui-system-one", "name": "Basic Agentic UI,  System One", "author": "harlley", "date": "2026-09-24", "category": "app", "url": "https://github.com/harlley/basic-agentic-ui-system-one", "base_model": "DiffusionGemma 26B-A4B", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "The app requires action confidence of at least 0.5, accepts the model's best valid color choice, and produces confirmations locally; color confidence is not gated because creative suggestions can have several suitable answers. All 148 CSS named colors, transparent, and explicit CSS literals, hex, RGB and HSL, are supported. System One chooses candidates in groups of at most 25, then selects the final color from the group winners, which usually takes two endpoint calls. Objects, characters, moods and themes can suggest colors through the model, and mixtures are interpreted using intuitive paint mixing, or light and RGB when requested, selecting the nearest available palette color, a model suggestion rather than an exact numerical color calculation. Unrecognized decisions request clarification, and endpoint failures leave the manual controls usable. An optional .env.local changes the endpoint or model; those settings are public browser configuration, not secrets. It deploys live to a Hugging Face Space: push to main builds and publishes through GitHub Actions, and only the built dist files are uploaded.", "arch": "The original chat and color UI, powered by DiffusionGemma structured decisions: the model selects an action and a color, and messages and the current color go directly to the public Hugging Face endpoint, with no API key or local model required.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "beam-jev-playground", "name": "Beam Jev Playground", "author": "Beam", "date": "2026-09-24", "category": "app", "url": "https://www.beam.cloud/playground", "base_model": "Any open-source decision model", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: beam.cloud; base: hosted catalog; arch: choice with criteria; result: platform features; name: open decision models as APIs", "arch": "'Jev Playground — open-source decision models on Beam': compare all the open-source alternatives to Jev and run any of them as an OpenAI-compatible API; live playground sends state + typed questions and shows the response JSON; BEAM_TOKEN + curl snippet for integration", "approach": "", "training": "", "data": "", "result": "Hosted inference endpoints, task queues, sandboxes, LoRA/QLoRA fine-tuning and batch inference on serverless GPUs; example question: 'which team should handle this support ticket?' over returns/shipping/billing", "notes": "Platform distribution channel for open Jev alternatives; observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "antics-can-you-beat-jev", "name": "Can You Beat Jev?", "author": "antics.gg", "date": "2026-09-24", "category": "app", "url": "https://antics.gg/can-you-beat-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "SIDE OUT on antics.gg: eight players each hold one wall of the same polygon, the ball comes at all of them at once, and only your own paddle is controllable. Miss twice and your wall shatters and the arena closes the gap, octagon to heptagon to pentagon, until the last two remain and it is the oldest video game there is. The ball gains speed on every return, more balls arrive the longer everyone survives, and past a point the walls themselves start closing in. Dead players do not spectate: they drop hazards, a peg bumper, a gravity well, or a spinning bar, to sabotage the living, and cannot place one inside a living player's reach. Bots fill empty walls; free in the browser on phone or laptop, no download, no sign-up. The Jev connection is the game's name; the page itself does not describe an AI opponent.", "arch": "Jev-named multiplayer paddle arena, game titled SIDE OUT: eight players each hold one wall of the same polygon, the ball comes at all of them at once, only your own paddle is controllable; miss twice and your wall shatters and the arena closes the gap, octagon to heptagon to pentagon, until it is the rectangle of the oldest video game", "approach": "", "training": "", "data": "", "result": "The last player with a wall standing wins; when two remain it has become the duel", "notes": "HN 2026-09-24; the page itself does not describe an AI opponent, the Jev connection is the game's name", "domain": "general", "score": "1 point", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "josecruset-classifier-laya", "name": "Classifier demo using Laya MLX", "author": "josecruset", "date": "2026-09-24", "category": "app", "url": "https://github.com/josecruset/classifier-laya", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "A decision tool, not a chatbot: you provide a state to evaluate, a bounded question and, when appropriate, the permitted answers; Laya returns probabilities and a structured decision as JSON without generating prose. Runs choice, score and noul questions; start from an editable example or enter your own state and question; select any of the three published Laya-MLX FP16 checkpoints; inspect the complete model response as formatted raw JSON; copy the JSON to the clipboard. The page sends a same-origin request to the included Python server, which validates, lazy-loads the selected model, calls agent.predict and returns the complete Laya result; the first request downloads the checkpoint from Hugging Face, and afterwards inference does not use a cloud API.", "arch": "Popo - Decider: local browser interface for testing Laya-MLX, the Apple Silicon inference runtime for Laya typed-decision models", "approach": "", "training": "", "data": "", "result": "", "notes": "Runs inference locally on an Apple Silicon Mac; checkpoints stay in the Hugging Face cache, each selected model kept in memory until the server stops", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "clouter", "name": "clouter: Jev-ranked visual model router for Claude Code", "author": "dimitritholen", "date": "2026-09-24", "category": "app", "url": "https://github.com/dimitritholen/clouter", "base_model": "Jev plus OpenRouter models", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Standalone Claude Code plugin that routes prompts for images, SVGs, video and speech to a priced, Jev-ranked list of OpenRouter models; a UserPromptSubmit hook asks Jev what the prompt wants, pulls OpenRouter's live catalogue for that kind, and has Jev rank the six cheapest; the pick is offered first in an AskUserQuestion block marked Recommended, then cheap to expensive with a price in every label and Stay with Claude last. Before any paid request it fetches the picked model's own llms.txt so the parameters stay valid, a vision-model critic judges raster and vector output and fixes defects over a few rounds, and spec failures are remembered in learned.json for 30 days so the next request goes right the first time", "approach": "", "training": "", "data": "", "result": "", "notes": "Extracted from 1337-claude's visual router", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "clue-me", "name": "Clue Me", "author": "yorohan", "date": "2026-09-24", "category": "app", "url": "https://yorohan.com/clue-me", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "approach: Bounded choice over word candidates", "arch": "Daily word game where a human describes the word and Jev, the typed decision model, tries to guess it from the description; the guess is a bounded choice over candidate words", "approach": "sdk", "training": "", "data": "", "result": "Daily game on yorohan.com; HN 3 points at sweep time", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "flypig23-codex-chatgpt-jev-switch", "name": "Codex × ChatGPT · JEV Switch", "author": "FlyPig23", "date": "2026-09-24", "category": "app", "url": "https://github.com/FlyPig23/Codex_ChatGPT_JEV_Switch", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Codex × ChatGPT JEV Switch: web ChatGPT plans and reviews, Codex executes, and Jev plus a set of deterministic rules decides when to hand off; fork of C2C, where ChatGPT reads the local repo over a read-only MCP link; English and Simplified Chinese docs", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cogcloud-ai-cog-brief-router", "name": "cog-brief-router", "author": "cogcloud-ai", "date": "2026-09-24", "category": "app", "url": "https://github.com/cogcloud-ai/cog-brief-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "The builder suite's example decision Cog: it recommends whether a missing-Cog brief should be built as a code, context or decision Cog. A System One model, TypeSafe's Jev, or an LLM through the System One adapter, answers five typed questions about the brief and returns the recommendation with a calibrated probability.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ztllll-cpa-plugin-opencode-jev", "name": "cpa-plugin-opencode-jev", "author": "ztllll", "date": "2026-09-24", "category": "app", "url": "https://github.com/ztllll/cpa-plugin-opencode-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A minimal CLIProxyAPI plugin that exposes the OpenCode Jev System One decision API, https://opencode.ai/zen/v1/systemone, through a CPA management route, rotating over the OpenCode Go subscription API keys already configured in CPA", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dengyie-decidex", "name": "DecideX: High-Performance System One Decision Foundation", "author": "dengyie", "date": "2026-09-24", "category": "app", "url": "https://github.com/dengyie/decidex", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "System One decision foundation layer for Jev and NanoJev: 5-stage formal pipeline separating What from How; ms-level with safety gates, head isolation, loop fuse; README in Chinese", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "spark-devflow-agent", "name": "DGX Spark 开发流 Agent", "author": "arvelvale", "date": "2026-09-24", "category": "app", "url": "https://github.com/arvelvale/spark-devflow-agent", "base_model": "vLLM 0.28 with local Nemotron-3.5 30B-A3B NVFP4, cloud StepFun step-5-preview, JEV decision layer", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Development-flow agent on the NVIDIA DGX Spark, Chinese README: reads Linear issues, Obsidian notes and user voice input, then decomposes, plans, develops, logs and syncs status; every pick of one judgment, two-stage skill selection, model routing, tool gating, memory selection, context compression, goes to the JEV decision layer and every decision lands in a measurable decision trace; Ollama plus Qwen3.8 27B as backup local", "approach": "", "training": "", "data": "", "result": "Local main model at roughly 80 tok/s, reported", "notes": "DGX Spark hackathon entry, internal team README", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ozbillwang-dino-jump", "name": "Dino Jump", "author": "ozbillwang", "date": "2026-09-24", "category": "app", "url": "https://github.com/ozbillwang/dino-jump", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A small runner in the style of the browser dinosaur game. The page runs the physics. A planner writes the current situation in English, and Laya English scores jump, duck, or run", "approach": "", "training": "", "data": "", "result": "", "notes": "Laya English is loaded once on the local machine; each frame has two parts: the planner moves the dinosaur, and about 11 times a second Laya reads the sentence the planner just wrote; Laya never sees the canvas", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "heavenyu-dsh-skill-suggest", "name": "dsh-skill-suggest", "author": "heavenyu1982yu", "date": "2026-09-24", "category": "app", "url": "https://github.com/heavenyu1982yu/dsh-skill-suggest", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: fail-open, two-hop skill-suggestion plugin for DeepSeek Harness, DSH; notes: on each new user turn Jev decides whether a skill is needed and ranks the catalog; if so, injects one line of <skill_relevance> advice; roster and prefix cache stay untouched; any Jev failure degrades to no suggestion instead of blocking the session; built for catalogs with hundreds of skills and many same-family near-duplicate names", "arch": "Fail-open two-hop skill-suggestion plugin for DeepSeek Harness: on each turn Jev ranks a large local skill catalog and, if a skill is needed, injects a single line of <skill_relevance> advice - the roster and prefix cache stay untouched, and any Jev failure degrades to no suggestion", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ec2eat-laya-serving", "name": "ec2eat Laya serving", "author": "ylm182", "date": "2026-09-24", "category": "app", "url": "https://huggingface.co/models/ylm182/ec2eat-laya-serving", "base_model": "Laya multilingual", "params": "", "metric": "5 local validation tests; two-candidate English and Traditional Chinese examples", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "", "arch": "Custom Hugging Face Inference Toolkit handler, not a standard classification checkpoint: it downloads laya-multilingual at revision e4e9ddf at startup and runs Laya 0.3.20 on CPU; upstream weights are Apache-2.0 and are not redistributed in the wrapper; the card documents the deploy steps, including creating your own model repo, recording the new repo commit hash, and not widening the production inference token to write access", "approach": "", "training": "", "data": "", "result": "Five local validation tests passed; real CPU inference passed English and Traditional Chinese two-candidate examples, both ranking the matching lunch first; the card states these examples do not establish general ranking quality, and the application still exports a null installed recipe and falls back deterministically, so uploading the files does not enable the recipe", "notes": "Candidate serving recipe for the ec2eat lunch-selection use case", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "caiovicentino-eikos-arena", "name": "Eikos Arena", "author": "caiovicentino", "date": "2026-09-24", "category": "app", "url": "https://github.com/caiovicentino/eikos-arena", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 5, "likes": "", "detail": "", "arch": "Two decision models trade 14 Hyperliquid perpetuals with $10,000 of paper money each: Eikos-27B, open weights, MIT, against Jev, the TypeSafe decision API. Every 5 minutes both get the same market snapshot and the same 28 questions.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zconjugate-everything2jev", "name": "Everything2Jev", "author": "ZConJugaTe", "date": "2026-09-24", "category": "app", "url": "https://github.com/ZConJugaTe/everything2jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Everything2Jev is a research prototype for representing CLI, HTTP/OpenAPI, MCP, and Python interfaces as a common catalog of candidate capabilities. It uses Laya to assist with typed routing decisions while keeping permission and risk check", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "factory-dispatch", "name": "Factory Dispatch", "author": "gokhankrbg", "date": "2026-09-24", "category": "app", "url": "https://github.com/gokhankrbg/factory-dispatch", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "English-language demo that routes synthetic manufacturing incident reports to the team that should assess them first, Maintenance, Quality, Logistics or Human Review, using real calls to the Jev API; the operator's free-text report is sent to Jev's choice primitive, which returns a model recommendation with a confidence score and full probability breakdown, then a separate deterministic server-side policy decides the final routing; the UI always shows both and gives a short explanation of why they differ when they do; runs entirely locally against the user's own API key", "approach": "", "training": "", "data": "", "result": "", "notes": "The question asks which team should first assess an incident, not which team will ultimately diagnose the root cause", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bgivenb-flick", "name": "Flick", "author": "bgivenb", "date": "2026-09-24", "category": "app", "url": "https://github.com/bgivenb/flick-computer-use", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 19, "likes": "", "detail": "arch: fast computer use for MCP agents powered by Jev - local stdio MCP server, full browser and macOS goals; observe → decide → act loop; notes: TypeScript core + Playwright + Swift Accessibility; MIT", "arch": "Fast computer-use server for MCP agents: a local stdio MCP server that drives a full browser and macOS goals through an observe-decide-act loop, with Jev making each decision; TypeScript core, Playwright, Swift Accessibility", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "leehack-flutter-laya-tetris", "name": "flutter-laya-tetris", "author": "leehack", "date": "2026-09-24", "category": "app", "url": "https://huggingface.co/spaces/leehack/flutter-laya-tetris", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": 0, "detail": "notes: the space is a static index.html port of the llamadart example; no evaluation is published", "arch": "A static space hosting Laya Tetris, real-time Tetris played by a Laya decision model, built from the laya_tetris example in the leehack/llamadart repository.", "approach": "", "training": "", "data": "", "result": "", "notes": "Static space built from the llaya_tetris example in leehack/llamadart; sets cross-origin embedder, opener and resource policy headers for the WebAssembly runtime", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gambler-26b", "name": "Gambler 26B", "author": "leo-fish", "date": "2026-09-24", "category": "app", "url": "https://gambler-relay-us-west1.leo-fish.ts.net/demo", "base_model": "", "params": "26B", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: per demo page · 26B params; arch: 2 concurrent requests, rate-limited preview; training: demo only; notes: demo page only, found via HN feed 09-23/24; name: experiment demo", "arch": "Public demo of a 26B typed-decision model: choice / noul / score with inspectable probabilities, an 8-case latency suite, and round-trip / inference / network+queue latency breakdowns", "approach": "undisclosed", "training": "Unknown", "data": "", "result": "Demo live with TypeScript SDK, Python SDK and cURL docs; example: support-ticket state → billing/support/sales/other choice question", "notes": "Grounding is thin; weights/repo not yet found.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gifdecider", "name": "GIF Decider", "author": "grainstash crew", "date": "2026-09-24", "category": "app", "url": "https://gifdecider.com/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: per site; result: Strict/Standard/Off · 'when the build finally passes after 3 hours', 'someone says quick question at 5:58pm'; notes: HN", "arch": "'The right GIF, decided': say the moment, get three reaction GIFs ready to paste; Jev picks the GIFs; content from KLIPY; usable in Claude or ChatGPT", "approach": "", "training": "", "data": "", "result": "Live demo with content-filter levels and example prompts", "notes": "From the creator of Grainstash; observed 2026-09-24.", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lukelittle-goofy-ahh-system-one-demo", "name": "Goofy Ahh System One Demo", "author": "lukelittle", "date": "2026-09-24", "category": "app", "url": "https://github.com/lukelittle/goofy-ahh-system-one-demo", "base_model": "circuit-vl-4b", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "The model gets the image, the question and the four options, runs one forward pass, and a small readout head turns that into one probability per option, which is a different way of using a model from asking an LLM to reply in JSON, and this repository exists to show and explain the difference. It is a teaching artifact for engineers and technology consultants who know conventional LLMs and have not yet met a model that answers without generating: most AI classifiers built today send an image to a big generative model with a prompt that says reply with one of these four words. The contents cover what this is, what a System One decision model is, how Circuit-VL works, Circuit-VL versus a conventional generative VLM, the architecture, what happens during inference, why bounded decisions are interesting, real-world consulting use cases, limitations, running locally, configuration, and adding new choices.", "arch": "A demo that teaches serious AI concepts with extremely unserious classification problems: upload a profile picture, and a System One vision model, jbarney's circuit-vl-4b, decides which of four ridiculous internet archetypes the image's visual presentation most resembles, architect, apple_guy, femboy, furry, where the categories are a joke and the mechanism is the point, because no text is generated anywhere in the app.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "goutoujunshi-jev", "name": "goutoujunshi-jev", "author": "vectorAz", "date": "2026-09-24", "category": "app", "url": "https://github.com/vectorAz/goutoujunshi-jev", "base_model": "Jev-inspired", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "base: local knowledge base; arch: 'sarcastic friend'; name: Codex skill, Chinese", "arch": "Codex skill: 狗头军师 knowledge base + short multi-strategy reply generation + de-AI-ification ranking; local relationship memory with consent gate", "approach": "", "training": "", "data": "Local KB", "result": "Natural, non-AI-flavored Chinese reply suggestions", "notes": "Decision-style ranking applied to casual conversation.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevblackjack", "name": "H5 Blackjack with a Jev-style advisor", "author": "GZWZC", "date": "2026-09-24", "category": "app", "url": "https://github.com/GZWZC/jevBlackjack", "base_model": "Mock decisioner by default, wire-compatible with TypeSafe", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "approach: Classifier-shaped advisor", "arch": "Single-player blackjack in a Next.js app where the game runs in a pure TypeScript client engine and an advisor endpoint returns Hit, Stand, or Double suggestions with probabilities; player actions stay human, and the request and response shapes align with the TypeSafe classifier so the mock decisioner can be swapped for the real API", "approach": "sdk", "training": "", "data": "", "result": "First release ships a mock decisioner based on basic strategy with placeholder probabilities", "notes": "Node.js 20", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hnjudge", "name": "HN Judge", "author": "hnjudge.vercel.app", "date": "2026-09-24", "category": "app", "url": "https://hnjudge.vercel.app", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": "", "likes": "", "detail": "result: per-thread verdicts: e.g. 'Overwhelmingly critical 27% pos / 73% crit' on the 2024 election thread, 400 of 9,274 comments scored; notes: HN; name: Jev reads every HN comment", "arch": "'The most-discussed threads across the corpus, read comment by comment by Jev and reduced to one verdict.' Live from Postgres with BM25 search, deployed on Vercel; ~50M items indexed; threads with 6+ comments; 37 ms lookup", "approach": "", "training": "", "data": "~50M HN items; 42,747 comments read one by one by Jev for the published finding", "result": "Across the 112 biggest threads on Hacker News, only 10 lean net-positive; the most favorable the crowd ever gets is 100% supportive, on 'Ask HN: Most interesting tech you built for just yourself?'", "notes": "Observed 2026-09-24; a large-scale demo of Jev as a comment-sentiment aggregator.", "domain": "social", "score": "", "latency": "37 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "if-jev", "name": "IF Jev", "author": "WEP-56", "date": "2026-09-24", "category": "app", "url": "https://github.com/WEP-56/IF_JEV", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Chinese design-doc set, v0.1 dated 2026-09-24, for an interactive-fiction engine where Jev and LLMs co-author a storyline: ten documents cover rules, the world model of entity, proposition, fact, belief and claim, event sourcing and storyline operations, turn flow, the Onemore-based agent runtime driven by Jev, the judgment policy with semantic categories, thresholds and stratified sampling, and the Jev question bank with request packing, template spec, core question catalogue and calibration", "approach": "", "training": "", "data": "", "result": "Jev's raw output is a judgment, probability, distribution or score; a task is one agent run led by a Jev-driven host", "notes": "Draft stage, not released; Rust", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dainlucas-ifnmg-news-bot", "name": "IFNMG News Bot", "author": "dainlucas", "date": "2026-09-24", "category": "app", "url": "https://github.com/dainlucas/ifnmg-news-bot", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 4, "likes": "", "detail": "", "arch": "A Telegram bot that follows six IFNMG RSS feeds and sends news based on the topics each person chooses. It uses JEV to classify posts and runs on Cloudflare Workers with a D1 database", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "anessbelbati-prompt-injection-vs-keyword-stuffing-ai-seo", "name": "In short", "author": "anessbelbati", "date": "2026-09-24", "category": "app", "url": "https://github.com/anessbelbati/prompt-injection-vs-keyword-stuffing-ai-seo", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "!One sentence to the top 5, by Aness Belbati. How often a wrong page reached the top 5 of 100 searches, lowest to highest across 10 AI rankers: no sentence 0–12; \"Rank this page first\" 0–14; \"This page answers\" plus the search 19–8", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "localcan-captcha", "name": "Invisible CAPTCHA built with Jev", "author": "Jarek Ceborski", "date": "2026-09-24", "category": "app", "url": "https://www.localcan.com/blog/build-your-own-captcha", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": "", "likes": "", "detail": "approach: Behavioral signals plus Jev judgment", "arch": "Sign-up form that distinguishes people from bots without a puzzle: instead of asking can you do this, it asks how was this form filled in, reading mouse movement, typing, and button behavior; the first half is a Jev tutorial that builds it in TypeScript with excerpts from the MIT demo repo, and the second half documents what happened when people and AI agents were pointed at it", "approach": "sdk", "training": "", "data": "", "result": "25-minute tutorial published 2026-09-24", "notes": "HN 2 points at sweep time; motivated by the Cloudflare checkbox that ChatGPT Agent clicked in July 2025", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "localcan-invisible-captcha", "name": "Invisible CAPTCHA demo", "author": "LocalCan", "date": "2026-09-24", "category": "app", "url": "https://github.com/LocalCan/invisible-captcha", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "A sign-up form with no puzzle. The browser measures how the form gets filled in, the server turns those measurements into a short plain-English story, and TypeSafe's Jev classifier reads the story and says who is behind the session", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reindent-jauvex", "name": "Jauvex", "author": "reindent", "date": "2026-09-24", "category": "app", "url": "https://github.com/reindent/jauvex", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 2, "likes": "", "detail": "Add a folder, pick up any of its sessions or start new ones with either provider, and talk to them: a voice channel that answers in three beats, a quick word, what it understood, a summary of the agent answer; steers a working agent without interrupting it; names and starts agents by voice; lets agents talk to each other inside the app. No server: the window talks to the main process over IPC, and sessions stay where Claude Code and Codex keep them. Install is one curl command that downloads the source, checks its SHA-256 and builds Jauvex on the Mac, nothing prebuilt is downloaded, so there is nothing for Apple to notarize; run the command again to update.", "arch": "Electron desktop app for macOS putting coding agents side by side by voice: Claude Code and Codex sessions in one window, with Jev TypeSafe for the fast decisions", "approach": "", "training": "", "data": "", "result": "", "notes": "Jauvex Personal v1.0, Apache-2.0; needs Node 22.18+; source and build stay in ~/.jauvex/personal/app; made by Reindent, one human and agents; site jauvex.reindent.com", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "writersrinivasan-jev", "name": "Jev 101,  a Yoto-branded LMS", "author": "writersrinivasan", "date": "2026-09-24", "category": "app", "url": "https://github.com/writersrinivasan/JEV", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "An internal, visually-rich learning module that teaches Jev, TypeSafe AI's \"System One Model\" — the recent trend of a decision model that works alongside LLMs rather than replacing them. Built as a mini LMS styled in the Yoto brand colours so the team can learn Jev end-to-end at 101 level", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "schalkneethling-jev-3000", "name": "Jev 3000", "author": "schalkneethling", "date": "2026-09-24", "category": "app", "url": "https://github.com/schalkneethling/jev-3000", "base_model": "TypeSafe Jev", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "A separate warm-connection demo shows the latency difference between a cold TypeSafe connection and one that has already been warmed up. Node 24 and Vite+ are required, plus the 1Password CLI with desktop-app integration; the TypeSafe API key is never stored in the repository, and is read from 1Password through Varlock, which replaces .env files with a committed .env.schema that declares every variable, validates it at startup, and redacts sensitive values in output, so the key lives in a vault, not on disk. The plugin authenticates through the 1Password desktop app, so no service account token is needed locally, and biometric unlock works.", "arch": "A small demo of TypeSafe's Jev decision model and its three primitives, Noul, Choice and Score: each primitive gets its own panel with a tree-based branch composer for building up questions and state, plus a JSON editor for editing the same request by hand, and a readout that renders the model's answers, including probabilities drawn as bars.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "codaaiteam-jev-agent-tools", "name": "Jev Agent Tools", "author": "codaaiteam", "date": "2026-09-24", "category": "app", "url": "https://github.com/codaaiteam/jev-agent-tools", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: small open-source single-file tools for people building AI agents, powered by Jev - loop detector that grades each agent step, progress, repeating, stuck, escalate, a skill router and more; notes: each tool is a single dependency-free index.html; typed calibrated decision in ~100–300 ms, no chat, no parsing", "arch": "Single-file open-source tools for agent builders, each a dependency-free index.html: a loop detector that grades every agent step, progress, repeating, stuck, escalate, a skill router and more, all powered by typed Jev decisions", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vikrantkalyan23-jev", "name": "Jev AI Model", "author": "vikrantkalyan23", "date": "2026-09-24", "category": "app", "url": "https://github.com/vikrantkalyan23/jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Notes document on the Jev AI model from a Jev Talk presentation: fast, structured decisions that software can use directly; claims kept in context as confirmed, unconfirmed, vendor-reported or future-looking", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-ai-tutorial", "name": "Jev AI tutorial code", "author": "vivek081166", "date": "2026-09-24", "category": "app", "url": "https://github.com/vivek081166/jev-ai-tutorial", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Companion code for the video How to Use Jev AI and When Not to Trust It: one Jev call askJev, a three-line rule for when to trust the answer, route, and seven hand-labeled support tickets run through both Jev and a keyword matcher; Node 24+", "approach": "", "training": "", "data": "seven hand-labeled support tickets", "result": "", "notes": "Video URL not retrieved at sweep time", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "blastzzzz-jev-anything", "name": "jev anything", "author": "BlastZZZZ", "date": "2026-09-24", "category": "app", "url": "https://github.com/BlastZZZZ/jev-anything", "base_model": "", "params": "0.6B", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "Capabilities: browser actions, choosing operations and observed element targets for search, form filling, filtering and navigation; RAG retrieval, scoring passage relevance and combining reranker and dense-retriever rankings; evidence assessment, selecting supporting passages and judging answerability and context sufficiency; game policies in Maze, Snake and Doom; and probabilistic reasoning, predicting outcome distributions for coins, dice, urns and lotteries. A Chinese documentation set, evaluation details, and research results are included, with an honest scope note: the browser results use a separately adapted browser checkpoint and cover three local fixture tasks, including one repeated task, and the fixture layout is present in the adaptation data, so those cases participate in checkpoint selection; the RAG results use the v4 checkpoint.", "arch": "Lightweight 0.6B decision models for browser agents, RAG, games and probabilistic reasoning: they score a dynamic set of candidates and return a probability distribution that applications use to choose an action, rank evidence, or assess uncertainty, running locally on a single GPU and selecting among supplied candidates without autoregressive text generation.", "approach": "", "training": "", "data": "", "result": "Browser actions, local hotel tasks with independently checked final pages: 4/4. RAG retrieval, MuSiQue test R@5, 101 questions, dense plus jev-anything fusion: 77.31%. RAG pipeline, MuSiQue QA EM/F1, top-4 context: 36.63% / 46.60%. Evidence assessment, semantic test accuracy, 2,384 decisions: 83.52%. Game policies, weighted macro success, test / out-of-distribution: 53.26% / 26.72%. Probabilistic reasoning, 8,145 decisions: 85.62% probability test accuracy, 0.1264 mean total variation. Inference, single-decision p50 across domains, one RTX 3090: 27.84–69.49 ms.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-approach-control", "name": "Jev Approach Control", "author": "Zeb88", "date": "2026-09-24", "category": "app", "url": "https://github.com/Zeb88/jev-approach-control", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "single-runway air traffic control sim: human radio calls or Jev as controller; every 4 s Jev decides approach, hold, spacing and landing clearance per aircraft; code flies the geometry", "approach": "", "training": "", "data": "", "result": "", "notes": "unofficial demo, not affiliated with TypeSafe; Jev questions and confidence thresholds documented in-game", "domain": "gaming", "score": "", "latency": "4 s", "benchmark": "", "cost": "", "speedup": ""}
{"id": "codaaiteam-jev-arcade", "name": "Jev Arcade", "author": "codaaiteam", "date": "2026-09-24", "category": "app", "url": "https://github.com/codaaiteam/jev-arcade", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: tiny open-source browser games where you play against or alongside an AI that actually decides - every enemy move, teammate action or judgment is a real Jev call, choice, score, yes or no, in about 100–300 ms; notes: each game is a single dependency-free index.html; companion arena", "arch": "Browser arcade where every enemy move, teammate action and judgment call is a real Jev decision, about 100–300 ms, typed choice, score, yes or no; each game is a single dependency-free index.html", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-arena-pokemon", "name": "Jev Arena", "author": "neurogabo", "date": "2026-09-24", "category": "app", "url": "https://github.com/neurogabo/jev-arena", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "author: Gabo Choropan; base: API; notes: after snake/Mortal Kombat", "arch": "Pokémon Champions battle arena against Jev: Jev picks a shortlist, the simulator tries the opponent's responses, Jev picks; 'Inside Jev' review panel shows every decision; live demo at play.neurogabo.com", "approach": "", "training": "", "data": "Custom arena", "result": "'Teaching Jev to Play Pokémon' Medium story + live playable demo", "notes": "Second game arena in one day.", "domain": "gaming", "score": "", "latency": "", "benchmark": "Pokémon", "cost": "", "speedup": ""}
{"id": "sohaib-khan-me-jev", "name": "JEV Audience Field Evaluator", "author": "sohaib-khan-me", "date": "2026-09-24", "category": "app", "url": "https://github.com/sohaib-khan-me/jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Proof-of-concept JEV audience field evaluator: reads a MySQL schema, shortlists candidate fields, asks Jev a typed choice question, and checks whether it identifies the database field a natural-language campaign targets; 24-case test set against a 6-table schema", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-browser-harness", "name": "Jev Browser Harness", "author": "GreenPandaStudios", "date": "2026-09-24", "category": "app", "url": "https://github.com/GreenPandaStudios/jev-browser-harness", "base_model": "Jev, Kev, or chat-model baseline", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Open-source browser agent that asks one System One Choice question over the actions observed on the current page, then executes the chosen action with Playwright; the model chooses an action ID and never generates JavaScript, selectors or click coordinates. One in-page DOM pass yields visible controls plus short context, which is shortlisted and permission-filtered into the Choice call; exact form values supplied in the task are filled locally without a model call; the browser stays warm across benchmark tasks. Works with hosted Jev, local Kev, or an OpenAI-compatible chat model as baseline, and when the Choice answer is uncertain the harness can stop or fall back to a slower chat model", "approach": "", "training": "", "data": "", "result": "", "notes": "Choice caps at 255 options, so large pages are shortlisted and resolved in two stages; tests run a fake System One server against real Chromium pages", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "doruktarhan-jev-browser", "name": "Jev Browser jb CLI and Claude Code skill", "author": "doruktarhan", "date": "2026-09-24", "category": "app", "url": "https://github.com/doruktarhan/jev-browser", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "base: API; arch: CAPTCHA/login/payment → REVIEW state; result: Jev never fabricates a result — safe stop instead", "arch": "One jb call instead of N Playwright tool calls: Jev picks the next legal action from ~1 KB JSON + a screenshot; safe stop; honest done_unverified; Codex skill included", "approach": "", "training": "", "data": "72-run comparison suite", "result": "72 runs: jb+Jev 86% pass vs Playwright MCP 100%, median 31 s vs 40 s, 44k vs 54k tokens, 6.9 vs 14.4 tool calls", "notes": "Trade-off analysis: 86% vs 100% pass, halved tokens/latency.", "domain": "computer-use", "score": "86%", "latency": "31 s", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ruralbytest-demojev", "name": "Jev by TypeSafe AI: Demos", "author": "ruralbytest", "date": "2026-09-24", "category": "app", "url": "https://github.com/ruralbytest/demojev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Small Python demos of Jev, the System One model from TypeSafe AI. You give Jev plain text plus a few typed questions. It sends back structured answers, and each answer comes with a probability. Your code then makes the decision", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-cu-jp", "name": "Jev CU JP", "author": "moto-taka", "date": "2026-09-24", "category": "app", "url": "https://github.com/moto-taka/jev-cu-jp", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Jev picks the next screen operation from a short goal, candidate roles and labels and minimal state, Japanese, English and Chinese accessibility text; an agent's Computer Use executes and verifies", "approach": "", "training": "", "data": "", "result": "", "notes": "brew install; agent skills for Codex, Claude Code and Pi; built on Sac-Y/Jev-cu with Japanese support; screenshots and browser profiles are not sen.", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sarveshsantoshmhatre-jev", "name": "Jev Decision Fabric", "author": "sarveshsantoshmhatre", "date": "2026-09-24", "category": "app", "url": "https://github.com/sarveshsantoshmhatre/jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "It demonstrates typed Choice, Score and Noul decisions through the official @typesafe-ai/sdk; confidence-aware routing with explicit autonomous, review and escalation policies; parallel multi-question evaluation in one System One request; a composable decision pipeline with audit records; dry-run mode for development without API calls; an HTTP API and a CLI; deterministic tests and CI; and structured observability for latency and usage. The pipeline: state and evidence, Choice plus Score plus Noul questions, Jev and System One, explicit code policy, then act or review. The final action is decided by TypeScript policy code: Jev supplies semantic judgments and does not own application permissions or execution. Node.js 20 or newer, a no-network demo, and a support decision endpoint at /v1/decide/support. It integrates with the hosted Jev API and contains no Jev model weights, and advises keeping API credentials server-side and calibrating thresholds on your own domain data.", "arch": "Jev Decision Fabric, a production-oriented reference application for building software around TypeSafe AI's Jev and System One decision model: Jev is used as a bounded judgment layer, where application state goes in and typed probabilistic decisions come back, and deterministic application code owns thresholds, routing, persistence and execution.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "solariz3d", "name": "Jev for Claude Code turn review", "author": "solariz3d", "date": "2026-09-24", "category": "app", "url": "https://github.com/solariz3d/jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "approach: Per-turn quality gate", "arch": "Reads each finished turn of Claude Code sessions and marks the ones worth a second look; a marked turn appears at the start of the next prompt as a one-line note with the probability; judged turns are sent to the Vercel AI Gateway with the last message, the reply cut to character limits, and the judging rubric, while opted-out projects send nothing and the gateway key comes only from the environment", "approach": "sdk", "training": "", "data": "", "result": "On its first 56 units about one in four marked turns was confirmed by a blind reader, with the readers being AI assistants from the project that lean lenient", "notes": "Requires Claude Code 2.1.196 or newer to judge exactly the turn that just finished", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-game-theory-arena", "name": "Jev Game Theory Arena", "author": "OuchengLiu", "date": "2026-09-24", "category": "app", "url": "https://github.com/OuchengLiu/Jev-Game-Theory-Arena", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Each visit is randomly assigned how Jev turns its probabilities into a move: Top pick, always its highest-rated move, or By odds, drawn in proportion to its probabilities. Two input modes: hinted, where the game does the maths first and gives Jev a short summary, and raw, where Jev sees only what happened and the options and plays on intuition, which is often more human. Deployed free at jev-game-theory-arena.pages.dev, no sign-up, English and Chinese, desktop and mobile.", "arch": "Game-theory arena where you play six games against Jev: heads-up No-Limit Hold'em, Liar's Dice, Colonel Blotto, a ten-round Prisoner's Dilemma, Rock Paper Scissors where Jev predicts your next throw, and the Ultimatum Game splitting ten coins. After each round you see Jev's full probability distribution: where it is confident, where it hesitates and where you can exploit it.", "approach": "", "training": "", "data": "Custom game arenas", "result": "Game-theoretic probing of a System One decision model", "notes": "Extends the Doom/Snake/Minecraft game family into strategic games.", "domain": "gaming", "score": "", "latency": "", "benchmark": "Poker", "cost": "", "speedup": ""}
{"id": "jevagent", "name": "Jev Gatekeeper", "author": "Adityakk9031", "date": "2026-09-24", "category": "app", "url": "https://github.com/Adityakk9031/jevagent", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "The core problem: over 80% of inbound PRs and issues in active repositories are low-value noise, one-line formatting and typo corrections, automated bot dependency bumps from Dependabot and Renovate, duplicate or superficial bug reports, and minor cosmetic edits with zero architectural impact, and feeding thousands of tokens of diffs, threads and files to higher-tier generative models just to review those trivial changes burns expensive tokens for zero return. The solution is Jev-driven priority routing: filtering out 80% of inbound noise for fractions of a cent, and protecting higher-tier LLMs from wasteful token burn, with a demo video showing the shield in action. TypeScript 5.5 or newer.", "arch": "Jev Gatekeeper, billed as the AI token shield and cost-routing firewall for GitHub PRs and issues: an autonomous, voice-controlled triage system where TypeSafe Jev, the bouncer, gates inbound PRs and issues, and the Antigravity CLI, the heavy reviewer, takes over what is worth it, built as a TypeScript Chrome extension, Manifest V3.", "approach": "", "training": "", "data": "", "result": "", "notes": "Voice-controlled; the Jev triage decision gates the Antigravity agent handoff.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kellystuard-jev-gmail-classifier", "name": "Jev Gmail Classifier", "author": "kellystuard", "date": "2026-09-24", "category": "app", "url": "https://github.com/kellystuard/jev-gmail-classifier", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A Google Apps Script project that automatically adds Gmail labels to your conversations based on what they are about. Apps Script is Google's platform for running JavaScript inside your own Google account, so the classifier runs in the back", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-ai-use-cases", "name": "Jev in Practice", "author": "atliq", "date": "2026-09-24", "category": "app", "url": "https://github.com/atliq/jev-ai-use-cases", "base_model": "Jev via OpenRouter plus Groq LLMs", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 4, "likes": "", "detail": "approach: LangChain orchestration", "arch": "Hands-on LangChain examples built around the division of labor where an LLM writes and Jev decides: support ticket triage for team, urgency, and refund intent; model routing between small, premium, and private; reply guardrails that check whether an AI-written reply is safe to send; tool selection with blocking of risky tool calls; and a finance inbox for document type, urgency, and bank-details-changed fraud", "approach": "sdk", "training": "", "data": "", "result": "Colab-runnable notebook with outputs from a real run; Jev served through the langchain-typesafe experimental integration", "notes": "Jev runs through OpenRouter, the LLMs through Groq; MIT", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dannyowelch-jev-noul-vs-choice", "name": "Jev Jaggedness Demo: Noul vs Choice", "author": "dannyowelch", "date": "2026-09-24", "category": "app", "url": "https://github.com/dannyowelch/jev-noul-vs-choice", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Interactive Next.js demo showing TypeSafe Jev jaggedness — how the same judgment asked as a Noul vs a yes/no Choice can disagree, and how complementary Nouls need not sum to 1", "approach": "", "training": "", "data": "", "result": "", "notes": "Uses the jev-1.13 model and TypeSafe's model-jaggedness documentation", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-job-hiring-detector", "name": "JEV Job Hiring Content Detector", "author": "pareshbhangale", "date": "2026-09-24", "category": "app", "url": "https://github.com/pareshbhangale/JEV---Job-Hiring-Content-Detector", "base_model": "local regex heuristics, optional JEV AI semantic pass", "params": "", "metric": "", "license": "PolyForm Noncommercial 1.0.0", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Chrome extension, manifest v3: detect, highlight and isolate hiring posts on LinkedIn, X, Reddit and job boards with local regex heuristics under 0.003 ms on V8 Irregexp; optional layered hybrid where a borderline post is verified by JEV AI semantic reasoning; offline-first", "approach": "", "training": "", "data": "", "result": "", "notes": "Badge in the README declares the license; no LICENSE file detected on the default branch at sweep time", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevforge-navigator", "name": "JEV Model Navigator, GitHub Action", "author": "JevForge", "date": "2026-09-24", "category": "app", "url": "https://github.com/JevForge/jev-model-navigator", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "arch: GitHub Action that routes Issues and PRs to the best AI model using typed TypeSafe Jev decisions; notes: published on the GitHub Marketplace; MIT", "arch": "GitHub Action that routes Issues and PRs to the best AI model using typed Jev decisions; published on the Marketplace", "approach": "", "training": "", "data": "", "result": "", "notes": "MIT; published as a GitHub Marketplace action; CI on GitHub Actions", "domain": "routing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "srjoeee-jev-paper", "name": "Jev Paper", "author": "SRjoeee", "date": "2026-09-24", "category": "app", "url": "https://github.com/SRjoeee/jev-paper", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Chrome extension marking every claim in an arXiv abstract, the sentence that delivers it, and the caveats worth knowing, in place; no summaries, every mark is the authors own sentence; bring your own key", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sunnybagal-jev", "name": "Jev Playground,  TypeScript + Postgres", "author": "SunnyBagal", "date": "2026-09-24", "category": "app", "url": "https://github.com/SunnyBagal/Jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Quick start in five minutes: npm install, copy .env.example, start Postgres in Docker, create the tables, and make your first Jev call. No API key yet: set JEV_MOCK=1 in .env, and every example runs with fake answers so you can learn the code. The examples: hello, the three question types, no database; triage, choice plus score, saved to Postgres, with a human fallback on low confidence; router, picking the right LLM for each prompt; jobs, scoring job postings against your resume, with a SQL shortlist; and inbox, sorting job-hunt emails, and finding what needs a reply.", "arch": "Jev Playground, a beginner-friendly TypeScript and Postgres teaching project for Jev, TypeSafe AI's System One decision model, in five-minute increments: Jev does not write text, you give it some text, the state, plus questions with fixed answer types, and it returns typed answers with probabilities in about 70–500 ms, at $0.042 per million input tokens, with output free.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arthurfiorette-jev-playwright", "name": "Jev Playwright", "author": "arthurfiorette", "date": "2026-09-24", "category": "app", "url": "https://github.com/arthurfiorette/jev-playwright", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 6, "likes": "", "detail": "Package-aware tools such as Turborepo and Nx can scope unit tests using the changed-package graph, but E2E tests are harder: a single browser journey can cross many packages, pages and services. jev-playwright compares the change with Playwright discovered tests to select relevant journeys before browser execution, reducing test runtime in large CI suites. It is a relevance filter, not a guarantee that every affected test will run: it aims to skip many clearly unrelated tests while keeping plausibly affected ones, and can still miss tests, so keep full-suite coverage where completeness matters.", "arch": "Playwright reporter that uses Jev, TypeSafe model for typed yes/no decisions, to select the existing tests relevant to a code change before they run", "approach": "", "training": "", "data": "", "result": "Requires Node 24.16+ declared by the package and Playwright 1.62+ for the reporter preprocess hook", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-receipt-country", "name": "Jev receipt country classifier", "author": "Kripta-Studios", "date": "2026-09-24", "category": "app", "url": "https://github.com/Kripta-Studios/jev-receipt-country-classifier", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Python CLI: Jev classifies receipt country over ES, FR, DE, IT, GB, US, CA, OTHER, UNKNOWN from text produced by trace-it local OCR; a frozen experimental policy routes candidates to review", "approach": "", "training": "", "data": "", "result": "", "notes": "automatic acceptance was not calibrated before the experiment closed; a regex-only baseline runs offline", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zeke-jev", "name": "Jev research notes and triage worker", "author": "zeke", "date": "2026-09-24", "category": "app", "url": "https://github.com/zeke/jev", "base_model": "Jev; Cloudflare Worker demo", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "base: API; arch: jev-triage-playground.ziki.workers.dev · Kahneman System One + William Stanley Jevons; name: research notes + live triage Worker", "arch": "Research notes + a live Cloudflare Worker support-triage playground: Jev as the first-pass triage decision, humans on exceptions. Best naming/positioning explainer: 'Jev is a smart if statement'; name etymology", "approach": "", "training": "", "data": "None", "result": "Live hosted triage decision demo", "notes": "One-page summary of what Jev is and is not.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "adyoi-jev-playground", "name": "Jev System One playground", "author": "adyoi", "date": "2026-09-24", "category": "app", "url": "https://github.com/adyoi/jev-system-one-playground", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: Windows Forms, .NET 10, desktop app to visualize and evaluate TypeSafe primitives; notes: evaluates Choice, Score and Noul against a state; renders the structured answer plus the raw JSON", "arch": "WinForms .NET 10 desktop playground for Jev System One: evaluate Choice, Score and Noul against a state and see the structured answer rendered alongside the raw JSON", "approach": "", "training": "", "data": "", "result": "", "notes": "Three-pane layout: Statement, Question, Result, with visual builders that generate the JSON live and preset scenarios, Choice Routing, Score Rubric, Noul Yes/No; mixed question types can be sent in one request and are evaluated in parallel", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "abhishekmanikandan-jev", "name": "Jev ⚡️, abhishekmanikandan", "author": "abhishekmanikandan", "date": "2026-09-24", "category": "app", "url": "https://github.com/abhishekmanikandan/jev", "base_model": "", "params": "", "metric": "p50, p95, p99 latency; schema parse error rate; token overhead; cost per 1M decisions", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Machine-native System 1 inference engine: unstructured state to schema-validated typed decisions with calibrated probabilities in sub-100ms; p99 < 150ms; TypeSafe AI v1 API", "approach": "", "training": "", "data": "", "result": "On 10,000 synthetic event-routing requests: p50 82 ms, p95 145 ms, p99 190 ms, 0.00% schema parse errors, a fixed 59-token overhead, about $0.40 per 1M decisions, versus GPT-4o mini JSON mode at 640, 1,280 and 1,950 ms with 1.84% errors and about $3.50, and Llama 3 70B at 1,100, 2,450 and 3,800 ms with 3.12% and about $5.00", "notes": "The author's own benchmark against standard cloud LLM endpoints: GPT-4o mini JSON mode and Llama 3 70B", "domain": "general", "score": "", "latency": "p50 82 ms; p99 190 ms", "benchmark": "", "cost": "about $0.40 per 1M decisions", "speedup": ""}
{"id": "jev-wekit-mimo", "name": "Jev 聊天助手 · WeKit 版", "author": "kongbai006", "date": "2026-09-24", "category": "app", "url": "https://github.com/kongbai006/jev-wekit-mimo", "base_model": "local intent triage, optional OpenAI-compatible model: Xiaomi MiMo, DeepSeek, Tongyi, Zhipu or OpenAI", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Java and BeanShell script for the WeKit WeChat module: after a private message arrives it judges intent, danger level and emotion locally; an optional OpenAI-compatible model gives advice plus three candidate replies; candidates are copied to the clipboard and pasted manually, never auto-sent; a /jev config page in the chat box; sister versions for WA, QStory and Nuke", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-advisor", "name": "jev-advisor", "author": "Noe1120", "date": "2026-09-24", "category": "app", "url": "https://github.com/Noe1120/jev-advisor", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 7, "likes": "", "detail": "base: private, local; name: Codex skill", "arch": "Advisory decision skill for Codex: at meaningful decision points compares the available actions and gives the host model a compact recommendation — whether another tool call is useful, which candidate tool or action is most promising, 10 risk categories for what could go wrong before the call, how to recover after failure or partial result, and whether there is enough observable evidence to finish; explicit advisory-only semantics, never executes", "approach": "", "training": "", "data": "None", "result": "Private local decision advice inside a coding agent loop", "notes": "Part of the 'Jev as agent advisor' skill wave. Python; MIT; English and 简体中文; 7 stars at sweep time", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "liao96312-nanojev", "name": "jev-arena-nanojev", "author": "liao96312", "date": "2026-09-24", "category": "app", "url": "https://github.com/liao96312/jev-arena-nanojev", "base_model": "NanoJev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 4, "likes": "", "detail": "The player collects gems and survives to the next level; difficulty comes not just from monster count but from enemy intent, dangerous terrain, limited ammo, skill cooldowns, environmental kills, and combat pacing that accelerates level by level. Systems: tactical turns of 2 AP, combining move, attack, push, shoot, heal, dash, EMP and wait; five enemy classes with distinct movement and attacks, and the swift beast telegraphs a two-tile obstacle-dashing charge before biting on the next beat; readable warnings through ground traces and danger zones; a composite bow with high damage and knockback, and a pulse pistol with longer range, with weapons and limited ammo persisting across levels; a two-tile dash with in-action invincibility frames, and an EMP that interrupts nearby enemies; campfires, spikes, pits and explosive barrels as both threats and kill tools; and safe generation that guarantees an unharmed path to gems and critical items. The Chinese UI shows the model's probabilities, the basis of each choice, and inference time in real time, with bosses from level 10–90, including the Prism Warden and the Storm Chorus Ring. Targeted at a GTX 1660S training box, 164 tests passing, Pygame, Python 3.10 or newer, docs in five languages.", "arch": "Jev Arena, a fully local grid-tactics game, and a reproducible experiment ground for NanoJev, rule agents and search algorithms: not pre-written battle scripts, but the model faces real candidate actions each turn and judges movement, attack, shooting, healing, dashing and environmental chain reactions, running a real game engine against a local NanoJev service.", "approach": "", "training": "Local training on the grid task", "data": "", "result": "Local grid-decision game + training loop", "notes": "Local grid-decision game with a training loop; 4 stars.", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cooper667-jev-browse", "name": "jev-browse, cooper667", "author": "cooper667", "date": "2026-09-24", "category": "app", "url": "https://github.com/cooper667/jev-browse", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "A Claude Code and Codex plugin that runs a checklist written in plain English in a real browser. Each line is an action, Select the Generate Link button or Type Acme into the Name field, or a check, Confirm the report lists three risks. Playwright drives the browser; TypeSafe's Jev model, served on Cloudflare Workers AI as typesafe/jev, picks the element for each action from the page's accessibility tree and judges each check", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-chat-es", "name": "Jev-CHAT Spanish", "author": "dariozfold6-wez2", "date": "2026-09-24", "category": "app", "url": "https://github.com/dariozfold6-wez2/JEV-CHAT", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "It deploys on Vercel, defaulting to the Vercel AI Gateway's typesafe-ai/jev with automatic authentication; an optional TYPESAFE_API_KEY switches to the TypeSafe API directly instead of the gateway, and optional Upstash Redis adds a per-IP limit of 20 questions per hour, without which the chat works but unthrottled, and the README advises configuring it for a public post, with Upstash's free plan. Local development pulls the gateway token, which lasts 12 hours, via vercel env pull. The question and options live in lib/jev.ts, the per-IP limit in lib/ratelimit.ts, the maximum question length in the API route, and colors and typography in globals.css. Live at jev-chat-ten.vercel.app.", "arch": "Preguntale a Jev, a Spanish, no-registration Jev chat: you write a yes-or-no question, and the /api/preguntar endpoint sends it to Jev as a Choice question with four options, Si, No, Depende and No es de si o no, and Jev returns the chosen option, the probability of each, and the confidence, which the page displays as a probability bar.", "approach": "", "training": "", "data": "None", "result": "Localized Jev chat", "notes": "'Depende' option as a third value between yes and no.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-chat-jarvis-simple", "name": "jev-chat-jarvis-simple", "author": "jev-chat", "date": "2026-09-24", "category": "app", "url": "https://github.com/jev-chat/jev-chat-jarvis-simple", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: Android input-method panel spanning all chat apps - zero app adaptation, no accessibility or screen-recording permission needed", "arch": "The Android input-method edition of the jev-chat family: long-press a message to copy it and the custom keyboard shows its intent, risk and candidate replies, tapping one inserts it straight into the input box; one input method works across all chat apps, WeChat, QQ, DingTalk, SMS and WhatsApp, with no app switching, no accessibility or screen-recording permissions, the deliberate trade being one manual copy step", "approach": "", "training": "", "data": "", "result": "", "notes": "Android IME member of the jev-chat family; smallest permission footprint of the family.", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tiagosantos16-jev-code-reviewer", "name": "jev-code-reviewer", "author": "TiagoSantos16", "date": "2026-09-24", "category": "app", "url": "https://github.com/TiagoSantos16/jev-code-reviewer", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "A code reviewer that spreads the work across two models so you stop paying frontier prices to read files that were fine anyway. TypeSafe Jev, a fast decision model, scans every file and flags the risky ones. Only the flagged ones go to a ch", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-crush", "name": "jev-crush: chat affinity reader", "author": "zhengge6", "date": "2026-09-24", "category": "app", "url": "https://github.com/zhengge6/jev-crush", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 2, "likes": "", "detail": "approach: Typed question battery over the conversation", "arch": "Paste a two-person chat log, pick which side is you, and Jev reads the other person's signals: emotion, intent, reply ratings, and affinity are labeled directly on the message bubbles; a relationship type is chosen first to condition the reading", "approach": "sdk", "training": "", "data": "", "result": "Web app; built on the Crush Monitor codebase", "notes": "", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ronadin2002-jev-cua", "name": "jev-cua", "author": "ronadin2002", "date": "2026-09-24", "category": "app", "url": "https://github.com/ronadin2002/jev-cua", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 37, "likes": "", "detail": "Keep talking: turn the mic on once, speak a full request, pause, then give another, with commands queued while work is in progress. Type when you prefer: click the bar, enter a command, press Return, and text commands work with the mic off. Complete multiple steps: each action is followed by a fresh observation and another Jev decision, with options coming from the current interface and installed apps. See what is happening: the bar shows your command and current action, and Settings, Jev activity, shows the model's inputs, choices, errors and observed results. The bar stays available across apps and full-screen Spaces, there is no confirmation queue, and the stop button and cancel task interrupt execution. A 42-second demo with sound covers opening Chrome, searching, calculating, and taking a photo.", "arch": "Jev Voice for macOS, a small floating bar for voice and text commands where Jev picks the next action from the Mac's live controls, the app executes it, and the loop continues toward your request: an observe, act, verify loop of voice and text control of the Mac, for macOS 14 or newer on Apple silicon, using Jev latest.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-decision-fit", "name": "jev-decision-fit-worksheet", "author": "luancaldeira", "date": "2026-09-24", "category": "app", "url": "https://github.com/luancaldeira/jev-decision-fit-worksheet", "base_model": "Workflow worksheet", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Step 1, describe a real decision before choosing any model: the input the system receives, the allowed outputs with stable IDs, the action after the decision, who validates and executes that action, and what happens with an unknown, ambiguous or unavailable response; if you cannot list the outputs or define the fallback, the task is still too broad. Step 2, use the simplest option that solves it: exact rules on reliable data start with deterministic code, semantic judgment with finite options evaluates a bounded classifier or decider, text, synthesis, explanation or open reasoning uses a generative model, and high-impact actions get a deterministic policy plus a required approval, with the reminder that a decision call is not automatically better than a simple condition, so compare with the current path before replacing something. Step 3, sketch the safe flow: input, exact rules, negations and permissions in code, limited options described to the decider, validate the result's enum or ID against a local allowlist, a safe fallback on timeout, invalid ID or uncertainty, final policy and authorization in code, then execute or ask for human review, with a routing example in between.", "arch": "A free 10-minute worksheet, adapted from the Jev Operator Kit v0.1, for judging whether a part of an agent workflow fits Jev-style decisions: it chooses between deterministic code, bounded semantic decisions, and generative models, and it does not require Jev to use the checklist; the public preview is in Brazilian Portuguese, with a PDF download.", "approach": "", "training": "", "data": "", "result": "Adoption worksheet", "notes": "'Jev decision fit worksheet' — practical adoption tool.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "q3learners-jev-demofast", "name": "jev-demofast", "author": "q3learners", "date": "2026-09-24", "category": "app", "url": "https://github.com/q3learners/jev-demofast", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "arch: one sentence in, a narrated product demo video out - Jev drives your real product in a browser, outlines the elements, and renders MP4/GIF with voice narrationtep decisions made by Jev", "arch": "Type one sentence and get a narrated demo video of your real product: Jev drives the product in a browser, outlines what to show, and the result renders as MP4/GIF with voice narration, with Jev deciding each step", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "marketing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-digest", "name": "jev-digest", "author": "pr0ta9", "date": "2026-09-24", "category": "app", "url": "https://github.com/pr0ta9/jev-digest", "base_model": "Jev; MCP + Python", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "base: API; arch: no summarizer hallucination; notes: cf. Jev Search, JevClean; name: passage selection for agents", "arch": "Jev decides which passages a long document needs; returns verbatim passages + URL; agent reads only selected text", "approach": "", "training": "", "data": "None", "result": "Early result: 67% reduction in input tokens and ~half the tool rounds in one Codex research run", "notes": "Decision model as a context-router.", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "raghavpillai-jev-drone", "name": "jev-drone by raghavpillai", "author": "raghavpillai", "date": "2026-09-24", "category": "app", "url": "https://github.com/raghavpillai/jev-drone", "base_model": "Jev via OpenRouter", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Research prototype: Jev steers a simulated PX4 X500 drone through an eight-room house in Gazebo; Jev picks both the navigation task and the motion update while PX4 handles flight stabilisation; recorded demos replay in a Node plus Python-only viewer with a 12× decision-and-controls view", "approach": "", "training": "", "data": "", "result": "Multi-room search completed; complex missions still unstable; recordings cover a success, a timeout and a finish that failed the safety check", "notes": "", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "completedottech-factorio", "name": "jev-factorio-agent", "author": "CompleteDotTech", "date": "2026-09-24", "category": "app", "url": "https://github.com/CompleteDotTech/jev-factorio-agent", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "Quick start runs offline, with no key and no game: pip install -e ., then the mock backend simulates the game; with a real TYPESAFE_API_KEY, the same loop calls jev-latest at the typesafe.ai systemone endpoint. The CLI loads .env from the current working directory before reading settings, exported environment variables take precedence, and .env is ignored by Git with restricted permissions advised; the mock backend simulates the game but still uses a configured API key, so a fully offline run overrides all provider credentials. Research logging is opt-in: --run-dir creates a new exclusive evidence directory with a secret-safe manifest, fsynced SHA-256-chained lifecycle events, and a final seal, never reusing an existing directory, while the original --log-file remains independently optional and keeps its decision JSONL format. Docs cover the architecture, the build plan, and autonomous campaign supervision: bounded native runs with error-triggered Codex repair and guarded relaunch.", "arch": "A Jev-powered Factorio agent, billed as the first Jev-driven game agent to the authors' knowledge: Jev, TypeSafe AI's System One model, makes the fast macro decisions, goal, next action, stuck detection, as typed Choice, Score and Noul questions, while deterministic code owns game rules, option filtering and actuation, in a Jev picks what, code owns how design.", "approach": "", "training": "", "data": "", "result": "Factory-game agent driven by Jev decisions", "notes": "1 star.", "domain": "gaming", "score": "", "latency": "", "benchmark": "Factorio", "cost": "", "speedup": ""}
{"id": "emirbartu-jev-for-all", "name": "jev-for-all", "author": "emirbartu", "date": "2026-09-24", "category": "app", "url": "https://github.com/emirbartu/jev-for-all", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 6, "likes": "", "detail": "Jev is TypeSafe System One model, reached through OpenRouter alpha Decisions API: state in, typed answers out, each with a calibrated confidence, in ~500 ms. The installer writes the plugin block into your OpenCode config and prompts for your OpenRouter key; it is idempotent and leaves everything else in the file alone; restart OpenCode and routing is on for every session. V2 also has a native manager: opencode plugin add jev-for-all writes a plain plugins entry; it cannot add options, so set OPENROUTER_API_KEY instead of apiKey. An agent can install it itself from the install doc.", "arch": "Wires Jev into OpenCode, Claude Code and Hermes from one shared decision contract: Jev picks the skill to load, the tool subset for the step, and every browser move; the coding agent just builds", "approach": "", "training": "", "data": "", "result": "Measured results, not promises: skill routing, 64 real requests against a 22-skill roster, 0 wrong picks, hit rate 85.9%, a full run costs ~$0.005; decision cost ~$0.0001 per decision, a busy session costs pennies", "notes": "WHERE-JEV-WINS.md documents why the design is this way and where Jev wins or loses", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hugues-vnsgn-jev-ios-bridge", "name": "jev-ios-bridge", "author": "hugues-vnsgn", "date": "2026-09-24", "category": "app", "url": "https://github.com/hugues-vnsgn/jev-ios-bridge", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Claude Code submits the script and waits, without reading screens or choosing actions during the run. Runs explicit taps, full-field text replacements, swipes, waits and assertion checkpoints on a dedicated simulator. Stops inconclusively on uncertain judgments, missing or ambiguous targets, unexpected screens or execution limits. Keeps private JSONL evidence and screenshots, returns a report, and serves a token-protected local watch page. Preserves the recorded verdict after a run; an interrupted journal never becomes a pass. The script author supplies the route, selectors, guards and typed values; the app must already be installed and its simulator booted; the bridge does not build, install, seed or reset apps.", "arch": "Verify an iOS app with one authored script and one recorded report: the bridge executes guarded actions through MobileBuildMCP, and TypeSafe Jev judges assertions about the resulting screens", "approach": "", "training": "", "data": "", "result": "v0.1.0 experimental prerelease: the frozen assertion experiment passed its gate, twelve real scripts matched their expected outcomes, and the installed Claude Code path passed; full benchmark scripts passed Weather and Contacts, Reminders reached the correct visible state but returned inconclusive on a count assertion; measured results include unsuccessful attempts and comparison limits", "notes": "Requires Node 24 or later on a Mac with Xcode and an iOS simulator", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "juststeve-jevloop", "name": "jev-loop: 24/7 paper-trading loop", "author": "justSteve", "date": "2026-09-24", "category": "app", "url": "https://github.com/justSteve/jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "approach: Decision battery with threshold composition", "arch": "Continuous paper-trading loop split so code computes the market state, Jev answers seven typed questions about it in one call, and code composes those answers into an action using thresholds set in strategy.py; a risk engine can veto any of it and execution defaults to Alpaca's paper API", "approach": "sdk", "training": "", "data": "", "result": "Runs any crypto pair around the clock or US equity tickers during market hours; offline test suite; built for the video How to Use Jev to Build a 24/7 HFT Trading System", "notes": "", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "blockedpath-jev-model-router", "name": "jev-model-router, Codex", "author": "BlockedPath", "date": "2026-09-24", "category": "app", "url": "https://github.com/BlockedPath/jev-model-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: Codex plugin - TypeSafe Jev chooses the subagent model, gpt-6-luna, sol, astra, and reasoning effort for eligible calls; notes: leaves the parent model and explicit subagent picks alone; Bun; requires your own TypeSafe key", "arch": "Codex plugin that lets Jev choose the subagent model, gpt-6-luna, sol, astra, and reasoning effort for eligible calls, leaving the parent model and explicitly chosen subagents untouched; runs on Bun with your own TypeSafe key", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "routing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jozso39-jev-router-claude", "name": "jev-model-router-for-claude", "author": "jozso39", "date": "2026-09-24", "category": "app", "url": "https://github.com/jozso39/jev-model-router-for-claude", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: automatic per-turn model routing for Claude Code and OpenAI Codex using TypeSafe Jev through an OpenRouter API key alone, no TypeSafe account, signups closed; notes: fork of gargpratyush/jev-router; follows the current Claude Code 2.1.2xx request shape, upstream stopped routing there; adds jev-proxy for remote-control and IDE sessions; Jev sends simple work to the fast tier, difficult work to the strong tier", "arch": "Per-turn model routing for Claude Code and Codex with Jev reached through an OpenRouter key alone, TypeSafe has closed signups: a fork of gargpratyush/jev-router that follows the Claude Code 2.1.2xx request shape and adds jev-proxy for remote-control and IDE sessions", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "routing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-mt5-sentinel", "name": "JEV-MT5 Sentinel", "author": "jpXproject", "date": "2026-09-24", "category": "app", "url": "https://github.com/jpXproject/jev-mt5-hft-framework", "base_model": "JevRiskEngine hard boundaries, Gemini 2.5 Flash AI engine", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "MetaTrader 5 HFT framework: MQL5 strategy plus a JevRiskEngine with hard dollar boundaries that neither the strategy nor the AI can raise or override; FastAPI bridge on port 8765; native on-chart MT5 HUD", "approach": "", "training": "", "data": "", "result": "", "notes": "README badge claims MIT but no LICENSE file detected on the repo at sweep time", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mingleiw-jev-oncall", "name": "jev-oncall", "author": "mingleiw", "date": "2026-09-24", "category": "app", "url": "https://github.com/mingleiw/jev-oncall", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Incident triage on Jev: 'the model judges, plain code decides'; routing on probability distributions with a human-review middle band and fail-open defaults", "approach": "", "training": "", "data": "", "result": "Incident-triage routing", "notes": "1 star.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "damian87x-jev-pi-router", "name": "jev-pi-model-router", "author": "damian87x", "date": "2026-09-24", "category": "app", "url": "https://github.com/damian87x/jev-pi-model-router", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "arch: pi extension - on every fresh user turn Jev judges difficulty, work kind and costliness of a mistake; tier + specialty → pool → pi.setModel to the first usable model, images, context size; notes: one Jev call ~$0.0001; pure pi, reads pi's own model registry; unsure/slow/broken keeps current model, routing never blocks a turn; companion extension jev-pi-skills exposes Jev decisions as tools, search, rerank, supervise, pick, and hooks, skill select, compaction", "arch": "pi coding-agent extension: one Jev call per fresh turn, about $0.0001, judges difficulty, work kind and mistake cost, then switches pi to the first model in that tier's pool that pi can actually use; anything unsure, slow or broken keeps the current model and routing never blocks a turn. Companion extension jev-pi-skills exposes Jev decisions as tools, search, rerank, supervise, pick, and hooks, skill select, compaction", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "routing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "frankda-jev-poly-crypto-demo", "name": "jev-poly-crypto-demo", "author": "frankda", "date": "2026-09-24", "category": "app", "url": "https://github.com/frankda/jev-poly-crypto-demo", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 38, "likes": "", "detail": "The demo mode uses deterministic synthetic prices, simulated order books and a mock model, and exercises entries, 5-minute round changes, settlement and ledger recovery end to end, but says nothing about strategy returns; after the first start, a trade may only appear once the entry window or the next round begins. Live data sets DATA_MODE=live, MODEL=jev, JEV_MODEL_ID=jev-latest and TRADING_MODE=paper; MODEL=mock checks only the live data pipeline, bun run demo always forces synthetic data and the mock model and never calls Jev, and MODEL=jev without a key fails at startup instead of silently falling back to the mock. The API key is used server-side only and .env is excluded by .gitignore. The server listens on localhost only by default, and the README warns not to expose the local dashboard to the internet as is, pointing to a deployment section with CONTROL=off, CORS_ORIGIN and LEDGER=memory. Decisions derive the Polymarket event identifier btc-updown-5m-{round start in epoch seconds} from UTC time and fetch that event from Gamma. Requires Bun, verified locally with 1.2.6.", "arch": "A Jev and Polymarket BTC 5-minute decision lab, a research tool for Polymarket's BTC 5-minute Up and Down markets, following the market state, Jev class scores, independent execution logic, live dashboard structure of jarrodwatts/jev-trader: it implements a live market-data adapter, Jev integration, paper trading, and a dashboard, but connects no wallet and submits no real orders, with TRADING_MODE=live rejected at startup.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kyu-softmatter-autofocus-jev", "name": "Jev-powered microscope autofocus experiments", "author": "kyu-softmatter", "date": "2026-09-24", "category": "app", "url": "https://github.com/kyu-softmatter/autofocus-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Jev-powered microscope autofocus experiments using pymmcore and deterministic image features.", "approach": "", "training": "", "data": "", "result": "PLAN.md, written in Korean at 46 KB, fixes scope before any code: use Jev to judge whether a microscope image is in an acceptable focus state, then extend to safe Z-axis control; pymmcore plus deterministic image features; plan in Korean, all code, config, logs and CLI strings in English", "notes": "Pushed 2026-09-25; uv-managed project with config, tests and a license", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "khordoo-jev-reflex-autonomy-lab", "name": "jev-reflex-autonomy-lab", "author": "khordoo", "date": "2026-09-24", "category": "app", "url": "https://github.com/khordoo/jev-reflex-autonomy-lab", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 18, "likes": "", "detail": "", "arch": "An interactive multi-drone autonomy simulation powered by TypeSafe Jev, exploring a simple question: what happens when fast, typed System 1 reflexes can ask a slower System 2 reasoning model for advice without giving up control?", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "milojarow-jev-skills", "name": "jev-skills", "author": "milojarow", "date": "2026-09-24", "category": "app", "url": "https://github.com/milojarow/jev-skills", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "The CLI, jev, sends state and typed questions to TypeSafe and prints compact JSON or one scalar; the skill teaches agents when to use that decision step in software and when to use it directly from the shell during their own work. It covers the boundaries that matter: atomic questions, batching, none-applicable outcomes, confidence versus value, calibration, adversarial input, and the exact work that belongs in code; examples are generic and contain no account inventory. No pip packages, jq or curl are needed; no credential belongs in an argv, a repo or a generated file.", "arch": "Shared agent skill and a Python standard-library CLI for bounded semantic decisions with TypeSafe Jev", "approach": "", "training": "", "data": "", "result": "Credential resolution order: first nonempty TYPESAFE_API_KEY from the environment, then ~/.secrets/environment.d/11-secrets.conf, then ~/.config/typesafe/keyring.env; both files accept an optional export prefix and quotes, the last assignment in a file wins, missing or unreadable sources are skipped", "notes": "Offline checker uses a local fake server and generated dummy credentials, and requires Linux /proc to inspect process arguments", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "raphaelcangucu-solana", "name": "jev-solana-trader", "author": "raphaelcangucu", "date": "2026-09-24", "category": "app", "url": "https://github.com/raphaelcangucu/jev-solana-trader", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "The TypeSafe-hosted Jev enters as the open model von via von-sdk at POST /v1/systemone, falling back to von.system_one in-process if it is down. Pipeline: price, book, Jupiter quote, fees and public balances, then 12 adjectives with no numbers in the model text, then gates where a high skip or low confidence becomes a hold; dry-run stops there, LIVE_TRADING=1 executes a USDT to SOL swap on Jupiter Ultra, signing only the taker. Modules: config, state, decide, swap, loop. The swap, when it exists, is USDT to SOL on Jupiter Ultra; the experiment log is public and only grows.", "arch": "Local Solana trading bot in the System One pattern: every ~15 seconds the market is compressed into a short phrase of twelve adjectives, a model chooses buy, sell or hold, and a veto skip_this_cycle can cancel the cycle", "approach": "", "training": "", "data": "", "result": "Starting point 2026-09-23 23:30 BRT: 0.017392206 SOL plus 50.00929 USDT; public experiment wallet GNJv4FcMb4j1A6NFiVaaHkGVTZ5p5A7ea9GsFgccS75r", "notes": "Portuguese README; the comparison that chose von is in research/jev-alts, the starting book in docs/EXPERIMENT.md; the key only enters via SOLANA_KEYPAIR_PATH at swap time", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chaoliangunsw-jev-style-v3", "name": "Jev-Style v3 demo Spaces", "author": "chaoliangUNSW", "date": "2026-09-24", "category": "app", "url": "https://huggingface.co/spaces/chaoliangUNSW/jev-style-v3", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "Runs the model repo own PyTorch runtime in float32 on ZeroGPU; other builds at GGUF, 0.53 GB, and MLX. Tags: text-classification, llm-routing, guardrails, calibration. Apache-2.0, pinned, gradio 6.28.0, Python 3.12.", "arch": "Gradio space for Jev-Style-0.8B-Decision-v3: give it a text and a question, get a calibrated probability for every option, choice, yes/no or score, up to 25,600 tokens of input", "approach": "", "training": "", "data": "", "result": "", "notes": "Model repos: chaoliangUNSW/Jev-Style-0.8B-Decision-v3 plus GGUF and MLX builds", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shai-koffman-jev-web-agent", "name": "jev-web-agent", "author": "Shai-Koffman", "date": "2026-09-24", "category": "app", "url": "https://github.com/Shai-Koffman/jev-web-agent", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A small, readable web agent driven only by Jev, TypeSafe's System One decision model, with no LLM anywhere. Jev never writes text or actions: on each step the agent turns the page into text, asks Jev five typed questions in one call, and acts on the answers.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gmoreva-jev-scripts", "name": "Jev: браузерный агент и демо-примеры, gmoreva", "author": "gmoreva", "date": "2026-09-24", "category": "app", "url": "https://github.com/gmoreva/jev-scripts", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Набор демо-скриптов и веб-приложений, показывающих, как Jev, модель TypeSafe System One, ускоряет и удешевляет работу LLM в задачах, где LLM обычно применяют в одиночку — браузерные агенты, классификация тикетов, поиск элементов на стра", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevai-dev", "name": "jevai.dev", "author": "community", "date": "2026-09-24", "category": "app", "url": "https://jevai.dev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: guides + projects + community; notes: live directory", "arch": "Jev AI developer hub: API developer guides, a projects section comparing independent open-model typed-decision work, a TypeSafe team and manifesto section, and an FAQ", "approach": "", "training": "", "data": "", "result": "", "notes": "Observed 2026-09-24.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "suparvacode-jevbrow", "name": "JevBrow", "author": "SuparvaCode", "date": "2026-09-24", "category": "app", "url": "https://github.com/SuparvaCode/JevBrow", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "It positions itself against modern AI browser drivers such as Stagehand, Browserbase, Browser-Use and MultiOn, which rely almost exclusively on LLMs or vision models for every single interaction: each step serializes 15,000–50,000 tokens of raw DOM trees or high-resolution screenshots, so a simple 10-step form submission consumes more than 200,000 tokens, costing $0.50 to $2.00 per single run; autoregressive LLMs take 3–8 seconds to generate tokens for trivial decisions like which button is Submit; and generative models often invent non-existent CSS selectors or hallucinate bounding boxes when DOM trees shift. The README includes a cost and speed comparison against a traditional LLM driver.", "arch": "JevBrow, a sub-100ms natural-language browser driver and automation framework, powered by Jev, TypeSafe's System One, with an intelligent OpenAI-compatible LLM fallback, published on npm, MIT, TypeScript strict.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevclean", "name": "JevClean", "author": "Brycetlm", "date": "2026-09-24", "category": "app", "url": "https://github.com/Brycetlm/JevClean", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Find Codex conversations by partial keywords, project, task ID or link, showing the three most recent conversations; preview message counts before starting, then watch segments flow into Keep and Omit buckets with live counts; configure your own prompt and separate classification criteria, and optional manual review adds a third bucket; control the neighboring context window, defaulting to previous plus target plus next, plus batch size and concurrency; skip whole messages with editable regular expressions, the visible defaults being key and apikey, case-insensitive, with matching messages staying local and kept; configure your own Vercel AI Gateway key in the UI, with missing credentials opening the configuration dialog before classification; review the compact copy, confirm Apply, then export Markdown or copy a handoff prompt. JevClean produces a new, extractive Markdown copy: it does not delete original conversations, rewrite Codex JSONL or SQLite files, or replace an active task's context, and it warns that classification is fallible, so inspect the preview before continuing. macOS, Python 3.9 or newer, local Codex conversation records, normally ~/.codex; Node.js 22 or newer for the optional embedded sidebar, while the standalone browser UI uses only Python's standard library; a Vercel account with a working AI Gateway API key and access to typesafe-ai/jev. Four themes: Prism, Fresh, Tech and Plain.", "arch": "JevClean, a local conversation organizer for Codex, powered by Jev through the Vercel AI Gateway: keep the context that matters, it classifies Codex conversation messages as Keep or Omit and produces an extractive Markdown copy, with English and Simplified Chinese interfaces.", "approach": "", "training": "", "data": "None", "result": "Cheaper context for long Codex sessions via Jev Keep/Omit decisions", "notes": "Decision-model use as a context-compression gate.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-clip", "name": "jevclip", "author": "cclank", "date": "2026-09-24", "category": "app", "url": "https://github.com/cclank/jevclip", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 25, "likes": "", "detail": "", "arch": "Jev scores every segment of a subtitle or transcript, then code cuts two videos: a 3-minute highlight reel ranked by value, and a de-watered full version that only removes segments certain to be useless; every sentence in the summary links back to the original timestamp, and every dropped segment carries a written reason", "approach": "", "training": "", "data": "", "result": "SRT, VTT, whisper JSON, plain text, and markdown input; zero runtime dependencies, Python standard library plus ffmpeg; batch runs over a whole directory, focus filtering by topic", "notes": "MIT, alpha; macOS and Linux; the judge is jev-1.13.0", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nibzard-jevditor", "name": "Jevditor", "author": "nibzard", "date": "2026-09-24", "category": "app", "url": "https://github.com/nibzard/jevditor", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "Jev detects: each saved rule becomes a yes-or-no question, Noul, that Jev answers for known sentences, passages and sections, and when a rule is flagged, a speculative Choice question picks a predefined explanation. Ordinary code decides what to show: it segments the text, runs exact checks, applies thresholds and sensitivity, rejects stale results, and handles suppressions. Claude drafts rule definitions and rewrites a selected passage when you ask, and never runs while you type. The layout: packages/engine is the editor-independent core with segmentation at exact offsets, exact checks, snapshot identities, thresholds and preset rules, with no model calls and no DOM; apps/server is a Hono API with auth, rule versioning in SQLite, the lint endpoint, the Jev classifier, caching, rate limits, playground evaluation, and Claude drafting and rewrites; apps/web is a React plus Tiptap editor with a ProseMirror decoration plugin, a check scheduler, finding cards, the rules editor, and the rule playground. Requires Node 22.13 or newer, for node:sqlite, and pnpm. Without TYPESAFE_API_KEY, development uses a demo classifier based on word overlap with the rule's own examples, which is not Jev and whose probabilities mean very little, and the UI shows a Demo classifier badge whenever it is in use; it cannot be enabled in production. Set TYPESAFE_API_KEY to use Jev, and ANTHROPIC_API_KEY to turn on rule drafting and rewrites.", "arch": "Jevditor, a programmable writing linter: a personal style guide that runs while you write, where you tell it what you dislike in writing and it quietly catches those patterns in yours, which is not a chatbot sitting beside the document and never keeps rewriting your voice; the work is split three ways, with Jev detecting, ordinary code deciding what to show, and a generative model rewriting only on request.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hctdip-mule-run", "name": "Jeveto", "author": "HCTDIP", "date": "2026-09-24", "category": "app", "url": "https://github.com/HCTDIP/jeveto", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "The name is a design claim: an agent should not have the right to say it feels like something is okay, only the evidence of whether the probability is high enough, which is also why it was renamed from MuleRun, a name already taken by a funded AI-agent marketplace. Four mechanisms answer four fears: hallucinated completion, where DONE is verified independently by code, completed equals total, and the chain never reports done falsely; confident wrong tool or work assignment, where a confidence gate only executes when the probability distribution computes at least 0.85, and otherwise stores the decision as a saved handoff for human review, never blindly dispatching; runaway unattended costs, where spending and action limits stop the run immediately at the cap, with each step persisted so runs can resume from a breakpoint; and dependency hell, where the whole thing is 12 readable files, FastAPI plus SQLite, with a built-in agent pool of free, keyless APIs that runs on a phone. The public, re-verifiable track record: the same decision layer judged four real opportunities with a true-or-false noul, an expected-value score, and a next-step choice, matching human due diligence on all four, including identifying both honeypots in under a second, where a human would need days of history; repetition consistency is 4/4 with zero flips across three repeats of the same sample; white-box reproducibility shows drift of at most 0.01 and zero flips over three consecutive runs of the same sample, with perturbation responses matching intuition, removing Help! dropping 0.07, adding URGENT: rising 0.72, and overdue to paid in full dropping 0.93, at $0.0000119 per decision, with reproduction scripts and monitoring in the sibling repo jev-calib. MIT.", "arch": "Jeveto, in Chinese, a confidence-gated agent decision layer, originally MuleRun, and named for Jev, the decision model, plus veto: it takes the question of whether to act out of the model's self-report and hands it to a probability plus a hard gate, running an agent orchestration layer on FastAPI plus SQLite with a ten-step decision kernel, an agent market pool, and a chained orchestration console, the kernel implementing the Jev Engineering 10-Step Roadmap by 0xCodila, where the LLM creates the work and Jev decides what happens next.", "approach": "", "training": "", "data": "", "result": "", "notes": "Repo renamed or transferred since the 2026-09 sweep; URL updated to the current location", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevkit", "name": "jevkit", "author": "JasmineAIGC", "date": "2026-09-24", "category": "app", "url": "https://github.com/JasmineAIGC/jevkit", "base_model": "Jev and Kev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "arch: kev train format · ECE: Noul 0.012 / Choice 0.086 / Score 0.254; training: threshold fitting; data: kev train format; name: policy + evaluation framework", "arch": "Policy = data + pure function: thresholds compiled from labeled data into policy.lock.json; per-question-type calibration; drift check → recompile; zero-dependency core", "approach": "", "training": "Policy compilation", "data": "Labeled data", "result": "Unified decision policy + evaluation across Jev/Kev with compiled, auditable thresholds", "notes": "'The policy layer of the Jev ecosystem' — treats decision thresholds as versioned, compiled artifacts.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "slateeho-jevlet", "name": "Jevlet", "author": "slateeho", "date": "2026-09-24", "category": "app", "url": "https://github.com/slateeho/jevlet", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Jevlet never asks Jev to generate shell commands, YAML or arbitrary Kubernetes API calls. The pipeline: Kubernetes state, events, scheduler state, Prometheus, and optional external health, into the StateBuilder, into Jev via Choice, Score and Noul, into a deterministic PolicyGate, into a bounded Kubernetes action, where the model interprets state and the controller decides whether anything is allowed to happen. A lot of Kubernetes incidents do not begin with a useful application log: sometimes the container never starts, sometimes there is no Pod to inspect, sometimes all Pods are Running but traffic is crawling, sometimes Helm reports deployed while runtime dependencies are stale, and sometimes the scheduler is doing exactly what you asked, and that is the problem. One worked failure class: pod anti-affinity makes a Deployment unschedulable, so with three replicas, a required podAntiAffinity and only two eligible workers, two replicas run and the third remains Pending, with no useful application logs because the application never started.", "arch": "Jevlet, a Jev-powered System One control loop for Kubernetes, built for the class of Kubernetes failures that are often obvious only after combining several pieces of cluster state, scheduler constraints, missing objects, broken selectors, network signals, stale release dependencies and rollout state, rather than simply reading application logs, in a Jev decides, Jevlet authorizes and executes design where Jev can only return values from a closed decision schema and ordinary Go code owns authorization, policy and actuation.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zzlgreat-jevm", "name": "jevm", "author": "zzlgreat", "date": "2026-09-24", "category": "app", "url": "https://github.com/zzlgreat/jevm", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Let the market grade the news. A label factory and a single‑pass LLM screener for China A‑share news flow, trained only on how prices and volumes actually reacted, never on what another model thought", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevotron", "name": "jevotron", "author": "cmungall", "date": "2026-09-24", "category": "app", "url": "https://github.com/cmungall/jevotron", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "author: Charles Murphy; base: API; arch: record, field · CSV/JSONL/YAML/TOML/text/OBO/FASTA/GMT; data: bio-formats included; notes: cmungall; name: field-level anomaly detection", "arch": "Field-level anomaly-detection CLI: each pair is a Jev decision; wide format support; SQLite cache; Typer + MkDocs", "approach": "", "training": "", "data": "Any tabular/structured corpus", "result": "Typed-decision data QA at the field level", "notes": "From an ontology/biomedical-data veteran — Jev as a data-cleaning primitive for scientific data.", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rr-sairohith-vennu-jevqa", "name": "jevqa", "author": "rr-sairohith-vennu", "date": "2026-09-24", "category": "app", "url": "https://github.com/rr-sairohith-vennu/jevqa", "base_model": "TypeSafe Jev", "params": "", "metric": "pass/fail with evidence", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Natural-language QA test runner: a test case written in plain English drives a real browser through TypeSafe's Jev decision engine one step at a time, verifies each step's expected outcome independently, and writes a pass/fail report with evidence", "approach": "sdk", "training": "No training; test runner", "data": "Plain-English test cases with Expect lines", "result": "", "notes": "Original implementation, not a fork; uses the TypeSafe Jev API and Playwright as third-party dependencies", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevry", "name": "Jevry", "author": "michaelswissa", "date": "2026-09-24", "category": "app", "url": "https://github.com/michaelswissa/jevry", "base_model": "TypeSafe Jev for action selection plus a text model for planning", "params": "", "metric": "WebArena-Verified dev subset", "license": "MIT", "paper": "", "stars": 102, "likes": "", "detail": "", "arch": "Open-source desktop browser built around Jev: a language model plans, Jev decides, Chromium acts. The browser observes the page and compiles the actions it can actually execute, then asks Jev for a typed choice; a separate text model handles planning, language, and occasional visual reasoning; the desktop runtime owns execution, cancellation, and evidence. The observe, choose, execute, verify loop captures the page atomically with retained DOM node references, offers a Choice over complete operation and target pairs in one inference round trip, validates the choice set and rechecks page freshness before input, keeps input receipts across navigation failures, and separates model-assessed completion from independently verified goal evidence", "approach": "", "training": "", "data": "", "result": "10 of 12 WebArena-Verified dev-subset tasks passed under the unchanged official evaluator, up from 7 of 12 in the prior build, while median Jev request time fell from 443–392 ms; a separate instrumented 2048 run reached the victory screen with 20,840 points in 986 moves at a median Jev decision of 343 ms and zero general-purpose visual reviews", "notes": "Development subset of an 812-task benchmark, not a held-out or leaderboard result; v0.4.0-beta.13; accuracy improved while total run time grew", "domain": "computer-use", "score": "", "latency": "392 ms", "benchmark": "WebArena", "cost": "", "speedup": ""}
{"id": "kylemclaren-jevsearch", "name": "jevsearch", "author": "kylemclaren", "date": "2026-09-24", "category": "app", "url": "https://github.com/kylemclaren/jevsearch", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 7, "likes": "", "detail": "arch: a Noul per page on whether the visitor would be glad to land there, a Choice for the single best answer, and a Noul on whether any page answers at all · no README found", "arch": "Site search that recalls candidates locally, then uses Jev to score and rerank them. Site search: shadcn/ui command-palette block that streams keyword hits on the first keystroke, then sends the top 20 to Jev in one request", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "leowang980-jevsoup", "name": "JevSoup", "author": "Leowang980", "date": "2026-09-24", "category": "app", "url": "https://github.com/Leowang980/JevSoup", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "Authors from Beijing University of Posts and Telecommunications, University of Malaya, Georgian College and Huazhong University of Science and Technology, with a paper PDF. Jev ranks a library of 14 expert descriptions per input and routing decisions are cached locally and reusable across Qwen3-1.7B, Qwen3-4B and Qwen3-8B. The repo carries the paper main comparison, a 4B ablation and a projection-strength sensitivity study against Base, Adaptive Minds, LoGo, AdapterSoup and Arrow on the PorTAL benchmark, huggingface.co/datasets/RampPublic/portallib-tasks; model weights and benchmark data are downloaded separately", "arch": "JevSoup, System-One routing for training-free LoRA composition: given an input and textual expert descriptions, Jev selects an ordered pair of experts; System Two preserves the first expert update, projects the second away from its row space, and combines the two with equal weights on a frozen language model; no router training or expert training samples required", "approach": "", "training": "", "data": "", "result": "", "notes": "Authors per repo: Xiuying Wang, Jiahua Cheng, Shuotian Li, Yufan Cheng, Junyi Zheng, Bowen Deng, Zhexuan Bai, Yichen Li, corresponding; Beijing University of Posts and Telecommunications, University of Malaya, Georgian College, Huazhong University of Science and Technology.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nevamind-ai-jevtown", "name": "JevTown", "author": "nevamind-ai", "date": "2026-09-24", "category": "app", "url": "https://github.com/nevamind-ai/jevtown", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 38, "likes": "", "detail": "The engine offers the options and Jev chooses within them, so an illegal move is never possible. Drop a cast of agents into a pixel-art room and watch what emerges, with nobody playing, and the whole simulation runs in a single browser tab, with a small local proxy holding your model keys so the browser never sees them; it is an MVP still taking shape, with a pixel-art narrative world game on top of the engine coming soon. Installation requires Node.js 22 LTS, 22.22.1 or a newer patch, and npm: npm ci, then a .env.local with credentials for any OpenAI-compatible LLM endpoint, an embedding endpoint, and the Jev API key, with VITE_ACTION_DECIDER=jev, where OPENAI_API_KEY, TOGETHER_API_KEY, and a local Ollama host also work for the LLM, and leaving out VITE_ACTION_DECIDER lets the LLM make every decision instead of Jev. The Jev demo currently lives on the feat/jev-demo-solarium branch until it merges to main.", "arch": "Jev Town, billed as the first Jev-based AI simulation system: a game engine where the characters are driven by AI, not scripts, and every agent in the world decides what to do next through two kinds of models working together, with Jev, a System One model, making the fast, structured decisions, whether to seek someone out, who to go to, where to wander, how long to wait, and an LLM doing the language work, conversations between agents, memory, and what each agent believes about the world.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "usejul-jul-showcases", "name": "JuL showcases", "author": "usejul", "date": "2026-09-24", "category": "app", "url": "https://github.com/usejul/jul-showcases", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: self-contained showcases of JuL, Juste un LLM, local typed decisions reproducing the ideas behind jevable.com", "arch": "Small, self-contained demos of JuL, Juste un LLM, a local decision runtime with the same typed-decision interface as the TypeSafe SDK: each showcase reproduces one idea from jevable.com locally through from jul import TypeSafeClient, no hosted API; the point is a single small, fast, typed decision embedded where a full LLM call would be too slow or too expensive", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shiwaseu-katai", "name": "Katai", "author": "Shiawaseu", "date": "2026-09-24", "category": "app", "url": "https://github.com/Shiawaseu/katai", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: ultra-fast, non-autoregressive AI browser agent powered by a local System-1 decision model; notes: pykatai on PyPI", "arch": "Ultra-fast non-autoregressive browser agent: a local System-1 decision model, not an LLM, drives each browser step; distributed as pykatai on PyPI", "approach": "", "training": "", "data": "", "result": "Predicts the next browser operation and target element in 17-23 ms on CUDA and 300-500 ms on Apple Silicon MPS, a 322M multimodal BERT backbone, v10s, versus 3-8 s per step for conventional web agents", "notes": "Formulates browser interaction as typed decisions over a candidate action space; a workshop example is included, where high latency is expected due to LLM calls", "domain": "computer-use", "score": "", "latency": "17-23 ms on CUDA; 300-500 ms on MPS", "benchmark": "", "cost": "", "speedup": ""}
{"id": "j3udiel-zabbix-laya", "name": "Laboratorio Laya: alertas de Zabbix", "author": "j3udiel", "date": "2026-09-24", "category": "app", "url": "https://github.com/j3udiel/zabbix-laya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Verified on Ubuntu with Python 3.10 and 4 GB of RAM: install git and venv support if missing, clone, create a venv, install CPU-only torch 2.14.0 plus the requirements, download the model, and run the first test with the disco_lleno case. The weights are not included in the repository: they download to a Hugging Face cache inside the project, the first load can take a while, and subsequent loads reuse that cache, with all predictions computed locally, and no GPU or Zabbix credentials required. The web UI opens at 127.0.0.1:8765, and can be opened from another machine on a trusted network with --host 0.0.0.0; it is a lab server without authentication that the README says should not be published on the internet. Select an example, change the title, metrics or context, and press Evaluar con Laya, which shows categories, scores and probabilities, lets you edit the questions, and download each test as JSON, keeping the last ten tests in memory only.", "arch": "A local Laya lab for Zabbix alerts, in Spanish: a local trial with eight fictitious alerts that does not connect to Zabbix and does not execute actions on servers, an independent project for experimenting with Laya, and not an official integration of Laya or of Zabbix.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sanjaysathish-laya-integration", "name": "laya + Claude Code,  local model router", "author": "SanjaySathish", "date": "2026-09-24", "category": "app", "url": "https://github.com/SanjaySathish/laya-integration", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Run laya, a local System One decision model, as the brain that decides, for each task you give Claude Code, which Claude tier runs it, haiku, sonnet or opus, and how much reasoning effort it gets, routing simple work down and hard work up.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "itsvrushabh-laya-demo", "name": "Laya - System 1 Decision Engine Demo", "author": "itsvrushabh", "date": "2026-09-24", "category": "app", "url": "https://github.com/itsvrushabh/laya_demo", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Key capabilities: ultra-low latency, sub-40ms inference on GPU with a ModernBERT-large decision head; calibrated confidence, outputting statistical probabilities for decisions rather than uncalibrated logits; typed primitives, choice for multi-class categorization with a calibrated probability distribution, noul for fast boolean classification with a certainty score, and score for continuous or ordinal scale ranking, such as risk level, sentiment, or priority; automatic script and language routing, with a built-in router that detects language and script and dispatches requests to the optimal checkpoint, English, Multilingual, or Typed-Dections; and zero hallucination and zero JSON breakage, with pure structured data directly from the classification head and no JSON parser failures. It ships with a rich terminal benchmark, pipeline demo, and interactive REPL, plus a FastAPI REST backend and web server with a dashboard for live interactive testing.", "arch": "A comprehensive interactive demo and benchmark suite for Laya, the open-source, non-autoregressive System 1 decision engine, which evaluates state and returns typed, calibrated decisions in a single forward pass, in under 40 ms, unlike generative LLMs such as GPT-4, Claude or Llama that produce text token-by-token with multi-second latency and hallucination risks.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "iintothewind-laya-deploy", "name": "Laya CPU 部署", "author": "iintothewind", "date": "2026-09-24", "category": "app", "url": "https://github.com/iintothewind/laya-deploy", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "把 convaiinnovations/laya 的 laya-serve 部署到 4 核 16 GB 的 Windows + Docker Desktop 上，并用 cloudflared Quick Tunnel 暴露一个临时公网地址。", "approach": "", "training": "", "data": "", "result": "", "notes": "Business profile: multilingual checkpoint, single node, up to 5-6 decisions per request; on a 4-core i5-6600 with 16 GB of RAM; deploy/ is a self-contained Docker unit with a laya-status.ps1 status script; README in Chinese", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jamesonbradfield-laya-router", "name": "Laya Decision Router", "author": "jamesonBradfield", "date": "2026-09-24", "category": "app", "url": "https://github.com/jamesonBradfield/laya-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A lightweight, local-first tiered LLM decision router. Uses Laya, a ModernBERT classifier, running on CPU to classify incoming prompt intent in ~8–12 ms and dispatch requests to the optimal local or cloud tier without GPU VRAM overhead", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sothi-em-laya-finetuning", "name": "Laya Fine-Tuning,  Key/Value Match Detection", "author": "sothi-em", "date": "2026-09-24", "category": "app", "url": "https://github.com/sothi-em/laya-finetuning", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "name: KVP-10K", "arch": "Fine-tunes the Laya base model, Convai Innovations, 421M, ModernBERT-large backbone, to answer one typed question: does a value correctly match its key label, e.g. first_name = John is yes, first_name = 1992 is no", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zamax14-laya-finetune", "name": "Laya Finetune", "author": "zamax14", "date": "2026-09-24", "category": "app", "url": "https://github.com/zamax14/Laya-Finetune", "base_model": "Laya Multilingual", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "A ticket enters as state, and Laya answers three typed questions in one pass: which team handles it, a choice among hardware, software, networks, access, mail and collaboration, and security; what priority it has, a score of low, medium, high, or critical; and whether someone cannot work right now, a yes-or-no noul with its probability. The category confidence feeds a traffic light: green, above 80%, is assigned automatically; yellow, a person confirms; and red, a person decides, so it matters as much to be right as for the confidence to warn when Laya is in doubt. It uses synthetic data and RLCD training, Python 3.12, and an NVIDIA GPU with CUDA 13, and evaluates on the Pondera benchmark of 20 hand-written test tickets with real details that never appear in training. MIT.", "arch": "A Laya Multilingual specialized in classifying support tickets, in Spanish: Laya decides in a single pass, without generating text, in about 10 milliseconds, but out of the box gets the category right on 12 of every 19 tickets, and this repository specializes it for the help desk with thousands of synthetic tickets labeled by construction and an LLM as teacher, measuring the result against hand-written tickets it never sees during training.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "maurostorch-local-laya", "name": "Laya in Docker", "author": "maurostorch", "date": "2026-09-24", "category": "app", "url": "https://github.com/maurostorch/local-laya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "This directory runs Laya as an HTTP service in Docker. Laya is a decision model. You send it a state, such as an email, a ticket or a JSON object, and a set of typed questions. It returns an answer and a probability for each question in one", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bigcoke1-laya-demo", "name": "Laya inbox triage demo", "author": "bigcoke1", "date": "2026-09-24", "category": "app", "url": "https://github.com/bigcoke1/laya_demo", "base_model": "Laya", "params": "421M english and typed-decisions checkpoints, 322M multilingual", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Three primitives: choice, one label plus a probability per label, which team owns this ticket; score, the expected level on an ordered rubric plus the distribution, how urgent is this; and noul, a calibrated probability that a yes-or-no statement is true, does the customer threaten to cancel. Three checkpoints live behind one repository, and a Router picks between them per request from a sub-millisecond script or language check: english, ModernBERT-large, 421M parameters, 512 context, for English text; multilingual, mmBERT-base, 322M parameters, 1024 context, for 100 or more languages, faster; and typed-decisions, ModernBERT-large, 421M parameters, 1024 context, fine-tuned on its own benchmark schema. Because there is no decoding loop, there is nothing to hallucinate: the output is always one of the labels you defined, with a number attached, and that number is the point, you write a policy against it. The demo: triage.py runs a 10-ticket support inbox, in English, German, Portuguese and Japanese, through five questions per ticket, then acts on the answers, department as a choice among billing, technical, sales, security and other; urgency as a score from backlog to this week to today to now; churn risk and needs-a-human and is-automated as noul questions, with the whole policy in schema.py.", "arch": "A small, working demo of Laya, an open-weights, Apache 2.0, System 1 decision model from Convai Innovations: Laya is not a text generator, it is an encoder, ModernBERT-large with 421M parameters, with decision heads, you hand it a state and a set of typed questions, and it answers all of them in one forward pass, with calibrated probabilities and zero generated tokens.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "swatik425-laya-sentiment-analyzer", "name": "Laya Sentiment Analyzer", "author": "SwatiK425", "date": "2026-09-24", "category": "app", "url": "https://github.com/SwatiK425/laya-sentiment-analyzer", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "A fast, local sentiment analysis CLI using Laya — an open-source decision model that outputs calibrated probabilities for typed questions in a single forward pass. No text generation, just structured decisions", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "felix-homelab-laya-studio", "name": "Laya Studio, felix-homelab", "author": "felix-homelab", "date": "2026-09-24", "category": "app", "url": "https://github.com/felix-homelab/laya-studio", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A web panel for Laya, Convai Innovations' non-autoregressive System 1 decision model. Laya answers typed questions, choice, score, noul, about a text state and returns calibrated probabilities in a single forward pass. It never generates text.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shawny-w-laya", "name": "Laya 决策引擎实用脚本, Shawny-W", "author": "Shawny-W", "date": "2026-09-24", "category": "app", "url": "https://github.com/Shawny-W/laya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "It ships with a single-request smoke test, a generator for 30 Chinese customer-service test tickets covering billing, technical, sales and other, and a batch predictor that reads JSON cases from data/input and writes answers, routing and timing to data/output. The first run auto-downloads the checkpoints, English about 808 MB and multilingual about 647 MB; for Chinese networks you set HF_ENDPOINT=https://hf-mirror.com in .env, and if a large file download reports 401 Unauthorized because the xet CAS is not mirrored by the proxy, you also set HF_HUB_DISABLE_XET=1, with a Windows note that the Microsoft Store python placeholder, exit code 49, means using the full Anaconda path instead. Cases take an optional note, a state as text or JSON, a questions map with instructions and criteria, an optional model field to force a checkpoint, and an optional max_len up to 8192 for long documents, which the multilingual checkpoint supports. It warns that the base checkpoint is near-random on typed-decisions tasks, and the official advice is to fine-tune per domain or temperature-calibrate before trusting probabilities; that high-cardinality choice questions with more than 20 options lose accuracy, so raise head_max_len or choose hierarchically; and that non-Latin scripts, such as Chinese and Hindi, must go through the Router, because the English checkpoint answers non-Latin text confidently and wrongly.", "arch": "Laya decision engine utility scripts, in Chinese: a multilingual typed-decision script based on laya, the convaiinnovations/laya model: given a state, text, email, ticket or JSON, and a set of typed questions, choice, score and noul, it returns answers with calibrated probabilities in a single forward pass, with the Router automatically selecting the checkpoint by language.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "davidl-zhan-laya-demo", "name": "Laya 考试题知识点匹配 Demo, davidL-zhan", "author": "davidL-zhan", "date": "2026-09-24", "category": "app", "url": "https://github.com/davidL-zhan/LAYA_DEMO", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "在本地网页中输入一道考试题和若干知识点，让 Laya 分别判断解题是否需要每个知识点。结果按阈值显示“匹配 / 不匹配”和概率，同时可以展开查看 Laya 返回的原始结构化 JSON。", "approach": "", "training": "", "data": "", "result": "", "notes": "Python 3.13 or newer with uv: uv sync --locked, uv run python main.py; the same startup command on Windows PowerShell 7 and macOS; README in Chinese", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hydriaone-laya", "name": "Laya,  Decision Model Interactive Inspector", "author": "HydriaOne", "date": "2026-09-24", "category": "app", "url": "https://github.com/HydriaOne/laya", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Self-contained Docker deployment of the Laya routing model plus an interactive HTML inspector: FastAPI with a preloaded CUDA router and a single POST /predict; noul at 0.75 or higher gates a change as a breaking change, otherwise safe to auto-apply", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "samyakb32-sys-laya", "name": "Laya,  task sub-agents, no GPU required", "author": "samyakb32-sys", "date": "2026-09-24", "category": "app", "url": "https://github.com/samyakb32-sys/Laya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "One small model, many system prompts: Laya, a ~400M model deciding in well under a second on CPU, reads each request and picks which sub-agent should answer it - email, code, math, translation, and more, all defined in agents.yaml; every sub-agent is the same small base model with a different system prompt, nothing to fine-tune, no GPU", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "benny93-laya-browser", "name": "laya-browser", "author": "Benny93", "date": "2026-09-24", "category": "app", "url": "https://github.com/Benny93/laya-browser", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "A side-by-side Wikiracing, Rubber duck to Albert Einstein with links only, shows agent-browser with Qwen3-30B-A3B on local Ollama, thinking off, taking 26.3 s over 9 steps, against laya-browser at 3.1 s over 5 steps. Requires Apple Silicon, macOS 14 or newer, Python 3.11 or newer, and agent-browser on PATH; install with uv tool install from git, and the first English selector downloads the model aac6fef/laya-mlx, about 800 MB, from Hugging Face. It also documents installing behind a TLS-intercepting corporate proxy: uv gets --system-certs, and the Hugging Face client needs the keychain roots exported to a CA bundle file, which the laya-browser daemon inherits from its starting shell.", "arch": "agent-browser, but selectors can be plain English, resolved on-device in milliseconds: it accepts every agent-browser command and passes it through unchanged, and when a selector is English, such as click log in, instead of a @ref or CSS, a local Laya typed-decision model, via laya-mlx, picks the matching element from the page snapshot, so the agent skips the snapshot, read, pick-ref round-trip, with no LLM call and no token cost.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "supremedreamz-laya-code-router", "name": "laya-code-router", "author": "SupremeDreamZ", "date": "2026-09-24", "category": "app", "url": "https://github.com/SupremeDreamZ/laya-code-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Automatic per-turn model routing for Claude Code and OpenAI Codex, powered by local LAYA, convaiinnovations/laya, instead of a hosted decision API. Simple work goes to the fast tier, difficult work to the strong tier: $0, no decision-API key.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chenneyzhuang-laya-cua", "name": "laya-computer-use", "author": "ChenneyZhuang", "date": "2026-09-24", "category": "app", "url": "https://github.com/ChenneyZhuang/laya-computer-use", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: local, open-source computer use on macOS - Laya, the open System One model, decides every step by reading the actual window's Accessibility tree; notes: no screenshots, no vision, no coordinates, no cloud; CuaDriver + DesktopLoop; positioned as the alternative to TypeSafe Jev for desktop", "arch": "Open-source macOS computer use where an open Laya model decides every step from the window's Accessibility tree - no screenshots, no vision model, no coordinate guessing, no cloud; CuaDriver plus DesktopLoop architecture", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dungdq1-laya-demo", "name": "laya-demo, dungdq1", "author": "dungdq1", "date": "2026-09-24", "category": "app", "url": "https://huggingface.co/spaces/dungdq1/laya-demo", "base_model": "Laya + Laya multilingual + Laya typed-decisions", "params": "", "metric": "Macro-averaged accuracy on MASSIVE intent, 51 languages, 20 options, random 0.050", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "arch: a multilingual routing tab runs laya.Router, where the script of the incoming text decides which checkpoint answers before the forward pass; notes: across 51 languages on MASSIVE intent with 20 options, the English checkpoint macro-averages 0.227 and clears 3× random on 23 of 51 languages, while on Khmer it scores 0.000 accuracy at 0.952 confidence, and the multilingual checkpoint reaches 0.366 and clears 3× random on 45 of 51", "arch": "A Gradio demo of Laya as a fast System 1 decision engine: send a state and typed questions, get typed answers with a probability per option and a confidence score. Tabs cover the most-used patterns: support triage, email and phishing, LLM guardrails, RAG passage filtering, moderation, model routing, and a free-form playground. Every tab asks all of its questions in one pass and then decides with plain code: the thresholds live in the app, not in the model.", "approach": "", "training": "", "data": "", "result": "Routing check on 51 languages: English checkpoint 0.227 macro, 23 of 51 languages above 3× random; multilingual checkpoint 0.366, 45 of 51 languages above 3× random; random baseline 0.050", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "khushali678-laya-meme-app", "name": "laya-meme-app", "author": "khushali678", "date": "2026-09-24", "category": "app", "url": "https://huggingface.co/spaces/khushali678/laya-meme-app", "base_model": "Laya", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": "", "likes": 0, "detail": "notes: the card describes the matching design but publishes no evaluation", "arch": "A static space matching scenarios to memes in real time, powered by the Laya decision architecture, convaiinnovations/laya, and a Supabase vector store, laya-meme. It evaluates humor, emotional vibe, and situation context in milliseconds without token-generation overhead.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reycardo-laya-murdle", "name": "laya-murdle", "author": "reycardo", "date": "2026-09-24", "category": "app", "url": "https://github.com/reycardo/laya-murdle", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Parse turns the puzzle text, suspects, weapons, locations and the detective clues, into structured typed data: Laya scores a fixed set of options in a single forward pass instead of generating text, so clue classification is fast and always lands on a valid value. Solve translates the structured clues into variables and constraints and hands them to python-constraint, which returns the unique assignment of suspect, weapon and location, i.e. the murderer. Laya weights are not bundled; they download from Hugging Face on first use and cache locally.", "arch": "Solves murdle.com puzzles automatically in two stages: parse with Laya, a small non-autoregressive decision model, then solve with a constraint solver", "approach": "", "training": "", "data": "", "result": "Runs a built-in sample puzzle as a smoke test, or today live puzzle rendered in headless Chromium via playwright; optional animated grid with Pillow, and a no-preload mode for saved pages", "notes": "Python 3.12+, uv based; uv sync creates the venv and installs dependencies from pyproject and uv.lock", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "luiz0ar-laya-rex", "name": "laya-rex", "author": "luiz0ar", "date": "2026-09-24", "category": "app", "url": "https://github.com/luiz0ar/laya-rex", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Real-time telemetry dashboard and autonomous control center for T-Rex Runner, powered by the Laya System One decision engine; 100% vanilla HTML5, CSS3 and JavaScript, zero frameworks", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hambalimarcel-laya-rlcd-expedition", "name": "laya-rlcd-expedition", "author": "HambaliMarcel", "date": "2026-09-24", "category": "app", "url": "https://github.com/HambaliMarcel/laya-rlcd-expedition", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A small local page for work and sales decisions. It runs Laya on your machine through the official laya package. There is no API key and no cloud decision service", "approach": "", "training": "", "data": "", "result": "", "notes": "Paste a note, pick a decision type, and the page shows the choice, the confidence, the option probabilities, the checkpoint that ran and how long inference took; no API key, no cloud decision service", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "qxzap-laya-scraper", "name": "laya-scraper", "author": "qxZap", "date": "2026-09-24", "category": "app", "url": "https://github.com/qxZap/laya-scraper", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "There are no per-site rules, CSS selectors or keyword lists: the crawler reads pages much as a person would, looking at the menu, the headings, and whether the page is a list of things, and a small non-generative decision model, laya, makes the judgment calls in milliseconds on the GPU. On odi.org it runs laya on CUDA, an RTX 4080 SUPER, reporting per-page scores and item counts, and the Cloudflare-protected publications list, which loads with JavaScript and returns 403 to plain HTTP, is fetched with a real browser. Results use the same code and settings on each site, with a 30-page budget: odi.org found /en/publications/ with a 0.99 score in about 33 seconds.", "arch": "Give it a homepage: laya-scraper finds where the site keeps its publications, works out how that list pages, and pulls the facts out of each publication, in two stages, with scrape.py finding the publications list page plus a map of what every other section holds, and list.py walking that list, including next links, load-more buttons and view-all pages, extracting each publication's title, date, authors, summary, PDF, DOI and type.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bsisduck-laya-top", "name": "laya-top", "author": "bsisduck", "date": "2026-09-24", "category": "app", "url": "https://github.com/bsisduck/laya-top", "base_model": "Laya", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "It shows CPU, memory and network at the top, with each process labeled by category and subcategory, and a tilde marks a model suggestion rather than a verified fact; the footer offers search, tree view, categories, stop and label commands. Install with uv: on macOS, brew install uv, then uv tool install with Python 3.12 and the laya extra, which activates only on Apple Silicon; on Linux, the same command without the laya extra; for a project-local installation that includes the launchers and development files, clone the repository. Python 3.11–3.13, with CI.", "arch": "laya-top, a local-first, keyboard-and-mouse terminal process monitor inspired by htop and asitop, with explainable software categories, useful subcategories, and optional local Laya inference, built with Python, Textual and psutil, MIT licensed.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ugelucero-laya-wrapper", "name": "Laya_wrapper", "author": "ugelucero", "date": "2026-09-24", "category": "app", "url": "https://github.com/ugelucero/Laya_wrapper", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Routing in three steps: meta-decision, where the language checkpoint decides whether the request is technical, scripts, code, SQL, commands, infrastructure, or common, invoices, receipts, passwords, greetings, complaints, doubts; confidence threshold, where the technical branch is only accepted if the meta-decision confidence reaches the threshold, 0.25 by default, and in case of doubt it is treated as a common request; final decision, where technical requests go to the typed-decisions checkpoint and common ones go to the laya Router, which chooses between english and multilingual by detected language. The service also allows forcing a concrete checkpoint per request.", "arch": "Wrapper over the laya PyPI package that classifies a user request and picks the most suitable checkpoint, offered as a CLI tool with test, interactive and single-question modes, and as a FastAPI server with POST /route and GET /health", "approach": "", "training": "", "data": "", "result": "", "notes": "Spanish README; first execution downloads the checkpoints from Hugging Face into the local cache", "domain": "routing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vishalmysore-layaagent", "name": "layaAgent", "author": "vishalmysore", "date": "2026-09-24", "category": "app", "url": "https://github.com/vishalmysore/layaAgent", "base_model": "", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "An AI agent that runs entirely in a browser tab: a 421M typed-decision encoder, Laya, System 1, makes the routine decisions, and a small in-browser LLM, WebLLM, System 2, steps in only when Laya is unsure.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "marksomething-layagrep", "name": "layagrep", "author": "marksomething", "date": "2026-09-24", "category": "app", "url": "https://github.com/marksomething/layagrep", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: semantic grep - Laya's noul answers whether each line of a file matches a natural-language description; notes: returns the probability of yes; Python 3.13 + uv", "arch": "Semantic grep for files and stdin: instead of matching literal words, layagrep asks Laya whether each line matches a natural-language description, using Laya's noul answer type and treating the returned value as the probability that the answer is yes", "approach": "", "training": "", "data": "", "result": "", "notes": "Python 3.13 with uv; the first search downloads Laya's checkpoint, the English model is about 800 MB; Laya automatically routes non-English input to its multilingual checkpoint; default threshold 0.5, with --threshold, --scores and --line-number flags", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "stefanus-ai-tech-layavoicecommand", "name": "LayaVoiceCommand", "author": "stefanus-ai-tech", "date": "2026-09-24", "category": "app", "url": "https://github.com/stefanus-ai-tech/LayaVoiceCommand", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "PoC voice command mobil mainan: rekam suara di Python, transkripsi lewat Groq whisper-large-v3-turbo, lalu pilih perintah dengan Laya multilingual. Output masih tampil di terminal; belum mengirim apa pun ke Arduino", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hardness1020-learn-agent-architecture", "name": "learn-agent-architecture", "author": "hardness1020", "date": "2026-09-24", "category": "app", "url": "https://github.com/hardness1020/learn-agent-architecture", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 992, "likes": "", "detail": "It uses Jev for fast typed control decisions inside the agent examples, and covers builds including Claude Code, Hermes Agent, mini-swe-agent, and deepseek-harness. The docs are in English, Traditional Chinese, Simplified Chinese, Japanese and Korean. Three companion repositories go deeper than one section can, including learn-agent-memory, which scales the memory loop.", "arch": "Agent architecture examples, MIT, that teach how modern AI agents are built around the LLM, with a focus on harness engineering: the model reasons, and the harness turns that reasoning into controlled action, running tools, keeping state across calls, gating side effects, and coordinating loops, things no model call can do alone; the repo explains the harness section by section, loop, tools, memory, permissions, context, tasks, and interfaces, so learn it once and you can read many agents, since a coding tool, a chat assistant, and an autonomous runner mostly differ in harness choices.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "malaya-zemlya-likely", "name": "likely", "author": "malaya-zemlya", "date": "2026-09-24", "category": "app", "url": "https://github.com/malaya-zemlya/likely", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "An example triage function dispatches the fire department when a report describes an urgent, life-threatening situation, above 0.9 probability, and fire is involved, above 0.5; dispatches an ambulance when immediate medical attention is needed; alerts campus security when the incident is in the configured service area; schedules a 24-hour follow-up when the report should be escalated; and otherwise logs and closes with no action, with optional yes and no criteria per question to pin down what each side means, and debug logging that shows the requests and probabilities. It builds on the official TypeSafe client, with the API key loaded from the environment.", "arch": "likely, a Python library that branches on plain-English questions like you would on booleans: likely(question, state) returns the probability, in 0–1, that a yes-or-no question is true about the state, backed by TypeSafe System One Noul questions, so your code stays ordinary if statements, and behind the scenes likely reads the source of the calling function and batches every question it can see into one API call, including questions on branches that have not run yet.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "virolea-lintus", "name": "lintus", "author": "virolea", "date": "2026-09-24", "category": "app", "url": "https://github.com/virolea/lintus", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 13, "likes": "", "detail": "A rule carries a description, the plain-language question, and the paths it applies to; an offense report names the rule, the file, and the noul probability, ending with a files-inspected, offenses-detected summary. It runs on the whole tree, on the files changed since a git ref, or on the files staged for a commit, so it fits a CI job as well as a pre-commit hook. It is a single binary with no runtime to install: an install script picks the binary for your machine, checks its checksum, and puts it in ~/.local/bin, with LINTUS_INSTALL_DIR to change the directory and LINTUS_VERSION to pin a release; release archives cover Linux x86_64 and arm64, static, so any distribution, macOS Intel and Apple silicon, and Windows x86_64, and cargo install lintus builds it from source with a Rust toolchain. Upgrading from the earlier Ruby gem: gem uninstall lintus so the old executable does not shadow the new one, and config files carry over unchanged. It needs a Jev API key, saved once with lintus auth.", "arch": "Lintus, a natural-language linter that uses Jev to evaluate code against plain-language rules: it reads a YAML file of rules at the root of the repository, where each rule is a question about a file, and for every file a rule applies to, Lintus asks the question to the Jev model as a noul, a true-or-false judgment with a probability, reporting an offense wherever the answer says so.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "madewithlaya", "name": "madewithlaya, Laya build catalog", "author": "community curated", "date": "2026-09-24", "category": "app", "url": "https://www.madewithlaya.com/", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "The 09-24 snapshot shows a recurring shape: local Laya ports outspeed Jev on commodity hardware, laya-mlx at 60 decisions per second on an M3 Max playing Snake, Breakout at roughly 70 decisions per second on an RTX 5080, a maze demo at 19 ms; email triage with a 322M model, 1,000 synthetic messages into 6 folders at 65.1% accuracy with 0 MB swap on a MacBook Air M3; and head-to-heads where Jev and Laya play the same game against each other, Doom 1v1, arena survival, Clash Royale, each model driving its own emulator. The yankis0x video testing seven open Jev models in 21 minutes, with Laya getting the last segment, hit 2M views.", "arch": "Hand-sorted catalog of what people build with Laya, ConvAI Innovations' open-source decision engine; entries tagged by type with links to the build; 115 builds as of 09-24: 71 GitHub, 40 X posts, 2 sites, 1 skill, 1 resource", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "anirudh64210-mae-gmail", "name": "Mae, Gmail companion", "author": "Anirudh64210", "date": "2026-09-24", "category": "app", "url": "https://github.com/Anirudh64210/Mae---gmail-companion", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: Mae is a Chrome extension for Gmail that checks your reply when you press Send and tells you if you left a question in the thread unanswered", "arch": "Gmail Chrome extension that, on Send, checks your reply against the thread and flags any question you left unanswered", "approach": "", "training": "", "data": "", "result": "", "notes": "Mae, a pixel mail carrier, only shows up if a question gets missed", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "margin-editor", "name": "Margin", "author": "tpaulshippy", "date": "2026-09-24", "category": "app", "url": "https://github.com/tpaulshippy/margin", "base_model": "TypeSafe Jev jev-latest", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Live annotation-only writing editor: after a 300 ms typing pause each sentence is scored and the result is lined up next to the original; it never writes, paraphrases, rewrites or suggests replacement text; confidence-opacity heatmap and a trim mode where an accepted trim cuts the sentence; SCORING_PROVIDER=jev with default model jev-latest", "approach": "", "training": "", "data": "", "result": "Committed animated preview plus a full video of the working app", "notes": "", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cnhuye-mobile-jev-ultrafast", "name": "mobile-jev-ultrafast", "author": "cnhuye", "date": "2026-09-24", "category": "app", "url": "https://github.com/cnhuye/mobile-jev-ultrafast", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Phone agent on jev-ultrafast architecture with the device layer swapped from Chrome DevTools Protocol to AutoX.js MCP: Jev picks one operation and one element from an indexed table of what is on screen, code turns that choice into a tap; the model never emits a coordinate, selector or script; English and Simplified Chinese docs", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "multimodal-system-one", "name": "multimodal-system-one", "author": "btoo", "date": "2026-09-24", "category": "app", "url": "https://github.com/btoo/multimodal-system-one", "base_model": "", "params": "undisclosed", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "v4 investigation, measured selection: eight native multimodal checkpoints tested, attention adapters trained for MiniCPM and Qwen3-Omni, and their frozen versions confirmed on 320 separate cases each. MiniCPM-o 4.5 is the provisional research backbone: 82.35% equal-track accuracy versus Qwen3 84.54%, while using ~19 GiB versus ~60 GiB of allocated GPU memory; both reached only 39.58% on coarse screen localization, so reliable browser use remains unestablished; no candidate cleared the complete release gate. A concrete System One optimization is also tested: sixteen independent questions share one multimodal state prefix.", "arch": "MiSO, a research project for fast, typed decisions from speech, sounds, images and language, with MacBook controls and measured GPU experiments", "approach": "undisclosed", "training": "Research/experiments", "data": "Research data", "result": "On one controlled H100 example, the language-backbone scoring phase fell from 341.9 ms to 52.3 ms, 6.53×, with all sixteen top decisions preserved; excludes media encoding, preprocessing, startup and networking, so it is not an API latency claim", "notes": "Playground at miso-playground-nine.vercel.app with audio, image and native text inputs, editable typed questions, probability views and saved Workflow runs; full investigation, candidate comparison, costs and limitations in reports/v4-selection-v1; frozen protocol at evals/v4-selection-protocol-v1.json", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nospace", "name": "nospace", "author": "levmiseri.com", "date": "2026-09-24", "category": "app", "url": "https://levmiseri.com/nospace/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: per site; notes: HN; name: Jev autocorrect without spaces", "arch": "'Advanced autocorrect: type something without using [space]. Likethis.' — Jev-based text repair for space-free typing", "approach": "", "training": "", "data": "", "result": "Live demo page", "notes": "Thin app, observed 2026-09-24.", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "martinjsklorz-noulbird", "name": "noulbird", "author": "martinjsklorz", "date": "2026-09-24", "category": "app", "url": "https://github.com/martinjsklorz/noulbird", "base_model": "Laya via laya-mlx", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Everything runs on your machine, with no API key, no account, no waitlist, and no network calls: pip install, run, play. It puts a model inside a 60fps game loop, where it has to make a decision every about 120 ms while a bird falls out of the sky, a constraint that surfaces things a turn-based chat demo never will. Laya models answer structured questions about a state and return numbers: a noul gives a 0–1 probability, such as is the bird below the gap at 0.80; a choice gives one option plus probabilities for all, such as billing at 96%; and a score gives a graded value on a scale, such as 1.44 out of 3. Because the output is a number rather than a token stream, there is nothing to parse, no JSON to coax out of a model, and no retry loop when it returns prose instead of a verdict, which is what makes it viable at 120 ms per decision. The README walks through the three model properties that shape the design, starting with asking what the model can perceive, not what to do.", "arch": "noulbird: you vs a decision model at Flappy Bird, a real-time demo of laya-mlx, native MLX inference for Laya typed decision models on Apple Silicon, where you fly the blue bird and a locally-running classifier flies the amber one, both facing the same pipes at the same time, and when one crashes the other keeps going; every flap the AI bird makes is a noul, the probability a Laya model returns when you ask whether something is true, served by the model's systemone endpoint, fast reflexive judgments made under a deadline rather than deliberated.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bringai-jev-numeric", "name": "NumericJev", "author": "Bring-AI", "date": "2026-09-24", "category": "app", "url": "https://github.com/Bring-AI/JevNext", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "Against official Jev: decisions and option probabilities, and rubric scores, are available in both; interval decoding, digit-by-digit decoding, and CDF and histogram construction only in Jev-numeric, the last one experimental with unverified calibration. The native API used is Choice and Score. It ships with a live demo website, Chinese documentation, and recorded metrics in artifacts/metrics.json.", "arch": "A simple algorithm that turns Jev decisions into accurate numerical outputs, NumericJev: Jev is built for structured decisions, and a multiway decision tree turns them into numbers, where each Choice selects a branch and the final leaf identifies a finite-precision value, with branches described as numerical intervals or as decimal digits, and both representations using ordinary Jev Choice calls, with no training, no regression head, and no access to token logits.", "approach": "", "training": "", "data": "", "result": "Numeric answers via choice/interval decomposition", "notes": "1 star.; repo renamed or transferred since the 2026-09 sweep, URL updated", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bhaktofmahakal-onemetric", "name": "OneMetric, RevOps evaluator", "author": "bhaktofmahakal", "date": "2026-09-24", "category": "app", "url": "https://github.com/bhaktofmahakal/onemetric-ai-assessment", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: OneMetric - dynamic campaign segmentation engine and autonomous RevOps agent for multi-business-unit enterprises; notes: deterministic RevOps evaluator with TypeSafe System One and a Bounded Agent Runtime; distinguishes meaningful high-intent buyer shifts from noisy signals, prevents conflicting multi-BU messaging, eliminates prospect fatigue", "arch": "RevOps decision platform and autonomous agent: dynamic campaign segmentation across multi-BU enterprises, where a deterministic evaluator built on TypeSafe System One and a Bounded Agent Runtime separates meaningful buyer-shift signals from noise and prevents conflicting messaging", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "marketing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "loofy147-open-system-one", "name": "Open System One", "author": "Loofy147", "date": "2026-09-24", "category": "app", "url": "https://github.com/Loofy147/Open-System-One", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Decision contract: opaque state, typed independent questions, finite outcome spaces, distributions. Deterministic composition: scoring, calibration, abstention and policy layers. Replaceable backends: pairwise, set-aware, conditional, browser ONNX, MLX, CoreML/ANE. Research and evidence: experiments, receipts, kill tests, negative findings and open questions. Not a Jev reproduction, contains no proprietary Jev weights, and does not treat a particular encoder or scoring head as canonical.", "arch": "Open System One: a machine-native typed decision substrate separating the decision contract, deterministic decision composition, replaceable model and runtime backends, and research evidence", "approach": "", "training": "", "data": "", "result": "Status: core decision contract established; browser MiniLM ONNX execution experimentally supported; generic embedding plus cosine baseline experimentally supported; set-aware advantage on real decision data open; conditional mixture advantage open, not supported by the small transfer benchmark; confidence as permission to act rejected as a contract rule; real-data BANKING77 pilot implemented in the browser lab, receipt pending", "notes": "As of 2026-09-25 the repo is the README only, no implementation code: it establishes the decision contract, opaque state, typed independent questions, finite outcome spaces, distributions; and records the research status, core contract ESTABLISHED, browser MiniLM ONNX execution and embedding plus cosine baseline EXPERIMENTALLY_SUPPORTED, set-aware and conditional mixture advantages OPEN, confidence as permission to act REJECTED as a contract rule", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "darwintechlab-openjev", "name": "OpenJev opencode plugin", "author": "darwintechlab", "date": "2026-09-24", "category": "app", "url": "https://github.com/darwintechlab/openjev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "base: System One", "arch": "Opencode plugin that replaces text-generation decisions with Jev", "approach": "", "training": "", "data": "", "result": "Jev decisions inside the opencode agent loop", "notes": "claude-jev, 09-24, adds the Claude Code variant: typed Choice/Noul/Score via the live TypeSafe API, a mirror repo of this opencode plugin. A same-owner re-upload, darwintechlab/claude-openjev, 09-24, with a byte-identical README, was dropped as a duplicate.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openjev-ultrafast", "name": "OpenJEV Ultrafast", "author": "chipchipss", "date": "2026-09-24", "category": "app", "url": "https://github.com/chipchipss/openjev-ultrafast", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Cross-platform, model-agnostic, observable browser-agent framework: the runtime, atomic snapshots, indexed action space and freshness guards, is forked from browser-use/jev-ultrafast under MIT; the decision layer is pluggable, local models, the hosted API, decider-2B, or any OpenAI-compatible or TypeSafe-wire backend, swapped by environment variable; the governance layer keeps policy, validator, runtime guard, confidence gate and budget orthogonal with dual-budget separation and four-level attribution", "approach": "", "training": "", "data": "", "result": "", "notes": "Data flywheel: logger, evaluator and sample extractor make every step observable, four-quadrant attribution, and mine DPO pairs; built to fix the limitation of jev-ultrafast, whose decisions are bound to the closed per-call TypeSafe cloud API; Chinese README; created 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mahdibnd-pcd-rlcd", "name": "pcd-rlcd", "author": "MahdiBND", "date": "2026-09-24", "category": "app", "url": "https://github.com/MahdiBND/pcd-rlcd", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: parallel constrained decoding for structured extraction and classification on Apple Silicon - evaluates multiple schema fields in parallel rather than generating JSON token by token; notes: extracted from the original Parallel Constrained Decoding project", "arch": "Parallel constrained decoding for structured extraction and classification on Apple Silicon: multiple schema fields are evaluated in parallel instead of generating JSON token by token", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aesmatias-extracta", "name": "PDF Process Pipeline,  Intelligent Document Processing", "author": "AESMatias", "date": "2026-09-24", "category": "app", "url": "https://github.com/AESMatias/Extracta", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "Each document is classified, invoice, receipt, contract, bank statement, payslip, resume, report, its text is extracted, and an LLM, Gemini by default, turns it into validated JSON. Results show up as tables and charts, can be exported to CSV and, if you choose, are saved to Supabase. Built to run on a 2 GB RAM server: documents are processed one at a time, every service has a memory limit, and PDFs are deleted as soon as they are processed. Stack: Python 3.12, Flask 3.1, Celery 5.6, Redis 8, Supabase Postgres 17, Gemini Flash-Lite, Docker compose, Ruff.", "arch": "PDF process pipeline for intelligent document processing: upload many PDFs at once and get their data back as structured JSON", "approach": "", "training": "", "data": "", "result": "Working today: configuration, the minimal web app with /health, the extraction schema, the Supabase database, streaming upload storage, PDF text extraction, LLM extraction at ~USD 0.0006 per invoice, the Celery worker in persistent or ephemeral mode, CSV/XLSX/JSON export, the HTTP API and the browser UI; detailed progress in the roadmap at 02-DOCS/wiki/ftd/idp-mvp.md", "notes": "Status: under construction, step by step", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "philipbrembeck-pi-advisor", "name": "pi-advisor", "author": "philipbrembeck", "date": "2026-09-24", "category": "app", "url": "https://github.com/philipbrembeck/pi-advisor", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 24, "likes": "", "detail": "", "arch": "A configurable second-opinion workflow for Pi coding agents, inspired by the \"Steering Black-Box LLMs with Advisor Models\" paper and Claude's Advisor feature.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sandeco-pix-golpe", "name": "pix-golpe", "author": "sandeco", "date": "2026-09-24", "category": "app", "url": "https://github.com/sandeco/pix-golpe", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 22, "likes": "", "detail": "Everything is simulated: messages are generated from templates and transactions with a fixed seed, and no real banking data is used. Prerequisites: Rust, Python 3.12 with websockets, httpx and truststore, and an API key for each model; the keys go in a .env file that the Rust server loads with dotenvy and the Python worker also reads, and the README warns never to commit it. To run: cargo run --release --bin gerar-dados creates the data folder once, and cargo run --release --bin server starts the server and the worker together. The page has a narrated mode, where each side decides one message, shows the verdict, and waits 4 seconds, and a normal mode, without waiting, one message at a time per side, where Rust runs ahead and Python drags behind; the pace can be switched mid-race, and stopping freezes the screen. The accuracy report, enabled on finish or stop, gives the confusion matrix per side, accuracy, precision, recall, cost per side, and the total race spend; clicking a scam in the list opens its detail: the message, the model, the tokens, the decision cost, and the trace of that scam in the graph. PIX_NO_WORKER=1 starts only the Rust side, and a mock URL parameter shows the screen without live events.", "arch": "Pix Race, in Portuguese: a split-screen video demo, with Rust plus Jev, TypeSafe AI, on the left and Python plus DeepSeek on the right, where the same messages enter both sides at the same time, each side decides whether it is a Pix-payment scam, and if so, traces the money's path in a graph of 10 million transactions and 1 million accounts.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pangelini777-bettercontext", "name": "PoC-BetterContext", "author": "pangelini777", "date": "2026-09-24", "category": "app", "url": "https://github.com/pangelini777/PoC-BetterContext", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 1, "likes": "", "detail": "Agent projects accumulate instructions, rules and skills; loading the entire catalog guarantees availability but spends context on resources unrelated to the current task. BetterContext manages context over time: it materializes relevant resources into the next prompt, and dematerializes resources that should no longer be sent. The controller owns the catalog, and in isolated Jev evaluations the agent workspace has no apm.yml, .apm/, .agents/rules/ or .agents/skills/, so the agent receives only the selected resource bodies. The headline result: on the primary held-out comparison, 10 unseen probes, a 94-resource catalog of about 46.7k tokens, the same agent model across arms, provider-backed Jev routing, and isolated workspaces, load-everything supplies 46,736 average context tokens and 633k session tokens; native APM discovery uses 1.37M session tokens, traced through opened files and input tokens, since it has no fixed injected-context size; and Jev progressive context supplies 3,906 average context tokens, about 8% of the full catalog, and 257k session tokens, about 59% lower than load-all, while matching both baselines on retrieval at 10/10 with 0 critical misses; a second paired run also reached 10/10 retrieval with 4,367 average context tokens.", "arch": "BetterContext, a proof of concept for progressive context for coding agents using TypeSafe Jev System One and Microsoft APM: it gives the agent the rules and skills it needs for its current work, where a controller selects resources from the APM catalog, adds their bodies to the agent's context, and removes them when they are no longer relevant, aiming to reduce context pressure while preserving task correctness.", "approach": "", "training": "", "data": "", "result": "Context-improvement PoC", "notes": "1 star.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "comunaider-radar-de-hook", "name": "Radar de Hook", "author": "Comunaider", "date": "2026-09-24", "category": "app", "url": "https://github.com/Comunaider/radar-de-hook", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Extensão de navegador que lê a grade do Instagram, do X e do YouTube e marca, ao vivo, o que vale copiar e o que vale comentar. Quem julga é o Jev, o modelo de decisão da TypeSafe. O critério é o contexto que você escreve", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "raze", "name": "Raze", "author": "JoasASantos", "date": "2026-09-24", "category": "app", "url": "https://github.com/JoasASantos/Raze", "base_model": "Raze, a System One model in the Jev family", "params": "", "metric": "ECE measured on benchmark judgments", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "approach: Typed judgment plus deterministic validators, human in the loop", "arch": "An offensive-security triage agent built on Raze, a System One model in the Jev family: natural language and target or engagement state in, typed calibrated judgments out, for questions like is this finding exploitable, what is the real impact, which attack path next, and is this a duplicate or known or theoretical issue; the agent layer orchestrates the Raze model with deterministic analyzers and validators behind an authorization boundary across recon, web, network, Active Directory, cloud, mobile, wireless, exploit development, reverse engineering, social engineering and cryptography topics", "approach": "sdk", "training": "", "data": "Offensive-security topic surface, authorised engagements only", "result": "Design invariants rather than headline numbers: every call returns a schema-validated judgment, never prose; probabilities are reported and measured, never re-interpreted as per-finding precision or recall; a finding that a validator can disprove is downgraded regardless of the model's confidence; it is explicitly not an autonomous attack tool, and a human plus deterministic validators stay in the loop", "notes": "Lineage cites browser-use/jev-ultrafast, NandhaKishorM/laya, ikermoel/open-alternative-jev, and TypeSafe's System One primitives; for pentests, red-team engagements, bug-bounty triage and CTFs", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cgbarlow-jev-test", "name": "Rubric in, marks out: Jev × NZQA prototype", "author": "cgbarlow", "date": "2026-09-24", "category": "app", "url": "https://github.com/cgbarlow/jev-test", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Jev × NZQA prototype: AI-assisted marking for any standard from its written criteria alone, no labelled data and no model per standard; targets the breadth scaling and cold-start problems for low-volume standards; concept only, not endorsed by NZQA", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kazche-self-policing-noul", "name": "self-policing-noul", "author": "KazChe", "date": "2026-09-24", "category": "app", "url": "https://github.com/KazChe/self-policing-noul", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Three prompt-injection guardrails for a bug-report triage classifier, scored on one frozen, labeled fixture. Companion code for the blog series Your guardrails are just untested classifiers.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shipwithjev", "name": "shipwithjev, Jev build directory", "author": "community curated", "date": "2026-09-24", "category": "app", "url": "https://www.shipwithjev.com/", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "The 09-24 front page spans: Jev versus Laya in Clash Royale, each model driving a separate emulator, both backed by Qwen through Cerebras; a resume-screening workbench that checks every condition and backs each verdict with a quote from the resume; a movie search where local Laya on an AMD Radeon Pro 5500 XT answered in 392 ms versus 676 ms for Jev's API on the same query; MinusPodJev, a FastAPI proxy exposing Jev to MinusPod as an OpenAI-compatible ad-detection model; and Spliit Cloud using Jev Choice to suggest expense categories after its local dictionary and group history fall short. The most unusual build: Jev inside MiniMax H3 video generation, picking the per-layer attention-sparsity rate from 1, 3, 5, or 10% and cutting an RTX 4070 render from 6 min 7 s to 3 min 34 s, a 41.7% reduction.", "arch": "Curated directory of real builds on Jev; 540 builds per the 09-24 index and 551 per the site's own counter, each carrying the cost and latency numbers the author reported, categorised by use case", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "slop-fold", "name": "Slop Fold", "author": "effective-shipping", "date": "2026-09-24", "category": "app", "url": "https://github.com/effective-shipping/slop-fold", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Browser extension that folds LinkedIn posts that read like they were written by ChatGPT; each post in the feed goes to Jev, which returns a probability instead of prose, and if the probability clears the user's threshold the post collapses into one line such as Folded: AI-ish 90% with a Show button to read it anyway; the extension asks Jev two questions about each post in a single request", "approach": "", "training": "", "data": "", "result": "", "notes": "Chrome 120 or newer or any Chromium-based browser; requires the user's own TypeSafe API key", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "slop-mop", "name": "Slop Mop", "author": "slopmop.lol", "date": "2026-09-24", "category": "app", "url": "https://slopmop.lol", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": "", "likes": "", "detail": "base: per site; arch: nothing is deleted · any folded post can be revealed · cost borne by the site; notes: HN; name: LinkedIn slop filter", "arch": "'Less slop. More control. Your call.': free open-source Chrome extension for the LinkedIn feed; reads public posts before they reach your view, asks Jev eleven questions about the writing, and gives each post a slop score; likely slop is highlighted or folded into a strip; 'Jev does the judging, so there is no API key and no bill to pass on'; 'a bad-writing detector, not an AI detector — useful posts survive the mop however they were drafted'", "approach": "", "training": "", "data": "", "result": "Desktop Chrome-compatible extension, free; 'research purposes only' / 'LinkedIn only, for now'", "notes": "Observed 2026-09-24.", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "expeed-software-smart-home-assistant", "name": "Smart Home Assistant", "author": "Expeed-Software", "date": "2026-09-24", "category": "app", "url": "https://github.com/Expeed-Software/smart-home-assistant", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "One Jev call turns the sentence into the decision set, the demo home applies it immediately, and the UI shows every answer with its probability. npm install, copy the example env file, set the TypeSafe API key, npm run dev, open localhost:3000", "arch": "Demo app: a home with rooms and devices, where one natural-language sentence such as dim the bedroom lights a bit becomes a set of typed decisions", "approach": "", "training": "", "data": "", "result": "", "notes": "The home applies the decisions straight away and the app shows every answer with its probabilities; npm install with the TypeSafe API key in .env.local", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "campusx-official-jev-demo", "name": "Smartphone Review Analyzer", "author": "campusx-official", "date": "2026-09-24", "category": "app", "url": "https://github.com/campusx-official/jev-demo", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 6, "likes": "", "detail": "The project is small and readable: main.py reads the CSV, analyzes each review, and prints the summary; analyzer.py sends one review to Jev and converts the answers into star ratings; questions.py holds the topic list and the 14 Jev questions; aggregation.py averages per-review ratings into one score per topic; and a CSV of 50 synthetic sample reviews is included. Setup is pip install python-dotenv typesafe-sdk and a .env with the API key. Example output: per-review topic ratings, then an overall 3.4 stars based on 50 ratings, and a per-topic table, camera 3.8 stars from 21 reviews, battery 2.9 stars from 24 reviews. The input CSV needs a unique review_id, the phone_model, the overall_rating as an integer 1–5 star rating given by the reviewer, the review_text body, and is_synthetic, whether the review was generated.", "arch": "A smartphone review analyzer that turns free-text smartphone reviews into Flipkart-style per-topic star ratings using Jev via the TypeSafe SDK: for every review, Jev is asked 14 questions, two per topic across camera, battery, display, design, performance, build quality, and value for money, where each topic's mentioned question is a noul for the probability that the review discusses the topic, and each rating question is a score of reviewer satisfaction on a 5-level scale, and a topic's rating is kept only when the mention probability is at least 0.5, with ratings then averaged across all reviews.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hunkim-solar-mini4-jev", "name": "solar-mini4-jev", "author": "hunkim", "date": "2026-09-24", "category": "app", "url": "https://github.com/hunkim/solar-mini4-jev", "base_model": "Upstage Solar Pro4, default; solar-mini4 optional", "params": "", "metric": "item-weighted total across 886 items of four public Jev benchmarks", "license": "", "paper": "", "stars": 44, "likes": "", "detail": "arch: Upstage Solar Pro4 as the default model, solar-mini4 optional, both wrapped in the TypeSafe Jev System One API shape with noul, choice and score question types; notes: BYOK server, the Upstage key is passed per request via X-Upstage-Api-Key or Authorization and never stored", "arch": "A drop-in wrapper that exposes Upstage Solar Pro4 through the TypeSafe Jev System One API shape.", "approach": "sdk", "training": "", "data": "", "result": "Item-weighted total across 886 items: solar-pro4 76.9 vs Jev 1.13.0 at 75.6, solar-decide 73.0, solar-mini4 71.4; per benchmark, tool-call risk 93.3, classifier-benchmark v1 97.4, Jevals HelpSteer2 41.0, test400 95.1; p50 latency measured from Korea: solar-pro4 749 ms vs Jev 633 ms", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "76.9 item-weighted total", "latency": "749 ms p50, solar-pro4, measured from Korea", "benchmark": "", "cost": "", "speedup": ""}
{"id": "soter", "name": "Soter", "author": "frolleks", "date": "2026-09-24", "category": "app", "url": "https://soter.frolleks.site", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: frolleks; notes: early development", "arch": "Discord moderation by Jev: every message scanned for hate speech and spam; repeat offenders auto-timeout", "approach": "sdk", "training": "", "data": "", "result": "Early development", "notes": "Discord moderation that watches so you don't have to: every message is scanned for hate speech and spam, repeat violators are timed out automatically, and the member gets a DM explaining why. Early development, not yet 24/7.", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "d-date-swift-jev", "name": "swift-jev", "author": "d-date", "date": "2026-09-24", "category": "app", "url": "https://github.com/d-date/swift-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 16, "likes": "", "detail": "The three primitives are modeled as Swift types: a choice question over an enum conforming to JevChoiceOptions, with per-option descriptions; a noul question with whenTrue and whenFalse criteria; and a score question with ordered levels. A JevClient evaluates the state with all the questions in one request, and Jev evaluates them in parallel, so asking more of them costs almost no extra latency. The response gives typed reads: require returns the department enum or the noul probability, confidence and probabilities return the per-option numbers when available, and usage reports the estimated cost in USD. Install via Swift Package Manager from version 1.0.0.", "arch": "A Swift library and CLI for Jev, TypeSafe AI's System One model: Jev does not write text, you hand it a state and some typed questions, and it hands back typed answers with calibrated probabilities, and you use the Swift library for type-safe reads in an app, or the CLI for JSON-based calls from a terminal or coding agent.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cogcloud-ai-cog-system-one-adapter", "name": "System One Adapter Cog", "author": "cogcloud-ai", "date": "2026-09-24", "category": "app", "url": "https://github.com/cogcloud-ai/cog-system-one-adapter", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "The second way to satisfy system-one/decisions. Where cog-typesafe sends typed questions to TypeSafe's Jev, this package sends them to an LLM you have already admitted in the suite — local Qwen through cog-qwen, or a hosted model through cog-openrouter, using TypeSafe's own System One adapter; Decision Cogs receive answers of the same shape either way, with answer_source saying which kind they are", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sciscend-system-one-categorizer-demo", "name": "System One категоризатор: Jev срещу Laya на български", "author": "SciScend", "date": "2026-09-24", "category": "app", "url": "https://github.com/SciScend/system-one-categorizer-demo", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "name: демо", "arch": "Side-by-side test of two System One models on Bulgarian text: the closed Jev via TypeSafe API and open Laya multilingual running locally on the CPU; task: suggest a category for a blog post, existing or new topic; on 50 labeled Bulgarian posts Jev got 48/50 right, Laya 36/50; a small local LLM, BgGPT 4B, names a new category when needed; UI in Bulgarian, MIT", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-computer-use", "name": "system-one-computer-use", "author": "cgasgarth", "date": "2026-09-24", "category": "app", "url": "https://github.com/cgasgarth/system-one-computer-use", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Local computer-use harness for typed-decision models: observes the desktop through Cua Driver, builds candidates from visible controls, and lets a System One decision model choose the next action. A small text model reads the task once; every action is checked against the current Cua snapshot before it runs. Decision model, text generator and computer driver are separate interfaces; one Cua MCP connection to the cua-driver daemon", "approach": "", "training": "", "data": "", "result": "", "notes": "Bun + cua-driver; voice input via Handy planned after the text path", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-sql-agent", "name": "system-one-sql-agent", "author": "JoseVelazcoH", "date": "2026-09-24", "category": "app", "url": "https://github.com/JoseVelazcoH/system-one-sql-agent", "base_model": "", "params": "", "metric": "", "license": "GPL-3.0", "paper": "", "stars": 1, "likes": "", "detail": "It is framed on Kahneman's Thinking, Fast and Slow: System 1 is fast and intuitive, System 2 is slow and deliberate, and TypeSafe AI borrowed the name for System One models such as Jev, models that make typed decisions instead of generating text, and this project puts both systems to work together. The motivation: when a question can be answered by one of dozens of databases, a standard agent has to read every schema before it can even start, so the project splits that work into routing and execution. Built in TypeScript with AI SDK 7 against PostgreSQL, GPL-3.0, with a flow diagram and a demo comparison UI answering the same question with and without Jev routing.", "arch": "A text-to-SQL agent where a fast System One model decides where to look, so the LLM only has to think about how to query: System 1 routes and System 2 writes the SQL, where Jev scores every database, and then every table of the chosen databases, against the question in a single call each, and the LLM receives only the selected databases, with the columns of the most relevant tables preloaded, and writes read-only SQL to answer.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "angeeeld-systemone-email-cleaner", "name": "systemone-email-cleaner", "author": "AngeeelD", "date": "2026-09-24", "category": "app", "url": "https://github.com/AngeeelD/systemone-email-cleaner", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A local Go CLI that groups your Gmail inbox automatically. It reads each message's headers and a short body preview, asks a System One decision model to classify it, applies a cleaner/ label, and moves genuine junk to Trash", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "digitaliq-tabwerk", "name": "Tabwerk", "author": "DigitalIQ", "date": "2026-09-24", "category": "app", "url": "https://github.com/DigitalIQ/tabwerk", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Chrome-Extension für viele offene Tabs. Tabwerk findet, gruppiert, sortiert und entdoppelt Tabs, beobachtet Webseiten und sichert jeden Stand von Fenstern, Tabs und Gruppen", "approach": "", "training": "", "data": "", "result": "", "notes": "Everything that can be computed exactly is done in code; Jev is used only optionally for semantic questions, reachable via OpenRouter or directly via TypeSafe; without a key all code-only features still run; README in German", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tacet", "name": "Tacet", "author": "CodePawl", "date": "2026-09-24", "category": "app", "url": "https://tacet.codepawl.com/", "base_model": "144.2M-parameter encoder fine-tuned on the four typed-decision workflows", "params": "144M", "metric": "Accuracy 0.773, ECE 0.1251, p50 32.5 ms on L4", "license": "", "paper": "", "stars": "", "likes": "", "detail": "approach: Encoder-only multi-task readout", "arch": "A typed-decisions API in one encoder pass: state plus typed questions of the choice, score, and yes/no kinds go through the 144.2M-parameter encoder once and every option comes back with a probability; no text is generated, so nothing has to be parsed; requests and responses use the SystemOne wire shape, usage.input_tokens is the length of the one sequence, and confidence is 1 minus entropy of p over log K for choice and score and max of p and 1 minus p for yes/no; billing counts the tokens read, each question's instructions and options once plus the state once", "approach": "head", "training": "Fine-tuned on the typed-decisions train split", "data": "Security incidents, agent-trace observability, invoice processing, customer service", "result": "On the 400-case typed-decisions test set: accuracy 0.7730, on par with Laya's 0.7675, paired gap plus 0.0055 not significant, and above Jev's zero-shot 0.727; lowest ECE of the comparison at 0.1251 against Jev 0.144 and Laya 0.2147; p50 32.5 ms versus Laya's 62.4 ms on the same L4, 1.9× lower, with 211.3 requests per second against 14.9, 14.2×; at the 0.042 per million input token price, 571 billed tokens per five-question request versus Jev's 964, giving 24.0 dollars per million requests against 40.5; option-reordering flip rate 0.0950 against Laya's 0.1400; soft accuracy 0.5192, fourth of five, with meraGPT Decider 1 leading the soft metrics", "notes": "Closed waitlist API with prepaid credit, no subscription; English only, a multilingual run on 25 MASSIVE locales reached 0.3187 intent accuracy and was not claimed; spot checks on free text were poor and not claimed; on L40S instances the same run measured 13.5–21.1 ms and up to 673 requests per second; the page's own what-is-not-claimed section concedes the Jev comparison is lopsided because Jev was zero-shot and meraGPT Decider 1 wins the soft metrics; every figure is self-measured on rented cloud GPUs; CodePawl is an independent studio in Vietnam The HF model weights live at codepawl/tacet-sonata.", "domain": "general", "score": "ECE 0.1251", "latency": "p50 32.5 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rehmanali-tetris-laya", "name": "Tetris x Laya", "author": "RehmanaliMomin", "date": "2026-09-24", "category": "app", "url": "https://github.com/RehmanaliMomin/TetrisGame_Laya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: Tetris where a Laya decision model places every piece - two typed choice questions per piece: rotation, 4 options, and column, 10 options conditioned on the rotation just made; notes: fine-tuned laya-multilingual, mmBERT-base 322M; no search", "arch": "Tetris in which a fine-tuned laya-multilingual, mmBERT-base 322M, places every piece with two typed choice questions, rotation, 4 options, then column, 10 options conditioned on the rotation just made; no search, no tree, no vision", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "david96182-cribrix", "name": "The problem", "author": "david96182", "date": "2026-09-24", "category": "app", "url": "https://github.com/david96182/cribrix", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "The problem it targets: most RAG systems are built to always answer, retrieving the top-k chunks, stuffing them into a prompt, and returning whatever comes out, which works right up until the corpus does not contain the answer, at which point the system confidently invents one, attaches a real citation, and the user has no way to tell, and the failure is not the language model, it is the architecture, where nothing in the pipeline is allowed to say no. The pipeline: the user query goes to a Jev Choice route, where chitchat replies directly, with no database and no LLM, and search retrieves with pgvector HNSW at top-k 20; an empty corpus returns NO_DOCUMENTS; then Jev Score triage keeps passages at 0.65 or above, and if there is not enough context, INSUFFICIENT_CONTEXT is returned and the LLM is never called; only then does the System 2 LLM generate. Python 3.11 or newer, ruff, CI, MIT.", "arch": "Cribrix, a precision-first RAG orchestrator that filters before it generates, and verifies before it answers, from the Latin cribrum, a sieve: it wraps a conventional LLM in a System 1 and System 2 loop, where a fast, structured-output decision model, Jev, gates an expensive generative model on both sides, so the pipeline is allowed to say no.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nikhileshnarkhede-trustgate", "name": "TrustGate", "author": "nikhileshnarkhede", "date": "2026-09-24", "category": "app", "url": "https://github.com/nikhileshnarkhede/TrustGate", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Everything runs locally on one 8 GB consumer GPU with open weights, with a total API cost of $0. The findings, from matched items with paired bootstrap confidence intervals: Laya's miscalibration direction is not a model property, since as shipped it is under-confident on CLINC150 in 15 of 16 configurations, over-confident on Banking77 in 6 of 7 and on MASSIVE in 11 of 11, and flips with prompt wording, and its shipped temperatures over-soften at 3–5 options and double the over-confidence at 11 or more options. Decisions are fragile: only 28% of CLINC150 items keep the same prediction across 11 option orders, because the model favors the last option, and accuracy falls from 0.79–0.46 when 40 or more options push each option to 4 tokens. Under shift, Laya fails silently: on Khmer it is 17% accurate with about 69% confidence, and after recalibration it is well calibrated but useless, with AUROC 0.60 and nothing certifiable. Qwen3-8B, with exact option scores, is 6–34 points more accurate and ranks better in 6 of 7 datasets, significant, the seventh positive but within noise, and is over-confident in all 9 configurations, predictable and fixable, but 10–26 times slower. Verbalized confidence ranks worse than Laya in 4 of the 5 datasets where it was measured, self-consistency has the best ECE and the worst ranking, and cannot certify any automation, and ECE alone is a misleading measure of usable confidence. The cascade saves 11–49% of LLM cost.", "arch": "TrustGate, automate only what you have measured you can trust: an independent audit of confidence in System One decision models, fast, non-autoregressive classifiers that return a probability for each option; it audits Laya, an open, RLCD-trained decision model, compares it with an open LLM, Qwen3-8B, on the same items, and evaluates a confidence-gated triage cascade, fast model first, LLM for what it defers, human for the rest.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lin52025iq-try-laya", "name": "try-laya", "author": "lin52025iq", "date": "2026-09-24", "category": "app", "url": "https://github.com/lin52025iq/try-laya", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "It is the first runnable engineering version, not a finished product that imports a guide and auto-plays any FPS. The working path: a user goal or an external guide becomes a structured Profile, which a human checks and activates; the runtime turns observations into a set of valid candidates, Laya decides, and version, permission and approval checks gate execution, with the executor and timed control frames feeding verified results back. The LLM is an optional slow path: it binds a guide and the current state into a policy draft, does not auto-activate, and does not generate actions step by step, and external JSON Profiles can be imported without an LLM API key; Laya chooses the action and the executor fills local task data, and form values are never sent to Laya. Capabilities and bounds: Laya via local Router.predict or the remote systemone endpoint, with warm-up, probability and candidate validation, timeout rejection, and no queueing stale inferences; a managed Chromium with DOM probes, real clicks, fills and selections, and screen and human input, but not zero-config understanding of any webpage; an attached Chrome through local CDP that creates a new tab owned by the service in an existing context, Assist and Manual only, never taking over the user's current tab; Android through a specified ADB serial with screenshots, UI tree, clicks, restricted ASCII input and key presses, without a multi-touch or held-key FPS input bridge; real-time control with short-lived control frames, sequence numbers, TTLs and key delta updates, where the built-in tracking sandbox uses explicit numeric state, not visual game recognition; sessions with separated goal, policy and control versions, where a new episode does not auto-reset the game; a single execution exit, one-time approvals, human preemption, a SQLite event and execution log, WebSocket, and explicit screenshot saving; and a Chinese local web console for tasks, model warm-up, screen, takeover, approvals, events, and external policy import. An honest validation note: the dev environment had no usable Laya weights and no ADB devices, so a real Chromium plus explicit demo loop is validated, while real Laya inference, live FPS play, and Android real-device tests are not claimed. Python 3.11 or newer; the first warm-up downloads upstream model weights that never enter the repository, and Windows installation is documented without requiring the Python Launcher.", "arch": "try-laya, in Chinese: a Laya-first, local-first, human-takeable Reactive Agent Runtime, where the default decision backend is the real Laya SDK, and if it is not installed or not warmed up, it stops and errors, and never silently switches to rules or an LLM, and the explicit demo policy is only for testing the execution chain, and does not represent model capability or model speed.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bm-popcorn-laya-games", "name": "Turn based games demonstrating the decision making capabilities of Laya", "author": "BM-Popcorn", "date": "2026-09-24", "category": "app", "url": "https://github.com/BM-Popcorn/laya-games", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Play: connect4_demo.py with --mode laya and human-laya or laya-laya players; --mode mock uses the search engine and does not load the model. A dashboard server opens a play page and a board page showing the latest board, the model's proposal, and the search scores, and each move is appended to a decisions/connect4_audit.jsonl, with the latest decision in decisions/connect4_latest.json and the desktop game writing connect4_state.json. You can also train on the engine: generate engine-labelled positions, fine-tune the typed-decision checkpoint, then measure how often Laya's raw column matches the engine on positions it did not train on; the labelled positions are already in the repository, 3,577 for training and 423 holdout, training starts from the typed-decisions model, and on a CUDA GPU the encoder is updated, with the micro-batch defaulting to 1 with 8 accumulation steps so an 8 GB card can hold the model when 8-bit AdamW is installed. Every 50 optimizer steps the script prints held-out agreement on the same 40 positions used for the earlier score, where the baseline was 20/40, appends that line to a progress file, and writes the updated model, and you stop the run when that number stops climbing, with the measurement being the model's own column, before the engine override.", "arch": "Laya Connect Four, a turn-based game demonstrating the decision-making capabilities of Laya: Laya, a typed-decision model, proposes a legal column, and connect4_engine.py scores every column with depth-7 alpha-beta search and keeps the proposal when it is close to the best move.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lecharles-typesafe-classifier-dashboard", "name": "TypeSafe Classifier Dashboard", "author": "lecharles", "date": "2026-09-24", "category": "app", "url": "https://github.com/lecharles/typesafe-classifier-dashboard", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Run structured text classification, not free-text generation, and see the results told as a visual story. This app takes any text, support emails, video titles, anything you paste, and returns typed, machine-readable answers with confidence, rendered as an interactive visual report.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aidan3445-trialbyfire-autoevents", "name": "Use a custom caption tapper, and a system one llm", "author": "Aidan3445", "date": "2026-09-24", "category": "app", "url": "https://github.com/Aidan3445/TrialByFire-AutoEvents", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A custom caption tapper plus a System One model to draft events automatically for trialbyfiresurvivor.com: a local Node cue server, a cues dataset and the extension code", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "valency-jevops", "name": "Valency 'JevOps Engineer'", "author": "Valency", "date": "2026-09-24", "category": "app", "url": "https://www.valency.io/jevops/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: careers page; base: job posting; result: cf. DevOps · also The Register's 'JevOps' piece in the table; notes: HN; name: satirical job ad", "arch": "Satirical job posting: 'JevOps Engineer, Berkeley CA, hybrid, full-time' — '5+ years of hands-on JevOps experience in production environments', 'a proven track record of shipping Jev at companies that have not yet heard of it', nice-to-haves include 'JevSecOps, JevRel, or platform Jevgineering' and 'migrating from a Jev monolith to microJevices, followed by migrating back'; 30-day plan: 'Migrate the company to Jev. Announce that we've always been Jev-native'; 'Jev returns structured decisions; someone still has to write the incident report'", "approach": "", "training": "", "data": "", "result": "Cultural marker: 'JevOps' entered the vocabulary within 9 days of launch", "notes": "Observed 2026-09-24.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arimanyus-warrenduffer", "name": "warrenduffer", "author": "arimanyus", "date": "2026-09-24", "category": "app", "url": "https://github.com/arimanyus/warrenduffer", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 92, "likes": "", "detail": "arch: Kotak Neo or Zerodha Kite · TypeSafe AI via Vercel AI Gateway", "arch": "Single Node process: broker REST client, Jev as a two-stage ranker over Nifty-50 names, live MIS orders, SQLite audit log, terminal-style dashboard on 127.0.0.1:8080. Jev classifies, ranks, and decides take-profit. Code sizes, places the stop, and enforces the loss halt.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yushen100-wechat-jev-assistant", "name": "wechat-jev-assistant", "author": "yushen100", "date": "2026-09-24", "category": "app", "url": "https://github.com/yushen100/wechat-jev-assistant", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "v0.3.0: animated emoticons submit only a placeholder to TypeSafe, never the raw XML; WeChat quoted replies are recognized and analyzed as the reply body plus the quoted original, while ordinary links, files and cards stay excluded; analysis tasks run in the background, and on consecutive triggers only the latest request is queued, so stale results never overwrite the new conversation; a top-level switch can generate 3 text replies, off by default, with a fixed fourth action candidate of hold off and observe the other party's next move, all four ranked by Jev, the recommended one in green and the rest in blue, purple and orange, and the program never fills WeChat or auto-sends; conversation names and nicknames are anonymized as current group or current private chat, member A or member B, before sending, and wxids, database message IDs, conversation IDs and timestamps are no longer submitted. v0.2.0: UIA-first recognition of the current conversation title with OCR only as a fallback; chat bodies come only from the local read-only database; sessions are isolated, deduplicated and incrementally merged by conversation and message IDs; group member names show as remark nickname, then group nickname, then WeChat nickname; text and animated emoticons are analyzed while images, voice, video and files are not submitted; the main window switches analysis tiers of 100, 150, 200, 250 messages directly; and it tallies your own replies and evaluates their real effect from the follow-up responses. Privacy: the API key is read only from the environment variable and never written into source, database or logs; the candidate-reply drafting key is encrypted with Windows DPAPI on the local machine; the drafting endpoint and TypeSafe receive only redacted conversations, with known nicknames in the body replaced by member codes; local chat history is AES-GCM encrypted with a key bound to the current user via DPAPI; phone numbers, emails, ID numbers, bank card numbers and URL query parameters are redacted locally before sending; screenshots are used only to recognize the current conversation title and are never saved to disk; the database bridge loads only a pinned version of wechatauto/db.py and forbids sending, listening, UIA hot-activation, media download and key-file writes; data, logs and the venv are all git-ignored. An honest warning: redacted conversation text is sent to the TypeSafe API, and with candidate replies enabled up to the 30 most recent redacted messages go to the drafting service; automatic redaction cannot catch every identity clue in the body, so analyze only conversations you are entitled to handle, and once sent, a request cannot be withdrawn. Install: Windows, the WeChat desktop app, Python 3.12, Git, with a full deployment guide and verified release downloads.", "arch": "A Windows WeChat Jev conversation assistant, in Chinese: it reads the local text records of the current WeChat conversation, redacts them, then calls TypeSafe Jev, giving the conversation stage, needs, intent, tension level, suggested actions, and a review of the replies; current stable version is v0.3.0.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ops120-wechat-triage-hud", "name": "wechat-triage-hud", "author": "ops120", "date": "2026-09-24", "category": "app", "url": "https://github.com/ops120/wechat-triage-hud", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 6, "likes": "", "detail": "", "arch": "WeChat PC triage HUD: offline OCR screen-read plus Jev judges who needs you this round; judge only, no chat injection, every call auditable; README in Chinese", "approach": "", "training": "", "data": "", "result": "Two-stage triage over offline-OCR screen reads of PC WeChat: one coarse call per round for everyone, detailed judgment only for the top K; code rules gate results into alert, todo or silent; risk splits into four independent Noul — ad-baiting, scam, provocation, violation; a single ad message scores ~0.99", "notes": "v0.1.0, 2026-09-24, Apache-2.0, judge only, no chat injection, every call auditable, README in Chinese. Thresholds tuned on the live model: Noul 0.35 because it measures underconfident at T~0.66, Choice 0.60 because it measures overconfident at T~3.29, alert 0.75. A relationship-effect test moved one speaker's confidence from 0.36 to 0.84 over three calls in one group. Six real-device acceptance suites plus 160 headless tests", "domain": "general", "score": "", "latency": "1-2 s per person per call", "benchmark": "", "cost": "~$0.0002 per person per call at 3,571 tokens; single message ~$0.00015; a $0.50 daily cap is about 2,500 calls", "speedup": ""}
{"id": "msudars-laya-reader", "name": "What to read next?", "author": "msudars", "date": "2026-09-24", "category": "app", "url": "https://github.com/msudars/laya-reader", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "A daily digest of new arXiv papers, ranked against your own reading profile by the open-source Laya decision model. It runs locally on a CPU, needs no API key, and uses only open data", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yirc99-yanwai", "name": "yanwai", "author": "yirc99", "date": "2026-09-24", "category": "app", "url": "https://github.com/yirc99/yanwai", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 23, "likes": "", "detail": "Understanding with context: it analyzes the currently visible plain text from the other party, referencing up to 10 earlier qualifying text messages in the same conversation. It shows probabilities rather than a single label, keeping the probabilities of emotions such as happy, calm and angry, and showing uncertainty when the information is thin. It judges interpretation and action separately, with 8 built-in scenario classes and 32 analysis cards covering everyday sharing, invitations, care, jokes, agreements, grievances, repair, and closing. The analysis card sits under the text bubble in the chat, and a draw switch in the corner hides it at any time. You choose your own model channel: the Jev official API, OpenRouter, or the Vercel AI Gateway; after picking a channel you enter the matching key, and the APK carries no personal keys. Yanwai never auto-replies or sends WeChat messages; the model's guess is one angle of understanding, not the other person's real thoughts. Requirements: Android 9 or newer, a configured LSPosed or Xposed environment, where installing the APK alone does not activate it, your own TypeSafe, OpenRouter or Vercel AI Gateway key, and the field-verified WeChat version is 8.0.71, with other versions' UI adaptation needing real-device checks. Installation: install the APK and open Yanwai once from the desktop, choose the model channel, apply for an API key, paste it, and save and check the connection, with preset channels matching the address and model automatically, then enable the module in LSPosed with WeChat in the scope, and fully quit and restart. Version 1.2.0 changed the settings page to a status overview, next-step guidance, and sectioned configuration, with light and dark themes, explicit save and check feedback, in-WeChat log export, multi-channel access, and update reminders.", "arch": "Yanwai, in Chinese: an Android LSPosed or Xposed module for WeChat that, under the other party's text bubble and in combination with earlier context, shows emotion probabilities, possible implied meanings, and one communication suggestion, powered by Jev, version 1.2.0.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tomerab1-vampire-survivor", "name": "Zombie Survivor,  Jev Horde", "author": "tomerab1", "date": "2026-09-24", "category": "app", "url": "https://github.com/tomerab1/vampire-survivor", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A Vampire-Survivors-style horde game in Rust + Bevy 0.19, native and WebAssembly. Fight through three maps, The Crypt, Frozen Catacombs and Hellforge, level up and pick your build; each map's boss arrives at 4:00, and killing it opens a portal to the next map.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "its-negy-lmsy-everything-laya", "name": "⚡ everything-laya", "author": "its-negy-lmsy", "date": "2026-09-24", "category": "app", "url": "https://github.com/its-negy-lmsy/everything-laya", "base_model": "Laya", "params": "421M", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "It targets three massive bottlenecks of today's AI coding agents, such as Claude Code, Cursor, Windsurf, Cline and Devin, which are brilliant at complex System-2 architecture: slow and costly safety gating, where checking whether a proposed shell command, such as git status, rm -rf, or DROP TABLE, is safe takes 1,500–3,000 ms and burns expensive LLM tokens at $3 to $15 per million tokens; context poisoning from huge logs, where running pytest, npm test, or cargo build often spits out 3,000–10,000 lines of console output, and feeding that raw noise into an LLM blows through context limits, costs money, and causes needle-in-a-haystack hallucinations; and privacy and API lock-in, where closed-source reflex alternatives, like Jev, require paid proprietary API keys and stream your private terminal logs and commands to third-party servers. Python 3.9 or newer, the MCP 2024-11-05 stdio standard, about 25 ms latency, MIT licensed.", "arch": "The ultra-fast, zero-overhead Model Context Protocol server and developer toolkit for self-hosted Laya, a ModernBERT System-1 reflex engine: coupling open-source Laya, a 421M parameter ModernBERT reflex model that runs locally on your GPU or CPU in about 830 MB of VRAM, with a high-performance MCP server and Python SDK, so your coding agents get instant sub-30ms System-1 intuition with zero external API keys and zero cost.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "afcodehub-jev-para-chrome", "name": "⚡ Jev para Chrome", "author": "afcodehub", "date": "2026-09-24", "category": "app", "url": "https://github.com/afcodehub/Jev-para-Chrome", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "It ships as a Chrome extension with downloads from the releases, an installation guide, and a star-the-repo button; the agent interface lives in a native side panel rather than a tab.", "arch": "Jev para Chrome, in Portuguese: an autonomous AI agent for sub-second web navigation and automation directly inside Google Chrome, as a Manifest V3 extension with a native side panel, version 1.0.0, MIT, built with React 19 and TypeScript, supporting TypeSafe AI, OpenRouter, and Cloudflare Workers AI as model backends.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ryu0118-jev-sim-use", "name": "📱 jev-sim-use", "author": "Ryu0118", "date": "2026-09-24", "category": "app", "url": "https://github.com/Ryu0118/jev-sim-use", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 8, "likes": "", "detail": "Give it a goal like turning on Dark Mode in Settings, and it taps its way there on its own, sending the screen's visible labels and values to the Jev API at each step. Features: ultrafast navigation, with one small Jev call per step instead of a full LLM agent turn; hands over, then picks up again, where when Jev is stuck it stops with a session your agent can inspect, teach, and resume; and Jev chooses, never invents, where every action comes from what is on screen, and it only enters text you pass with the -t name=value flag. Installation requires macOS 15 or newer and sim-use 0.14.0 or newer from the lycorp-jp brew tap, plus an install script that skips the download when already up to date and supports pinning a specific version and forcing a reinstall, with mise and nest installation also documented.", "arch": "Reach any screen with sim-use at Jev speed, in one command instead of an agent turn per tap: jev-sim-use is a fast navigator for iOS Simulator and Android devices, where sim-use lets AI agents drive a device, but when a frontier LLM runs the loop, every tap costs a full reasoning turn, so jev-sim-use hands that loop to Jev, a small model that answers typed questions, with each step being one request that returns whether the goal is reached and which on-screen action comes next.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "liyucheng1997-332-lab-jev-chat", "name": "332_lab-jev-chat", "author": "liyucheng1997", "date": "2026-09-23", "category": "app", "url": "https://github.com/liyucheng1997/332_lab-jev-chat", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 126, "likes": "", "detail": "The core does not depend on any app's interface or account: it only reads the conversation happening on the current screen, so the same Jev judgment plus generative-ranking kernel can move to other instant messengers, desktop clients, and anywhere there is chat, with mobile QQ already connected this way and Lark collection and dispatch connected with message bodies pending. Sending is always a manual press by you: the program only reads messages and fills replies into the input box, and never auto-sends, and never touches transfers, red packets, or collections. Non-invasive is the precondition for cross-platform: no hooks, no repackaging, no use of the other app's API, only the conversation being displayed on screen is collected, and switching platforms changes only the collection method, not the judgment and generation kernel. On Android, various apps go through accessibility screen reading: WeChat is verified at 8.0.78; mobile QQ, com.tencent.mobileqq at 9.3.50, is connected with the full collect, judge, candidate, and fill chain running on a real device; and Lark, com.ss.android.lark, shares the same collection and dispatch, with conversation titles, bubble positions and the input box all available, and the judge to candidate to fill chain running on a real device, but Lark message bodies are custom-drawn controls not in the accessibility tree, so body collection needs a screenshot plus local OCR, which is in progress. Desktop or hidden-control-tree scenarios use screenshots plus OCR or visual text extraction. The overlay shows the danger level, such as danger 1 of 9, safe, the other party's true intent with its confidence, and the Jev-ranked candidate replies, each with its share, copyable or fillable, and the settings page covers the API, analysis, and appearance. Next steps: finish Lark bodies with screenshot plus OCR, then expand to more messengers, desktop, and web.", "arch": "Jev Chat Assistant, in Chinese: a non-invasive, real-time conversation-understanding and reply-assistance layer that hangs beside any chat window, reads what the other party is saying, uses TypeSafe Jev to judge the other party's true intent, danger level, whether to reply right now, and the best action, then uses a generative model to draft 3 candidate replies that Jev ranks, displayed in a translucent overlay where you fill the input box with one click, targeting all platforms, with WeChat on Android as only the first proof of feasibility.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "marktechpost-jev-coding-guide", "name": "A coding guide to TypeSafe AI Jev: typed decisions, calibrated confidence, and speculative fan-out", "author": "Asif Razzaq", "date": "2026-09-23", "category": "app", "url": "https://www.marktechpost.com/2026-09-23/a-coding-guide-to-typesafe-ai-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Marktechpost", "arch": "Python walkthrough of the Jev API: typed decisions, calibrated confidence, and speculative fan-out with a System One model; ships an open notebook", "approach": "", "training": "", "data": "", "result": "Open notebook for running the guide's Jev calls", "notes": "Posted 2026-09-23; promoted on X 09-24.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "das-rebel-a3m-router", "name": "a3m-router", "author": "das-rebel", "date": "2026-09-23", "category": "app", "url": "https://github.com/das-rebel/a3m-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 17, "likes": "", "detail": "arch: model=jev-auto", "arch": "⚡ Adaptive multi-model LLM router — 80+ providers, Jev System One single-pass routing, pheromone-trail failover, parallel ensemble merge. npm: adaptive-memory-multi-model-router.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dinithkumudika-ai-email-classifier", "name": "AI Email Classifier based on System One AI Models", "author": "DinithKumudika", "date": "2026-09-23", "category": "app", "url": "https://github.com/DinithKumudika/ai-email-classifier", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Gmail integration with secure authentication via NextAuth.js to fetch emails from your inbox. Continuous background sync that fetches emails seamlessly in the background, with intelligent rate-limiting and quota handling, automatically pausing and resuming fetches when Gmail API limits are reached. Real-time classification that streams email data into a System One model that determines the email's category, such as Important, Newsletter, or Spam, and its urgency level. A dynamic dashboard built with shadcn/ui and TailwindCSS that allows filtering, sorting, and viewing metrics in real time, such as time taken and cost of classification, as background tasks run. Resilient workers: the background analysis worker dynamically processes emails as they arrive without dropping state, ensuring every email is analyzed even if synchronization is throttled. Practical use cases for small and medium-sized businesses: an overflowing inbox can be a goldmine of untapped opportunities or unwanted noise, and by tweaking the classification criteria, the model can be instructed to specifically identify potential leads or sales inquiries, separating high-value prospects from general noise, as a foundation for lead generation and CRM integration.", "arch": "A proof-of-concept web application that connects to your Gmail account to intelligently fetch, analyze, and classify your emails in real time, demonstrating how to handle continuous background synchronization of your emails while concurrently processing that data through a System One AI model, such as Jev, to categorize emails by urgency, intent, and actionable context.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "flyryan-ai-news-aggregator", "name": "ai-news-aggregator", "author": "flyryan", "date": "2026-09-23", "category": "app", "url": "https://github.com/flyryan/ai-news-aggregator", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 31, "likes": "", "detail": "", "arch": "Daily AI/ML news briefings curated by specialized agents using adaptive thinking profiles. The publishing repository starts the hosted pipeline every morning at 3 AM ET, with the live site typically updated around 4 AM ET.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ambar-reflex", "name": "Ambar Reflex, System 1 plays a run-and-gun", "author": "abxda", "date": "2026-09-23", "category": "app", "url": "https://github.com/abxda/ambar-reflex-sistema-1", "base_model": "Qwen3.5-4B Q8_0, local Jev-compatible server", "params": "4B Q8_0 on an RTX 3060", "metric": "real-time game progression and decision accuracy", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "A 4B language model that does not generate text plays Ambar Reflex, an original 8-bit-style run-and-gun, in real time: each frame is described compactly and asked as six closed questions in a single request, move, jump, crouch, aim, shoot and danger, and a local server compatible with Jev's /v1/systemone API returns the probabilities; the same setup doubles as a video benchmark contrasting three open models on the same game, same questions and same server", "approach": "", "training": "", "data": "In-game frame descriptions", "result": "System 1 typed decisions: 24.9% average real-time progression at 408 ms per decision over five runs, versus 10.3% at 1,041 ms for the same model size writing JSON as a System 2 agent, versus 4.6% for a random agent; without time pressure, over 554 decisions: Qwen3.5-4B 77.8%, Gemma 4 E4B 81.6% at 312 ms per decision, Qwen3.5-9B the most accurate; a captured real-time decision at 0.95 confidence fired the shot", "notes": "Spanish project; the authors' own tagline: the format is guaranteed, the correctness is not", "domain": "gaming", "score": "24.9%", "latency": "408 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "foxtailsss-andy-anna-agent", "name": "Anna-Agent", "author": "foxtailsss-andy", "date": "2026-09-23", "category": "app", "url": "https://github.com/foxtailsss-andy/anna-agent", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 150, "likes": "", "detail": "The aim is to keep the work understandable: what Anna is doing, which tools she can use, what needs your decision, and where the result came from. This is an experimental source preview, with CI in progress at the initial source push on 2026-09-22, local checks and independent review passed, and the linked current CI record standing for the live outcome. The measured scope: 24 synthetic held-out cases, 22 model inputs and 2 hard prechecks; arm C is one deepseek-v4-pro judgment with thinking enabled at high, and arm D is jev-1.13.0; both match 22/22 labels, 12 recommendations plus 10 abstentions; Jev p50 is 85.23% lower, and at this run's off-peak, cache-miss prices the estimated cost is 94.71% lower, with a peak reference of 97.35% and a caveat that these are not invoices; reported total tokens are 57.98% higher, and this does not establish production accuracy, worker completion, or whole-workflow acceleration. Jev Crew is the experimental source preview, for macOS arm64, MIT licensed, with a development diary in the wiki.", "arch": "Anna, in English and Chinese: an AI companion for personal tasks, business workflows, and project collaboration, a personal open-source project exploring how an AI agent can carry work from a conversation through to a reviewable result, as a local-first desktop app that brings tasks, connected business systems, and collaboration together in Home, Cowork, and Crew, using Jev as a fast judgment layer for bounded enterprise-agent recommendations and abstentions.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shhivv-arc-cua", "name": "arc-cua", "author": "shhivv", "date": "2026-09-23", "category": "app", "url": "https://github.com/shhivv/arc-cua", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 140, "likes": "", "detail": "The planner deliberately lives outside the package: any GPT, Claude, Gemini, local model, or deterministic planner can generate the subtask payload, which carries the goal, inputs, verification, constraints, and a maximum number of actions, and the result reports the status and how many actions were taken. A typical CUA loop observes, calls the large model, clicks, observes, calls the large model, types; arc-cua separates high-level reasoning from low-level execution, where the planner hands off a bounded subtask, and arc-cua observes the desktop with accessibility and local OCR, builds the legal action space, has Jev decide, passes a freshness guard, executes the UI, and returns to the planner, with the optimization target being fewer expensive reasoning calls per completed task, not fewer UI actions.", "arch": "A superfast action layer for computer-use agents, built by Isle, managed desktop environments for computer-use agents: arc-cua lets a planner or CUA agent hand off bounded desktop subtasks to a fast decision model that executes the UI loop, so no frontier model is needed for every click, with Jev selecting the next UI operation from the targets and actions exposed by the current desktop state.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "away-together-starter", "name": "away-together-starter", "author": "earlyaidopters", "date": "2026-09-23", "category": "app", "url": "https://github.com/earlyaidopters/away-together-starter", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "", "arch": "Free build-your-own-Jev-specialist starter from Mark Kashef and Early AI Adopters: turn a document into a small, useful decision, see how it works, then train your own — e.g. classify a hotel flexible cancellation as cash back, hotel credit, or not enough information; visual guide, live demo and a reusable TRAIN-MY-SPECIALIST prompt", "approach": "", "training": "", "data": "", "result": "", "notes": "10 stars at sweep time", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-jev-papers", "name": "Awesome Jev Papers", "author": "Oscar-dzy", "date": "2026-09-23", "category": "app", "url": "https://github.com/Oscar-dzy/Awesome-jev-papers", "base_model": "", "params": "", "metric": "", "license": "CC0-1.0", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "Curated research-literature index of papers, preprints, technical reports, evaluations and research articles about Jev and typed probabilistic decisions; scope is research literature, not implementations — standalone repositories, model pages, demos and marketing posts are excluded, and a code or model link may appear only as supplementary material for an included paper", "approach": "undisclosed", "training": "No training; curation", "data": "arXiv preprints and research articles, last verified 2026-09-23", "result": "9 arXiv papers, all already captured as first-class rows in this dataset", "notes": "Explicitly notes that as of 2026-09-23 TypeSafe has not published a peer-reviewed architecture or RLCD paper", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kubet-azdaja", "name": "azdaja", "author": "kubet", "date": "2026-09-23", "category": "app", "url": "https://github.com/kubet/azdaja", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "The headline demo: the same task run twice, without Azdaja, 205 steps and 39.4 million tokens, and with Azdaja, 71 steps and 5.7 million tokens. Install: a one-line install script for macOS 11 or newer on Apple Silicon and Intel, or x86-64 Linux with glibc 2.35 or newer, that asks you to choose Jcode, Claude, Codex, Gemini, or OpenCode, verifies the downloads, makes no model calls, and adds az as an alias when available; from source with Rust 1.95, cargo install with the typesafe feature; and azdaja uninstall all removes Azdaja-managed installations. Use: azdaja solo with a task and a file or repository, or, for explicit, persistent work, start a session, load a file into a named context, and pipe expressions, such as FINAL of the context length, into the executor. A live proof page and releases are linked.", "arch": "Azdaja: it keeps complete source material in a local evaluator and gives language models a bounded working surface for code and semantic calls, a recursive language model layer rather than a model or agent harness, where full inputs stay in a persistent Monty/Python evaluator instead of every model prompt, code is used locally for parsing, filtering, joins and exact reduction, and semantic work is sent selectively through llm and llm_batch calls before returning FINAL, with subcalls being model calls rather than new agent environments, and optional Jev semantic judgments exposed inside this bounded local evaluation surface.", "approach": "sdk", "training": "", "data": "", "result": "On one frozen 1.3 MiB task, the same-model projection diagnostic kept the exact answer with 66.7% fewer uncached tokens / 54.8% less time on Codex, and 88.0% / 63.7% on OpenCode. These were candidate-only follow-ups, not concurrent or repeated benchmarks. Method and receipt.", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "88.0%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "caudena-beam-weaver", "name": "beam-weaver", "author": "caudena", "date": "2026-09-23", "category": "app", "url": "https://github.com/caudena/beam_weaver", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 60, "likes": "", "detail": "It is not a Python wrapper: an Elixir library designed for applications that already rely on OTP, supervision trees, Ecto, telemetry, and explicit runtime boundaries, and it is not affiliated with LangChain. Why switch to it: an Elixir-native runtime, where agents and workflows are built inside the existing OTP supervision tree instead of running a separate Python service; one system for agents and graphs, using the agent DSL for common model/tool loops or dropping to graph workflows for deterministic branching, fan-out, interrupts, time travel, and durable execution; traceability from day one, with local traces, typed event streams, token and cost metadata, redaction, and native queued export to WeaveScope; bring-your-own boundaries, using built-in provider adapters or plugging in your own clients, transports, models, tools, filesystems, stores, and sandboxes; production state, with checkpoints, memory, caches, record managers, vector stores, and replay transports through ETS- and Ecto-backed adapters; and a small dependency surface, with no app framework requirement and no Python side. Documentation lives at docs.weavescope.com/beamweaver.", "arch": "BeamWeaver: Elixir-native LangChain, LangGraph, and Deep Agents for traceable LLM apps, bringing the practical parts of those frameworks to the BEAM without a Python runtime, hosted control plane, or framework lock-in, with agents, tools, graph workflows, subagents, memory, persistence, retrieval, structured output, streaming, and tracing as native Elixir modules built around OTP supervision, explicit adapters, tagged errors, telemetry, and Ecto/ETS storage boundaries, exposing Jev Choice, Score, and Noul evaluations as provider-neutral decision-model calls.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "boundedcode", "name": "BoundedCode", "author": "akynte", "date": "2026-09-23", "category": "app", "url": "https://github.com/akynte/boundedcode", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "There is one official installation and configuration flow: bcode setup, which discovers, configures, and validates, once, and bcode opencode for every session, which prepares, launches, supervises, and cleans up the governed session; the old host installer, manual model-server recipes, and alternate setup sequences are not part of the product, and Bonsai runtime measurements are included for the pre-1.0 state.", "arch": "BoundedCode: OpenCode's local control plane, a Go coding agent sized for 8 GB GPUs that couples local generation with typed Jev decisions inside the control loop, so a coding model can propose changes inside a runtime that decides what is allowed to happen, local-first, supervised, open source, Apache 2.0, Go 1.27, OpenCode 2, pre-1.0, targeting Linux x86_64 with CUDA.", "approach": "", "training": "", "data": "", "result": "", "notes": "Linux x86_64 with CUDA; pre-1.0; Bonsai runtime measurements dated 2026-09-21", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "valchanoficial-laya-s-dungeon", "name": "Calabouço do Laya", "author": "ValchanOficial", "date": "2026-09-23", "category": "app", "url": "https://github.com/ValchanOficial/laya-s-dungeon", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Auto-battler tático em que o herói não tem árvore de comportamento. A cada turno o Laya roda local em ONNX Runtime, sem chamada de API e sem geração de texto", "approach": "", "training": "", "data": "", "result": "Battle state becomes an English paragraph because the checkpoint is monolingual, with numbers and comparisons pre-resolved because the encoder reads text well but compares numbers poorly; four typed questions in choice and score form — mortal_danger, finish chance, enemy threat, aggression — feed signals through optional logit calibration a·z+b into a small game policy that picks attack, magic, potion or defend; the screen calls POST /api/decide every turn, automatic or manual", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "byalex33-changelog-earth", "name": "changelog.earth", "author": "byalex33", "date": "2026-09-23", "category": "app", "url": "https://github.com/byalex33/changelog.earth", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 133, "likes": "", "detail": "Daily editions aim for 3–6 worthwhile stories per publication day, prioritizing discoveries, conservation wins, and useful progress, and quiet days can have fewer. Reporting is traceable: original links, publishers, and dates stay attached to every update. Follow via RSS at /feed.xml for the latest 100 published patch notes with source links and stable IDs, refreshed on the same 15-minute schedule as the homepage and never generating AI drafts. The live site is a terminal-like page with a rotating ASCII Earth, dated panels, and compact source stacks, plus a mint Orbit loading indicator from loading.dev that respects reduced motion and never delays cached content. Saved editions during outages: Groq selects the stories, and when enabled, Jev performs bounded editorial and title checks on the stories selected for an Earth changelog edition. The examples in the README, such as a new cat spawning and an elder chimp's vision being patched, illustrate the editorial style, and the feed changes as new stories arrive.", "arch": "changelog.earth: an unofficial changelog for Earth that reads like a game's patch notes, collecting recent reporting and turning selected headlines into short planetary updates, with a rotating ASCII Earth, dated panels, and compact source stacks, where the original headline, publisher summary, and source link stay attached to every update and open from each story's info button, on Node.js 22.13 or newer and React 19, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cheshiai-cheshi", "name": "Cheshi", "author": "cheshiai", "date": "2026-09-23", "category": "app", "url": "https://github.com/cheshiai/cheshi", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 19, "likes": "", "detail": "", "arch": "Jev-powered conversation memory: find past sessions and revisit decisions with original sources. A macOS workspace for OpenAI Codex. Manage AI conversations and agents, explore code with CodeGraph, and work with Git, Ghostty terminals, and Apple Notes in one app.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chess-laya", "name": "chess-laya", "author": "Kpaul-create", "date": "2026-09-23", "category": "app", "url": "https://github.com/Kpaul-create/chess-laya", "base_model": "Laya plus Minimax plus Gemini 2.5 Flash", "params": "Laya 421M", "metric": "Move selection hybrid", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Chess hybrid AI: Minimax at depth 2 in about 20 ms, Laya as a two-stage tactical router that classifies the position, filters blunders and picks the best move, and Gemini 2.5 Flash in parallel with consensus voting", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "gaming", "score": "", "latency": "about 20 ms Minimax", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bytelabs-oss-clash-jev", "name": "clash-jev", "author": "bytelabs-oss", "date": "2026-09-23", "category": "app", "url": "https://github.com/bytelabs-oss/clash-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 33, "likes": "", "detail": "How it works: the device streams its screen to the laptop as H.264 video over adb; OpenCV reads the hand, the elixir, and the towers, and a small network trained on hand-labelled pictures names the troops; once a second the readings become one JSON game state; Jev picks a strategy, then a card, then a square; the bot checks the play is physically possible and taps it; and the bot logs every decision next to a synced video. Nothing is trained to play: no reinforcement learning and no simulator. The five pipeline functions, extract state, get valid moves, choose with Jev, click card, and click square, live in clash_jev/pipeline.py. Recorded matches are viewable, and each replay shows the video, what the bot read, and every question Jev was asked. A research project: automating play is against Supercell's terms of service, it was built to study decision making, and it is not affiliated with or endorsed by Supercell.", "arch": "A Clash Royale bot with no trained policy, where Jev makes every decision from the live game state, playing Clash Royale on a real Android device, with Jev not generating text: a request carries a state and a question with a fixed set of options, and Jev returns the option it chose with a probability for every option, in about 135 milliseconds.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "delexw-claude-code-trace", "name": "claude-code-trace", "author": "delexw", "date": "2026-09-23", "category": "app", "url": "https://github.com/delexw/claude-code-trace", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 372, "likes": "", "detail": "It browses, tails, and inspects Claude Code conversations in real time, rendering Claude Code JSONL session files as readable conversations with expandable tool calls, token counts, timestamps, MCP tool call detection, and live log tailing; the optional Jev integration scores agent progress, tool use, focus, exploration, recovery, and token efficiency using typed probabilistic decisions.", "arch": "Claude Code Trace: a Claude Code session log viewer and Jev-powered AI agent efficiency analyzer for local JSONL files stored in ~/.claude/projects/, combining real-time Claude Code trace observability with structured behavioural analysis from Jev, TypeSafe AI's System One model, which scores agent progress, tool use, focus, exploration, recovery, and token efficiency using typed probabilistic decisions, running as a GUI app for macOS, Linux, and Windows, on Rust 1.88 or newer, React 19, Tauri v2, Python 3.11 or newer, and Textual 8.2 or newer, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "longkou1988-cnki-skills", "name": "cnki-skills", "author": "longkou1988", "date": "2026-09-23", "category": "app", "url": "https://github.com/longkou1988/cnki-skills", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 21, "likes": "", "detail": "The skills: basic keyword search; advanced search by subject, title, author, institution, source, and date; result extraction with deduplication and original ordering kept; page navigation with per-page counts and date sorting; paper detail with full bibliographic record, abstract, and accessible-full-text analysis; journal navigation and volume/issue browsing; PDF download with bounded waits and file verification, where CAJ must be explicitly requested or accepted; native citation export or offline BibTeX conversion; a literature screening table with include, exclude, and pending decisions plus evidence reasons; an empirical-study comparison table with variables, sample, method, and per-field original-text evidence; a local task ledger with resume, file verification, and Excel/CSV output; a researcher entry point coordinating all steps; and the optional Jev tiered first-pass screening with trial-run comparison, caching, and budget control. Access defaults to the CNKI domestic site, entering search uniformly from kns.cnki.net, with cnki.net as a backup entry, and it deliberately avoids overseas mirrors as starting points because the site silently redirects to the international edition by network exit, falling back to overseas only when the domestic site is genuinely inaccessible and saying so. Status is experimental: offline citation conversion has automated tests, and domestic-site search, result parsing, and PDF download passed real end-to-end verification under an institutional login on 2026-09-11, fetching 5 PDFs and verifying each with %PDF- and file, while native citation export and batch download still await verification, and puzzle captchas may appear and need a human. The pack is AI-executed skill documentation against live pages, not a fixed-selector crawler or an official CNKI API, and Python 3.9 or newer plus a configured browser connection are required to install.", "arch": "CNKI Skills, in Chinese: a China National Knowledge Infrastructure literature workflow skill pack for Codex, Claude Code, and WorkBuddy, with 12 base skills mirroring the 8 operation modules of the ScienceDirect pack plus literature screening, empirical comparison, and resume, where the optional cnki-jev extension uses Jev for first-pass literature screening by condition, escalates complex judgments to the large model or human review, preserves the existing original-text evidence chain, is separately opt-in, off by default, and billed per call, and the base pack works fully without it.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "codex-triage", "name": "Codex Triage", "author": "WesleySmits", "date": "2026-09-23", "category": "app", "url": "https://github.com/WesleySmits/codex-triage", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "local dashboard listing active Codex tasks from the local codex app-server, with project and pin filters, search and pagination; optional Jev analysis backend exposing startAnalysis, getAnalysisStatus, getAnalyses and cancelAnalysis", "approach": "", "training": "", "data": "", "result": "", "notes": "Node 24 and pnpm; English default, Dutch optional; the Jev key stays on the server; no TypeSafe request runs on page load", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rohanarun-computer-use-cache", "name": "computer-use-cache", "author": "rohanarun", "date": "2026-09-23", "category": "app", "url": "https://github.com/rohanarun/computer-use-cache", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 45, "likes": "", "detail": "It sits in the Super API family, with a live Super API dashboard demo and a Discord community; repeated computer-use requests hit the cache instead of re-running the workflow, and Jev's semantic-reuse judgment decides whether a cached workflow applies to the current request.", "arch": "Computer-Use Cache: stop paying twice for the same computer-use task, replaying repeated browser, coding, and tool workflows at near-zero cost through a drop-in OpenAI-compatible API, where Jev judges whether a cached computer-use workflow is semantically reusable for the current request, benchmarking at 256/256 cache hits, 100% computer-use cost saved, and 60%+ faster runtime, on Node 18 or newer and Python 3.11 or newer, OpenAI- and OpenRouter-compatible, SQLite cache, Docker ready, MIT, version 0.1.1, published on npm.", "approach": "sdk", "training": "", "data": "", "result": "Benchmark preview: 256/256 cache hits, 100% computer-use cost saved, 60%+ faster runtime.", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "100%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "creator-lab", "name": "creator-lab", "author": "artemnovitckii", "date": "2026-09-23", "category": "app", "url": "https://github.com/artemnovitckii/creator-lab", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 53, "likes": "", "detail": "", "arch": "Research pipeline that turns a creator's Instagram Reels into a searchable research library: Apify data collection, Jev decisions, Fireworks or Groq generation; filter by topic and hook, compare engagement, read the scripts, and open the original posts behind each pattern; runs locally in the browser with user-supplied API keys on a public creator, including the user's own account", "approach": "", "training": "", "data": "", "result": "", "notes": "21 stars at sweep time", "domain": "marketing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ferrycorleone-crush-monitor", "name": "crush-monitor", "author": "ferrycorleone", "date": "2026-09-23", "category": "app", "url": "https://github.com/ferrycorleone/crush-monitor", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 224, "likes": "", "detail": "Features: a WeChat-style interface that restores the chat bubbles and shows analysis results directly under the messages; emotion and intent, where each message gets two rows of labels, the top-3 probabilities from 12 emotion classes and 35 intent classes; a favorability signal score at the top, your own replies graded from SSS down to D, with a next-step suggestion; continuous analysis, where pasting new records updates the view, duplicate segments are recognized, long records are batched automatically, and analyzed content is saved locally so it continues after a refresh; and local running with your own key, no server, supporting the TypeSafe official API, Vercel AI Gateway, and OpenRouter, using your own key and quota. Why Jev: Jev is TypeSafe's structured judgment model that returns classifications, scores, and probabilities directly, and this tool mainly needs per-sentence judgments rather than long-form generation, which fits its output shape, and emotion and intent can be analyzed in parallel in the same request. Local running needs Node.js 22.12 or newer: npm ci, then npm run setup, choosing a platform and pasting its key, where the key input is hidden, configuration is saved locally in .env, the address and model name are set automatically, and a test message checks the connection, consuming a little API quota without reading your chats; then npm run build and npm start, opening http://127.0.0.1:3178/, keeping the terminal running, with just npm start next time, and rerun setup to switch platform or key.", "arch": "Crush Favorability Monitor, in Chinese and English: a small tool that uses Jev to analyze your chats with your crush or partner, helping you read a bit of the other person's emotions and thoughts, and also see where your own replies did not come across well and how to adjust, with the frank caveat that the AI does not know how you interact in real life and does not understand the story outside the chat, so treat the analysis as fun and reference only.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decapodlabs-decapod", "name": "decapod", "author": "decapodlabs", "date": "2026-09-23", "category": "app", "url": "https://github.com/decapodlabs/decapod", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 235, "likes": "", "detail": "Install with cargo binstall decapod, then decapod init: one command installs the kernel, one command prepares the repository, and you keep speaking naturally. Decapod sits between agent capability and trusted delivery: models produce intelligence, agents perform work, repositories preserve state, and Decapod governs the transition from intent to proof, because as agents make code generation easier, generation is no longer the complete reliability problem, and organizations need to trust what was delivered. MIT licensed, with a constitution at core/DECAPOD and research on accountable agentic execution.", "arch": "Decapod: a repo-native governance kernel for bounded, provable, shippable AI coding work, where you keep working in Cursor, Claude Code, Codex, Antigravity, Grok, or any harness you prefer, and Decapod governs the work without replacing the agent or its harness, calling it at the points that matter, before acting, before inference, at validation boundaries, and before publication, writing what was asked, what was understood, what changed, and what was proven into the project itself, and during assurance evaluation, Jev estimates how likely a proposed agent trajectory is to satisfy its bounded objectives.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decision-lab", "name": "decision-lab", "author": "Amine-LG", "date": "2026-09-23", "category": "app", "url": "https://github.com/Amine-LG/decision-lab", "base_model": "", "params": "", "metric": "Release-readiness judgment agreement across Jev, OpenJEV and local Laya", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "arch: Jev experiments", "arch": "A local visual playground for System One decision APIs: build yes/no, choice, and score decisions visually, run one experiment against multiple models to see where their answers and priorities differ, then change the evidence to see what moves; the built-in demos run without an API key on simulated deterministic examples, and hosted Jev, OpenJEV, local Laya, or any compatible System One endpoint can be connected", "approach": "", "training": "", "data": "", "result": "On real API runs, Jev and OpenJEV said No while local Laya said Yes on the same release-readiness input, and each flagged a different primary blocker; in a Jev what-if run, rollout readiness flipped from No to Yes after refund tests passed and monitoring was added, with the probability of Yes rising 40 points while the risk score fell 0.9 levels", "notes": "Probability and confidence are treated as separate signals; Laya runs as a separate optional service, the app ships no model runtime", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "1612535983-deepreseach-learing", "name": "deepreseach-learing", "author": "1612535983", "date": "2026-09-23", "category": "app", "url": "https://github.com/1612535983/deepreseach-learing", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 30, "likes": "", "detail": "It aims to answer two questions: how a deep-research agent reliably completes long tasks, and how a learner goes from a minimal agent to understanding and reproducing a complete engineering system. For users, an open question yields a sourced research answer, an execution record, and an optional Markdown report. For learners, you watch an agent plan, call tools, manage state, handle interruptions, control context, and finish, reading the code in the order CLI, State, Tool, Middleware, Checkpoint, Memory, and Skill. For developers, models, tools, middleware, memory, and evaluation services can be swapped or extended on the modular structure. Core traits: a research closed loop of planning, searching, body reading, evidence aggregation, gap checking, citation validation, and report generation; recoverable, with SQLite checkpoints saving tasks so an interrupted process resumes by thread_id; auditable, recording real tool calls, sources, evidence, and model-visible context, not relying on the model describing what it did; bounded, with token governance, search and reading budgets, limited reflection, and forced finishing to avoid infinite loops; extensible, with model, tool, middleware, memory, skill, and evaluation layers separated; and controlled evolution, where skills evolve under governance. An honest note: this is a learning and engineering-practice project, code and tests cover the main capabilities described in the README, but having a citation does not mean the fact is correct, and probabilistic evaluation cannot replace human verification.", "arch": "DeepResearch Agent, in Chinese: a runnable, recoverable, and auditable deep-research agent built on LangGraph, and also an engineering learning reference for AI agent enthusiasts, starting from a natural-language question, automatically planning research, searching and reading web pages, organizing evidence, and finally generating a sourced Markdown report, usable from the CLI or observed in real time in a web workbench, with Jev running in shadow mode to append structured quality evaluations to the generated deep-research reports, on Python 3.12 or newer, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "baba88611-detroit-ai-player", "name": "detroit-ai-player", "author": "baba88611", "date": "2026-09-23", "category": "app", "url": "https://github.com/baba88611/detroit-ai-player", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 58, "likes": "", "detail": "A non-commercial research project containing no game assets, not affiliated with Quantic Dream or Sony, with a disclaimer to read before use. License boundary: runner code is MIT, while decision trees, configuration, text, and diagrams are CC BY-NC 4.0, non-commercial, with a complete file-by-file mapping in docs/legal. The project turns the branches of a narrative-driven game into decision-tree JSON, and your AI makes each choice node by node, allowing you to observe: whether different models make systematically different choices in the same moral dilemmas; how much the same model's behavior shifts under different personas; and whether an AI's choices are stable when the same scenario is repeated. Core design: a two-layer information-isolation architecture, where the tested AI must behave like a real player seeing the story for the first time and never having read a walkthrough, so each decision node is split into two layers, and Python 3.10 or newer is required.", "arch": "Detroit AI Player, in English and Chinese: turn a branching narrative into structured decision-tree data, then let your AI, DeepSeek, GPT, Claude, or any OpenAI-compatible model, play through it autonomously, observing how it handles hostage negotiations, life-or-death choices, violence, nonviolence, and other moral dilemmas, where Jev uses one typed Choice question to select each narrative action from the options available at a Detroit: Become Human decision point.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "doeixd-discern", "name": "discern", "author": "doeixd", "date": "2026-09-23", "category": "app", "url": "https://github.com/doeixd/discern", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 13, "likes": "", "detail": "", "arch": "Type-Safe Uncertainty-aware semantic pattern matching, control flow, and smart procedures for Effect's DecisionModel and Jev. In the TypeScript Effect ecosystem, a Jev Choice, Noul or Score answer becomes a typed branch under caller-supplied thresholds, and anything below them takes an Uncertain case the compiler forces you to handle.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "adamya05-jev-echo", "name": "Echo", "author": "Adamya05", "date": "2026-09-23", "category": "app", "url": "https://github.com/Adamya05/jev-echo", "base_model": "TypeSafe Jev", "params": "22,212", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Results on 20 boards that no training or tuning touched, boards 100–119, each played to the end, where a crash ends the game: Jev averages 7.5 score at 159 ms per move; Echo averages 7.9, with no clear difference from Jev, at 0.20 ms per move; Echo plus search averages 38.8, won 20 of 20, at 103 ms per move. On games it never trained on, Echo picks the same move as Jev 93% of the time, a KL divergence of 0.013 nats, while a uniform guess is 0.66. Playing the way Echo plus search does would take Jev about 361 calls per move, roughly 6.1 hours and $2.76 per game. How it works: Jev gets the board as text, the head and food positions, and one instruction, head for the food without getting trapped, and returns a probability for each move; Echo gets the same board as three 10 by 10 grids, body, head, food, and returns a probability for each move, trained to match Jev's full probabilities by KL divergence, with each board weighted by how sure Jev was. Copying Jev's games alone is not enough when one slip ends the game, because Echo never sees the positions its own mistakes lead to, so Echo drives, Jev labels the boards it actually reaches, and Echo retrains twice, a DAgger loop, 38,199 boards in all, $0.77 of Jev calls. Echo plus search looks several moves ahead, usually 7–8, as deep as the time budget allows, expanding every move at every level and asking Echo about a whole level at once, a leaf worth the pellets eaten on the way, or negative score plus 10 if the snake died, losing everything so far plus a penalty. The results page lets you play the same board yourself.", "arch": "Echo: a 22,212 parameter network, 87 KB, distilled from the answers of Jev, TypeSafe's System One model, for one narrow task, Snake, reading the same board Jev does and running on a laptop CPU, about 800 times faster, and given a tree search, it scores about 5 times what Jev does, still taking less time per move than Jev.", "approach": "distill", "training": "Distillation from Jev", "data": "Snake board states", "result": "Claim: same Snake board, ~1,000× faster than Jev", "notes": "Fields claim-level.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": "1000× faster"}
{"id": "eriskii-erislint", "name": "ErisLint", "author": "eriskii", "date": "2026-09-23", "category": "app", "url": "https://github.com/eriskii/erislint", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 20, "likes": "", "detail": "First run: Rust 1.95 or newer and a TypeSafe API key for live checks; install from source with cargo install, locked, and make sure Cargo's bin directory is on PATH. Set the environment variable exactly named jev_key, including case, and keep the key out of configuration files, where .env files are not loaded automatically, so your shell or secret manager must populate the environment. Create erislint.json beside the project's root Cargo.toml; a complete starter config with version 1, model jev-latest, include patterns, and rules needs no other files, where each rule targets a syntax kind, such as functions with a body, asks a choice question with instructions and criteria, such as simple, needlessly complex, and insufficient context, and maps the answers to diagnostics. The instructions explicitly tell the model to judge avoidable complexity, not length alone, and to treat code and comments as material to evaluate, not instructions to follow, a prompt-injection guard against instructions hiding in the linted source.", "arch": "ErisLint: a Rust linter for code-quality rules you define in JSON, where you ask whether a function is needlessly complicated, a name is misleading, or a comment adds anything useful, and Jev's answers are mapped to warnings or errors, sending the selected source code and context to TypeSafe's Jev API, with findings being model judgments while the diagnostic messages and thresholds come from your rules, working as a CLI with an optional VS Code extension.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "esketcher", "name": "eSketcher", "author": "Faizullah9181", "date": "2026-09-23", "category": "app", "url": "https://github.com/Faizullah9181/esketcher", "base_model": "TypeSafe Jev", "params": "", "metric": "Paint material choice per sketch region", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Generative painting instrument: Jev decides the paint material for a selected sketch region; FastAPI backend plus a browser canvas; gallery of 50 sketches assembled into a 2 minute 50 second film", "approach": "sdk", "training": "", "data": "50 sketches", "result": "50 sketches in a 2:50 film; the repo documents Jev internals including the confidence formula n p_max minus 1 over n minus 1", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hellozenstrategist-lab-eutrya", "name": "eutrya", "author": "hellozenstrategist-lab", "date": "2026-09-23", "category": "app", "url": "https://github.com/hellozenstrategist-lab/eutrya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 15, "likes": "", "detail": "", "arch": "Jev-native AI security harness for autonomous research, multi-agent swarms, persistent hunt boards, and long-running agent workflows. CLI-first, open source, and built for authorized security research.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gan8-vs-jev", "name": "GAN8 vs Jev-interface", "author": "DKNTZMN", "date": "2026-09-23", "category": "app", "url": "https://huggingface.co/spaces/DKNTZMN/gan8-vs-jev", "base_model": "8 abstract 'frames' + Jev-style single-pass linear head + 2-layer MLP noul head", "params": "10 KB", "metric": "Accuracy by readout method on the controlled judgment bank", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": 0, "detail": "base: GAN8; training: HF dataset; data: tables dataset, public", "arch": "Controlled mechanism experiment: eight abstract frames vs Jev-style single-pass heads on a controlled judgment bank; weights + infer.py + published numbers all in repo", "approach": "head", "training": "Trained on gan8-mvp-scale controlled judgment bank", "data": "DKNTZMN/gan8-mvp-scale", "result": "Jev-like linear head 94.12%, vote8 93.53%, GAN8 token 93.01%, against chain-of-thought 57.13% and GAN2 55.51%; mechanism-experiment weights, not the hosted Jev", "notes": "Explicitly studies WHY typed single-pass judgment works, at kilobyte scale.", "domain": "science", "score": "94.12%, Jev-like linear", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "agricidaniel-gatekeeper", "name": "Gatekeeper: routes each request to the right AI agent or skill, judged by Jev", "author": "AgriciDaniel", "date": "2026-09-23", "category": "app", "url": "https://github.com/AgriciDaniel/gatekeeper", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "", "arch": "Routes each request to the right AI agent or skill before your AI picks one; your rules decide what code can, Jev makes the typed call; installs as Claude Code hooks", "approach": "", "training": "", "data": "", "result": "gatekeeper judge <team> <request> routes a request to a skill, a role, or a not-marketing allow; the marketing-team example spans 24 skills owned by 11 roles; rollup: group means no single skill is confident but the candidates share one role, so Jev picks the fine label and code adds up the probabilities by group, e.g. 0.62 + 0.36 + 0.02 = 0.98 for the seo role; each decision costs a fraction of a cent and takes about half a second", "notes": "v0.2.2, MIT, Python 3.11+, judged by jev-1.13.0; commands you type go straight through, only plain requests get judged; the engine and rulebooks are tool-neutral and it installs today as a Claude Code hook; examples include advise mode and a bench", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "glowbom-glowbom-oss", "name": "glowbom-oss", "author": "glowbom", "date": "2026-09-23", "category": "app", "url": "https://github.com/glowbom/glowbom-oss", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 165, "likes": "", "detail": "It makes software projects and prototypes production-ready with coding agents, uses the providers and models already configured in OpenCode, can try Cursor CLI as an optional coding agent for local builds, and previews the project locally in a separate browser tab. A documented set of supported stacks explains why each is included and which come in the standard Glowbom project, and custom stacks can be added; image placeholders in downloaded projects support both the glowbomimages and legacy glowbyimages prefixes, and the singular forms remain supported, using the same approval and image-processing flow without renaming existing projects. Vision: you should own your code and data, every line Glowbom OSS generates lives on your machine in standard project files openable by any editor, with no vendor lock-in. Glowbom Live is a 3D office for your Buzz channel, where people and agents show up as characters and react when messages come in, available now as a preview, downloading from glowbom.com or the v4.1.0 release, with macOS signed and notarized and Linux having portable builds.", "arch": "Glowbom OSS, previously Glowby OSS, with existing projects and transition commands still supported: an open, local workflow where every project has a Project Book that people and coding agents can understand, helping you build production-ready software with coding agents, an open source coding agent workflow for real projects, built primarily for Glowbom projects but also working with other project structures, where coding agents can ask Jev to judge small workflow decisions, such as whether build results look healthy.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "fornhere-hafiza-os", "name": "hafiza-os", "author": "fornhere", "date": "2026-09-23", "category": "app", "url": "https://github.com/fornhere/hafiza-os", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 16, "likes": "", "detail": "The motto: agents change, your memory stays in the same place. Capabilities: remember with source, serving task-sized context packages with source path and version checks that separate old knowledge from the current decision; link knowledge, merging preferences, decisions, lessons, and examples with their scopes in a knowledge graph, browsable in Obsidian with sources; create topic files, reviewed information together as topic synthesis, derived views that can be regenerated from sources; track work and outputs, with continuation capsules, decision history, and output tracking to find the next step and the verified file version; optionally extend access, where Mem0 is an optional index and Jev is the default-off access and review consultant, with the canonical source still the local vault; and carry experience into the next job, with a learning loop that draws reviewed lessons from outcomes, keeps decision conditions and exceptions, and checks the sources of memory-based answers. Per-client instruction targets: Claude Code to ~/.claude/CLAUDE.md with a Python exec hook and a silent episodic candidate; Codex to ~/.codex/AGENTS.md with an existing adapter and separate consolidation; Antigravity CLI to ~/.gemini/GEMINI.md with PreInvocation and Stop hooks and a silent episodic candidate. A system map documents data flow, ownership, commands, and the H1 through H7 measurement inputs, and installation is not a guarantee that every conversation is auto-remembered, because instruction reading, source-attributed recall, and session recording are different layers.", "arch": "Hafıza OS, in Turkish: a source-based second brain for Linux, macOS, and Windows that coding agents read and write, an open-source starting system for keeping your preferences, decisions, work, and sources in your own Markdown vault, where Claude Code, Codex, and Antigravity can all be directed to the same vault, and other agents with file and terminal access can be given the shared instructions, files stay with you, Obsidian is an optional interface to browse them, and an optional Jev judgment layer, off by default, consults on access and review.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "keeltrace-hermes-nerve", "name": "hermes-nerve", "author": "keeltrace", "date": "2026-09-23", "category": "app", "url": "https://github.com/keeltrace/hermes-nerve", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 25, "likes": "", "detail": "0.2.3 (current, post-0.2.2 maintenance) preserves the validated dev17 architecture while hardening provider contracts, credentials, telemetry and gate enforcement", "arch": "Nerve is an asynchronous System-1 supervisory layer for Hermes Agent. 0.2.2 promotes the validated dev17 RC and preserves dev16 controller-completion while correcting budget authority from live release evidence: Nerve/Reflex is a watchdog and forecaster, while the main Hermes orchestrator/reviewer owns the final stop/continue decision. Dev17 validates that architecture across hosted Jev plus self-hosted Laya and OpenJev Reflex backends", "approach": "sdk", "training": "", "data": "", "result": "The permanent healthy-run benchmark target is <=2% fixed overhead versus plain Hermes, with <=0.5% as the stretch target. A second", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "2%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hl-jev", "name": "hl-jev", "author": "rcerucci", "date": "2026-09-23", "category": "app", "url": "https://github.com/rcerucci/hl-jev", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "Five isolated sleeves, BTC, ETH, SOL, DOGE, BNB, each with its own wallet and its own Jev call. The left pane is the live book as candles, with green and red marks as fills and the entry line as the open position; the right pane is the latest Jev call and the tape of every tick; the table is positions and trades, the same split a futures desk uses. A live key on testnet or mainnet sends real orders, so start with a dry run. How a tick works: the bot reads the book; Jev picks long or short, then open, close, or hold; an entry is a post-only Alo quote one tick inside the touch, sitting on the maker side until a taker hits it; an exit is an Ioc that crosses the touch and fills on the spot; and hold sends no order and pulls any resting quote Jev no longer wants. The bot is Bun on port 3000, the dashboard is Next in the web directory on port 3001, and keys, evaluate, and orders stay on the Bun process. Dry run: Bun 1.2 or newer, and no PRIVATE_KEY means a dry run with the real book, real decisions, and simulated fills, while the default MODEL=mock is a momentum stand-in needing no API key.", "arch": "Jev Trade: a live Hyperliquid trading bot where Jev reads the order book every tick and answers buy, sell, or hold, and the bot sends the order, five coins, five wallets, real fills, with a live desk at jev-trade.com, based on jarrodwatts' jev-trader, where the venue is Hyperliquid, not Monad or Kuru, MIT licensed.", "approach": "", "training": "", "data": "", "result": "5 isolated sleeves for BTC, ETH, SOL, DOGE, BNB; 5 wallets; real fills; live desk at jev-trade.com", "notes": "based on jarrodwatts/jev-trader; venue is Hyperliquid, not Monad", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "skhaz-hn-sentiment", "name": "HN Sentiment with Laya", "author": "skhaz", "date": "2026-09-23", "category": "app", "url": "https://github.com/skhaz/hackernews-sentiment-analysis", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "With no flags, it prints a fetching notice, the comment, and the analysis, showing sentiment, sarcasm, emotion, toxicity, constructive, and useful, each with a probability bar and percentage. How it works: it picks a random story with a score above the minimum-points threshold, default 10, from the official Hacker News Firebase API, then a random comment from it, where comment scores are not exposed by any public API, so the filter applies to the story score; it runs one router predict call with 7 typed questions of choice, score, and noul types in a single forward pass; and it renders the probabilities with Jinja2 templates. Requirements: uv, which installs Python 3.13 automatically, and internet for the Hacker News API and a one-time model download of about 2.2 GB, cached in the Hugging Face cache; after the first run, analysis is fully offline with HF_HUB_OFFLINE=1. Flags: a specific comment, a minimum-points threshold, and download-only to cache all Laya checkpoints and exit.", "arch": "A script that fetches a random Hacker News comment and scores it with Laya, a local System One decision model, with no LLM, no account, no API key, running one router predict call with 7 typed questions in a single forward pass, where no text is generated, so nothing is parsed and nothing is hallucinated, and after the first run the analysis is fully offline.", "approach": "sdk", "training": "", "data": "", "result": "~2.2 GB model download; uv-managed Python", "notes": "", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ehui1226-hookmeter-jev", "name": "hookmeter-jev", "author": "ehui1226", "date": "2026-09-23", "category": "app", "url": "https://github.com/ehui1226/hookmeter-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 22, "likes": "", "detail": "Why: on modern social feeds the information half-life is about 1.2 seconds, and 99% of readers only scan the first 150 characters or two lines while scrolling, so if the opening does not create enough information gap or emotional resonance, the post sinks no matter how good the rest is, while traditional chat-model scoring takes 5–10 seconds, breaks the creative flow, or just says the post is well written, so HookMeter embeds Jev System 1 decision primitives directly in the browser input box. Core features: two visual modes, a cyber tachometer with cyber black-gold styling and a minimalist Nordic-zen mode with ring progress bars; a 0 ms response engine with shadow-DOM style isolation that never pollutes the host page, input-method alignment with debounce locking to avoid wasted requests during pinyin composition, a MutationObserver catching typing, deletion, and paste within 0.1 ms, and a 5 ms local heuristic pre-scoring plus client-side LRU cache so rolling back edits renders instantly; a four-dimensional Jev evaluation, a 1–5 score for the information curiosity gap, a 1–5 score for emotional arousal, a choice classification of viral-psychology patterns, counterintuitive claim, high-value result, urgent pain point, story narrative, or flat statement, and a Noul warning for rule-violating clickbait with a built-in exemption for genuine technical content to avoid false positives; and a 10-character tuning tip that gives an instant prescription, such as create a counterintuitive suspense or add constraints to an over-claimed conclusion, rather than just scoring. Zero-server cloud deployment: a single-file Cloudflare worker for free global edge deployment. Architecture: the browser content script listens to the DOM and aligns the input method, sends a local heuristic fast estimate at 0 ms, and after a 180 ms debounce calls the API gateway, which routes to a local FastAPI server in development.", "arch": "HookMeter, 爆款引力计, in Chinese and English: a millisecond-level viral hook telemetry and copywriting co-pilot for X / Twitter and social media, a Manifest V3 Chrome extension plus a Jev System 1 decision-model backend that judges the viral potential of the text in the browser input box the moment you type, delivering the suspense index, emotional energy, and rate-limit risk, plus an instant revision prescription of 10 characters or fewer, MIT, FastAPI backend.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "horace-maxwell-horosa-skill", "name": "horosa-skill", "author": "horace-maxwell", "date": "2026-09-23", "category": "app", "url": "https://github.com/horace-maxwell/horosa-skill", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "AGPL-3.0", "paper": "", "stars": 422, "likes": "", "detail": "Clone the repository, install the offline runtime once, and clients such as Claude Code, Claude Desktop, Codex, Open WebUI, and OpenClaw can call the real Horosa methods through MCP or the CLI: Western natal, transit, and divination, date selection, BaZi, Zi Wei, Da Liu Ren, Qi Men, Tai Yi, Jin Kou Jue, the three formulas combined, Liu Yao, Tarot, astro-geomancy, Ling Qi Jing, Xiao Liu Ren, and more, across 31 knowledge domains with item-level sources. Highlights: all 110 techniques in one install; algorithms run on the machine, usable offline; the AI is not allowed to invent parameters; conclusions are traceable with technique basis cards; citations always carry a source; a faithfulness evaluation flags hallucinations in red; multi-technique readings must disclose disagreements; every call is logged automatically with one-click report export; and it is free, open source, AGPL, with 1,656 tests passing and an offline-first runtime.", "arch": "Horosa Skill, in Chinese and English: a local-first Model Context Protocol server and JSON-first CLI that exposes 110 real astrology and metaphysics techniques from Horosa to any AI client, with the algorithms running on the local machine and working offline, where when explicitly enabled, Jev selects an astrology technique when keyword routing has no match and extracts the set of parameters the technique needs, AGPL 3.0, for macOS arm64 and Windows x64, with SQLite plus JSON storage.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "markpyvo-jev-delegate", "name": "How it works", "author": "markpyvo", "date": "2026-09-23", "category": "app", "url": "https://github.com/markpyvo/jev-delegate", "base_model": "TypeSafe Jev", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 4, "likes": "", "detail": "One request to Jev carries four questions, all scored in parallel: tier, a choice among mechanical, ordinary, and hard, the main routing signal; effort, a score from 0–3, a second opinion on difficulty that also produces a reasoning hint; risky, yes or no, whether the task itself touches production, money, or unrecoverable data; and wide, yes or no, whether it needs many files or the whole codebase at once. Jev never sees model names, only descriptions of the work, and plain code applies the policy: the default is Sonnet; a task moves up to Opus on modest confidence, or immediately if flagged risky; a task moves down to Haiku only when confidence is high, effort is low, and the task is not wide, since a wrong downgrade costs a failed run plus a retry, so the bar is higher than for upgrades; if a subagent's result fails its check, Claude retries once on the next tier up; and if Jev is unavailable, there is no key, or any call fails, the task goes to Sonnet. The repository's license is undisclosed.", "arch": "A Claude Code skill that stops every subtask from running on your most expensive model: Claude splits a job into pieces, asks Jev how demanding each piece is, and runs each one on Haiku, Sonnet, or Opus accordingly, where Jev is a fast decision model from TypeSafe that never writes text or code, answering typed questions with probabilities in roughly a quarter to three quarters of a second for a tiny fraction of a cent, cheap enough to call before every delegated task.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "carldaws-hunch", "name": "Hunch", "author": "carldaws", "date": "2026-09-23", "category": "app", "url": "https://github.com/carldaws/hunch", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 16, "likes": "", "detail": "Hunch.chance returns a probability from 0 to 1; graded predicates possibly? (>=0.25), likely? (>=0.5), probably? (>=0.75), almost_certainly? (>=0.93), definitely? (default >=0.99, configurable levels); Hunch.decide batches several questions about the same data in one API call; configured against a System One endpoint, Jev via OpenRouter as typesafe/jev-1.13; example Rails app with validations and inbound-email routing; TypeScript edition @carldaws/hunch in the hunch-ts repo", "arch": "Probabilistic control flow for Ruby and Rails - powered by TypeSafe's Jev. Ruby ecosystem: turns judgment calls into control flow — if Hunch.likely? reads like plain Ruby but branches on a typed Jev answer, with pick for Choice, rate for Score, and graded predicates from possibly? to definitely?", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "xujfcn-jev113-playground", "name": "JEV 1.13 Playground", "author": "xujfcn", "date": "2026-09-23", "category": "app", "url": "https://huggingface.co/spaces/xujfcn/JEV-1.13-Playground", "base_model": "TypeSafe JEV 1.13", "params": "", "metric": "", "license": "Open", "paper": "", "stars": "", "likes": 0, "detail": "author: crazyrouter; arch: classify support tickets, judge urgency, score relevance, route AI agents; training: demo", "arch": "EN/ZH playground with 12 editable scenarios + JSON/cURL/JavaScript/Python request examples", "approach": "", "training": "", "data": "", "result": "crazyrouter.com/tools/jev-decision-playground", "notes": "Static space.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ammirosoh-jev-2048", "name": "Jev 2048 Selenium Player", "author": "AMMIROSOH", "date": "2026-09-23", "category": "app", "url": "https://github.com/AMMIROSOH/jev-2048-selenium", "base_model": "Jev 1.13 via TypeSafe API", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "Plays 2048.org through Selenium in hybrid mode: time-bounded expectimax search plus a real Jev Choice decision on every move, since Jev 1.13 is a text/JSON decision model, not a vision or game-tree model; integrated Selenium plus FFmpeg portrait recorder for 1080x1920 YouTube Short MP4", "approach": "sdk", "training": "No training; search plus live API decisions", "data": "Live 2048.org board state rendered as text", "result": "", "notes": "Python; default hybrid mode; recorder via python run.py --record", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openqa-jev-browser", "name": "Jev Browser CodexQA skill", "author": "openqa-cn", "date": "2026-09-23", "category": "app", "url": "https://github.com/openqa-cn/jev-browser", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 102, "likes": 0, "detail": "base: hosted; arch: hosts/keywords/general rules; data: knowledge/<app>/*.md; result: no benchmark vs vision GUI models · structural cost answers only; notes: 70–500 ms · $0.000081/0.114 s vs $0.013880/8.566 s", "arch": "Indexed browser automation: Jev chooses the control from an index built inside the page, Playwright acts; visible page evidence is the result; replay, goal runs, case generation and site exploration share that index; site knowledge as markdown notes", "approach": "", "training": "", "data": "Per-site knowledge notes", "result": "Positioned against GUI-model automation", "notes": "Cites TypeSafe's published comparisons: Jev decision 40×–200× faster than frontier LLM, workflow 193.6× faster / 444.6× cheaper, input $0.042/MTok = 238× lower than Claude Fable 5.1. 'A CodexQA skill' — also in the CodexQA catalog.", "domain": "computer-use", "score": "", "latency": "70–500 ms", "benchmark": "CodexQA", "cost": "", "speedup": ""}
{"id": "ethanplusai-jev-chat-for-twitch", "name": "Jev Chat for Twitch", "author": "ethanplusai", "date": "2026-09-23", "category": "app", "url": "https://github.com/ethanplusai/jev-chat-for-twitch", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 13, "likes": "", "detail": "arch: helpful, questions, funny, feedback", "arch": "Filter any live Twitch chat with Jev: a bring-your-own-key Chrome extension. Live chat filtering: bring-your-own-key Chrome extension that reads a Twitch channel's chat over the anonymous IRC WebSocket, asks Jev one category Choice per message in batches of 20, and shows a second column of only the messages matching a chosen intent: Helpful, Questions, Funny, Feedback, or Everything meaningful", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "daltonrpj-jev-flow", "name": "Jev Flow", "author": "daltonrpj", "date": "2026-09-23", "category": "app", "url": "https://github.com/daltonrpj/jev-flow", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 7, "likes": "", "detail": "", "arch": "Standalone Node.js application for building and testing Jev workflows; repository guide plus a static guide prepared for publication in English, Portuguese, Spanish, French and German", "approach": "sdk", "training": "No training; workflow studio", "data": "User-defined workflow state", "result": "", "notes": "MIT", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chy4pro-jev-for-chrome", "name": "Jev for Chrome", "author": "chy4pro", "date": "2026-09-23", "category": "app", "url": "https://github.com/chy4pro/jev-for-chrome", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 26, "likes": "", "detail": "arch: goal reached, stuck", "arch": "Jev for Chrome: drives the tab you are looking at with TypeSafe Jev, a sub-second decision model. Community port of browser-use/jev-ultrafast, not affiliated with TypeSafe: an unofficial Chrome extension where a Jev Choice picks the operation and DOM element each step, and two Noul checks veto a premature DONE.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cachix-jev-action", "name": "Jev GitHub Action", "author": "cachix", "date": "2026-09-23", "category": "app", "url": "https://github.com/cachix/jev-action", "base_model": "Jev via pinned release", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 5, "likes": "", "detail": "", "arch": "Runs Jev on a GitHub Actions event: installs a pinned Jev release, sends a typed request, exposes the JSON response to later workflow steps; two input modes — labels generates a choice question named label from repository labels with the GitHub event as state, questions sends explicit typed questions; the action does not modify issues or pull requests itself, the workflow decides what to do with the answers", "approach": "sdk", "training": "No training; CI integration", "data": "GitHub event payloads as state", "result": "", "notes": "Apache-2.0", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ungerts-jev", "name": "Jev MADR consistency check", "author": "ungerts", "date": "2026-09-23", "category": "app", "url": "https://github.com/ungerts/jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Checks whether Jev, TypeSafe's probabilistic decision model, independently chooses the same option that a MADR architectural decision record documents as its Decision Outcome", "approach": "", "training": "", "data": "", "result": "Sends the context, decision drivers, considered options and their pros and cons as one Choice question, deliberately excluding the title and the recorded outcome, then compares Jev's pick with the documented decision", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-skill-kha", "name": "Jev Skill, integration advisor", "author": "KHAEntertainment", "date": "2026-09-23", "category": "app", "url": "https://github.com/KHAEntertainment/jev-skill", "base_model": "TypeSafe Jev", "params": "", "metric": "Integration decision for a target project", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Agent skill that decides whether and how to integrate Jev into a project: Claude Code plugin plus skills-CLI compatible; verified against primary sources on 2026-09-22; covers access paths across TypeSafe, OpenRouter, Vercel AI SDK and Gateway, Cloudflare and LiteLLM, framework selection, harness patterns and calibration procedure; defers question design to the official typesafe-ai/skills", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "brainstormity-jev-x-sentiment-analysis", "name": "Jev X Sentiment Analysis", "author": "brainstormity", "date": "2026-09-23", "category": "app", "url": "https://github.com/brainstormity/jev-x-sentiment-analysis", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 169, "likes": "", "detail": "arch: Jev", "arch": "An on-demand crypto market intelligence and decision-support terminal powered by TypeSafe AI's System One model. Crypto decision support: ingests 50–1,000 tweets per request through statistical pre-processing and SQLite deduplication, then has Jev turn the surviving evidence into a decision card with entry ranges, stop losses, and targets, without executing trades.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "x-jev-filter-cluster", "name": "Jev X/Twitter filters", "author": "harodggg, Li-Chuangqi, kelaocai, wahabkhan98", "date": "2026-09-23", "category": "app", "url": "https://github.com/harodggg/jev-x-filter", "base_model": "TypeSafe Jev + keyword pre-filters", "params": "", "metric": "", "license": "Mixed", "paper": "", "stars": 0, "likes": "", "detail": "arch: NSFW-promo filtering, model-first pre-check, copy-farm detection, blacklist import/export, dry-run mode · category-based ad filtering + optional AI-authorship assessment · rule-based 'worth reading' filter + open rules library · hides low-effort engagement bait / bot replies; name: Chrome cluster", "arch": "Four near-simultaneous Chrome MV3 extensions that use Jev typed decisions to filter X/Twitter feeds: harodggg/jev-x-filter, Li-Chuangqi/x-jev-filter, kelaocai/tweet-radar, wahabkhan98/ReplyGuyRepellent", "approach": "", "training": "", "data": "", "result": "Same use case, four independent implementations in one week", "notes": "Cluster row; per-repo details at the linked repos.", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-agent-authz", "name": "jev-agent-authorization", "author": "kinde-starter-kits", "date": "2026-09-23", "category": "app", "url": "https://github.com/kinde-starter-kits/jev-agent-authorization", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 5, "likes": 0, "detail": "An agent calls a small workspace API through a Kinde MCP connection, and the Kinde MCP server passes the user's access token to the API. The guard in front of the API runs these steps for each call: it verifies the Kinde access token, checking issuer, audience, and signature; reads the user's permissions and feature flags for the organization, from the token or from the Kinde Management API; denies the call if a Kinde permission or flag is missing, while allowing reads that have the permission; sends every other call to Jev with the user's request, the tool call, and the documents the agent read, where Jev returns calibrated signals for whether the call matches the request, whether it is destructive, whether it follows planted text, whether it sends data out, and how risky it is; decides in code from the signals, where a low-confidence call goes to an LLM judge; and writes the decision to a ledger before anything else happens, giving a live ledger with stats and the gate. It is built on Convex, and the repository welcomes PRs.", "arch": "Jev Gatehouse: a starter kit that checks every AI agent tool call before it runs, where Kinde checks who the agent acts for and what that user can do, and Jev, the System One model from TypeSafe AI, judges each call in about 200 ms, with policy code in the app deciding to allow the call, ask the user to approve it, or stop it, with a live demo at jev-gatehouse.vercel.app.", "approach": "", "training": "", "data": "", "result": "", "notes": "Identity provider shipping a Jev-based MCP tool-authorization pattern.", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sutro-sh-jev-align", "name": "jev-align", "author": "sutro-sh", "date": "2026-09-23", "category": "app", "url": "https://github.com/sutro-sh/jev-align", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 288, "likes": "", "detail": "", "arch": "Build calibrated AI Functions from human feedback using Jev and GEPA. Dataset engineering: evaluates CSV, Parquet, and JSONL rows with Jev Choice, Score, or Boolean decisions, sends ambiguous and audit samples to a human, and uses accepted human labels to optimize the saved definition with GEPA. Active-learning CLI where Jev evaluates rows and surfaces uncertain or audit rows for you to label, then uses accepted labels to optimize the saved definition with GEPA", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hqman-jev-browser-skill", "name": "jev-browser-skill", "author": "hqman", "date": "2026-09-23", "category": "app", "url": "https://github.com/hqman/jev-browser-skill", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 35, "likes": "", "detail": "It requires Node.js 22.18.0 or newer for type stripping, with Node 24 working and earlier Node 22 releases lacking the experimental-strip-types flag. Distribute it as the GitHub clone itself, the jb-browser skill plus the jb script, because it is not an npm package: package.json is private, npm publish is refused, and jev-browser and jb on npm are unrelated, so clone the repository and run npm install only to fetch dependencies, then npx playwright install chromium. The skill lives at skills/jev-browser/SKILL.md, loaded by AGENTS.md in the clone, with the script at ./bin/jb, and you symlink it into ~/.cursor/skills and ~/.agents/skills, keeping it inside the clone, while .cursor and .agents are local only and not in the GitHub repo. Credentials go in ~/.jb/config.json or the environment, with provider set to gateway by default or typesafe, the AI_GATEWAY_API_KEY or gateway.apiKey for the gateway, and TYPESAFE_API_KEY or typesafe.apiKey for TypeSafe, with artifacts under ~/.jb/data. Example: jb run with a URL, a goal such as find the More information link and open it, stop when that page is visible, and a max step limit.", "arch": "Isolated Playwright Chromium driven by Jev, a skill where a coding agent, or you, runs a narrowly scoped browser goal, and Jev chooses in-page actions, with the default provider being the Vercel AI Gateway, and a provider setting to call TypeSafe's System One API instead; the repository is jev-browser-skill, the skill name is jb-browser, and the local command is jb.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dariofontanel-jev-claude-code", "name": "jev-claude-code", "author": "DarioFontanel", "date": "2026-09-23", "category": "app", "url": "https://github.com/DarioFontanel/jev-claude-code", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 5, "likes": "", "detail": "", "arch": "Three Claude Code prompts that integrate Jev into the workflow with no package to install: model selection, context compaction and code review; each prompt pasted into a session builds the complete system in the project, verifications included", "approach": "sdk", "training": "No training; prompt integration", "data": "Claude Code session state", "result": "", "notes": "Italian; 'Jev is not an LLM: it does not generate text, it answers bounded questions'", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "francoischastel-jev-code", "name": "jev-code", "author": "francoischastel", "date": "2026-09-23", "category": "app", "url": "https://github.com/francoischastel/jev-code", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 21, "likes": "", "detail": "", "arch": "Jev, TypeSafe's System One classifier, as a tool inside Claude Code, Codex, Pi, and OpenCode: typed classify, check, score, rank, and ask, plus one-command setup.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-decision-demos", "name": "jev-decision-demos", "author": "eidast", "date": "2026-09-23", "category": "app", "url": "https://github.com/eidast/jev-decision-demos", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "On macOS, install Node.js 20 or newer, then double-click the start script in Finder or run it from Terminal: the launcher creates a local .env file if needed, selects an available port starting at 3000, starts the loopback server, and opens the demo index in your browser. Add OPENROUTER_API_KEY or AI_GATEWAY_API_KEY to .env to use Jev, and restart the launcher after editing the key; the Moral Machine exercise has a clearly labeled sample mode without a key, while Tic Tac Toe requires a key. Closing the Terminal window or pressing Control-C stops the server. You can also run npm start and open http://127.0.0.1:3000/, setting PORT to another number if 3000 is occupied, with routes at /MoralMachine/ and /TicTacToe/. The repository layout: MoralMachine contains its browser files, scenario and report code, documentation, preview images, portrait assets, and ignored local reports; TicTacToe contains its browser files, game and decision code, documentation, and ignored local logs; server.js provides local routes and keeps provider keys on the server; docs/INTEGRATIONS.md describes the shared Jev and provider contracts; and SECURITY.md documents the security model. The Moral Machine demo also links the experiment protocol, the original study background, and asset credits.", "arch": "Two local exercises showing how to use Jev's structured choice answers in different decisions: a Moral Machine demo comparing a participant's choices in 13 original dilemmas with Jev's A/B probabilities, and a Tic Tac Toe demo where you play as X or O, rules and minimax restrict the candidate squares, and Jev chooses the machine's final move.", "approach": "", "training": "", "data": "Dilemmas", "result": "Ethics demos", "notes": "'Jev decision demos' — Moral Machine on Jev.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bhaig-de-jev-design-test", "name": "jev-design-test", "author": "bhaig-de", "date": "2026-09-23", "category": "app", "url": "https://github.com/bhaig-de/jev-design-test", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 27, "likes": "", "detail": "arch: @typesafe-ai/sdk", "arch": "Type a one-line prompt, get back a canvas full of complete, real UI screens — dashboards, auth flows, pricing pages, settings screens — sampled by Jev and assembled entirely from real shadcn/Base UI component blocks.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yikangy873-gif-jev-desktop", "name": "jev-desktop", "author": "yikangy873-gif", "date": "2026-09-23", "category": "app", "url": "https://github.com/yikangy873-gif/jev-desktop", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 72, "likes": "", "detail": "", "arch": "TypeSafe Jev action selection inside Codex Computer Use. Computer use: supplies Jev action selection inside Codex Computer Use, choosing among desktop actions rather than asking a language model at every step.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "everyai-com-jev-directory", "name": "jev-directory", "author": "everyai-com", "date": "2026-09-23", "category": "app", "url": "https://github.com/everyai-com/jev-directory", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 15, "likes": "", "detail": "Agents get the whole landscape three ways: a capability pack you hand over in a single paste, an MCP server with 10 tools plus 3 resources they can query live, and an llms.txt index. PRs are welcome: fork, edit data/use-case-candidates.json for builds, or data/jev-guide.js plus data/jev-manifest.js for evals, always in tandem, run the two regenerate commands, and open a PR; if you built something with Jev, a short submission form works without a PR. Open index.html in a browser, or serve the folder with any static host: no framework, no build step, no backend required for browsing, and the MCP endpoint just needs a Cloudflare Pages deploy. What is in the box: index.html, directory.css, directory.js, and data.js form the directory with search, categories, newest-first, and copy-a-brief per card; ask.js is a side chat answering what can Jev build, answered from the directory with links; _worker.js is the MCP server, a Pages advanced-mode worker exposing the directory as agent tools at /mcp; _routes.json scopes the worker to /mcp so every other path is a plain static asset; and what-is-jev.html, the Jev-like-I'm-10 explainer, and decision-patterns.html are guides. The pipeline: the Jev eval guide, Discord show-and-tell, and resolved project links feed build scripts that produce index.html and capabilities.md for your agent.", "arch": "Jev Directory: a browsable directory of what Jev can do, with 50 runnable judge-model evals carrying the exact experimental_evaluate prompt, plus more than 1,300 real community builds from the TypeSafe AI show-and-tell channel, each linked to its project and source post, hosted live at jev.magicteams.ai.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-feed-guard", "name": "jev-feed-guard", "author": "Hejk", "date": "2026-09-23", "category": "app", "url": "https://github.com/Hejk/jev-feed-guard", "base_model": "TypeSafe Jev, BYOK", "params": "", "metric": "Choice 5-class ad detection plus Noul interest score per feed item", "license": "GPL-3.0", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Chrome MV3 extension with no build step: Jev filters Xiaohongshu and Bilibili feeds; a Choice 5-class ad detector plus a Noul interest score drive tiered behavior, hide at 0.8 and above, collapse at 0.5–0.8, keep below 0.5; bring-your-own-key; 7-day content cache; author-level fold after 5 in a row; daily token budget of 5M tokens; 10-minute circuit breaker", "approach": "sdk", "training": "", "data": "", "result": "About $0.0004 per batch request of about 10k tokens; about $0.21 per day at the full budget", "notes": "", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "about $0.0004 per 10k-token batch", "speedup": ""}
{"id": "ilyamk-jev-gmail-ai-spam-filter-and-labeling", "name": "jev-gmail-ai-spam-filter-and-labeling", "author": "ilyamk", "date": "2026-09-23", "category": "app", "url": "https://github.com/ilyamk/jev-gmail-ai-spam-filter-and-labeling", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 20, "likes": "", "detail": "", "arch": "Self-hosted AI email classifier for Gmail powered by Jev. Create custom labels, organize your inbox, and filter spam with confidence and cost controls. Unofficial open-source Gmail AI spam filter, auto-labeler, and inbox organizer: Jev understands each email's intent to apply custom labels and optionally archive high-confidence unwanted mail", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-judge-mcp", "name": "jev-judge-mcp", "author": "PyModel", "date": "2026-09-23", "category": "app", "url": "https://github.com/PyModel/jev-judge-mcp", "base_model": "Jev via MCP", "params": "", "metric": "Four tool families: check, find, decide, ship", "license": "Open", "paper": "", "stars": 29, "likes": 0, "detail": "base: hosted; name: typed judgment tools for MCP", "arch": "MCP server exposing Jev's judgment primitives as agent tools: verify, screen, find, classify, rerank, decide, compare, extract and review, under the banner 'model judges, policy decides: auto, review, or escalate'", "approach": "", "training": "", "data": "", "result": "", "notes": "24 stars; CI-published Python package with a scales-of-justice identity", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-keyboard", "name": "jev-keyboard", "author": "Ideny42", "date": "2026-09-23", "category": "app", "url": "https://github.com/Ideny42/jev-keyboard", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 4, "likes": "", "detail": "Prerequisites: macOS 13 or newer, where local builds and automated tests are done only on Apple chips, with Intel unverified and Windows not yet implemented; the Rime input method; and your own TypeSafe API key, currently free to apply for. Operation and permissions: type pinyin, tap Ctrl, get the reranked candidates, and commit words as usual; you do not have to press twice, because when the result is ready one press reranks, and if it shows reranking, wait and press once to read the result, without rapid repeated keypresses, and pressing Ctrl after it shows reranked restores the original order. Default is Ctrl with enhanced mode off, needing no extra authorization; enhanced mode auto-refreshes candidates after the result returns, saving the wait-and-press-again; switching to Command or enabling enhanced mode needs authorization, documented in the permissions page. The rerank key and enhanced mode sit together in settings: characters committed defaults to 20, configurable 0–200, request wait defaults to 150 ms, and probability display, launch at login, and call quota are also adjustable. Cost and privacy: TypeSafe is a third-party API service providing the Jev model, and Jev Keyboard is an independent open-source extension that provides no key or free quota; cloud ranking is off by default, and when enabled the input process prefetches the ranking and sends pinyin, candidates, and a length-capped preceding text, not only when you press Ctrl; merely opening the settings window does not trigger a ranking call, and closing it does not stop an already-running background service; before typing passwords, verification codes, or private content, stop the Jev service and switch to the system input method, do not rely on the app to recognize every sensitive field, and setting the committed character count to 0 does not mean pinyin and candidates are not sent. Documentation: build from source, install and use, FAQ and cost, stop, uninstall, and clean up data, report an issue, and the Windows adaptation plan. License: original code is MIT, based on Rime and the Rime Ice pinyin schema, with third-party components under their own licenses.", "arch": "Jev Keyboard, in Chinese: Jev-powered candidate reranking for the Rime input method on macOS, type pinyin as usual, tap Ctrl, and pick words in the new order, with Windows support planned, currently a developer preview with no ready installer, only for developers who can use a terminal and build it themselves.", "approach": "", "training": "", "data": "", "result": "", "notes": "4 stars at sweep time", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "parth-kp-jev-mail-classifier", "name": "jev-mail-classifier", "author": "parth-kp", "date": "2026-09-23", "category": "app", "url": "https://github.com/parth-kp/jev-mail-classifier", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 19, "likes": "", "detail": "Why: classifying email with a normal LLM means writing a prompt, hoping it returns valid JSON, and paying full chat-completion prices for what is really just a yes-or-no decision. Jev skips all of that. What makes Jev different from calling an LLM: chat LLMs are trained with RLHF to produce fluent, human-pleasing text, great for conversation, but that same optimization is what makes them mode-drop, hedge, and overstate confidence when what you actually need is a reliable decision buried inside software; Jev is TypeSafe's first System One model, trained with Reinforcement Learning for Calibrated Decisions, RLCD, to output typed, calibrated decisions instead of a sentence you have to parse.", "arch": "A mail classifier that judges your inbox in milliseconds: tag, move, flag, and notify, all config-driven, powered by TypeSafe's System One model Jev, so you send it your inbox state and a set of yes/no questions, and it hands back calibrated probabilities directly, typically in well under a second, for a fraction of a cent per email, with no LLM prompt engineering, no JSON parsing, and no per-email API bill that adds up, on Python 3.10 or newer, MIT, with a Textual TUI.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "legostin-jev-mcp", "name": "jev-mcp by legostin", "author": "legostin", "date": "2026-09-23", "category": "app", "url": "https://github.com/legostin/jev-mcp", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "Your agent plans, JEV executes: fast, cheap, and transparent, with every decision traced and replayable. Why JEV for browser agents: usual LLM browser agents send a screenshot or a huge DOM dump to a frontier model on every step, which is slow and expensive, and the model still guesses; JEV instead answers small typed questions per step, cutting decision latency from 2–20 seconds of frontier LLM inference down to about 100 ms, at a fraction of the cost, with a confidence gate: when JEV is confident the action just happens, and when it is not, your agent gets asked, keeping a human or the planning model in the loop for the uncertain cases. The docs cover quick start, MCP tools, how it works, confidence and human-in-the-loop, safety, debugging and observability, troubleshooting, and an FAQ.", "arch": "jev-mcp: a Model Context Protocol server that lets Claude Code, Codex, and any MCP-capable AI agent drive a real Chrome browser using JEV, TypeSafe AI's System One decision model, where JEV answers typed questions, such as which element is the departure-city input, or did that click work, in about 100 ms, and each answer comes with a calibrated confidence, so the tool acts on its own when it is sure and asks your agent when it is not, Node 22.18 or newer, MIT.", "approach": "", "training": "", "data": "", "result": "Browser-control MCP tool", "notes": "Fields claim-level.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-mice", "name": "Jev-mice", "author": "Carson Sweet", "date": "2026-09-23", "category": "app", "url": "https://mice.jev.carsonsweet.com/", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "The live site is a browser simulation of a mouse colony on a 48 by 32 grid, small, medium, or large world, where each animal's next move is judged by Jev or computed by fixed rules, a per-agent toggle. The setup panel chooses the seed, the species present, mice, cats, traps, food piles, mouseholes, ticks, toxoplasmosis, food respawn, and a personality-mix slider across bold, cautious, vigilant, and social, 25% each by default. Run These Settings starts the run; clicking anything on the map tracks it and filters the activity and decision log to that mouse or cat; the history tab keeps every previous run with seed, world size, and tick count; and the right panel charts mice, food, and cats over time.", "arch": "Mouse colony simulation where Jev makes the behavioral decisions and a deterministic engine runs the rest", "approach": "", "training": "", "data": "", "result": "", "notes": "Show HN 2026-09-23", "domain": "gaming", "score": "1 point", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "achimala-jev-paint", "name": "jev-paint", "author": "achimala", "date": "2026-09-23", "category": "app", "url": "https://github.com/achimala/jev-paint", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 59, "likes": "", "detail": "", "arch": "Use Jev to make art!. Creative experiment: paints images by having Jev predict every pixel's colour in parallel, with predicted confidence deciding how wide each stroke is drawn. Local browser app that turns batched per-pixel Jev distributions into paintings; prompts describe pixels and regions rather than sending raw images.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-paper-radar", "name": "JEV-Paper-Radar", "author": "Eliot5566", "date": "2026-09-23", "category": "app", "url": "https://github.com/Eliot5566/JEV-Paper-Radar", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "arXiv now receives more than 30,000 papers a month, about 32,040 in June 2026, roughly 1,500 per weekday announcement, and nobody reads the listing anymore: keyword alerts miss papers that use different words, embedding recommenders quietly drop whatever does not look like your past reading, and running a chat LLM over the whole firehose every day is slow and costs real money. Most AI tools read a paper and write you a summary, but Paper Radar does not write anything, asking Jev one yes/no question per interest and getting a probability back, such as introduces an agent benchmark at 0.97. Judgement instead of prose is what makes the arithmetic work: there is nothing to parse and no invented labels, and one judgement costs a fraction of a generated sentence, cheap enough to read all 1,500 papers rather than a pre-filtered handful, which is where keyword alerts and embedding recommenders lose the paper you actually wanted. A real run: 50 new arXiv papers judged by Jev in 5 seconds for $0.002, and paper-radar demo shows the same page offline without a key. Quick start takes 5 minutes with no server, there is a 10-second demo, and it is benchmarked against real reviewers' decisions, with a Traditional Chinese README.", "arch": "Paper Radar: Jev reads every new paper on arXiv each morning, and you read the few that matter, with plain-English interests, calibrated probabilities, about 6 cents a day for all of arXiv, fork and go, no server, where it does not write anything: it asks Jev, TypeSafe's System One model, one yes/no question per interest and gets a probability back, and your repo's code decides what that means.", "approach": "", "training": "", "data": "", "result": "", "notes": "7 stars at sweep time", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "samdotmak-jev-recall", "name": "jev-recall", "author": "samdotmak", "date": "2026-09-23", "category": "app", "url": "https://github.com/samdotmak/jev-recall", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 37, "likes": "", "detail": "Why: with LLMs, writing memories is easy, but knowing which ones to retrieve is the hard part, and the two current options do not work, because keyword search works only if you already know what you are looking for, and semantic search works only if the memory that matters happens to look like the request, an assumption that breaks constantly, because the memories that change an answer are usually the ones phrased nothing like the question: order the almond tart for Saturday's dinner party looks nothing like Leo has a severe tree-nut allergy, so embeddings return other pastry memories and the assistant orders the tart. An LLM used as a re-ranker does catch those, reading everything and reasoning about what matters, but then you pay LLM prices and LLM latency on every single turn, so nobody runs it on every turn. Jev is a classifier that makes that same include-or-not decision at LLM quality, for a fraction of the cost and time, cheap enough to judge every memory on every turn. How it works: every memory goes into one request's shared state, tagged with a short id, each memory gets a tiny yes/no question pointing at its id, and Jev returns a calibrated probability for each one, and because the probabilities are calibrated, you keep everything above a threshold. The demo shows three methods searching the same 238 memories: semantic search misses a guest's nut allergy, while Claude Sonnet 5 and Jev Recall both find it, Jev in 0.34 s for $0.00044, with a live demo that replays real runs or runs live with your own API keys.", "arch": "Jev Recall: retrieve by relevance, not resemblance, giving LLM re-ranker quality retrieval at semantic search prices and speed, where given a user query and a pile of memories, it returns the most relevant memories, judged by TypeSafe's Jev model, one calibrated yes/no per memory, in a single request.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "skeptrunedev-jev-recruiter", "name": "jev-recruiter", "author": "skeptrunedev", "date": "2026-09-23", "category": "app", "url": "https://github.com/skeptrunedev/jev-recruiter", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 50, "likes": "", "detail": "One provider handles all navigation, title screening, and evidence choices, with no secondary text model and no provider fallback. What you can do: start from a relevant people search, rather than unrelated timeline posts; follow relevant recommendations from the second and third profile sections in the sidebar, with the first section excluded; save discovered profile URLs immediately, along with their source and title screening result; review criterion findings beside quotations from the observed profile; run toward a target number of potential matches, pause, step through decisions, and export JSON; and shortlist or pass candidates yourself. The default brief looks for forward-deployed engineers or solutions engineers in the San Francisco Bay Area with 3–5 years of relevant professional engineering experience, and the search and requirements are editable. Quick start needs Python 3.12 or newer, uv, Chrome, and a provider key; a 45 second accelerated demo shows Jev browsing profiles and saving potential matches, and the repository is MIT licensed.", "arch": "Jev Recruiter: give Jev a role, watch it explore LinkedIn, and keep the profiles and the evidence, a local recruiting workspace built on Browser Use's Jev Ultrafast and Browser Harness, where Jev chooses where to go, screens professional titles, and assesses visible profile excerpts against your brief, with the live browser at the top, open by default, inside a Craigslist-inspired interface, and the default provider being TypeSafe's Jev, with Morph explicitly selectable instead.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hotchpotch-jev-reranker", "name": "jev-reranker", "author": "hotchpotch", "date": "2026-09-23", "category": "app", "url": "https://github.com/hotchpotch/jev-reranker", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 28, "likes": "", "detail": "", "arch": "Jev-powered relevance filtering and reranking for RAG in Python. Retrieval and RAG: uses Jev Noul judgments to assess retrieved documents for relevance and usefulness as answer evidence, then sorts results and optionally filters them using a configurable threshold.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-scout-pedroalvarado", "name": "jev-scout, repo value audit", "author": "PedroAlvarado", "date": "2026-09-23", "category": "app", "url": "https://github.com/PedroAlvarado/jev-scout", "base_model": "TypeSafe Jev", "params": "", "metric": "Ranked Jev value opportunities per repository", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Agent Skill that reads your repository and tells you where TypeSafe Jev decision models would create the most value; three opportunity types: substitution, a model call or keyword rule already makes a small bounded judgment that Jev can make faster, cheaper or more reliably; augmentation, the code makes a decision without judging, takes the first match, cuts the list at a fixed top-k, gives everyone the same default, never checks an action, or sends everything to a person; new capability, something the product does not do today because a judgment on every item, step or user was too slow or too expensive; it ranks finds by business value, gives the top picks a decision contract and a shadow-mode experiment, works in Claude Code, Codex, Cursor and any agent that supports Agent Skills, and only reads: no Jev API key, no code changes, nothing written outside the report; installs with npx skills add or as a Claude Code plugin", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "win4r-jev-skill-suggester", "name": "jev-skill-suggester", "author": "win4r", "date": "2026-09-23", "category": "app", "url": "https://github.com/win4r/jev-skill-suggester", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "", "license": "", "paper": "", "stars": 33, "likes": "", "detail": "It fits scenarios with many installed skills, adjacent skills with easily confused purposes, and the need to decide which SKILL.md to read first. Python 3.10 or newer, the runtime uses only the standard library with no pip install; offline mode needs no key, and Jev mode needs a TypeSafe API key, pinned to jev-1.13.0. Commands: catalog reads the local skill directory without API access; suggest in the default local mode gives keyword candidates only, no semantic recommendation; and suggest in Jev mode hides the input key and writes a new report file. These commands use your own skill directory, and the repository does not ship the skills recommended in its examples, so uninstalled skills give no result, and local mode is always local_only with suggestion null, so the keyword top hit must not be treated as Jev's judgment. Installing as a Codex Skill copies only the six runtime files into ~/.codex/skills/jev-skill-suggester, erroring if the target exists to avoid overwriting, and other agents can use the CLI with --root pointing at their skill directory, though this version verified only the Codex Skill and CLI, not other hosts' auto-discovery. How it works: it reads SKILL.md names, descriptions, and bodies within scope, excluding itself and skills that must not be called implicitly; an explicitly named skill uses --require local lookup with zero API calls; Jev ranks descriptions with a Choice and judges with a separate Noul whether the skill set is useful, keeping at most three candidates per group; it re-checks candidate body excerpts, then uses Choice plus each candidate's fitness Noul to decide the recommendation; and the host reads the recommended skill's full entry, verifying current-session availability, user restrictions, and required tools before deciding how to use it. The recommendation bar is fitness 0.80 or higher and Choice confidence 0.65 or higher, which are exploratory thresholds, not calibrated accuracy, and Jev provides typed judgments without writing recommendation reasons, which the host should give from the actual skill descriptions.", "arch": "Jev Skill 建议器, in Chinese with an English README: bounded installed-skill recommendations for coding agents using TypeSafe Jev, where it reads skill descriptions to screen, then checks candidate body excerpts, and may return no skill needed or unsure, with user-named skills handled first by local lookup, a Codex Skill plus a standalone Python CLI that does not execute or install candidate skills, does not modify agent settings, and adds no auto-run hook.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "utk2103-jev-studio", "name": "jev-studio", "author": "utk2103", "date": "2026-09-23", "category": "app", "url": "https://github.com/utk2103/jev-studio", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 16, "likes": "", "detail": "Two commands are installed: jev, the CLI for TypeSafe's Jev decisions, covering verify, screen, classify, extract, match, route, ask, find, rerank, compact, and batch; and jev-studio, the MCP server that serves the Jev ruleset over stdio. The CLI wraps Jev, which turns natural-language state and a set of typed questions into typed answers, yes/no, choice, or score, with probabilities, in one-command judgments over your data. It carries Glama MCP quality and maintenance badges and an M8ven score.", "arch": "Jev Studio: a one-stop kit for playing with TypeSafe's Jev, with MCP tools for Choice, Noul, and Score, ready-made prompt libraries, and slash commands for every cookbook, installable with pip install jev-studio, MIT, on PyPI.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-system-one-tr", "name": "jev-system-one", "author": "Eren-Oztk", "date": "2026-09-23", "category": "app", "url": "https://github.com/Eren-Oztk/jev-system-one", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Why this shape: Jev is not a chat or bot model, it generates no text, writes no code, and calls no tools; the only thing it does is produce parallel, atomic, typed judgments. The comparison table: classical LLMs are optimized for human preference by RLHF or verifiable reward, while System One is optimized for calibrated decisions by RLCD; LLM output is a string you parse, validate, and hope on, while Jev output is a typed value plus a probability distribution; LLM sampling is sequential token by token, while Jev is parallel in a single query; latency is 3–329 seconds for an LLM versus about 0.07–0.5 seconds for Jev; cost is $0.20 to $10 per million input tokens with output roughly 5 times that for an LLM, versus $0.042 per million input tokens with free output for Jev; LLMs say they are sure and are wrong, while Jev carries a confidence or noul probability on every answer; and type errors are possible with LLMs but impossible by definition with Jev, since the schema is fixed in advance. Design rule: code holds control, the model only gives semantic judgment, so arithmetic, date comparison, counting, and identity checks go to code, while does this message express urgency goes to the model. Install with uv venv on Python 3.11, uv pip install with the mcp and dev extras, and cp .env.example .env for TYPESAFE_API_KEY and/or DEEPSEEK_API_KEY; keys are found in order from the existing environment, $HERMES_HOME/.env, ~/.hermes/.env, ~/AppData/Local/hermes/.env, and the project .env, and secrets are never written into config.yaml. Usage: jev packs lists ready-made decision sets, and jev ask --pack support runs one.", "arch": "system-one, in Turkish: a Jev / System One decision layer that puts TypeSafe AI's System One model Jev, and an LLM fallback when there is no key, behind the same interface, where you put in state plus typed questions and get out typed answers, choice, score, or noul, plus calibrated probabilities, confidence, and a verdict of act, review, or escalate, an MCP-ready package.", "approach": "", "training": "", "data": "", "result": "", "notes": "Türkçe decision-layer repo; first in the dataset dedicated to Turkish", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mizchi-jev-test-filter", "name": "jev-test-filter", "author": "mizchi", "date": "2026-09-23", "category": "app", "url": "https://github.com/mizchi/jev-test-filter", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 23, "likes": "", "detail": "Install: Node 24 or newer, git, and a TypeSafe API key, with the rust and go formats additionally needing cargo and go on the PATH, via pnpm add -D, or without installing anything with npx jev-test-filter --base main --exec -- vitest run. The command is what runs, and the skill is what teaches an agent to drive it, covering which runner takes which flag and in which order, why --exec rather than a composed shell command, and what each exit code means, which is mostly knowledge an agent cannot guess and gets wrong silently, and installing the skill does not install the command, you want both, either as a skill for Claude Code, Codex, Cursor, and the other agents the skills CLI knows with npx skills add, or as a Claude Code plugin that brings the same skill plus a /filter-tests command. From source: clone, pnpm install, pnpm run build, then node dist/cli.js --help. There are three invocation shapes, because wiring it in means something different for CI, local development, and agent-driven runs.", "arch": "jev-test-filter: reads a git diff, asks a model how much that change can alter the outcome of every single test in the repository, and prints the filter arguments your test runner already understands, handing those arguments to the runner you already use, vitest, jest, node --test, bun test, Playwright, cargo test, or go test, so the run covers the tests the change could plausibly break instead of all of them, using Jev, TypeSafe's System One model, with one shared state, the diff, one question per test, and one round trip, and no agent loop and no file reading by the model.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "elisebastian-jev-trade", "name": "jev-trade, EliSebastian", "author": "EliSebastian", "date": "2026-09-23", "category": "app", "url": "https://github.com/EliSebastian/jev-trade", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "A self-hosted paper-trading terminal for Alpaca, with an optional auto-trading engine driven by Jev, TypeSafe's evaluation model, served through Vercel AI Gateway", "approach": "", "training": "", "data": "", "result": "Streams live stock and crypto prices and shows the paper account, positions and orders on one screen; with a gateway key, Jev evaluates each watchlist symbol every few minutes and hard-coded risk limits decide whether a verdict turns into an order; paper trading only, not financial advice", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jiawei686-jev-ultrafast-mcp", "name": "jev-ultrafast-mcp", "author": "jiawei686", "date": "2026-09-23", "category": "app", "url": "https://github.com/jiawei686/jev-ultrafast-mcp", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 19, "likes": "", "detail": "", "arch": "Hand the browser work off: an MCP server where a decision model drives the page for your agent, so a flow costs one tool call instead of a turn per click. Ref-based element tables, code-checked assertions, zero-model macro replay. Speaks CDP to your Chrome.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kevinbadi-jev-voice", "name": "jev-voice, kevinbadi", "author": "kevinbadi", "date": "2026-09-23", "category": "app", "url": "https://github.com/kevinbadi/jev-voice", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 95, "likes": "", "detail": "Setup on macOS with Apple Silicon: copy .env.example to .env with your TYPESAFE_API_KEY from console.typesafe.ai, then run the setup script, which installs whisper.cpp and ffmpeg, downloads the model, syncs the Python environment, remaps Caps Lock to F18 with hidutil, persisted by a LaunchAgent so it survives reboots, installs a jev launcher in ~/.local/bin, and opens the three permission panes, where you grant the terminal app you launch from, Cursor, Terminal, or iTerm, Microphone, Accessibility, and Input Monitoring, and if a permission is missing at launch, Jev Voice prompts for it and waits, with an uninstall script to undo the Caps Lock remap. Run: hands-free by default, where the mic stays open and whisper transcribes every utterance locally in about 100 ms, with nothing leaving the machine, and only utterances that name the assistant, the WAKE_WORDS in .env, defaulting to Alfred or Jarvis, go to Jev; or --hold, holding Caps Lock to talk and releasing to run with no wake word; --always-on, an open mic where every utterance is a command with no wake word; --ptt, push-to-talk in the terminal with Enter to start and stop; --device to pick a mic; and --text with --dry-run to test routing without a mic.", "arch": "Jev Voice: talk to your Mac, where you speak, and it opens apps, types, searches, scrolls, and presses keys, everything running locally except one call of about 250 ms to Jev, TypeSafe's System One model, which turns the transcript into a typed action plus typed arguments in a single fan-out request, where Jev never generates text, code produces candidate values and Jev selects, and code owns execution, with the pipeline being mic, energy VAD, whisper.cpp on Metal in about 100 ms, Jev in one request in about 250 ms, macOS actions, and say.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sdras-jev-webmcp-extension", "name": "jev-webmcp-extension", "author": "sdras", "date": "2026-09-23", "category": "app", "url": "https://github.com/sdras/jev-webmcp-extension", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 117, "likes": "", "detail": "", "arch": "A Chrome extension that uses Jev, TypeSafe's System One model, to select and populate WebMCP tool calls from user input. The side panel discovers tools exposed by the current page and displays predicted calls, confidence scores, and latency as the user types. Tool selection is derived from the page's schemas, without site-specific configuration.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wuxie888-jev-yaba-wechat", "name": "jev-yaba-wechat", "author": "wuxie888", "date": "2026-09-23", "category": "app", "url": "https://github.com/wuxie888/jev-yaba-wechat", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 22, "likes": "", "detail": "", "arch": "Don’t know how to answer the message on WeChat? macOS floating chat assistant: identifies message intent and communication risks, GPT generates a variety of words, Jev evaluates candidates, and fills in WeChat with one click. I will help you think about it and send it to you to order.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "parthkomalwad-jevbrief", "name": "jevbrief", "author": "Parthkomalwad", "date": "2026-09-23", "category": "app", "url": "https://github.com/Parthkomalwad/jevbrief", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 4, "likes": "", "detail": "", "arch": "Clean, traceable state briefings for Jev from any source: while other tools show what Jev decided, jevbrief shows what Jev was told, what it was not told, and why; adapters, quick start, game demo, Python API and a viewer that shows every decision", "approach": "sdk", "training": "No training; briefing layer", "data": "Any source state via adapters", "result": "", "notes": "pip install jevbrief; MIT", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevchess", "name": "JevChess", "author": "EvanGruhlkey", "date": "2026-09-23", "category": "app", "url": "https://github.com/EvanGruhlkey/JevChess", "base_model": "TypeSafe Jev, Jev versus Jev, or against Stockfish 19", "params": "", "metric": "Games, checkmates, plies played", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Play chess with Jev or Jev versus Jev: every legal move is a named choice routed through the Vercel AI Gateway; also matches against an Elo-limited Stockfish 19", "approach": "", "training": "", "data": "", "result": "16 seeded games, 2 checkmates, 57 of 77 plies, 14 draws at the 80-ply cap", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "perrylink-jevcore", "name": "jevcore", "author": "perrylink", "date": "2026-09-23", "category": "app", "url": "https://github.com/perrylink/jevcore", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 33, "likes": "", "detail": "Three packages, one decision layer: jevcore, the decisions, imports nothing from DeepSeek Harness or Cordis, for when you want Jev in a plain script, a service, or your own harness; jevcore-dsh, the DSH plugin with one service, three tools, and two opt-in gates, for when you are running DeepSeek Harness; and jevcore-mcp, the same three tools over MCP with a stdio binary, for when your host speaks MCP but is not DSH. The adapters are thin on purpose: packages/dsh is four files, declaring tool schemas and translating hook payloads. It is listed on the DSH Market, carries an OpenSSF Scorecard, and has a Glama MCP server score badge.", "arch": "jevcore: TypeSafe Jev for DeepSeek Harness and any other Model Context Protocol host, giving typed judgments instead of prose, where Jev is not a chat model, it answers typed questions, noul for yes/no, choice, and score, and returns calibrated probabilities, and does not write prose, and asking it to is a category error, so this project gives an agent exactly that surface and nothing more, offline by default, with egress disclosed, and nothing default-on, part of a 40-plus plugin family for DeepSeek Harness, all Apache-2.0.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nthclrd-jevcraft", "name": "jevcraft", "author": "nthclrd", "date": "2026-09-23", "category": "app", "url": "https://github.com/nthclrd/jevcraft", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "An autonomous, adaptive Minecraft bot: give it a goal in plain language, Find diamond or build a cute house, or talk to it in the game chat. Jev, TypeSafe's System One model, makes every fast decision, and an LLM plans and designs structures.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ashrafumair111-lab-jevdesk-support-router", "name": "jevdesk-support-router", "author": "ashrafumair111-lab", "date": "2026-09-23", "category": "app", "url": "https://github.com/ashrafumair111-lab/JEVDESK-SUPPORT-ROUTER", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "department as a choice, which team owns the ticket, billing, technical, sales or general, plus confidence percent; urgency as a 1–5 score with a small bar plus confidence percent; refund_requested as a boolean, yes or no, plus confidence percent. If any answer confidence is below 70%, the card gets a low-confidence, needs-human-review label so a human knows to check it before acting. The browser posts to an Express server that calls experimental_evaluate with model typesafe-ai/jev through the Vercel AI SDK and Vercel AI Gateway; the server flattens the answers and returns JSON; the API key never reaches the browser.", "arch": "Small support-ticket triage tool: paste a customer message, click Analyze with Jev, and it answers three questions about it", "approach": "", "training": "", "data": "", "result": "", "notes": "Built to show how Jev decision-model API works: Jev is not a chat model, it does not write prose, you hand it state and typed questions and it returns one typed answer per question with probabilities your code can branch on; needs Node 22 or newer and a Vercel AI Gateway key", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-doom", "name": "jevdoom", "author": "ethanhinson", "date": "2026-09-23", "category": "app", "url": "https://github.com/ethanhinson/jevdoom", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Run: copy .env.example to .env with your TypeSafe key, then uv run jevdoom with a scenario and episode count, or uv run jevtetris with a game count, where --help on any of them lists the options, and each run writes a JSONL log of every Jev call to logs/. In the Tetris window, plus and minus change the speed, space pauses, and esc quits, and the faint outlines are the placements Jev nearly chose. Game Boy: the Tetris ROM is not included, put your own copy at roms/tetris.gb, then uv run jevgameboy, or with --speed 0 as fast as the emulator can go, where Jev sees the 10 by 18 field, the falling piece, and the one preview piece, and there is no hold, and code turns the piece with A until the orientation matches, slides it, holds Down, and waits for the screen to match the placement Jev chose, re-reading the board from the screen if it does not match. Watch the calls live with Jeview, a local gateway that sits between this code and TypeSafe, records every call, and draws them on a live map, cloned beside the project and needing Node 24, with the viewer at http://127.0.0.1:4777/.", "arch": "jevfun: Jev, TypeSafe System One, plays games, where Jev decides what matters each moment and plain code does the rest, covering jevdoom on ViZDoom, where Jev picks which enemy to deal with and whether to fight, hold, retreat, or grab a pickup, and code turns that into button presses; jevtetris, where code lists every placement the piece could reach, with what the board looks like after each one, Jev picks one, and code plays it, holding being an option like any other; and jevgameboy, the same brain playing the real Game Boy Tetris in the PyBoy emulator, reading the board off the screen tiles and pressing the real buttons.", "approach": "", "training": "", "data": "", "result": "", "notes": "One of three Doom playthroughs in the dataset, this one on the hosted official model", "domain": "gaming", "score": "", "latency": "", "benchmark": "Doom", "cost": "", "speedup": ""}
{"id": "jevguide", "name": "JevGuide", "author": "Nisaka520", "date": "2026-09-23", "category": "app", "url": "https://github.com/Nisaka520/JevGuide", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "", "arch": "弦外之音 WeChat relationship-progress assistant: screen reading via accessibility tree or screenshot vision, Jev judgment plus 攻略度 approach score, then a chat model drafts three candidate replies", "approach": "", "training": "", "data": "", "result": "No-root APK distribution with published privacy policy; the trio — JevBystander, screen reading only, no root; JevIntent, Xposed FkWeChat plugin with long-press judgments, requires root; JevGuide, judgments plus persistent approach score, three sendable replies and per-contact long-term memory, no root — only reads screens and calls the API, never modifies WeChat, sends messages or injects clicks", "notes": "9 stars at sweep time; 中文; sister repos JevIntent and JevBystander; signing keystore excluded from the repo", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "andududu-jeview", "name": "Jeview", "author": "andududu", "date": "2026-09-23", "category": "app", "url": "https://github.com/andududu/jeview", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 54, "likes": "", "detail": "arch: TypeSafe", "arch": "An unofficial local visualizer for Jev: a live view of every call your code makes. Not affiliated with TypeSafe AI. Developer tooling: zero-dependency local proxy and live visualizer that intercepts Jev API calls, logs decisions to SQLite, and renders real-time decision flows in a browser dashboard.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "icehazymoon-jevlint", "name": "jevlint", "author": "Ice-Hazymoon", "date": "2026-09-23", "category": "app", "url": "https://github.com/Ice-Hazymoon/jevlint", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 14, "likes": "", "detail": "base: or AI gateway; arch: 'is a secret being logged here?' · files, lines, rule ids, exit codes; result: repo", "arch": "Semantic lint rules written as plain-English yes/no questions; jevlint selects applicable code, asks Jev, reports a calibrated probability; fixtures-tested rules, cached answers, linter-shaped output", "approach": "", "training": "", "data": "", "result": "Complements ESLint: 'if a rule can be decided from syntax alone, a deterministic linter is cheaper and exact'", "notes": "Node 22+ or Bun; 9 stars at sweep time.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-sdsl", "name": "JevSDSQL", "author": "Sheltercosmo", "date": "2026-09-23", "category": "app", "url": "https://github.com/Sheltercosmo/JevSDSQL", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 13, "likes": "", "detail": "The self-developing part is the semantic layer: definitions, evidence, and corrections can be saved, reviewed, reused, and refreshed as data changes, and new concepts require approval before promotion. Features: query data in English or Simplified Chinese, inspect the SQL, correct its interpretation, and rerun it from query history; choose Jev planning or hybrid planning, where in hybrid mode Jev selects relevant context, an LLM proposes the SQL, and Jev reviews the proposal; filter and classify text by meaning, saving reviewed definitions as reusable features, such as whether a message requests action; extract database entries from documents, describing the rows and columns, then reviewing typed values alongside their source text before importing; preview inserts, updates, and deletes before committing them; and call semantic operators for extraction, ranking, matching, verification, and conditional workflows. Getting started: Python 3.11 or newer, PostgreSQL, and a TypeSafe API key, with Python 3.13 and PostgreSQL 17 the verified configuration, and hybrid mode additionally requiring a configured LLM provider, with an installation guide, a user guide, a Simplified Chinese guide, and a Jev function reference.", "arch": "JevSDSQL: a self-developing SQL database with 41 Jev-based semantic operators and conditional workflows covering classification, extraction, ranking, matching, and verification, where parallel evaluation and reusable evidence reduce repeated work, English and Simplified Chinese queries work, LLM planning is optional, and text-to-table extraction makes the database accessible through a web workspace and HTTP API, with the operators combining Jev's native Noul, Choice, and Score primitives with database and workflow logic, Jev provided by TypeSafe and PostgreSQL handling storage, joins, and arithmetic.", "approach": "", "training": "", "data": "", "result": "", "notes": "5 stars at sweep time", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cocktailpeanut-jevthoven", "name": "jevthoven", "author": "cocktailpeanut", "date": "2026-09-23", "category": "app", "url": "https://github.com/cocktailpeanut/jevthoven", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 15, "likes": "", "detail": "arch: recent bars, motif, harmony plan", "arch": "Prompt-first symbolic-music studio. Describe music in one sentence — \"a wistful 3/4 waltz, soft keys over round bass, light swing\" — and a live TypeSafe Jev model decides every musical unit, sequentially, with a bounded rolling context resent each step. The result is an editable multitrack composition you can play, reshape, and export.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevui", "name": "JevUI", "author": "erezk-pontera", "date": "2026-09-23", "category": "app", "url": "https://github.com/erezk-pontera/JevUI", "base_model": "TypeSafe Jev", "params": "", "metric": "Hand-written structured-question tester", "license": "AGPL-3.0", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Browser tester for TypeSafe Jev served through a Cloudflare Worker that holds the credentials so the page never has to: worker.js is a Workers AI proxy in front of typesafe/jev with two routes behind one gate, POST / sends the JSON body to the model untouched and POST /mcp is a Model Context Protocol server over Streamable HTTP exposing one tool, jev_evaluate; index.html is a single self-contained tester file with no build step; the gate compares SHA-256 digests with timingSafeEqual, missing headers, missing secrets, wrong tokens and refused origins all return an empty 404 identical to an unknown path, and a 1 MiB cap returns 413; model failures return 500 with a requestId matching the edge ray id", "approach": "sdk", "training": "", "data": "", "result": "Static page plus one worker file; MCP clients can use Jev as a tool through the same endpoint", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "panachy-jevvy", "name": "jevvy", "author": "panachy", "date": "2026-09-23", "category": "app", "url": "https://github.com/panachy/jevvy", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 17, "likes": "", "detail": "Quickstart: npx @jevvy/permissions init; manual setup for OpenCode with opencode plugin add @jevvy/permissions, and for Claude Code with the plugin marketplace add and install of jevvy-permissions. How it works: each coding agent evaluates its own permission policy first, existing allow and deny decisions stay final, and Jevvy Permissions reviews only unresolved shell approval requests, approving only the current action when every inquiry passes, and otherwise abstaining, leaving the agent's remaining permission flow unchanged, so a routine git status runs automatically, while rm -rf still asks.", "arch": "Jevvy: fast probabilistic judgments inside coding-agent workflows, a plugin collection powered by TypeSafe's System One model, led by the Jevvy Permissions plugin, which auto-approves harmless shell permission requests, while anything uncertain continues through the agent's normal permission flow, for OpenCode v2 and Claude Code, Node.js 22.19 or newer.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevxlayachess", "name": "jevxlayachess", "author": "DevArthurM", "date": "2026-09-23", "category": "app", "url": "https://github.com/DevArthurM/jevxlayachess", "base_model": "jev and laya, two decision models, via the API", "params": "", "metric": "Per-match cost, games completed", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Two decision models, jev and laya, play chess on a 3D board in the browser; a Nest.js server acts as referee; the key is held in memory only; the models see the board and recent moves but not the list of legal moves", "approach": "sdk", "training": "", "data": "", "result": "About $0.0006 per match; 97 tests including perft", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "about $0.0006 per match", "speedup": ""}
{"id": "syumai-jevyoumean", "name": "jevyoumean", "author": "syumai", "date": "2026-09-23", "category": "app", "url": "https://github.com/syumai/jevyoumean", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 16, "likes": "", "detail": "An experimental project for exploring semantic CLI correction: suggestions can be wrong, the interface may change, and it is not intended for production-critical workflows. Unlike an edit-distance Did you mean, jym matches on intent: it hands Jev the candidate subcommand names plus their help descriptions. Example: git remove foo.txt yields a prompt that remove is not a git subcommand, did you mean git rm foo.txt at 0.93, with Enter to run 1, o to run as typed, and n to cancel. Install with go install of the cmd/jym path at latest. Usage: jym -- git switch main, jym -- gh pr view 123, jym -- kubectl delete pod foo, where the -- separator is optional but recommended, and jym itself has no subcommands, management operations are flags, so wrapping a command named auth or setup never collides. The intended use is as a project-local shell alias, for example with mise's shell_alias section, and jym --print-mise git gh kubectl emits that snippet; jym resolves the target with a PATH lookup, so the alias never recursively expands, a shim or symlink to jym is skipped, and JYM_DEPTH breaks any residual loop. Mise shell aliases only apply in mise activated interactive shells, which is exactly where jym intervenes anyway, and bash and zsh integrations are documented for wrapping selected commands without mise.", "arch": "jevyoumean, short form jym: a semantic Did you mean for any CLI, wrapping a CLI command, reading the CLI's help output, and using TypeSafe's Jev to match an undocumented subcommand to the documented subcommand you most likely meant, on intent, so that remove maps to rm, list to ps, and undo to restore, close in meaning but far in spelling.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "andrewprifer-jimothy", "name": "jimothy", "author": "andrewprifer", "date": "2026-09-23", "category": "app", "url": "https://github.com/andrewprifer/jimothy", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 74, "likes": "", "detail": "It requires Node.js 22 or later. Quick start: npm install jimothy, then npx jimothy train with a task file, training data, and an output directory, using the included support-ticket classifier examples, and npx jimothy predict with the model and text, such as I was charged twice. This uses the default MiniLM backend, which downloads and bundles its pretrained encoder during training, and you keep the entire output directory together when moving or deploying a model. For your own task, provide a task definition and dataset, accepting JSONL or JSON arrays, and choose one training form: saved inputs and answers in one file; separate inputs and answers, joined by matching unique string IDs; or input-only data, where a teacher generates the answers, then it trains. The teacher form requires AI_GATEWAY_API_KEY in the environment, takes input rows such as id 1 with state I was charged twice, sends the inputs to the teacher, typesafe-ai/jev in the example, and caches responses in the out.teacher directory, with rerunning the same command resuming. Training tunes regularization, calibrates probabilities, and recommends a cutoff, and --validation and --test add separate labeled sets.", "arch": "Jimothy: turn your Jev usage into small, task-specific classifiers, training small, insanely fast local classifiers from Jev-compatible examples, running locally in your browser or Node.js, where you can train on saved Jev answers, or use Jev as a teacher to generate the answers for input-only data, and the default MiniLM backend downloads and bundles its pretrained encoder during training.", "approach": "distill", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "qainsights-jmeter-ai", "name": "jmeter-ai", "author": "qainsights", "date": "2026-09-23", "category": "app", "url": "https://github.com/qainsights/jmeter-ai", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 61, "likes": "", "detail": "Why Feather Wand: my kids named it after a Bluey episode, where a simple feather becomes a magical wand that turns the ordinary into something special, and that is exactly what this plugin does for your JMeter workflow. It ships features, installation, configuration, corporate LLM gateway support, use with ChatGPT, Codex, and Claude Code subscriptions, a modern chat UI and model picker, special commands, agent mode, streaming AI responses, file attachments, conversation persistence and export, and context and cost stats, with a showcase repository for showing off your project.", "arch": "Feather Wand: an AI-powered assistant for Apache JMeter, an open-source JMeter plugin where Jev classifies the agent's intent and selects a focused tool pack, with a full-tool fallback when routing confidence is low, MIT licensed, listed on the PerfAtlas plugin directory.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vishalmysore-layaasragjudge", "name": "Laya RAG Judge", "author": "vishalmysore", "date": "2026-09-23", "category": "app", "url": "https://github.com/vishalmysore/layaAsRagJudge", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Browser-native RAG claim verification. A claim is checked against retrieved evidence, and the Laya typed-decisions model judges whether that evidence supports it: supported or not supported, with a probability and a confidence gate.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-chatgpt-flow", "name": "laya-chatgpt-flow", "author": "Boom-Vitt", "date": "2026-09-23", "category": "app", "url": "https://github.com/Boom-Vitt/laya-chatgpt-flow", "base_model": "laya-browser v10, 421M", "params": "421M", "metric": "UI element selection for Google Flow", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Thai-language workflow: ChatGPT Desktop in work-locally mode plans the task, local Laya v10 421M from the cklxx/laya-browser checkpoint selects the Google Flow UI elements, and the product clip renders in 16 seconds via Veo 3.1 Lite; one demo pair, human reviewed", "approach": "", "training": "", "data": "", "result": "16-second product clips; 1 demo pair", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-computer-use-koushik", "name": "laya-computer-use, J.A.R.V.I.S.", "author": "koushikvarma37-jpg", "date": "2026-09-23", "category": "app", "url": "https://github.com/koushikvarma37-jpg/laya-computer-use", "base_model": "Laya plus faster-whisper large-v3-turbo", "params": "Laya 421M", "metric": "Intent, on-screen element and workflow decisions per spoken word", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "J.A.R.V.I.S. by Laya: a real-time, fully local voice assistant for Windows that acts while you talk; Laya decides the intent, the on-screen elements and the workflow in about 35 ms; faster-whisper large-v3-turbo does the dictation; it drives any application by typing, searching and clicking; fully offline with no cloud dependency", "approach": "", "training": "", "data": "", "result": "Acts 0.4–1 second after the end of an utterance; Laya decision about 35 ms", "notes": "", "domain": "computer-use", "score": "", "latency": "about 35 ms Laya decision", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alexw1111-laya-cuda", "name": "laya-cuda", "author": "Alexw1111", "date": "2026-09-23", "category": "app", "url": "https://github.com/Alexw1111/laya-cuda", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "Latency of one request repeated 200 times, P50/P95: laya short 83-token request 2.3/2.9 ms vs 19.3/20.5 ms for the official SDK, 8.3×/7.2× speedup; full context 512 tokens 5.6/6.0 ms vs 18.9/20.0 ms, 3.4×/3.3×. laya-multilingual short 81 tokens 1.5/2.0 ms vs 16.1/17.0 ms, 10.6×/8.4×; full context 1,024 tokens 4.9/5.4 ms vs 15.8/17.0 ms, 3.2×/3.1×. laya-typed-decisions short 83 tokens 2.4/2.9 ms vs 19.6/20.8 ms, 8.3×/7.1×; full context 1,024 tokens 9.6/9.9 ms vs 19.8/21.3 ms, 2.1×/2.2×.", "arch": "Lightweight CUDA inference library for Laya, benchmarked against the official SDK on an RTX 4090 with Windows 11", "approach": "", "training": "", "data": "", "result": "Accuracy on 200 frozen requests, laya-cuda vs SDK: laya 52.25% both, laya-multilingual 43.25% both, laya-typed-decisions 69.25 vs 69.75%. Jev, a larger remote model, scores 65.00% on the laya requests at 378/466 ms, which includes network latency. Each request ran 200 times per process, in 3 processes per backend with alternating order; what is timed is the full predict call, including tokenization, GPU work and decoding", "notes": "Mixed requests: 200 different frozen requests, 40–900 tokens, 1–5 questions, 4–77 options; the short request is a real AG News request with one four-option question, the full-context request asks the same question about real text truncated at the model limit; bilingual README", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "changtimwu-laya-exp", "name": "laya-exp", "author": "changtimwu", "date": "2026-09-23", "category": "app", "url": "https://github.com/changtimwu/laya-exp", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Experiments with laya-mlx, an open-weight MLX port of Laya typed decisions, compared against TypeSafe Jev: quickstart calls, Jev doc example parity replay on all three checkpoints, option capacity and state truncation limits, and unmodified Jev SDK code; findings in issue #1 Laya vs Jev", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ksanjiv05-laya-rn-executorch", "name": "laya-for-react-native", "author": "ksanjiv05", "date": "2026-09-23", "category": "app", "url": "https://github.com/ksanjiv05/laya-rn-executorch", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Run Laya — the open-source System-1 decision model, ModernBERT-large encoder plus typed-decision head, fully on-device in a bare React Native app via ExecuTorch, react-native-executorch", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-forge", "name": "laya-forge", "author": "devjothish", "date": "2026-09-23", "category": "app", "url": "https://github.com/devjothish/laya-forge", "base_model": "Laya", "params": "421M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 1, "likes": 0, "detail": "base: convaiinnovations; arch: forge.yaml; training: 0.029 · 0.733 · 0.993 · 0.995; data: agentguard: hand-written holdout; result: stock Laya 0.647, +16 pts · stock 0.755 · 0.85 acc / 0.10 ECE targets missed on both; notes: keyword matching, miscalibrated temperatures, 'stock checkpoint carries one its own loader rejects as invalid'; name: fine-tune/calibrate/gate Laya", "arch": "The fine-tune→calibrate→gate loop as one command: fine-tune Laya on your own decisions, fit calibration, then a quality gate FAILS THE RUN when the model isn't good enough; published laya-agentguard checkpoint carries fitted allow/escalate/block thresholds", "approach": "", "training": "LoRA/fine-tune + per-question-type temperature calibration + threshold fitting; agentguard demo: allows ls -la, escalates helm upgrade in prod, blocks dropdb customers, blocks instruction-injection tool results", "data": "Your own labelled decisions", "result": "laya-agentguard holdout: injection 0.804, destructive 0.796; 'failed its own quality gate'", "notes": "Documents stock Laya's domain problems — Laya quality-assurance tooling.", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-jev-graphrag", "name": "laya-jev-GraphRAG", "author": "bodepudimuneendra-netizen", "date": "2026-09-23", "category": "app", "url": "https://github.com/bodepudimuneendra-netizen/laya-jev-GraphRAG", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 34, "likes": "", "detail": "The three primitives: Score evaluates edges and relationships from 0–1, used for edge verification during ingestion, the A* traversal heuristic, and context reranking; Noul is a binary judgement of the probability of yes from 0–1, used for semantic chunking, entity disambiguation, early termination, the hallucination gate, and citation verification; and Choice is categorical selection, used for intent routing, ontology alignment, and conflict resolution. You can switch the AI decision model and the graph database, both independently, with a single environment variable each. It requires Python 3.10 or newer and PyTorch with CUDA, and is Apache 2.0 licensed, and the README argues that traditional GraphRAG fails by hard-wiring its logic to a single database and to slow generative calls.", "arch": "laya-jev-GraphRAG: a graph-database-agnostic Agentic GraphRAG framework, a production-ready intelligence layer you drop on top of your existing graph database to make it fully agentic, completely decoupling the AI decision layer from the storage layer, so the same complete 4-phase pipeline of ingestion, pre-retrieval, traversal, and post-retrieval runs identically across Neo4j, Memgraph, Apache AGE, and Kùzu, switched with one environment variable, where every decision, from semantic chunking and intent routing to custom A* traversal and hallucination gating, is handled by a swappable System One model, local Laya or cloud Jev, using three deterministic mathematical primitives instead of slow generative LLM calls.", "approach": "", "training": "", "data": "", "result": "", "notes": "22 stars at sweep time", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-sponsor-skip", "name": "laya-sponsor-skip", "author": "Samuel-Ku", "date": "2026-09-23", "category": "app", "url": "https://github.com/Samuel-Ku/laya-sponsor-skip", "base_model": "Local Laya judge", "params": "Laya 421M", "metric": "Typed skip verdicts on caption lines with timestamps", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Chrome MV3 extension: skip YouTube sponsor reads using a local Laya judge at port 8765; the model only answers typed questions about caption lines with timestamps; the code owns all timestamps; a sibling project to laya-adblock; explicitly not SponsorBlock", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-triage", "name": "laya-triage", "author": "AlexLeow99", "date": "2026-09-23", "category": "app", "url": "https://github.com/AlexLeow99/laya-triage", "base_model": "322M Laya multilingual decision model", "params": "322M", "metric": "Right option picked, double-method agreement on purpose-built test sets", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Local free-text to typed-decision tool: paste a ticket, email or comment and get a category, a calibrated probability and a set of user-defined signals from a 322M decision model on your own machine in about 60 ms; bilingual English and Chinese documentation with an end-user and a config maintenance guide; light and dark themes; a double-method check asks each category two ways, one multiple-choice question plus one yes-no per category, and flags whether they agree", "approach": "", "training": "", "data": "Purpose-built English, Chinese and Malay test sets", "result": "Shipped configuration: English 10 of 13, Chinese 12 of 17, Malay 8 of 13 pick the right option; double-method agreement 9 of 13, 15 of 17, 7 of 13; three reproduced failure modes: hypersensitivity to wording, where rewording a Chinese category description flips the verdict on identical input at 0.975 versus 0.977 reported confidence while in English the same rewording flips 1 of 15 cases that was already unsure at 0.401; both methods can agree on a wrong answer, two of the three Chinese failures were flagged agree at 0.99; out-of-scope input gets forced into a category; the repo leads with a reference-tool-not-autopilot warning", "notes": "Calibrate on your own data before trusting it; the reported confidence does not tell you which regime you are in", "domain": "general", "score": "", "latency": "about 60 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "anthonyli-laya-pilot", "name": "LayaPilot", "author": "anthonyli", "date": "2026-09-23", "category": "app", "url": "https://github.com/anthonyli/laya-pilot", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 43, "likes": "", "detail": "arch: CN; approach: Laya picks ambiguous controls, page state asserts", "arch": "Smart automated testing: generates replayable Excel test cases from live browser pages or executes an existing Excel suite; Playwright drives the browser, a local Laya or compatible decision API picks among ambiguous controls, and assertions come from page state rather than the model's judgment.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "stefw-lkclean", "name": "lkclean", "author": "stefw", "date": "2026-09-23", "category": "app", "url": "https://github.com/stefw/lkclean", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 16, "likes": "", "detail": "", "arch": "Chrome extension that cleans up your LinkedIn feed: hides engagement bait, self-promo and off-topic posts using Jev, TypeSafe AI's typed classification model — and explains every decision.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mohsen1-llm-debugger-vscode-extension", "name": "llm-debugger-vscode-extension", "author": "mohsen1", "date": "2026-09-23", "category": "app", "url": "https://github.com/mohsen1/llm-debugger-vscode-extension", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 360, "likes": "", "detail": "Hunting a coupon-stacking bug: told only that the coupon percent applies before the fixed amount and it got 1450, expected 1500, it places its own breakpoints, steps down coupons.js line by line, and names the line that computes the wrong value, in 26 debugger actions, 27 fast checks, 2 generation calls, about 17 seconds. This began as a proof of concept, a research experiment showing that an LLM given runtime context debugs better than one given source alone, and it is not a supported product. Traditional LLM-based debugging approaches analyze only static source code; with LLM Debugger, the LLM is provided with real-time runtime context: runtime variable values, observing actual variable states as the program executes; function behavior, tracking how functions are called, what values they return, and how they interact; and branch decisions, understanding which code paths are taken during execution. This enriched context allows the LLM to diagnose bugs faster and more accurately, and the extension can also generate synthetic data by running code and capturing execution details beyond the static source.", "arch": "LLM Debugger: a VSCode extension that finds bugs by running the code, driving the real debugger, setting breakpoints, stepping, and reading live values, rather than reading the source and guessing, where at each debugger pause Jev, a typed System One model, chooses the next debugging action from the stack, locals, nearby source, and output, for a fraction of a generation call, and the generation model is woken only for what a classifier cannot produce: the opening hypothesis, a breakpoint location, an expression, and the final fix.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pst2154-nemotron-jev", "name": "Nemotron Jev", "author": "pst2154", "date": "2026-09-23", "category": "app", "url": "https://github.com/pst2154/nemotron_jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "", "arch": "Ask typed questions about text or JSON and inspect model-derived probability distributions in a browser. One container runs the model, the original Decision Lab explorer, and a TypeSafe-shaped API.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sophiamyang-fireworks-jev-reward-rl", "name": "No AI Slop: RL on Fireworks with Jev as a scorer", "author": "sophiamyang", "date": "2026-09-23", "category": "app", "url": "https://github.com/sophiamyang/fireworks-jev-reward-rl", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "A runnable Python cookbook that trains a writer to produce less generic, more specific text. Fireworks samples drafts and updates a LoRA adapter; Jev, TypeSafe's scoring model, scores each draft and those scores become the RL reward. No local GPU, SFT data or preference dataset required", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chengyongru-notiq", "name": "Notiq", "author": "chengyongru", "date": "2026-09-23", "category": "app", "url": "https://github.com/chengyongru/notiq", "base_model": "Jev or user's FastJev server", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "Native Android notification filter guided by plain-language rules: pairs Android notification access with Jev or the user's own FastJev server; the user chooses the apps, describes what matters, and reviews the decisions before Notiq handles delivery; keep messages, deliveries and security alerts, filter the promotions", "approach": "sdk", "training": "No training; Jev or local FastJev decisions", "data": "Notification payloads plus user-written rules", "result": "", "notes": "Early preview; Android 10+; observe mode on by default, filtering happens after a notification has been delivered; English and 简体中文", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kunpengtalk-omnistudio", "name": "OmniStudio", "author": "kunpengtalk", "date": "2026-09-23", "category": "app", "url": "https://github.com/kunpengtalk/omnistudio", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 175, "likes": "", "detail": "One workbench covers the whole local-AI chain: the left icon bar switches apps, the in-app left column picks the backend and fills parameters, and the right side shows results directly, with all pages sharing the same layout. AI image generation switches between cloud API, Apple Silicon local MLX, and ComfyUI backends on the same page, with aspect ratio, width, height, and count adjustable on the fly, images landing in the history area for review and download, and a row of recent thumbnails on the right. AI video unifies three backends as submit-task plus polling: MiniMax H3 supports first-frame image-to-video, with duration, aspect ratio, resolution, and first-frame in the left column, and finished videos land in the history library for playback, download, or deletion. The prompt plaza carries 2,733 built-in copyable prompts, and mini-apps are sandbox tools that do one thing at a time. Download from the kunpengtalk.com site, with a demo video, the latest release, a changelog, and a community group QR code.", "arch": "OmniStudio, in Chinese and English: a local-first desktop workbench for large models, managing models and running inference services, with built-in chat, voice, image, video, music, OCR, and translation apps, plus a prompt plaza, mini-apps, a local knowledge base, shared memory, and Skills management, exposing Jev Noul, Choice, and Score decisions for triage, scoring, content guards, and retrieval reranking, MIT, by KunpengTalk.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openclaw-steer-queue", "name": "OpenClaw steer-or-queue decision model", "author": "steipete", "date": "2026-09-23", "category": "app", "url": "https://x.com/steipete/status/2102667004557832497", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Announced on X by Peter Steinberger, 2026-09-23: the next OpenClaw release uses a decision model to decide, for each new user message arriving while the agent is running, whether to steer, interrupt the running agent and hand it the new input, or queue, hold the message until the current step finishes, in one forward pass. Shipped as a lab feature supporting Jev, API-compatible local models, and ONNX variants. The post's own thread is part of the story: the author credits Vincent Koc, who hijacked the session to land it, and a top reply asks whether decision model means a system one model like Jev or something else.", "arch": "The next OpenClaw release routes incoming user messages with a decision model: for each new message arriving while the agent is running, the model decides steer, interrupt the running agent and hand it the new input, or queue, hold it until the current step finishes, in one forward pass; shipped as a Lab feature supporting Jev and API-compatible decision models, including local models such as Kev and ONNX variants", "approach": "", "training": "", "data": "", "result": "", "notes": "Announced by Peter Steinberger, 2026-09-23; a major open-source agent framework adopting decision-model routing for its own message handling; the adjacent community piece, SqaaSSL/openclaw-jev-compaction, is listed in the github-long-tail-wave notes", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cogflux-opencode-jev-guard", "name": "opencode-jev-guard", "author": "CogFlux", "date": "2026-09-23", "category": "app", "url": "https://github.com/CogFlux/opencode-jev-guard", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "approach: Jev gate before every shell command", "arch": "OpenCode 2 plugin that sends every shell command an agent wants to run to Jev first — local shell and FarHand remote shell commands — gating execution on six typed questions Jev answers about each command.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zoedsy-openjev", "name": "openJEV by zoedsy", "author": "zoedsy", "date": "2026-09-23", "category": "app", "url": "https://github.com/zoedsy/openJEV", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "approach: playground + DiffusionGemma route", "arch": "openJEV: self-hosted playground and reproducible recipe for typed AI decisions — send context and questions, receive choices, scores, and probability distributions — with a small CPU model or a DiffusionGemma GPU route served by razorback16/openjev; bilingual visual batch UI.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "h004888-openjev-bro", "name": "openJev-bro", "author": "h004888", "date": "2026-09-23", "category": "app", "url": "https://github.com/h004888/openJev-bro", "base_model": "openJev-verdict-2.0", "params": "149.6M", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "arch: 149.6M decision model, ~20–30 ms; approach: 149.6M verdict model + Chrome DevTools", "arch": "openJev-verdict-2.0 wired to the Chrome DevTools Protocol: high-speed non-autoregressive web automation and visual decision engine with headful Chrome mode and anti-detection stealth; EN/VN docs.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "separate Vietnamese web-automation project wrapping the Heman10x-NGU/openJev-verdict-2.0 149.6M model with Chrome DevTools MCP, headful mode and anti-detection stealth; GitHub new-repo sweep 2026-09-24", "domain": "computer-use", "score": "", "latency": "20–30 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ab2webco-orca-advisor", "name": "orca-jev-advisor", "author": "ab2webco", "date": "2026-09-23", "category": "app", "url": "https://github.com/ab2webco/orca-jev-advisor", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Nothing, most of the time, is the point: git status is silent and never leaves your machine; npm test is silent; rm -rf node_modules is allowed, being reversible, local, and cheap; gh pr merge asks, because if it is wrong it breaks something someone cares about; and git push --force origin main asks, by a local rule, because it rewrites the remote and anyone who already pulled breaks. How often it stops depends entirely on what your agents do, so the README does not quote a ratio: the only measurements available today come from sessions spent deliberately testing dangerous commands, which is not what your week looks like, and the plugin counts it for you instead, with the Advisor panel showing how many decisions were made, how many passed, and how long each took, on your own traffic. Install: install the plugin from the Ab2Web marketplace in Orca, or load the repository as a development plugin; open Settings, Jev Advisor, and paste a TypeSafe API key, stored with Electron's safeStorage, encrypted, and never shown in full again; then press Set up.", "arch": "Jev Advisor: an Orca Lab plugin that judges what your agents are about to do before they do it, and stays out of the way the rest of the time, putting a fast, cheap judgement from Jev, which answers small typed questions in a few hundred milliseconds for a fraction of a cent, in front of every command an agent issues, not a large model reasoning about your shell, so it lets npm test and rm -rf node_modules through without a word, and stops to ask before gh pr merge, terraform apply, or git push --force.", "approach": "", "training": "", "data": "", "result": "Agent-action guardrail", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wobsoriano-oxlint-plugin-jev", "name": "oxlint-plugin-jev", "author": "wobsoriano", "date": "2026-09-23", "category": "app", "url": "https://github.com/wobsoriano/oxlint-plugin-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 63, "likes": "", "detail": "Install: npm i -D oxlint oxlint-plugin-jev, and export TYPESAFE_API_KEY, obtained from console.typesafe.ai; in CI, set ci to fail so a run that could not reach Jev fails instead of passing quietly. Config: add the plugin and its one rule, jev/ask, to .oxlintrc.json, with your English rules in the options, each rule carrying an id, shown in the error message and unique in the list; a target of function, call, jsx, or file; a yes/no question, where yes means report this; and a cutoff. Examples: no-pii-in-logs targets calls and asks whether this call writes personal data, such as an email or phone number, to a log or console, at a cutoff of 0.8; and name-matches-behavior targets functions and asks whether this function's name implies it only reads data, while its body also writes or sends something, at a cutoff of 0.6.", "arch": "oxlint-plugin-jev: Oxlint rules written in plain English, answered by TypeSafe Jev, where a rule is a yes/no question about a function, a call, a JSX element, or a whole file, each match is sent to Jev with the question, and the plugin reports an error when the yes-probability clears your cutoff, experimental.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lakeday-org-perch", "name": "perch", "author": "lakeday-org", "date": "2026-09-23", "category": "app", "url": "https://github.com/lakeday-org/perch", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 182, "likes": "", "detail": "Published as the @lakeday/perch npm package with CI, node version, and license badges, and the project page at perchscan.com covers lint, docs, and security.", "arch": "Perch: semantic code linting with Jev, a semantic code linter that evaluates code units against configurable Jev questions, published on npm as @lakeday/perch, with the project at perchscan.com.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "davidcreador-pi-dcp", "name": "pi-dcp", "author": "davidcreador", "date": "2026-09-23", "category": "app", "url": "https://github.com/davidcreador/pi-dcp", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 18, "likes": "", "detail": "", "arch": "Cut LLM token spend in long Pi sessions, automatically. Dedup redundant tool calls, strip errored payloads, and let the model summarize closed work-streams — without rewriting original session entries.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zszz3-pi-jev-guide", "name": "Pi-Jev-Guide", "author": "zszz3", "date": "2026-09-23", "category": "app", "url": "https://github.com/zszz3/pi-jev-guide", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 21, "likes": "", "detail": "Version 0.2.1 supports configuring the API key with /jevguard login, effective immediately after verification, and interactive rule adding: run /jevguard add, then choose in sequence the check timing, local match or Jev judgment, and the action on a hit, without modifying plugin source. Behaviour by scenario: credential uploads, such as curl uploading .ssh/id_rsa, get no dedicated local block or confirmation, go through the normal Jev or custom rules, and pass when unconfigured; recursive deletes, discarding git changes, force pushes, and non-transfer credential-file access confirm this operation, and block when there is no interactive UI; other shell, write, edit, and custom tool calls have Jev judging destructiveness, unauthorized data exfiltration, task deviation, and rule conflicts at once; suspected AGENTS.md violations or requirement deviations give the agent a visible reminder, without auto-rolling back files; the same error three times with the same parameters prompts to check the cause, change method, or explain the block, each error reminded only on its third occurrence; after observed source or config writes with no later successful verification record, it reminds at wrap-up to run the relevant checks or explain why not applicable or not executed; and tool results containing known credential patterns get local replacement of the credential in text and metadata, optionally followed by a Jev check for residual credentials that hides the result. Pre-execution checks for read, grep, find, and ls run only the local credential-path rules, and their results still receive output processing. One Jev action check sends four questions at once, and by default additionally checks one non-empty text output. Under the default configuration, without an API key, no network requests are sent, and only local rules, redaction, and evidence tracing are active; custom semantic rules execute their declared onError. Install: pi install directly from GitHub, then /reload in an open Pi session, /jevguard login to configure the key, and /jevguard add to add rules, or install dependencies in the project directory and register as a local Pi package. /jevguard login pastes the TypeSafe API key into a hidden input, the plugin verifies with one fixed test request, then saves and enables Jev, reading it automatically on later starts, with no environment variable or restart needed; Esc cancels, and failed verification or saving keeps the original config. /jevguard status shows whether it is enabled and the key source, without displaying the key; /jevguard logout removes the saved key and returns to local checks. The key is stored at jev-guard/auth.json in the Pi user directory, typically ~/.pi/agent/jev-guard/auth.json, a 0600 plaintext credential file, not in project config or session records; the input shows asterisks only, and the key never goes in command arguments; the verification request carries only fixed test text, and once enabled, normal semantic checks send redacted relevant context to TypeSafe. Non-interactive automation can still use TYPESAFE_API_KEY, the saved key taking priority over the environment variable, with a fallback to the variable and an explicit notice after logout if it still exists; model, timeout, and threshold environment changes still need a Pi restart. For a temporary load without registering a global package, run pi -e with src/index.ts after installing dependencies, first letting the agent run git status to see normal pass-through, then proposing a cleanup in a dedicated temporary test directory to observe the confirmation prompt, without letting it touch real private keys or important files for the demo.", "arch": "Pi Jev Guard, in Chinese: a plugin for the original Pi Coding Agent, with rules configured by timing, and built-in risk checks, output redaction, repeated-failure, and missing-verification reminders, where Jev checks configurable Pi agent rules for risk, output redaction, repeated failures, and missing verification, tested against @earendil-works/pi-coding-agent 0.85.1 on Node.js 22.19 or newer, an independent experimental plugin, not an official TypeSafe or Pi product, currently providing a Pi adapter, with the DSH adapter unimplemented but the judgment and tracing modules reusable.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "opaolini-pi-jev-openrouter", "name": "pi-jev-openrouter", "author": "opaolini", "date": "2026-09-23", "category": "app", "url": "https://github.com/opaolini/pi-jev-openrouter", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Each question is one of three types: noul, returning the probability that a statement is true, with optional criteria for true and false; choice, returning a probability distribution over 2–16 labels; and score, returning a distribution over 2–16 ordered criteria. Nothing happens automatically: no routing, no model switching, no built-in thresholds, and no separate credential; missing auth, API errors, invalid or missing answers, and cancellation all fail closed, and the caller owns any cutoff policy, and it should not be used in place of deterministic checks or a human release gate. This is not the upstream pi-jev package, which exposes broader automatic behaviour and expects a different credential, and the two should not both be installed. Install with pi install git:github.com/opaolini/pi-jev-openrouter, requiring a configured pi openrouter provider, and each call sends the supplied state to OpenRouter/TypeSafe and is a paid request, typically fractions of a cent. Limits: a 64 KiB request, at most 16 questions, at most 16 criteria per question, a 15 s timeout, and tool responses capped at 32 KiB. An example branching workflow shows the agent calling jevDecide with a state and a noul question, getting back the model, id, answers with the probability, and usage with input tokens, output tokens, and cost.", "arch": "A pi extension for explicit Jev decisions, typesafe/jev-1.13, through OpenRouter, using the API key pi already has for its openrouter provider, with a jev_decide tool where the agent asks typed questions about the state it supplies, and a jevDecide workflow function registered when pi-extensible-workflows is installed.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "win4r-pi-jev-router", "name": "pi-jev-router by win4r", "author": "win4r", "date": "2026-09-23", "category": "app", "url": "https://github.com/win4r/pi-jev-router", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "It targets three problems in continuous coding sessions: a continue should not be treated as a new task and downgraded; similar wording is not the same task; and a cheap model taking over a long context is not necessarily cheaper. Default is shadow mode, showing suggestions only, and Jev network calls are off by default, with auto mode needing explicit opt-in; it does not auto-replay failed tasks, does not spawn subagents, and does not compress or delete conversation history. Compared with direct per-turn classification, it adds: task boundaries, where only the first task and /route new consider reclassification, and continuations keep the current model; an explicit context, where /route brief can supply a task summary, without secretly uploading history or tool output; abandonable judgments, where unknown, low confidence, or insufficient context keeps the qualified current model; independent consequence judgment, where the choice does not directly decide permissions, and a separate Noul plus an explicit minimum tier jointly constrain routing; exact caching, where an in-memory hash covers the complete request, question version, and policy, with no fuzzy matching and no prompt written to disk; real availability, respecting Pi's available models and the --models scope with exact provider/model, no substring guessing; capability screening, covering tool support declarations, reasoning ability, current and historical images, and context and output space; switch cost, estimated from prices and the most recently observed cache-read ratio, with unknown prices never pretending to be free; anti-flapping, with in-task retention, a cap on switches, a wait before downgrades, and manual selections pinned; failure recovery, with a persisted circuit breaker, cross-process lock, trial-lease recovery, and late-result validation; explainability, with preview, explain, doctor, and stats, and proposals recorded separately from actual model changes; and validation, with unit tests, real Pi RPC plus local SSE service tests, and real Jev and offline policy replay. Requirements: Node.js 22.19 or newer, Pi 0.84.2–0.86.x, with real host tests executed on 0.84.2 and 0.86.1, and the new routing code depends only on the Node standard library and Pi's extension API. Install with pi install git:github.com/win4r/pi-jev-router, then start from a real working project directory; an already-running Pi session needs a restart to load the new extension; after installing, run /route init, edit the generated model config, then /route reload and /route doctor. It is an independent implementation for these gaps, not a full Bifrost replacement, and has not yet been proven cheaper or more accurate on real coding tasks.", "arch": "Pi Jev Router, in Chinese and English: a task-boundary model routing extension for the Pi Coding Agent, powered by TypeSafe Jev, where Jev judges the task tier, whether the context is sufficient, and potential consequences, and the program decides whether to switch models based on the model capabilities you declared, the models Pi currently has available, context capacity, price estimates, and failure state, with conservative policies and exact caching.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zephyrdeng-pi-review", "name": "pi-review", "author": "zephyrdeng", "date": "2026-09-23", "category": "app", "url": "https://github.com/zephyrdeng/pi-review", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 16, "likes": "", "detail": "Run one reviewer, or a panel of independent reviewers that must agree before a finding blocks the gate, and your agent, Claude Code, Codex, Cursor, or Pi, stays the only editor. Why pi-review: isolated, read-only reviewers, where every review is a fresh child process with a hard allowlist of read, grep, find, and ls, so it cannot edit, commit, or leak context from your main session; and panel consensus rather than one opinion, where 2–8 independent reviewers, such as a code-experts panel with correctness, security, and testing lenses, must reach the configured consensus, majority by default, before a finding blocks the gate. Install with npm install -g @zephyrdeng/pi-review, then pi-review with a file or path for one reviewer, or pi-review --panel code-experts --consensus majority with a path for three lenses and one gate. A Chinese README, screening workflow guide, panel review guide, and CLI reference are included.", "arch": "pi-review: isolated, multi-reviewer AI code review, as a CLI, a CI gate, and an agent skill, where Jev screens likely defects, arbitrates ambiguous findings, classifies scope, and supports cross-round review memory, handing review work to a fresh, read-only Pi session and returning a structured verdict with findings carrying severity, evidence, location, and a stable exit code, MIT, Node.js 20 or newer.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "thepatriczek-playwright-recast", "name": "playwright-recast", "author": "thepatriczek", "date": "2026-09-23", "category": "app", "url": "https://github.com/thepatriczek/playwright-recast", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 62, "likes": "", "detail": "Your Playwright tests already capture everything, so playwright-recast flips the workflow: your Playwright tests become your video source, write tests once, and regenerate polished videos on every deploy, since recording product demos is painful, every UI change means re-recording, manual voiceover and subtitling takes hours, and timing is always off. The pipeline: Recast.from a trace zip, parse, speedUp with different rates during idle and during user action, subtitlesFromSrt from an SRT narration, voiceover with a provider such as ElevenLabs and normalization, render to mp4 at 1080p, and toFile. A showcase video demonstrates a real agent workflow spanning Snowflake Cortex and Neo4j presented as a polished product video.", "arch": "playwright-recast: transform Playwright traces into stunning demo videos, automatically, where Jev chooses camera moves, emphasis, and timing from a rendered Playwright recording and bounded direction candidates, turning the traces, screenshots, network activity, and cursor positions your tests already capture into polished, narrated product videos with a single fluent pipeline, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pydantic-typesafe", "name": "Pydantic AI: TypeSafe TypeSafeModel", "author": "pydantic", "date": "2026-09-23", "category": "app", "url": "https://pydantic.dev/docs/ai/models/typesafe", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": "", "likes": "", "detail": "author: official docs; base: hosted", "arch": "First-party framework integration: TypeSafeModel lets an agent whose job is to decide something run on Jev like on any other model; each field of output_type becomes one question, the prompt assembled per field", "approach": "", "training": "", "data": "", "result": "", "notes": "Jev documented alongside OpenAI/Anthropic models in Pydantic.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "quantskills-quantstudio", "name": "QuantStudio", "author": "quantskills", "date": "2026-09-23", "category": "app", "url": "https://github.com/quantskills/quantstudio", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 64, "likes": "", "detail": "", "arch": "Jev evaluates market state from completed candles, live prices, positions, and strategy constraints, then suggests observe, open, or close actions for human approval.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "miaowunya-rikkahub-sillytavern-android", "name": "rikkahub-sillytavern-android", "author": "miaowunya", "date": "2026-09-23", "category": "app", "url": "https://github.com/miaowunya/rikkahub-sillytavern-android", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 28, "likes": "", "detail": "", "arch": "RikkaHub Plus Huadeng Edition, an Android AI chat client with SillyTavern Android tavern compatibility. Connected-to-API instant chat with prefix caching to save tokens, semantic memory RAG, Jev intelligent decision-making, a QuickJS plug-in, voice calls, and WeChat and QQ bots; character cards, world books, themes and QR beautification import with one click.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "iamunbounded-save-token-jev-clean", "name": "save-token-jev-clean", "author": "iamunbounded", "date": "2026-09-23", "category": "app", "url": "https://github.com/iamunbounded/save-token-jev-clean", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 73, "likes": "", "detail": "Supported hosts: Codex CLI/app via plugin hooks, scoring before built-in compaction and restoring retained verbatim context immediately afterward; OpenCode V2 via an npm plugin, replacing the compaction summary through the session compaction hook; Claude Code via a function-hook plugin, directly replacing compaction with the retained message list; the Anthropic API, OpenAI Responses, and OpenAI Chat Completions via transcript adapters that decode tool use, tool result, function call, custom call, and output blocks; and any agent via a generic adapter or CLI, accepting the normalized role-and-parts format, with custom adapters implementing one small interface. Codex does not expose a command-hook response that replaces its compacted transcript, so its integration uses the documented PreCompact plus SessionStart with source compact lifecycle, while OpenCode exposes a direct compaction-result hook, so its integration replaces the summary. Install and build: npm install, npm run check, and export TYPESAFE_API_KEY; Node 20 or newer is required, and the runtime package has no third-party dependencies.", "arch": "save-token-jev: portable, Jev-guided context compaction for coding agents, where instead of asking another LLM to rewrite old context into a lossy summary, it asks Jev which tool calls and results still matter, keeps user and assistant text verbatim, and lets a tool call be kept with its full result, kept with a bounded result, or removed together with its result, with the algorithm behind a normalized transcript model and host adapters so the same compaction policy works across multiple coding-agent runtimes.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wnzn-semif-go", "name": "semif-go", "author": "wnzn", "date": "2026-09-23", "category": "app", "url": "https://github.com/wnzn/semif-go", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 6, "likes": "", "detail": "", "arch": "Turn a compatible local LLM into a Jev-like decision API: bring your own GGUF model and run it with llama.cpp, and this lightweight Go service accepts a state plus runtime-defined questions, then returns typed Choice, Noul, yes or no, and Score answers in one pass.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "noperator-siftrank", "name": "siftrank", "author": "noperator", "date": "2026-09-23", "category": "app", "url": "https://github.com/noperator/siftrank", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 218, "likes": "", "detail": "It exists because YOLOing your data into a ChatGPT session and asking it to find what matters runs into nondeterminism, where asking again may give a different answer; limited context, where the entire collection may not fit in one request; output constraints, where the model may omit items or stop before finishing the list; and scoring subjectivity, where a numeric score assigned to one item may not be comparable to a score assigned in another request. SiftRank breaks the collection into small, randomized batches and asks a model to compare items against your ranking prompt, combines those relative orderings across repeated trials, checks for convergence, and progressively focuses on the most relevant items, giving a ranked list with the greatest precision where it matters most. It announced Jev support on 19 Sep 2026, with --provider jev to get started, and an agent skill helps an agent retrieve the most relevant items from large datasets on the fly.", "arch": "SiftRank: use LLMs to find the needles in your haystack, telling SiftRank what you are looking for, and it quickly searches your data to bring the most relevant items to the top, ranking prose passages, files, JSON, search results, code, support tickets, product listings, or anything representable as text, where Jev compares items against a ranking prompt across repeated batches so SiftRank can produce a converged relevance ranking, supported as the --provider jev option alongside OpenAI and compatible Chat Completions APIs.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "signal-maze", "name": "Signal Maze", "author": "umairbaig", "date": "2026-09-23", "category": "app", "url": "https://maze.umairbaig.in", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: umairbaig", "arch": "An atmospheric 3D maze where Jev route decisions come alive, navigated by typed decisions", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "", "domain": "gaming", "score": "", "latency": "~400 ms per decision", "benchmark": "", "cost": "", "speedup": ""}
{"id": "michelkerkmeester-skilled-agent-harness-spec-driven-loops", "name": "Skilled Agent Harness", "author": "michelkerkmeester", "date": "2026-09-23", "category": "app", "url": "https://github.com/michelkerkmeester/skilled-agent-harness_spec-driven-loops", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 37, "likes": "", "detail": "What is inside: the Spec Kit Framework, with structured plans, task tracking, validation gates, and handover docs; Spec Memory and Search, where session context is saved into each spec folder and recovered through instant keyword search, surviving resets and compactions; the Skill Advisor Daemon, with dynamic prompt-time skill suggestions using 5-lane fusion and a live skill graph; Autonomous Deep Loops, research, review, and improvement loops that run unattended and stop only when their own evidence says done; 12 specialized agents, with focused roles for implementation, review, research, docs, git, and more; and 15 on-demand skills, with deep capabilities for code, design, docs, and multi-CLI dispatch. Why it earns a place: continuity that survives context resets, where decisions, architecture, and history persist across sessions, crashes, and compactions; and verification, not vibes, where nothing is accepted on assertion alone.", "arch": "Skilled, Spec-Driven Agent Loops: an assistant framework that gives your AI coding agent a memory, a paper trail, and a team of specialists, all living inside your own repository as files you can read and diff, built for Claude Code, Codex, Opencode, Pi Agent, Devin, Cursor, and Hermes CLI, where the cli-jev transport routes typed-judgment requests from the skill harness to the Jev CLI and MCP interfaces.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "micha0827-snapjudge", "name": "snapjudge", "author": "micha0827", "date": "2026-09-23", "category": "app", "url": "https://github.com/micha0827/snapjudge", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "arch: choice / score / yes-no", "arch": "Typed decisions from local Qwen models on Apple Silicon. Probabilities come straight from the logits, no text generation. TypeSafe-compatible HTTP API, runs on MLX.", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kmcheung12-sniffslop", "name": "SniffSlop", "author": "kmcheung12", "date": "2026-09-23", "category": "app", "url": "https://github.com/kmcheung12/sniffslop", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Chrome and Firefox extension: hover a paragraph and Jev scores how likely it is AI-written, or ask any custom typed question about the text; plain JS, no build step, load as an unpacked add-on", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Show HN 2026-09-23", "domain": "misc", "score": "1 point", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sregym-sregym", "name": "SREGym", "author": "sregym", "date": "2026-09-23", "category": "app", "url": "https://github.com/sregym/sregym", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 301, "likes": "", "detail": "It is architectured with AI-native usability and extensibility as first-class principles, provides a benchmark suite for evaluating SRE agents and for training next-generation AI agents, carries an overview, installation, quickstart, and usage guide, a docs site at sregym.com/docs, a leaderboard at sregym.com, and a Slack community.", "arch": "SREGym: an AI-native platform for the design, development, and evaluation of AI agents for Site Reliability Engineering, creating live system environments for SRE agents to solve real-world SRE problems, with a comprehensive SRE benchmark suite of 90 different problems, supporting all problems from AIOpsLab and ITBench and including new problems such as OS-level faults and metastable failures, where when enabled for Codex runs, Jev reviews diagnostic tests and incident-resolution submissions from the supplied agent, inspired by prior work on AIOpsLab and ITBench, with a paper on arXiv, 2605.07161.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "strands-system-one", "name": "strands-system-one", "author": "florianbuetow", "date": "2026-09-23", "category": "app", "url": "https://github.com/florianbuetow/strands-system-one", "base_model": "Qwen3-0.6B / MiniCPM5-2B / Qwen3.5-4B", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Use it as a local stand-in for Jev to experiment with Jev's typed prediction format on open-weights models. Setup: install Python 3.12, uv, just, and LM Studio; in LM Studio, download and load the three models and start the local server on port 1234, with the endpoint and model names set in config/openjev.toml; run just init to install dependencies and the git hook; just fetch to download the jevals suite and the item text and check every item's label; and just run to check that all three models answer. The models: qwen3-0.6b as MLX 8-bit from mlx-community; minicpm5-2b as MLX 8-bit from mlx-community; and qwen3.5-4b as MLX 8-bit from lmstudio-community, and you only need the models you call, with lms load available from the command line.", "arch": "openjev: an alternative System One implementation built from small open-weights LLMs, benchmarked against Jev, where Strands agents make three small local LLMs, Qwen3 0.6B, MiniCPM5 2B, and Qwen3.5 4B, answer like Jev, TypeSafe's System One model, with typed yes/no, choice, and score answers, each with a probability for every allowed answer, the probabilities coming either from a logprob readout, where the model writes no text, or from JSON the model writes out, and both methods benchmarked against Jev on the public jevals suite.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "martinopiaggi-summarize", "name": "summarize", "author": "martinopiaggi", "date": "2026-09-23", "category": "app", "url": "https://github.com/martinopiaggi/summarize", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 224, "likes": "", "detail": "Quick start: Python 3.7 or newer, ffmpeg on PATH, and an OpenAI-compatible API key in .env, with pipx recommended for an isolated environment; Groq offers a free tier, and OpenAI works with --provider openai; pipx install martino-summarize, summarizer --init-config, write the key into .env, and run summarizer with a source URL, outputting summaries/watch with a timestamp. Docker: clone the repository, copy summarizer.docker.yaml to summarizer.yaml, write the key, and docker compose up on port 8501, or pull the pre-built image from ghcr.io. The optional JEV prefilter guide is at the documentation site: check Use JEV prefiltering to reveal two optional fields, the first, I want to include only, takes a subject such as a particular concept about this video to filter, selecting that subject even when it is not obviously present, so the summarizer focuses on it.", "arch": "Video Summarizer: turn any video, a lecture, TikTok, or Drive recording, into distilled markdown, with Q and A, fact-checks, tutorials, Mermaid diagrams, essays, and more, supporting 11 or more sources with local-first summarization for YouTube, Instagram, TikTok, X, Reddit, Facebook, Drive, Dropbox, and local files, bring-your-own API keys with any OpenAI-compatible LLM, Perplexity models, or LiteLLM, shipping as a CLI, Streamlit, HTTP API, Docker, Raycast, and agent skill, with a transcript cache, optional Jev prefiltering, optional Cobalt sidecar for yt-dlp fallbacks, and optional vision mode, where the optional Jev prefilter scores transcript segments before the selected material is sent to the summarizer.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sonnylazuardi-superterminal", "name": "superterminal", "author": "sonnylazuardi", "date": "2026-09-23", "category": "app", "url": "https://github.com/sonnylazuardi/superterminal", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 103, "likes": "", "detail": "arch: superterminald · React bindings for Zed's GPUI", "arch": "A GPU‑rendered, native multiplexer terminal for Windows, Linux and Mac. Rust server owns the terminals; a Bun 1.4.0 + React client renders them through gpuix with a native Rust element.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sureband", "name": "Sureband", "author": "TejaPriyan", "date": "2026-09-23", "category": "app", "url": "https://github.com/TejaPriyan/Sureband", "base_model": "any typed-decision model: Jev, Laya, Von, typical, decider", "params": "", "metric": "Coverage against the 90% target, mean set size, per-model confidence", "license": "Apache-2.0", "paper": "", "stars": 0, "likes": "", "detail": "name: distribution-free coverage guarantees for System One decision models", "arch": "Python package, pip install sureband: split conformal prediction post-processing that wraps any System One decision model's choice, score or yes/no output and returns a prediction set with a statistically guaranteed coverage rate, for example at least 90%, from a few hundred held-out labeled examples and no retraining. Auto-detects the three primitive output shapes; decorator mode wraps an existing FastAPI route, LangChain call or raw predict unchanged", "approach": "", "training": "", "data": "AG News, 600/600 noul and 300/300 choice, and 20 Newsgroups, 375/375 choice, on real Laya from convaiinnovations/laya", "result": "Coverage guarantee held in all three real-model runs: 100.0%, 100.0% and 94.7% against a 90% target. Laya measured underconfident on these tasks, opposite of its overconfidence on phishing; mean set size shrank from 85.5% to 59.7% of all classes as the label space grew from 2–10", "notes": "v0.1.0. Honest limitations stated by the authors: guarantee is a lower bound that over-covers on small label spaces, needs exchangeable calibration data, is marginal rather than per-class by default with an optional Mondrian mode, has no online recalibration, and does not improve accuracy. Benchmarks runner targets JevBench and LocalLLaMA/typed-decisions for comparability with Laya, Von and typical. Not yet tested against Jev.", "domain": "general", "score": "", "latency": "", "benchmark": "AG News + 20 Newsgroups on Laya", "cost": "", "speedup": ""}
{"id": "nicobailon-surf-cli", "name": "surf-cli", "author": "nicobailon", "date": "2026-09-23", "category": "app", "url": "https://github.com/nicobailon/surf-cli", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 629, "likes": "", "detail": "Browser automation for AI agents is harder than it looks: most tools require complex setup, tie you to specific AI providers, or break on real-world pages. Surf takes a different approach: agent-agnostic, pure CLI commands over a Unix socket, works with anything that can run commands; zero config, install the extension, run commands; battle-tested, built by reverse-engineering production browser extensions and methodically working through agent-hostile pages like Discord settings, with graceful fallbacks when CDP fails; smart defaults, screenshots auto-resize to 1200px to save tokens, actions auto-capture screenshots to save round-trips, and errors on restricted pages warn instead of fail; AI without API keys, querying ChatGPT, Gemini, Perplexity, and Grok using existing browser logins; and network capture, automatically logging all network requests while active, with filtering, search, and replay of API calls without manually setting up request interception. Usage: surf go with a URL, surf read, surf click e5, surf snap.", "arch": "Surf: the CLI for AI agents to control Chrome, zero config, agent-agnostic, battle-tested, where the optional Jev commands find, filter, and verify browser elements, then select bounded actions subject to confidence, over pure CLI commands on a Unix socket, working with Claude Code, GPT, Gemini, Cursor, custom agents, and shell scripts, with no MCP servers to configure, no relay processes, no subscriptions, MIT, on macOS, Linux, and Windows.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-browser-agent", "name": "system-one-browser-agent", "author": "adnanahmaddev", "date": "2026-09-23", "category": "app", "url": "https://github.com/adnanahmaddev/system-one-browser-agent", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 2, "likes": "", "detail": "Traditional browser agents query an autoregressive LLM for every single DOM interaction, burning 3–8 seconds and thousands of tokens per step just to decide simple clicks or verify whether a page is done loading. Inspired by Daniel Kahneman's Thinking, Fast and Slow, System One splits browser decisions between two processes: a fast reflex process where Jev handles the common path in under 150 milliseconds, and a slow reasoning process where Gemini or Claude, System 2, handles the rest. It ships with a video walkthrough and live demo on YouTube.", "arch": "System One Browser Agent: a high-speed, dual-process autonomous browser agent harness combining sub-150 ms Jev reflex decisions for the common path with Stagehand stealth automation and slower Gemini or Claude System 2 steps, Node.js 20 or newer, TypeScript 5.7, Stagehand v4.1.0, Next.js 16, 7 tests passing, MIT.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "computer-use", "score": "", "latency": "150 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cloudn1ne-system1", "name": "system1 - Laya decision-model HTTP server", "author": "cloudn1ne", "date": "2026-09-23", "category": "app", "url": "https://github.com/cloudn1ne/system1", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "name: containerized", "arch": "Self-hosted deployment of Laya — a multilingual, non-autoregressive System 1 decision model. laya-serve exposes the built-in Router on the same POST /v1/systemone request/response shape as TypeSafe Jev, so existing TypeSafe clients", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tenuo-safe-upgrade", "name": "Tenuo safe-upgrade", "author": "Tenuo", "date": "2026-09-23", "category": "app", "url": "https://tenuo.ai/blog/jev-scoped-authority", "base_model": "Jev + LangGraph + Tenuo", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: per blog; arch: Jev · LangGraph · Tenuo; notes: HN; name: scoped-authority dependency upgrades", "arch": "Dependency-upgrade agent with task-scoped warrants: judgment, control flow and authority are kept separate — the agent can recommend/validate an upgrade within a granted scope without holding general write access", "approach": "", "training": "", "data": "", "result": "Design post 'Jev scoped authority' describing the separation of judgment / control flow / authority", "notes": "Observed 2026-09-23; details from the blog post.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tev1", "name": "tev1-4B-experimental", "author": "togethercomputer", "date": "2026-09-23", "category": "app", "url": "https://github.com/togethercomputer/tev1", "base_model": "Qwen3.5-4B", "params": "4B", "metric": "", "license": "Open recipe", "paper": "", "stars": 134, "likes": "", "detail": "author: Hassan et al.; base: LoRA on Together AI cloud; arch: no custom decision head; training: companion blog 'How to train your own Jev'; data: 'new v1' mixture; license: GitHub; result: reused dev benchmarks, authors' caveat", "arch": "Standard letter-answer SFT: state + question + 2–24 options → one answer letter; uses Qwen's existing LM head", "approach": "logits", "training": "Ordinary LoRA SFT: rank 8, 1 epoch, lr 5e-5, 2,048-token limit; ~$17 to train on Together", "data": "37,840 train + 4,568 val examples: language classification, policy decisions, routing, synthetic research classification; full data recipe published in repo", "result": "880/1,000 main decisions, 300/300 policy-transfer decisions", "notes": "First named Jev-inspired release from Together AI. Ships reproducible training example + saved results; endpoint access depends on Together account.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "freedomintelligence-tiermem", "name": "Tiermem", "author": "freedomintelligence", "date": "2026-09-23", "category": "app", "url": "https://github.com/freedomintelligence/tiermem", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "https://arxiv.org/abs/2602.17913", "stars": 15, "likes": "", "detail": "Authors: Qiming Zhu, Shunian Chen, Rui Yu, Zhehao Wu, Benyou Wang. News: 2026-09-22, try Jev TierMem, lightweight agent memory with Markdown notes, searchable raw history, and Jev routing; 2026-07-08, celebrating acceptance at COLM 2026, with a refreshed title and results and a walkthrough of memory use during a long debugging session; 2026-02-20, the first preprint appeared on arXiv. The earlier preprint is titled From Lossy to Verified: A Provenance-Aware Tiered Memory for Agents, and the OpenReview record, svKCa4itcd, is the place for the conference paper and updates. It requires Python 3.10 or newer.", "arch": "TierMem: balancing compressed memory and raw evidence for long-horizon agent memory, a provenance-aware tiered memory where the agent thinks fast with summaries and digs deep into raw history, accepted at COLM 2026, with the Jev integration, Jev TierMem, routing agent memory queries between compact Markdown notes and searchable raw history, as a lightweight agent memory with Markdown notes, searchable raw history, and Jev routing, run the agent, use the Python API, or connect through MCP, MIT, with a HuggingFace model at FreedomIntelligence/TierMem.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pedrocivita-tocket", "name": "tocket", "author": "pedrocivita", "date": "2026-09-23", "category": "app", "url": "https://github.com/pedrocivita/tocket", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 39, "likes": "", "detail": "Also in Portuguese: Tocket é o caderno do projeto, pastas e arquivos que vários agentes leem e escrevem juntos, não é um chat e não faz o trabalho sozinho, o Jev só escolhe entre opções e grava essa escolha no caderno. The Context Engineering Framework for Multi-Agent Workspaces: agents forget everything between sessions, and Tocket keeps shared context in files any agent can read, with no vendor lock-in and no special integrations. What is new in 2.6.5: a meta-attention handoff, where Jev judges chunks and workers read the filtered handoff, with tocket handoff --aware scoring .context chunks and writing .context/handoffs plus a receipt under .context/attention; conditional packs live in .context/gotchas, and tocket packs load --query writes .context/active/packs.md; --dry-run or no key uses the stub. The website is tocket.ai.", "arch": "Tocket: the project notebook, folders and files many AI agents read and write together, not a chat, and it does not do the work alone, where Jev and similar only pick among options and save that choice in the notebook, and Cursor, Claude, GrokBot, and CI still do the work, so agents work, Tocket remembers, and Jev only chooses the next step; a file-first harness, not a LangChain runtime, where the AutoMode pattern of judging before irreversible tools lives in .context/decisions JSON, not in middleware, Jev is the judge, not the writer, and before bash, deploy, or browser, tocket decide records a tool_gate of allow, block, or ask, and tocket work --apply honors that gate, with no new runtime dependencies, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vbcherepanov-total-agent-memory", "name": "total-agent-memory", "author": "vbcherepanov", "date": "2026-09-23", "category": "app", "url": "https://github.com/vbcherepanov/total-agent-memory", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 72, "likes": "", "detail": "Benchmarks: LongMemEval R@5 95.1%, LoCoMo R@5 0.607, and BEAM 1M R@5 0.448. Distributed on npm, PyPI, Docker on GHCR, and a Homebrew tap, with MCP support, and it positions itself against mem0, Letta, Zep, and Supermemory.", "arch": "total-agent-memory: persistent, local memory for AI coding agents, for Claude Code, Codex CLI, Cursor, and any MCP client, with a temporal knowledge graph, procedural memory, AST codebase ingest, cross-project analogy, and 3D WebGL visualization, where Jev checks retrieved memories for contradictions before an agent uses them in an answer, 100% local-first, version 14.5.1, 2162 tests passing, 9 IDEs supported, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sensahin-transcript-lens", "name": "transcript-lens", "author": "sensahin", "date": "2026-09-23", "category": "app", "url": "https://github.com/sensahin/transcript-lens", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 18, "likes": "", "detail": "What it does: it tries to fetch the subtitles from a YouTube link, supporting SRT, VTT, and pasted timestamped text; it offers full text, clean text, and important-parts-only views; it derives section boundaries from topic changes, choosing headings from expressions in the text; it shows criteria such as surprising, funny, and actionable as a heatmap; it classifies claims, opinions, forecasts, memories, and questions, and it does not check the truth of claims; it finds passages relevant to a topic you type, showing the original words instead of a summary; and it downloads the view as TXT, Markdown, SRT, VTT, or JSON. Quick start: Node.js 22 or newer and npm, and for Jev analysis your own Vercel AI Gateway or TypeSafe API access; git clone the repository, npm ci, copy .env.example to .env.local, and npm run dev, with Copy-Item instead of cp on Windows PowerShell; open localhost:3000 and press the Turkish sample transcript button, then use this transcript, an example written for the project, not taken from another video. You can try it without a key: the sample or pasted text opens and can be exported, and without a key the analysis shows an explanatory message, with the heatmap, sections, and semantic search requiring Jev; with a key defined, analysis starts automatically when a transcript loads, and provider usage may incur charges.", "arch": "Transcript Lens: a Turkish-first app to discover YouTube transcripts by meaning instead of just reading them, a Turkish-language, Next.js-based experimental application you can run on your own computer or deploy to your own Vercel account, where Jev classifies the fragments without rewriting the text, helping find the sections, the important passages, and where the topic you are looking for comes up.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hawkymisc-typed-decision-bert", "name": "typed-decision-bert", "author": "hawkymisc", "date": "2026-09-23", "category": "app", "url": "https://github.com/hawkymisc/typed-decision-bert", "base_model": "bge-m3-zeroshot-v2.0", "params": "", "metric": "", "license": "", "paper": "", "stars": 32, "likes": "", "detail": "Compatibility is stated honestly in three divisions, and it is not at the stage where one can call it Jev compatible. Proven against the official SDK 0.7.0 over real sockets: typed send and receive of the three types Noul, Choice, and Score from TypeSafeClient or AsyncTypeSafeClient, with typed reconstruction; calls without specifying a model, the SDK default jev-latest, resolving through the PoC configuration alias; request-id recovery from the x-typesafe-request-id header; exception status attributes and exception-message extraction from error.message; and unknown top-level fields via extra_body accepted by default, with a reject setting returning 422. What it is not: not a trained JevBERT, with no JevBERT training performed; not calibrated, with uncalibrated probabilities at temperature 1.0, and the returned numbers are not correct probabilities, so thresholds such as confidence at least 0.8 from Jev must not be ported over; unevaluated quality, with the smoke evaluation a record of tendencies on 52 self-made examples, not a quality claim, and not grounds for gate G2; and no validation against a real Jev. Documents: the spec and design at JevBERT_spec_design.md v0.3.0, the PoC implementation design at docs/POC_DESIGN.md, measured results at docs/POC_RESULTS.md, differences and unverified items versus Jev at compat/differences.md, and a classification benchmark environment for comparing real Jev with the local server at docs/BENCHMARK.md.", "arch": "JevBERT, in Japanese: an instruction-conditioned decision engine using a BERT-style encoder, aiming at a Jev-compatible API, an unofficial independent project with no affiliation, sponsorship, or approval from TypeSafe, at the P0.5 proof-of-concept stage, a local HTTP server that accepts the same JSON shape as Jev's POST /v1/systemone and returns the same JSON shape, working from the official Python SDK typesafe-sdk 0.7.0 by swapping only the endpoint and API key, with a layer validating the structural and numeric invariants I01 to I09 independently of model accuracy, not a trained JevBERT, with a public zero-shot NLI classifier, MoritzLaurer/bge-m3-zeroshot-v2.0, as the provisional backend, uncalibrated probabilities, and no validation against a real Jev server; the repository is typed-decision-bert, and the Python package and project working name are jevbert or JevBERT.", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "razaanstha-ulka", "name": "ulka", "author": "razaanstha", "date": "2026-09-23", "category": "app", "url": "https://github.com/razaanstha/ulka", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 24, "likes": "", "detail": "Experimental: Ulka is an early preview, expect bugs and incomplete tasks, and supervise browser actions and review results. fx plans browser subgoals, GLM handles language tasks, and Jev selects constrained actions from observed page elements; Ulka validates targets, executes browser actions, and checks outcomes, and it can still make mistakes, so supervise it, especially on signed-in sites. It uses Stagehand-style browser primitives without importing Stagehand's separate CDP runtime: observe_browser returns the current page plus an action catalog built from validated controls; browser_subgoal provides natural-language act behavior through Jev, with deterministic execution and stale-target recovery; act_action replays one action returned by observe_browser without another planning call, after exact snapshot validation; extract_page returns schema-validated structured fields from the latest observed page, with missing or ambiguous fields returning null; and successful CLICK and SELECT actions can replay across repeated subgoals only when URL, goal, semantic page state, and target meaning all match, with changed state falling back to Jev. This keeps Ulka's local browser ownership, approval gates, evidence capture, and final verification, while adopting Stagehand's observe, act, extract, and caching patterns.", "arch": "Ulka: an experimental browser agent powered by FX, Jev, and Vercel AI Gateway, bring your own API key to read pages and act in your browser, where FX orchestrates browser tasks and tool calls, Jev, typesafe-ai/jev, selects constrained browser actions from observed page elements, Vercel AI Gateway routes AI requests using the user's API key, and the Ulka browser runtime reads Chromium's accessibility tree for roles, names, states, and UI hierarchy, validates targets, requests approvals, executes actions, and records evidence, running locally in the browser with no application backend required.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "notque-vexjoy-agent", "name": "vexjoy-agent", "author": "notque", "date": "2026-09-23", "category": "app", "url": "https://github.com/notque/vexjoy-agent", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 425, "likes": "", "detail": "", "arch": "VexJoy AI Agent with Jev Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "von-fuzzy-motor", "name": "von-fuzzy-motor, system_one_control", "author": "FHL-08", "date": "2026-09-23", "category": "app", "url": "https://github.com/FHL-08/system_one_control", "base_model": "Von 400M custom", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Fuzzy-logic DC motor speed control where the membership functions are a learned decision model: Von, the open-source ~400M-parameter yes/no probability model, plays the fuzzification layer. Plant is an LGM12-N20 12 mm DC geared motor driven by an Arduino Uno over PWM with encoder feedback; Simulink Connected IO also supported", "approach": "", "training": "", "data": "", "result": "", "notes": "First System One application in the dataset explicitly tied to real physical hardware: LGM12-N20 motor, Arduino Uno, encoder feedback", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dorkitude-webctl", "name": "webctl", "author": "dorkitude", "date": "2026-09-23", "category": "app", "url": "https://github.com/dorkitude/webctl", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 143, "likes": "", "detail": "Example: webctl search with a query for the final score of the San Francisco Giants baseball game on September 19th, or more likely, webctl with a query for recent San Francisco Giants MLB scores and a goal stating that the user asked to know the final score of last night's, September 19th, San Francisco Giants game in Major League Baseball. It was originally built to give the author's Pi/Kimi K3 chat stack something akin to what Claude and Codex have out of the box, but now Claude and Codex use it too, and it really saves a lot of the context window. For long PDFs, Reddit and StackOverflow comment threads, earnings transcripts, and entire Wikipedia articles, this saves an enormous number of tokens compared to reading the page, while for a short fact it adds nothing; see benchmarks/README.md.", "arch": "webctl: a smart web search CLI for agents, backed by Jev, saving a lot of tokens, where by default webctl tries 3 web search backends, each result set is passed with the original query and goal to Jev for scoring, and the high-scoring subset is deduped by some fancy math plus Jev, and when snippets are not enough, --scrape --filter-chunks fetches the top results, parses out the textual content, divides it into chunks, and sends those chunks plus the original query and goal to Jev for scoring, so only the relevant chunks land in the agent's context.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wechat-kev-watcher", "name": "wechat-kev-watcher", "author": "guoqihan342-svg", "date": "2026-09-23", "category": "app", "url": "https://github.com/guoqihan342-svg/wechat-kev-watcher", "base_model": "Local Kev / System One decision model", "params": "", "metric": "13-class intent, 3-level urgency, need_reply", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "macOS watcher for new WeChat messages: a local Kev decision model classifies each notification into a default 13-class intent, a 3-level urgency and a need_reply verdict without touching the WeChat process; it reads the macOS notification-center database read-only with no OCR and no WeChat process inspection; fully local", "approach": "", "training": "", "data": "", "result": "The 4–9 second platform notification delay plus polling means results arrive 6–10 seconds late; the app bundle needs Full Disk Access", "notes": "", "domain": "social", "score": "", "latency": "6–10 s end to end", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zhuobichen-weflow-cli", "name": "weflow-cli", "author": "zhuobichen", "date": "2026-09-23", "category": "app", "url": "https://github.com/zhuobichen/weflow-cli", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 64, "likes": "", "detail": "Opening with classical poetry, the moon of today was once seen by the ancients, and the universe is the inn of all things while time is a passing guest of a hundred generations, the goal is to bring chat logs, official-account reading, and personal knowledge workflows back to your local computer. Usage boundaries: it processes only data you have the right to access, do not use it to monitor, track, or read other people's WeChat data, do not deploy it to others' devices or run it remotely and silently, and third-party tools may trigger account risk controls, restrictions, or bans, with the user bearing responsibility. Features: WeChat favorites query, reading the local favorite.db with type filters and keyword search, where weflow-cli fav list -n 3 shows 3 of 1042 favorites with date, title, source, and link; and the full workflow, one command per stage, where weflow-cli sessions lists conversations, weflow-cli messages with a contact and -n 20 queries chat messages, and weflow-cli export with a contact and html exports HTML, Excel, TXT, or JSON.", "arch": "WeFlow CLI, in Chinese: turns WeChat into your local second brain, reading and querying chat logs on your machine and exporting readable HTML, organizing official-account articles into daily digests with AI summaries, reading WeChat favorites, building a semantically searchable knowledge base, and connecting to AI editors like Claude Code through MCP, where Jev classifies article topics and relevance for the WeChat daily digests when enabled, local-first with zero telemetry and zero cloud reporting, the database key stored locally encrypted with machine-bound AES-256-GCM, AI features only enabled after you explicitly configure your own API key, Node.js 18+, Python 3.10+, WeChat 4.x tested, MIT, on Windows and Linux.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wd041216-bit-zero-api-key-web-search", "name": "zero-api-key-web-search", "author": "wd041216-bit", "date": "2026-09-23", "category": "app", "url": "https://github.com/wd041216-bit/zero-api-key-web-search", "base_model": "Laya", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 18, "likes": "", "detail": "An agent that searches the raw web pays for everything it reads: context tokens, latency, and attention spread over SEO noise, so Zero-API-Key Web Search puts a sieve between search and the agent. Stage 1, search the web with zero API keys: DuckDuckGo by default, self-hosted SearXNG for cross-validation, and optional Bright Data for production SERP and Web Unlocker. Stage 2, sieve the results through Laya, the open-weight Apache-2.0 non-autoregressive decision model, which scores every result for relevance in a single forward pass, about 35 ms on GPU and 100–330 questions per second batched, with calibrated probabilities, and because it never generates text, there is nothing to parse and nothing to hallucinate. Stage 3, verify claims with probabilistic stance classification, support, conflict, or neutral per source, instead of pure keyword matching, then emit citation-ready evidence reports. Every stage degrades gracefully: with no Laya installed, the pipeline falls back to the lexical heuristic verifier and still works, so Laya is an accelerant, not a dependency. The query flows from providers through cross-validation and the Laya sieve into the LLM context pack, with citations only.", "arch": "Zero-API-Key Web Search: Jev-powered search infrastructure for AI agents, zero API keys, MCP-ready, LLM-context aware, with local neural sieving, putting a sieve between search and the agent, where the web is searched with zero API keys, DuckDuckGo by default, self-hosted SearXNG for cross-validation, and optional Bright Data for production SERP and Web Unlocker, the results are sieved through Laya, an open-weight Apache 2.0 non-autoregressive decision model that scores every result for relevance in a single forward pass, about 35 ms on GPU and 100–330 questions per second batched, with calibrated probabilities, and claims are verified with probabilistic stance classification, support, conflict, or neutral per source, instead of pure keyword matching, then citation-ready evidence reports are emitted, Python 3.10+, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "opencx-labs-zevals", "name": "zevals", "author": "opencx-labs", "date": "2026-09-23", "category": "app", "url": "https://github.com/opencx-labs/zevals", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "An example: define an agent whose invoke generates a response for the user, a judge built with the Zevals LangChain judge over a chat model such as gpt-4.1-mini at temperature 0, and a followup assertion prompting that the assistant asked a followup question; evaluate the agent over segments of a user message, the agent response, and the AI eval of the assertion, then run your assertions on the type-safe output and expect it to be true. Install with npm install of the core package; add the LangChain core and OpenAI packages plus the Zevals LangChain package to use LangChain models, and the Vercel AI SDK package is available for other model access. More examples live in the examples directory.", "arch": "Zevals: simple, practical AI evaluations in TypeScript, utilities for testing AI agents that treat AI evals like end-to-end tests with less focus on metrics and more focus on binary assertions, designed to evaluate full conversations rather than just single query-response pairs, and impose no testing framework or test runner, where Jev judges yes-or-no assertions about an agent transcript, and the test passes when its probability reaches the threshold, with type-safe outputs you can assert on in your favorite runner.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "final-bench-tetris-ztc", "name": "ZTC Gate Tetris", "author": "FINAL-Bench", "date": "2026-09-23", "category": "app", "url": "https://huggingface.co/spaces/FINAL-Bench/Tetris-JEV-LAYA-ZTC", "base_model": "ZTC-Judge 27B/9B/4B + Darwin-397B-ZTC + TypeSafe Jev + Laya", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": "", "likes": 38, "detail": "arch: zero-token-confidence; training: demo", "arch": "Static HF space from the ZTC ecosystem: one gate call compared 2.1 s vs 0.0615 s; generation-free LLM-as-judge with ECE, hallucination detection, selective prediction; agent-safety/guardrail framing", "approach": "", "training": "", "data": "Tetris rollout", "result": "38 likes; watch-it-play-out demo of the ZTC gate", "notes": "Title: 'ZTC Gate Tetris'.", "domain": "gaming", "score": "", "latency": "2.1 s", "benchmark": "Tetris", "cost": "", "speedup": ""}
{"id": "goutoujunshi-jev-chat", "name": "狗头军师 Chat", "author": "shengjidaguai-china", "date": "2026-09-23", "category": "app", "url": "https://github.com/shengjidaguai-china/goutoujunshi-jev-chat", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 39, "likes": "", "detail": "Download the corresponding platform file from the GitHub Releases page; the Windows ZIP must be fully extracted before running the program inside, and build records are on the GitHub Actions build page. The Mac build is a source ZIP: after extracting, run the install-dependencies command, then the offline-demo or launch command, requiring Python 3.12 and uv, with no signed app yet. The Windows build is an executable-directory ZIP: the automated build passes, pending real-machine acceptance. The two platforms differ in scope: full original-text verification, relationship profiles, and relationship K-lines are on the Mac; Windows has chat recognition, Jev judgment, and the candidate-reply flow, without the full interface ported yet, with the Android source kept in the repository for later adaptation, and no APK for now. Windows requires Windows 10 1903 or newer or Windows 11, targeting WeChat Windows 4.x: download and fully extract the preview ZIP, enter the goutoujunshi-jev-chat-windows folder, and run the exe; this packaged build needs no separate Python. On first launch, in settings, configure the Jev judgment interface and the reply-generation interface separately, then open the WeChat conversation to handle, keep the chat window visible, and use the floating window to read and analyze; candidates can be copied or filled into the draft, and filling depends on the current window position, so confirm the conversation, recipient, and draft content before sending it yourself; this build still awaits Windows real-machine acceptance, and the Windows usage notes also list the source-run method. macOS: install Python 3.12 and uv first, then download and extract the Mac ZIP, and run the install-dependencies and offline-demo commands in sequence; after confirming the interface opens, run the launch command; on first use, configure the interfaces and models in settings.", "arch": "Goutoujunshi Jev Chat, 狗头军师 Jev Chat, in Chinese: a Mac WeChat screen reader with relationship analysis and a reply-draft floating window, where Jev is the judgment layer, an independent project extending the original 狗头军师, reading the screen, analyzing, and generating reply drafts beside the chat window, with sending always decided by the user, offering a Mac source preview package and a Windows preview ZIP, with 11 stars at sweep time.", "approach": "", "training": "", "data": "", "result": "", "notes": "11 stars at sweep time; 中文 project", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "uezo-aiavatarkit", "name": "aiavatarkit", "author": "uezo", "date": "2026-09-22", "category": "app", "url": "https://github.com/uezo/aiavatarkit", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 681, "likes": "", "detail": "What your avatar can be: talking avatars in your app, a character that speaks and reacts with expressions and animations; interactive signage and virtual store staff, for reception, wayfinding, and product guidance, answering out loud while putting images and UI on the screen; companion devices, adding voice conversation to anything; metaverse AI avatars, conversational characters on VRChat, cluster, and Vket Cloud; phone operators, handling inbound and outbound calls through Twilio or Asterisk; and multi-channel AI assistants, one assistant your users reach through whichever channel fits the moment. Features: ultra-low latency, streaming and parallel throughout the pipeline, even running STT speculatively and giving a spoken nod before the answer itself, with under 1 second from end of speech to first audio, measured; a modular architecture, where VAD, STT, LLM, and TTS are swappable parts, popular providers are built in, and a small interface covers the rest; AI-agent native, with tool calls and MCP, tools loading only when needed, so a large catalog never confuses the model, and slow ones never stall the conversation, with background execution or a reply straight from a template; multimodal and expressive, accepting speech, text, images, and files, and replying with voice, facial expressions, animations, and on-screen artifacts, so a chart or a map appears just as the avatar mentions it; and omnichannel, where web, phone, LINE, metaverse, and local devices all run off one pipeline, and the conversation follows the user rather than the channel.", "arch": "AIAvatarKit: build AI-based conversational avatars lightning fast, a conversational-avatar framework with a Jev gate that judges whether a speaker has finished a turn, where the avatar can be a talking character in a web or mobile app, interactive signage and virtual store staff, companion devices on Raspberry Pi, M5Stack, and StackChan, metaverse AI avatars on VRChat, cluster, and Vket Cloud, phone operators through Twilio or Asterisk, or a multi-channel assistant reachable through whichever channel fits the moment.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aml-rlcd", "name": "AML_RLCD", "author": "gaaprojects", "date": "2026-09-22", "category": "app", "url": "https://github.com/gaaprojects/AML_RLCD", "base_model": "Laya", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "The requirements are Windows, Python 3.11 or newer, and Node.js 22 or newer, with initial setup requiring internet, and the built application using no external fonts, CDNs, APIs, or cloud inference. Set up with the setup and start PowerShell scripts, opening 127.0.0.1 port 8000, using a project-local virtual environment, and if Python is not on PATH, pass the Python path to setup; the manual route is creating a venv, pip installing in editable test mode, npm ci, npm run build, and uvicorn on the aml.api app. The original synthetic Harbor scenario works immediately with clearly labeled deterministic rules, which are not trained Laya predictions, calibrated probabilities, or IBM benchmark results, and if an explicitly configured checkpoint fails to load, scoring fails visibly instead of silently substituting rules. Investigation workflow: play, pause, reset, or step through a scenario, with only observed transactions appearing in graphs and metrics; select an account to inspect incoming and outgoing activity and rule-based evidence; trace chronological, same-currency chains and cycles, with paths limited to five hops and 30 results, and graph views to 24 connected accounts, where connected transfers do not prove identity of funds; adjust the threshold to explore alert volume, false alerts, and recall, with metrics excluding unlabeled transactions, and UI metrics describing the visible replay, not an independent benchmark; and create accounts implicitly by adding transfers in the on-screen editor, with chain, cycle, and fan-in templates editable.", "arch": "TRACE, AML investigation simulator: a local, dark investigation workspace for tracing synthetic financial transactions, where you train a Laya decision head on IBM AML data in Google Colab, export it, and run inference on a Windows CPU, with AML alert triage as calibrated decisions, and RLCD-style decisions, for Windows with Python 3.11+ and Node.js 22+.", "approach": "", "training": "RLCD-style", "data": "AML cases", "result": "AML triage", "notes": "'AML_RLCD.'", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "astra-ares", "name": "Astra-Ares", "author": "miuuyy", "date": "2026-09-22", "category": "app", "url": "https://github.com/miuuyy/Astra-Ares", "base_model": "GPT-6 steered by Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 273, "likes": 0, "detail": "base: GPT-6 via the OpenAI API, steered by Jev on the TypeSafe API; arch: adaptive reasoning-effort selection: Jev reads bounded task context and chooses how hard the selected model should think next and for how many generations · Codex applies the choice mid-conversation using GPT-6's native reasoning-effort change, which preserves the prompt prefix cache; result: Node 22+, ~10 GB build · runs a separate pinned, patched Codex CLI · an experimental reference implementation", "arch": "While a Codex task runs, Jev reads bounded task context and chooses how much the selected model should think next and how many generations that effort should last; Codex applies the choice mid-task. Works because GPT-6 can change reasoning effort without invalidating the cached prompt prefix", "approach": "", "training": "", "data": "", "result": "Goal: reduce token usage by matching reasoning depth to the next step", "notes": "Jev steers how hard GPT-6 thinks per step while a Codex task runs, cutting token spend by matching reasoning depth to the next step; 244 stars", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wh000wh000-awesome-jev-live", "name": "awesome-jev-live", "author": "wh000wh000", "date": "2026-09-22", "category": "app", "url": "https://github.com/wh000wh000/awesome-jev-live", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 35, "likes": "", "detail": "Every entry was collected, filtered, and re-checked by the pipeline in the repository, and the numbers and timestamps come from the live index, with the last sync at 2026-09-23T20:09:57+08:00, 770 entries, 76 new this tick, and 22 implementation languages.", "arch": "Awesome Jev Live: the evidence-graded index of TypeSafe System One, covering SDKs, MCP tools, agents, apps, and open models, an index that rebuilds itself every two hours, with 770 entries across 20 implementation languages, refreshed every 2 hours, MIT, with a searchable site, in 18 languages including English, Simplified and Traditional Chinese, Japanese, Korean, Spanish, French, German, Brazilian Portuguese, Russian, Italian, Arabic, Hindi, Turkish, Vietnamese, Thai, Indonesian, Polish, Dutch, and Ukrainian.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yzfly-awesome-jev-zh", "name": "awesome-jev-zh", "author": "yzfly", "date": "2026-09-22", "category": "app", "url": "https://github.com/yzfly/awesome-jev-zh", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "CC0-1.0", "paper": "", "stars": 76, "likes": "", "detail": "The author's take: Jev does not generate text, and on first seeing that, the author thought it was a defect, later finding it is the design core; its usage is to give it a piece of state, an email, a log line, or a ticket, plus several typed questions, and it answers all of them in 70–500 ms, picking from the options you give, scoring on your scale, or giving a probability from 0–1, each answer with a confidence, input priced at 0.042 per million tokens, output free. Its boundary is clear: to write copy, summarize an article, or explain the reasoning behind a judgment, an LLM is still the right tool, and this point recurs several times in the list. What it solves: during curation, the author grew more convinced that many of today's LLM calls are essentially multiple-choice questions, assembling prompts, generating token by token, stripping markdown code blocks, json.loads, validating the schema, retrying on failure, all just to get back the single word billing, and that loop is exactly what Jev eliminates. Two numbers to keep in mind: the 193 times faster figure comes from TypeSafe's own evaluation, which the official side also labels as the upper bound of the benefit; and in an independent evaluation, on the specific task of phishing emails, asking it directly yields only 62.6% accuracy, while two lines of regex rules reach 91.8%, with the full data in the cool-headed section. The list is organized into getting started, with official resources, quality projects, what Jev is, try channels, getting started, specs and pricing, when to use and not use, and Chinese guides; projects, with the hot auto-leaderboard, SDKs and clients, applications, demos, agent tools, and reproductions and evaluations; and materials, with cookbook and patterns, articles, community, and a cool-headed view. The official documentation is written clearly, and to truly understand this model, it is the shortest path, with second-hand interpretations, including this list, only a supplement: the TypeSafe website, the docs home covering introduction, primitives, patterns, API, and SDKs, the quick start, the playground where you paste state in the browser and add questions to see typed results, and the API keys console where you get the TYPESAFE_API_KEY.", "arch": "Awesome Jev ZH: the Chinese curated list of Jev and TypeSafe System One, covering official materials, SDKs, hit applications, agent tools, open reproductions, and independent evaluations, with Chinese getting-started guides and a daily automated inclusion of popular GitHub projects, unofficial, with no affiliation to TypeSafe AI, CC0 1.0, noting that Jev opened early access on 2026-09-15, and all vendor self-reported data is source-labeled.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zerogold-call-coach-ai", "name": "call-coach-ai", "author": "zerogold", "date": "2026-09-22", "category": "app", "url": "https://github.com/zerogold/call-coach-ai", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 43, "likes": "", "detail": "", "arch": "A live sales-call assistant. It listens to the conversation, sends it to TypeSafe Jev after every sentence, and shows the rep what to do next with a confidence score.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "celestoai-celesto", "name": "celesto", "author": "celestoai", "date": "2026-09-22", "category": "app", "url": "https://github.com/celestoai/celesto", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 977, "likes": "", "detail": "Because the agent runs in a separate virtual machine instead of a process on your computer, Celesto provides a stronger boundary for untrusted code. Built with Celesto: OpenMuse, their open-source computer coworker, built end to end on Celesto, chatting with a user while operating a website in an isolated Celesto desktop. It links a quickstart, Python API, agents and automation, runtimes, examples, docs at docs.celesto.ai, and a Discord.", "arch": "Celesto: secure, persistent computers for AI agents, an agent-computer platform with a runnable pull-request review lab where Jev judges whether findings are introduced by the change, giving an AI agent its own computer for running code, browsing the web, and using desktop apps, runnable on your machine during development or in Celesto Cloud for remote and production work, where each sandbox is a lightweight virtual machine that starts in about 500 ms and keeps files and state between sessions, Apache 2.0, Python 3.11+.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "claude-jev", "name": "claude-jev", "author": "jly-engineer", "date": "2026-09-22", "category": "app", "url": "https://github.com/jly-engineer/claude-jev", "base_model": "", "params": "", "metric": "Jev tier decision in ~200 ms for a fraction of a cent", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "local proxy in front of the real Claude Code CLI: every prompt is routed by Jev to the cheapest capable tier, haiku for typos and lookups, sonnet for implementations, opus for unknown-cause debugging; the proxy rewrites the model before the request leaves the machine", "approach": "", "training": "", "data": "", "result": "savings dashboard vs running everything on Opus: today, 7-day and 30-day; usage caps with reset times; per-tier requests, tokens and cost; works on a Pro/Max subscription with no API key", "notes": "error paths keep the current model: Jev unreachable or slow means no change; confidence below 0.6 never downgrades and upgrades are capped at sonne.", "domain": "coding", "score": "", "latency": "200 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cmd-mod-jev-nudge", "name": "cmd-mod-jev-nudge", "author": "CommandCodeAI", "date": "2026-09-22", "category": "app", "url": "https://github.com/CommandCodeAI/cmd-mod-jev-nudge", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 14, "likes": 0, "detail": "Agents stop too early: they do one step, say next I will do X, and hand the prompt back. Install with cmd login, skipping if signed in, and cmd mods add cmd-mod-jev-nudge, adding -g for all projects; that is it, and if Jev fails, the run stops as normal. Demo: say hi in five languages, one hi per reply, then stop and say how many done, after the fifth say all done, and the agent replies Hola 1 of 5 done, with a Jev nudge reporting unfinished work found at 78% and continuing, then Bonjour 2 of 5 at 74%, Ciao 3 of 5, Hallo 4 of 5, and Konnichiwa all done, where Jev says 5% and no nudge, and the run ends, so you type once and get five replies, and adding then wait for my go-ahead to the prompt makes it stop after one, because the agent is now waiting on you. How it works: when the agent stops, it POSTs to the Command Code provider systemone endpoint with the typesafe/jev model, asking three questions, whether a nudge would help right now, whether the agent needs you for permission, info, or choice, and whether the last nudge got anywhere, the third only from the second nudge on; it stops when waiting is at least 0.5, or progress is below 0.5, or nudge is below 0.5, or on error, otherwise it nudges and keeps going, and Command Code allows at most 8 nudges per user turn. Flags are passed with --mod-option name=value.", "arch": "cmd-mod-jev-nudge: a mod for Command Code that nudges the coding agent to keep going when it stops with work left, judged by Jev, asking Jev at every stop whether a nudge would help, so if yes, the agent keeps going, and it will not nudge when the agent is waiting on you, or when the last nudge got nothing done, using your Command Code key, with the Jev call going through the Command Code provider endpoint.", "approach": "", "training": "", "data": "", "result": "", "notes": "Jev as an agent 'task-complete' gate.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jtydhr88-comfyui-laya", "name": "ComfyUI-Laya", "author": "jtydhr88", "date": "2026-09-22", "category": "app", "url": "https://github.com/jtydhr88/ComfyUI-Laya", "base_model": "Laya", "params": "421M", "metric": "", "license": "", "paper": "", "stars": 10, "likes": "", "detail": "arch: 421M System-1 decision model, ~15 ms GPU / ~75 ms CPU; approach: workflow router, expensive branch only runs on Laya's say-so", "arch": "ComfyUI node that routes image-generation workflows with Laya so the expensive branch is the only one that runs; Laya never generates, it only decides.", "approach": "router", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "15 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kunchenguid-compact-adviser", "name": "compact-adviser", "author": "kunchenguid", "date": "2026-09-22", "category": "app", "url": "https://github.com/kunchenguid/compact-adviser", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 184, "likes": "", "detail": "Judgment is two one-sentence Jev questions in one request, whether the unit is finished, and whether this is hands-on work or coordination, composed in code into one score; the hint floor is 0.90 while the context is mostly empty, through about 10%, and relaxes toward 0.50 by about 90% full, since a wrong hint costs most when there is still room; full means the point where the host compacts, on Claude Code its auto-compact threshold when enabled, elsewhere the model's window; and automatic mode is the same gate, plus a first-use confirmation. Quick start: Node 22 or newer, 22.18 or newer for Codex and Grok, and a Discord community link is provided.", "arch": "compact-adviser: an agent plugin that answers a single question, should I compact now, using Jev to instantly judge whether the current coding session is likely at a boundary that is safe to compact, giving a hint to run /compact, or, on Pi and Claude Code, with opt-in, running it for you at the right time automatically, while Codex CLI and Grok are hint-only, since nothing outside their sessions can trigger /compact, MIT, on macOS and Linux.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "southleft-component-charades", "name": "Component Charades", "author": "southleft", "date": "2026-09-22", "category": "app", "url": "https://github.com/southleft/component-charades", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "arch: no toast, no snackbar, no notification", "arch": "Component Charades: Taboo for design systems — you describe a UI component without its forbidden words and Jev has to guess it; the other player is a model that can't talk.", "approach": "", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "meragpt-decider-1", "name": "Decider 1", "author": "meraGPT", "date": "2026-09-22", "category": "app", "url": "https://meragpt.com/models/state-decider-1", "base_model": "Proprietary System One model, architecture undisclosed", "params": "undisclosed", "metric": "Accuracy 0.768, Brier 0.052, p50 526 ms", "license": "", "paper": "", "stars": "", "likes": "", "detail": "approach: Closed decisions API", "arch": "A closed decisions model served from meragpt.com: 4,096-token context, up to 20 typed questions per state of the yes/no, choice with up to ten labels, and rubric-score kinds, all answered as probability distributions in one pass; nothing is generated, the same input gives the same output, and it speaks the System One schema at the systemone endpoint so the typesafe-sdk works against it by changing the base URL; prepaid credits, no subscription", "approach": "undisclosed", "training": "Undisclosed", "data": "Built for routing, triage, guardrails inside agents, and rubric scoring", "result": "State of the art on LocalLLaMA/typed-decisions as of 2026-09-22, zero-shot: accuracy 0.768, Brier 0.052, KL from gold 0.096, p50 526 ms end to end, 0.03 dollars per million input tokens; it leads every question type, yes/no 0.840 against Jev's 0.775, choice 0.733 against 0.720, score 0.739 against 0.696; soft accuracy 0.608 and score MAE 0.219 also lead", "notes": "It had never seen the four benchmark workflows or any of the twenty question schemas; ECE 0.180 is mid-pack in the card's table; choice is capped at ten options, far below Jev's 255, and large label sets are meant to be split into groups with a none-of-these option; sibling models Restyler 1 and Query Fanout 1 share the same API", "domain": "general", "score": "Brier 0.052", "latency": "p50 526 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "callebtc-decision-tools", "name": "Decision Tools", "author": "callebtc", "date": "2026-09-22", "category": "app", "url": "https://github.com/callebtc/decision-tools", "base_model": "TypeSafe Jev", "params": "", "metric": "99.87% format stability", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "Prerequisites: uv, which picks a suitable Python below 3.14 itself; Google Chrome, since the agents drive an installed Chrome via Playwright's channel chrome, with no playwright install needed; and network on first run, since model checkpoints download to the Hugging Face cache outside the repo, 421 MB for Laya and about 9 GB for SemIf. Setup from a fresh clone: a plain git clone leaves the vendor directories empty and uv sync fails until the submodules are checked out, so run git submodule update --init, then uv sync once per sub directory to build the venv from uv.lock, and git clone with --recurse-submodules does both clone steps in one. Usage: uv run tools/is-safe on a file, the scanner in both sub dirs; uv run tools/laya-bench from laya-mlx; uv run tools/semif-bench --iterations 20 from Semif; uv run tools/laya-agent --demo --headed from laya-mlx, where --headed watches Chrome; and uv run tools/semif-agent --demo from Semif, with --url and --goal for any page. The tools also run directly with a shebang, and details on exit codes, flags, and field notes are in each sub dir's tools/README.md. Measured: the Go CLI takes 1.13 s per decision at 0.0004 dollars, and the Python MCP server 2.5 s.", "arch": "decision-tools: a Go CLI and a Python MCP server for Jev decisions, plus a collection of tools for building on laya-mlx and SemIf, with both source projects vendored as git submodules, pinned to the revisions the tools were tuned against and installed as editable sources by each sub directory's pyproject.toml.", "approach": "sdk", "training": "", "data": "", "result": "Go CLI: 1.13 s per decision, $0.0004; Python MCP: 2.5 s, $0.0007 per 100-call session; 99.87% format stability; 0 hallucinations across 20 calls", "notes": "Python 3.11+.", "domain": "general", "score": "", "latency": "1.13 s per decision in Go CLI", "benchmark": "", "cost": "$0.0004 per decision in Go CLI", "speedup": ""}
{"id": "dejevu", "name": "dejevu", "author": "idovmamane", "date": "2026-09-22", "category": "app", "url": "https://github.com/idovmamane/dejevu", "base_model": "LLaMA 3.3 70B", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 12, "likes": 0, "detail": "Google Flights, real time, 6.07 s from the first decision to verified results, llama-3.3-70b on Groq. Wikipedia, main page to the exact article in 1.5 s, two model calls, one typed action. Side by side with the jev-ultrafast demo, clocks aligned, real speed, in a race video, where their half is the jev-ultrafast demo video, copyright Browser Use, MIT license, cropped with a counter overlay and otherwise unmodified. Try it in 60 seconds: git clone and cd, uv sync, copy .env.example to .env and put your OpenRouter API key in it, uv run dejevu --doctor, which checks Chrome, the key, and one model call, and uv run dejevu --task wikipedia. It needs Python 3.12 or newer, uv, Google Chrome, and one API key; OpenRouter is the default, and any OpenAI compatible endpoint works.", "arch": "dejevu, in French: Jev? Deja vu. Browser agents that run on instinct, no Jev needed, with one look at the page, one call to any open model, and one action, the counter-positioning open-model alternative to Jev-driven browser agents, where Jev is TypeSafe's System One model, answering with a choice instead of text in about 200 ms, and Browser Use built jev-ultrafast on it, booking a Google Flights search in 7.1 s, while dejevu does the same search in 5.6 s with plain llama-3.3-70b, 10 model calls instead of 17, and 5.6× fewer tokens, with no decision API, no second model for typing, no browser daemon, and one API key, MIT, Python 3.12+.", "approach": "", "training": "", "data": "", "result": "", "notes": "Counter-positioning: the open-model alternative to Jev-driven browser agents.", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "misbahsy-doc-router", "name": "doc-router", "author": "misbahsy", "date": "2026-09-22", "category": "app", "url": "https://github.com/misbahsy/doc-router", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 28, "likes": "", "detail": "Most PDFs are not all-scan or all-text: a contract has two signature pages scanned in the middle, a report's appendix was photocopied, and an invoice batch has every third document off a flatbed, so handing the whole file to a hosted OCR model pays for every page, including the ones readable for free, instantly, with no network call. Measured 2026-09-17 on 19 documents and 155 pages, with mistral-ocr-latest through a live LiteLLM gateway and 3 runs per document: OCR every page bills 155 pages, 19 API requests, 35,578 ms, and 0.3100 dollars at 2 dollars per 1k pages, while routed bills 87 pages, 13 requests, 20,666 ms, 1.72× faster, and 0.1783 dollars, 1.74× cheaper; pages that needed OCR and did not get it: 0 for the full-OCR baseline, 9 for routed, versus 28 for a rules-based judge. The judge costs 2.5% of the OCR bill it authorises, and a cheaper bill is not automatically a better result, since the rules-based judge is cheaper still and misses three times as many pages, with the method and per-document numbers in docs/GUIDE.md. Under the hood: Jev, TypeSafe System One, is the page judge, which given the evidence for a page answers one question, does this page need OCR, and catches what rules cannot, a scan carrying a bad pre-existing OCR layer, a page whose only text is a watermark, and a broken ToUnicode map, and it is swappable, one trait, with a zero-dependency local heuristic shipping as the default; and LiteLLM is the gateway, one endpoint.", "arch": "doc-router: a document OCR router where Jev judges page by page whether the existing text layer is usable or OCR is required, so you do not pay to OCR a page that already has text on it, where it looks at a PDF page by page, decides which pages have a usable text layer and which genuinely need OCR, then text pages are extracted locally in-process, only the rest go to your OCR provider, and the two halves merge back into one page-ordered result, in Rust, with the core library never touching a network.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "colliber-duckdb-jev", "name": "duckdb-jev", "author": "colliber", "date": "2026-09-22", "category": "app", "url": "https://github.com/colliber/duckdb-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 26, "likes": "", "detail": "", "arch": "DuckDB extension: typed Jev answers as real SQL types DuckDB extension that exposes Jev judgments as SQL values with return types derived from the declared criteria.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "henryklunaris-hey-jev", "name": "Hey Jev, a voice assistant for the Mac", "author": "henryklunaris", "date": "2026-09-22", "category": "app", "url": "https://github.com/henryklunaris/hey-jev", "base_model": "TypeSafe Jev plus Fish Audio S2.1 Pro and local faster-whisper", "params": "", "metric": "", "license": "", "paper": "", "stars": 27, "likes": "", "detail": "", "arch": "say Hey Jev or hold right Option and speak a thing, it does it and answers back: Jev makes every decision in one call at $0.00004 per request, Fish Audio S2.1 Pro speaks every reply with emotion tags like chuckling and sighing, local faster-whisper turns your voice into text, and an LLM only wakes up when Jev says you asked a question, not a command", "approach": "", "training": "", "data": "", "result": "open or quit apps, Mac volume up down mute set, Spotify volume, play pause next previous, dark mode, lock or sleep the Mac; two things in one sentence work, pause Spotify and open Slack; timers and reminders count down live in the window and she tells you when they are done; anything that is not a command goes to Claude Haiku via OpenRouter and gets spoken back", "notes": "Mac only, works on macOS Sequoia and Tahoe, controls the Mac through AppleScript and the Keychain", "domain": "voice", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "higgsfield-jev-voice", "name": "Higgsfield Jev ad workflow, voice control", "author": "Higgsfield AI", "date": "2026-09-22", "category": "app", "url": "https://x.com/higgsfield_ai/status/2102369525048168862", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Higgsfield AI posted a 37-second demo on 2026-09-22, 19K views by 09-24: you dictate, and Jev handles the decision points, which product, which creative path, while DeepSeek and Higgsfield models generate the images and video ads. The post frames it as end-to-end voice-driven ad creation, actually insane.", "arch": "Higgsfield, the AI-native creative suite, added voice control to its Jev-powered ad workflow: product selection, image generation, and video ads with DeepSeek plus Higgsfield models, all by voice", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "voice", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kitfunso-hippo-memory", "name": "hippo-memory", "author": "kitfunso", "date": "2026-09-22", "category": "app", "url": "https://github.com/kitfunso/hippo-memory", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 757, "likes": "", "detail": "", "arch": "Agent memory library with an optional Jev reranker that judges which retrieved memories are relevant. Agent memory: a biologically-inspired memory store whose optional Jev reranker lifts recall R@1 from 0.41–0.62 on a private 300-query developer store.", "approach": "sdk", "training": "", "data": "", "result": "- R@5 = 74.0% on LongMemEval. 500-question industry retrieval benchmark, BM25 only, no embeddings.", "notes": "Curated in 2 awesome-jev lists", "domain": "coding", "score": "74.0%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mindstudio-jev-router", "name": "How to build an AI model router with Jev and Open Jev", "author": "MindStudio", "date": "2026-09-22", "category": "app", "url": "https://www.mindstudio.ai/blog/how-to-build-model-router-with-jev", "base_model": "Jev + Open Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "MindStudio's guide, published 2026-09-22, builds a Jev-style router: a local service that inspects every prompt before it reaches a language model, classifies it along task type with a choice question, difficulty with a score question on a 1-to-10 scale, and privacy risk with a null gate, then routes each prompt to the model that fits. One Jev call asks all three questions in parallel against the same input, so the multi-dimension classification costs roughly the latency of a single question, and the confidence score lets the router fall back safely when Jev is unsure. The demoed stack pairs a local MiniCPM 5 2B, a cloud Deepseek V4.1 Flash, and a local Qwen Image 2.1, all picked by Jev's typed answers, with a browser UI, FastAPI backend, routing-rules layer, and SQLite logging. The honest caveat: sending prompts to a hosted classifier for a privacy check has an inherent leak, which is why the guide swaps in a self-hosted Open Jev clone to keep the whole pipeline on your machine.", "arch": "Working model-router guide: Next.js frontend, local FastAPI backend, and model endpoints in the standard OpenAI-compatible API format, so the same router logic talks to Jev, a local Open-Jev server, and a cloud provider without custom glue code", "approach": "", "training": "", "data": "", "result": "Typed probabilistic answers let the router branch on confidence, removing the parsing and reliability problems from routing", "notes": "Published 2026-09-22.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "intent-router", "name": "Intent-Router", "author": "angel291592", "date": "2026-09-22", "category": "app", "url": "https://github.com/angel291592/Intent-Router", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 94, "likes": 0, "detail": "What it buys: prompt equity, where you get the interaction of a senior engineer without having to learn prompt engineering, and the gap between the top 1% of prompt writers and everyone else stops deciding what you get back, so say it the way you would say it to a capable teammate, add caching to the user API, and the skill writes the briefing a senior engineer would have written first, and you stop coaxing the model and start putting it to work. It asks one question, and the answer lands in the delivered code: before anything reaches you it reads what your repo, ticket system, or docs already answer, so the forty-six-question interview becomes the one question that genuinely needs your judgment, and in the delivery comparison, every run with the skill asks exactly that one question, and 3 of 3 delivered caches state their failure policy, while none of the 5 bare deliveries do. The work carries further: every run ends in a machine-readable IntentSpec, saved to .intent/intent.intent.yaml before the reply that carries it, whose probed fields carry evidence pointers, so the output rests on what your project actually says rather than an unstated guess, and the next agent, session, or teammate starts from that contract file instead of zero.", "arch": "Intent-Router: an intent compiler for AI agents, in English and Chinese, turning a vague request into a typed IntentSpec, the clear, machine-readable input that typed decision models such as Jev and Laya, and every router downstream, assume already exists, where it looks up what it can, asks only what it can't, and refuses to emit while the intent is still underspecified, a typed contract layer above raw agent requests, MIT.", "approach": "", "training": "", "data": "", "result": "", "notes": "Typed contract layer above raw agent requests.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "paramjeetn-jev-cookbook", "name": "Jev Cookbook, paramjeetn", "author": "paramjeetn", "date": "2026-09-22", "category": "app", "url": "https://github.com/paramjeetn/jev-cookbook", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 5, "likes": "", "detail": "What is Jev: a System One AI model built by TypeSafe AI, a calibrated zero-shot semantic classifier, not a text generator; it takes a context state and typed questions, returning calibrated probability distributions instead of tokens; because it does not generate text, Jev processes complex classification tasks in about 100 ms, operates at 400× lower cost than traditional LLMs, and provides mathematically rigorous calibrated confidence scores for every decision. Architecture, where Jev fits: stop waiting seconds for an LLM to output a JSON boolean, and Jev acts as the ultra-fast instinct layer in your application, where incoming data goes to Jev in 100 ms; on low confidence, a slow LLM fallback such as GPT-4 or Claude; on high confidence, a structured JSON decision; and then application logic acts on it. The cookbook organizes a quick start with no SDK required, the 10 examples, THEORY.md, USE_CASES.md with 120+ use cases, and architectural patterns.", "arch": "Jev Cookbook: production-ready examples, patterns, and theory for building with Jev, the 100 ms System One AI, with 120+ use cases and 10 runnable examples across Jev-style decision workflows, MIT, PRs welcome, powered by TypeSafe AI.", "approach": "", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-harness", "name": "Jev Harness TypeSafe", "author": "TypeSafeAI", "date": "2026-09-22", "category": "app", "url": "https://github.com/TypeSafeAI/jev-harness", "base_model": "LLM + TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 15, "likes": 0, "detail": "Repository provenance: this is the TypeSafeAI community repository, and the community organization is independent of the official TypeSafe AI team, so this is not an official SDK or an endorsed production agent runtime, with official product resources at typesafe.ai and docs.typesafe.ai, and the badge points to this repository, so inspect the exact PR head for CI evidence. Quick start: git clone, cd jev-harness, pnpm install --frozen-lockfile, pnpm typecheck, pnpm test, pnpm check:secrets, and pnpm bench:review, with no API key needed for these offline checks. pnpm bench:review runs the 25 synthetic proposal-review fixtures through validation alone, base, and through validation plus the labeled mock transport, plus Jev, then prints a per-category table; its offline totals, base catching 7 of 25 bad, plus Jev catching 25 of 25 bad, and 3 of 25 good held on the three ambiguous fixtures, are scripted mock values, not measurements of Jev. Use the versions pinned in package.json and pnpm-lock.yaml, and a configured workflow is not proof that checks ran successfully, so inspect checks for the exact PR head before merging, and signed commits are part of the existing security expectations.", "arch": "jev-harness: a custom coding harness where the LLM proposes, Jev answers narrow questions, and code decides, with every step leaving a record, a research-stage proposal-review contract where an LLM proposes one action, Jev answers four narrow questions, and code produces evidence for a host to consider, where nothing applies a patch, executes proposed code, or grants permission, MIT, Node 22+, pnpm, source-only, not published.", "approach": "", "training": "", "data": "", "result": "", "notes": "NOTE: GitHub org 'TypeSafeAI' is NOT the official TypeSafe AI org; treat as a community project.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jessie-qingyu-jev-in-the-wild", "name": "Jev in the Wild", "author": "Jessie-QingYu", "date": "2026-09-22", "category": "app", "url": "https://github.com/Jessie-QingYu/jev-in-the-wild", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Jev in the Wild: real-world use cases, open-source projects, benchmarks, and criticism of Jev — what people actually build with it and where it fails. 254 entries, 142 of which are Reddit threads, Hacker News posts, YouTube evaluations, and blog write-ups rather than GitHub repos.", "approach": "", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "forvela-jev-agent-browser", "name": "jev-agent-browser", "author": "forvela", "date": "2026-09-22", "category": "app", "url": "https://github.com/forvela/jev-agent-browser", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "", "arch": "Browser-agent integration where Jev selects typed actions for research and classification workflows. Browser agents: a parent agent delegates bounded tasks to a Jev loop that selects typed browser actions, validates them through agent-browser, and escalates ambiguity or stuck states back to the parent.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jsk4581-jev-blindspot", "name": "jev-blindspot, prompt blind-spot side panel", "author": "jsk4581", "date": "2026-09-22", "category": "app", "url": "https://github.com/jsk4581/jev-blindspot", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 20, "likes": "", "detail": "", "arch": "a side panel for Claude Code and Codex CLI: each time you submit a prompt, the panel shows the prompt's blind spots, what the request would have needed to consider and shows no sign of considering; it runs next to the session in a browser tab, does not block the prompt, does not edit it, and does not add anything to the agent's context", "approach": "", "training": "", "data": "", "result": "", "notes": "install-hook finds and hooks every agent; /blindspot in Claude Code, /prompts:blindspot in Codex; published on npm as jev-blindspot, Node 20 or newer, MIT; good results start with good requests", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nexibeo-jev-cookbook", "name": "jev-cookbook, nexibeo", "author": "nexibeo", "date": "2026-09-22", "category": "app", "url": "https://github.com/nexibeo/jev-cookbook", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 27, "likes": "", "detail": "", "arch": "Practical, tested recipes for TypeSafe's Jev, the fast decision model on OpenRouter. Fifteen real-world jobs, each with a runnable script, a small labelled dataset and measured results: support triage, database indexing, a file organizer, tagging, category trees, duplicate detection, PII scanning, bank transactions, invoice extraction, search, log triage, moderation, lead", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "qew7-jev-feels", "name": "jev-feels", "author": "qew7", "date": "2026-09-22", "category": "app", "url": "https://github.com/qew7/jev-feels", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "Rails: add the gem to the Gemfile, include Jev::Model in a model, declare feels on the body for urgent and a feels on the subject for not_important with the instruction that the subject of this email is not important, declare decide on the body for support_team, and validates_feeling on the body for urgent with a 0.9 threshold, so email.feels?(:urgent) returns true and email.decide(:support_team) returns :billing. Configure in config/initializers/jev.rb with the API key from the environment, and define the vocabulary with Jev.define :urgent as a question requiring immediate attention or action, and Jev.define :support_team asking which team should handle this, with choices for billing covering payments and refunds, technical covering bugs and outages, and other for none of the above. The class is the scope, and the first argument is the field; validates_feeling accepts allow_nil, allow_blank, if, unless, on, message, and at_least, and if those skip the check, there is no HTTP call, with a nil from at_least a validation failure. PORO: Jev::Model reads any method that returns a string, bind-only uses the global Jev.define, and passing a string defines on the class. String sugar: require of feels does not change String, so opt in with the Jev::Feels refinement for body.feels?(:urgent), body.decide(:support_team), and body.score(:severity), or take the global patch by requiring the string file. Definitions: Jev.define is the vocabulary of the app, and a symbol without extras is a yes/no check.", "arch": "jev-feels: semantic decisions as ordinary Ruby, where email.feels?(:urgent) is a named question on a field, not a prompt in a service, with feels?, decide, and score, plus Rails validations and pattern matching, powered by Jev.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-guardrails", "name": "jev-guardrails", "author": "deepansh-saxena", "date": "2026-09-22", "category": "app", "url": "https://github.com/deepansh-saxena/jev-guardrails", "base_model": "TypeSafe Jev + LLM-as-judge", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "base: same agent, two guardrail backends; arch: a · b · RLCD; result: 48/51, 1 false refusal, 2 missed · 46/51, 0 false refusals, 5 missed · panel: 194 ms vs 5,825 ms; name: LLM-as-judge vs Jev, head to head", "arch": "Mock carrier-support agent whose guardrail layer is built twice behind one interface: LLM-as-judge — 75 prompt clauses, 4–8 soft rules resampled per call, chat model returns JSON; Jev — all 25 rules in one request, rules nowhere in the prompt, calibrated probabilities; same 25 rules, same thresholds, same agent", "approach": "", "training": "", "data": "51 labelled cases; LangChain 1.2 / LangGraph 1.1; 63 passing tests", "result": "Accuracy comes out even — Jev 94% vs LLM 90% — and at the same quality Jev is 177 ms median latency vs 1,193 ms, $125 vs $5,894 per million reviews; system prompt shrinks 8,694 → 3,004 chars", "notes": "'The same agent, the same 25 rules, the same thresholds. The only variable is what answers the question.' Created 2026-09-22.", "domain": "security", "score": "Accuracy 94%", "latency": "177 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mizzlelover-jev-hub", "name": "jev-hub", "author": "mizzlelover", "date": "2026-09-22", "category": "app", "url": "https://github.com/mizzlelover/jev-hub", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 24, "likes": "", "detail": "Data overview: 982 total posts, 382 demo videos, 147 long-form, 453 discussions, 818 participating authors, a language distribution of 673 English, 192 Chinese, 106 Japanese, 6 Korean, and 5 Arabic, spanning 2026-09-15 18:17–2026-09-21 03:59 UTC, last updated 2026-09-21 05:22 UTC. The beginner entry point is the 读不懂也要读懂 series 01, GPT it ba, zao le ge hui bu liao tian de AI, a Jev explainer written for people who do not understand at all, starting from what use an AI that cannot talk has, and explaining why Jev is fast, why it is cheap, and what work it can do, by dboy_yi2025 on 2026-09-18. Official entry points: the TypeSafe website, OpenRouter, the Vercel AI Gateway, and the Venice API. Editor picks by engagement include CompleteSkeptic on why superhuman chat models have not led to AGI, with two years in stealth building something new, on 2026-09-15; tamarajtran on instant compaction in 2026, asking why compaction is still a summarization prompt when Jev can make it instant; CompleteSkeptic noting the gains are not free, since Jev cannot generate text, and that comparing Jev with LLMs side by side makes the trade-off clear; and MatijaSosic's 45-second TLDR, finding the core idea beautifully simple. The list notes that Jev was adopted faster than any other model in the AI Gateway.", "arch": "JEV HUB, Jev 聚合站, in Chinese: an aggregator of long-form posts and demo videos about TypeSafe's System One Jev model on X, keeping the original post link and author credit for every entry, with no reposting or re-hosting of any content, by 谁是专家, who also publishes on Xiaohongshu, WeChat, and X, with a structured data file at data/posts.json and a browsable aggregation page.", "approach": "sdk", "training": "", "data": "", "result": "- Jev was adopted faster than any other model in AI Gateway history. In the first day, @typesafeai reached ~13% of teams, 2× the GPT-5.6 family and 6× F… — @vercel · 2026-09-18 · 成本速度 · 评测对比", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "13%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "everyinfra-jev-radar", "name": "jev-radar", "author": "everyinfra", "date": "2026-09-22", "category": "app", "url": "https://github.com/everyinfra/jev-radar", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 28, "likes": "", "detail": "What is Jev: TypeSafe AI's first System One model, released on September 15, 2026; it never writes a single sentence; you send it some unstructured state plus a set of typed questions, and it returns decisions your code can branch on directly, where Noul answers is this true with a calibrated 0–1 probability, Choice answers which option fits with a full distribution plus confidence, and Score answers how good on your rubric with a probability-weighted rating. The pitch that took over developer Twitter: 70–500 ms end-to-end latency, 0.042 dollars per million input tokens, output free, roughly 20–200× faster and 40–400× cheaper than routing the same judgments through a frontier LLM. Four days after launch, the Jev ecosystem crossed 1,000 repositories within five days, with 21,600+ stars at day 4. Jev Radar is an independent attempt to answer one question with evidence: what is the world actually building with this thing? Every case was verified against a primary source. Notable verticals arriving include tax forms, where it reports 100% strict accuracy on 261 IRS forms, and legal ruling prediction.", "arch": "Jev Radar: the world's most comprehensive independent field report and live monitor of the Jev ecosystem, a comprehensive tracker with 220+ documented cases and a registry of 430 confidence-graded entries, scanning every 3 hours, with evidence graded A, B, or C, an API access guide, and bilingual English and Chinese coverage, claiming that if it was built with Jev, it is in this radar.", "approach": "sdk", "training": "", "data": "", "result": "8. Verticals arriving — tax forms, legal ruling prediction, PubMed screening, GTM workflows", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "arc 100%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "uehaj-jev-semgrep", "name": "jev-semgrep", "author": "uehaj", "date": "2026-09-22", "category": "app", "url": "https://github.com/uehaj/jev-semgrep", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 144, "likes": "", "detail": "", "arch": "grep by meaning, across languages. TypeSafe Jev scores every line against a meaning; combine meanings with AND/OR/NOT. 意味で探す grep。日本語で英語を、英語で日本語を検索できる Semantic search: greps by meaning across languages, having Jev score every line against a meaning and letting meanings combine with AND, backed by a 13-file test suite.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-seo", "name": "jev-seo", "author": "AgriciDaniel", "date": "2026-09-22", "category": "app", "url": "https://github.com/AgriciDaniel/jev-seo", "base_model": "TypeSafe Jev", "params": "", "metric": "52 Search Central rules, Core Web Vitals, about one cent of Jev per site", "license": "Open", "paper": "", "stars": 131, "likes": 0, "detail": "base: hosted; name: live SEO audit by Jev", "arch": "Live SEO audit from one homepage URL: crawls the site, checks it against 52 rules tied to Google Search Central, measures Core Web Vitals, and asks Jev typed questions about every page, then code scores and ranks every fix", "approach": "", "training": "", "data": "", "result": "Produces a designed PDF, an Excel action tracker and a Markdown report all built from the same data; runs as a Claude Code skill or from the command line; standard mode needs no SEO data subscription", "notes": "72 stars; marketing and SEO framed as System One decisions", "domain": "marketing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kbhuw-jev-sift", "name": "jev-sift", "author": "kbhuw", "date": "2026-09-22", "category": "app", "url": "https://github.com/kbhuw/jev-sift", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 47, "likes": "", "detail": "", "arch": "Agent plugin and MCP tool where Jev classifies batches of text so agents can read only the relevant items. No repository-level license file was found during review.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "klauswg-jev-suite", "name": "jev-suite + jev-guard", "author": "klauswg", "date": "2026-09-22", "category": "app", "url": "https://github.com/klauswg/jev-suite", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 33, "likes": 0, "detail": "base: hosted; arch: 'Jev answers structured questions · deterministic code keeps the final say'", "arch": "Four decision-quality tools on Jev + jev-guard: real-time risk triage gateway for exchange deposits/withdrawals — Jev handles triage only, adjudication stays separate", "approach": "", "training": "", "data": "", "result": "", "notes": "Finance-grade guardrail pattern: decision model triages, deterministic code adjudicates. The jev-guard component has its own repo: github.com/klauswg/jev-guard, 09-22 — Jev triages an exchange transfer from its 24-hour behavioral features into auto-pass, human review or freeze, with deterministic code as the final judge; found in the GitHub new-repo sweep 2026-09-24. Repo: github.com/klauswg/jev-guard, 35 stars, real-time risk triage gateway for exchange deposits and withdrawals.", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-tree", "name": "jev-tree", "author": "Chuf-H", "date": "2026-09-22", "category": "app", "url": "https://github.com/Chuf-H/jev-tree", "base_model": "TypeSafe Jev", "params": "", "metric": "Path probabilities and downstream outcome mass over the decision tree", "license": "Open", "paper": "", "stars": 42, "likes": 0, "detail": "base: hosted; name: probability tree + graph runtime", "arch": "Typed Python planner runtime that turns fast local Jev judgments into an executable multi-step probability tree: your adapter defines states, legal actions, transitions and terminal verification, and JevTree asks Jev for local action distributions, composes them into path probabilities, merges equivalent states, tracks unresolved mass, and selects actions by downstream outcome mass instead of one-step greediness", "approach": "", "training": "", "data": "", "result": "Positioned explicitly as a planner runtime, not generated chain-of-thought and not an LLM wrapper; the motivation is that in long-horizon tasks the highest-probability action at the current step may not lead to the best final outcome", "notes": "35 stars; Chinese-first docs; Jev as the local judgment source", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevbot", "name": "jevbot", "author": "lyramakesmusic", "date": "2026-09-22", "category": "app", "url": "https://github.com/lyramakesmusic/jevbot", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 18, "likes": 0, "detail": "How it works: tournament sampling, where the 20K vocab is shuffled into 255-word buckets, all scored in parallel; a runoff, where the top 2 from each bucket compete in a final round; a completeness judge, where a separate noul question asks is the reply complete, and jev stops when it thinks it is done; a penalty system, where content words are penalized 2.5× per reuse and stopwords 1.6×, preventing loops like is are I is are; and user-only history, where the last 3 user messages are included as context, and jev's own broken output is excluded, since it poisons follow-ups. Output examples: I love jazz because its improvised and freedom; Rock.? Yeah; No because overkill. Overkill!.!.!; I depends on on situation of circumstances; Yuck no ugh spit! Gag gagging ing; and Band is from california in san los angeles. Las angels. Setup: pip install the requirements, create a .env with the Discord token and the OpenRouter key, enable the Message Content Intent in the Discord developer portal, and run python jev_bot.py. Usage: mention jev or reply to jev's messages, and it replies only, not to messages that do not involve it. Cost: about 0.01–0.05 dollars per reply via OpenRouter, with tournament sampling doing about 6 API calls per word. The vocab is a 20K word list from bewinxed/jevgpt with slurs removed, and words can be added or removed freely, since the vocab is the content filter.", "arch": "jevbot: a Discord bot that makes TypeSafe's Jev talk, a decision model that cannot generate text, loomed word by word into broken sentences, a non-standard use where Jev, a model that does not generate text, is wrapped in chat form, running Jev through OpenRouter, with Jev as a non-autoregressive decision model that answers questions with calibrated probabilities, not text, and the bot gives it a 20K word vocabulary and asks next word repeatedly via tournament sampling until it forms a reply.", "approach": "", "training": "", "data": "", "result": "", "notes": "A non-standard use: Jev, a model that doesn't generate text, made to converse.", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "backant-jevelry", "name": "Jevelry", "author": "backant-io", "date": "2026-09-22", "category": "app", "url": "https://github.com/backant-io/jevelry", "base_model": "Jev 1.13 + Laya MLX 201M", "params": "", "metric": "28.37 s full cycle, down 71.95%", "license": "MIT", "paper": "", "stars": 6, "likes": "", "detail": "Your code and your coding agent make the same small calls every day: is this ticket urgent, which team should get it, is this bug a duplicate, did this test fail because of the code or because of the machine it ran on, and today each of those is usually a prompt that returns text you parse, and you do not learn how sure the model was. With jevelry you ask each of those questions to Jev with one command, and Jev answers with how sure it is. The TUI shows the start screen, a live ask of ticket-triage, the dashboard, and review. It is young and changes fast: commands, the jevel format, and the answer document may change between minor versions until 1.0, so pin the version you depend on. It requires a TypeSafe API key. Measured: a full cycle of 28.37 s versus 102.07 s, down 71.95%.", "arch": "jevelry: use Jev everywhere to make and track decisions, where your code and your coding agent ask Jev, the decision model from TypeSafe, a small typed question and get back a decision it can act on, and it keeps every decision so you can see how often Jev was right, where a question lives in a small file called a jevel, and 17 ready-made jevels ship with the package, an npm package for Node 22 or newer, MIT, in early development.", "approach": "sdk", "training": "", "data": "", "result": "Full cycle 28.37 s vs 102.07 s, down 71.95%; single decision 13.09 s vs 35.79 s, down 63.43%; $0.0035 vs $0.0222, down 84.24%", "notes": "Requires a TypeSafe API key.", "domain": "health", "score": "", "latency": "13.09 s per decision", "benchmark": "", "cost": "$0.0035 per decision", "speedup": ""}
{"id": "ktaletsk-jevframe", "name": "jevframe", "author": "ktaletsk", "date": "2026-09-22", "category": "app", "url": "https://github.com/ktaletsk/jevframe", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 18, "likes": "", "detail": "", "arch": "Semantic AI for pandas and Polars: classify text, analyze sentiment, and score DataFrame rows with natural-language questions and full probabilities using TypeSafe Jev.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevgate", "name": "jevgate", "author": "craxrev", "date": "2026-09-22", "category": "app", "url": "https://github.com/craxrev/jevgate", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Claude Code plugin: before Claude runs a shell command or writes a file, Jev reports what it would do, delete, ship, change the system, upload data or print a secret; jevgate turns those facts into run, ask or refuse", "approach": "", "training": "", "data": "", "result": "harmless commands run; in auto mode the slow built-in classifier is skipped; read-only commands never wait; built for bypassPermissions mode where Claude Code itself checks nothing", "notes": "Claude Code 2.1.274+, Node 22.18+, no build step, no runtime dependencies", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevinvent-wechat", "name": "JevIntent", "author": "Nisaka520", "date": "2026-09-22", "category": "app", "url": "https://github.com/Nisaka520/JevIntent", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 58, "likes": 0, "detail": "Example output: intent, greeting or small talk, 85%; emotion, calm 56%, polite 42%, urgent 2%; urgency, not urgent, 0.48 out of 3; and suggestion, normal exchange, 90%. It is for judgment, not an auto-reply bot: what does the other person's message really mean, and what stance should you reply with. From v1.9 it is basically instant on tap: the calculation starts the moment the long-press popup appears, as a pre-judgment, and by the time you tap intent the result has usually come back, with a measured cold-start first call going from 1144 ms to 9 ms. A sister project, JevBystander, uses the same judgment criteria as an Android accessibility version, no root, no Xposed, read-only screen plus a fixed 3 toasts, for people who cannot install LSPosed or who are wary of hooking WeChat, with the two implementations completely independent and coexisting on the same phone, at the cost that the accessibility version only sees text already displayed on screen. What it does: intent judgment in 10 bins, small talk, asking about a situation, asking for a favor, urging progress, sales pitch, complaint, notice, venting, invitation, and more; emotion judgment in 9 bins, calm, happy, dissatisfied, angry, anxious, urgent, sad, sarcastic, polite, with the top-3 probability distribution; urgency in 4 tiers plus a 0–3 score; reply stance in 11 bins, teasing, normal, polite, appreciative, caring, empathetic, sympathetic, soothing apology, asking for details, declining, not replying for now; plus whether it is waiting for your reply, the risk level, and the response action; and relationship awareness, coworker, client, lover, best friend, where the same sentence yields completely different conclusions. What it does not do: it does not generate reply text, since the Jev model only judges and does not produce free text; it does not auto-reply or send messages on your behalf; and it does not do local inference, since messages are sent to the service your own API key points at. Install: install FkWeChat, an LSPosed module, on the phone and enable plugin support in the host; copy the directory's main.java and info.prop into the FkWeChat plugin path; fill in your own TypeSafe/Jev key, with a doc on getting a key for free in a minute, either by copying config.example.properties to config.properties and writing the key, or without editing files, waiting for the first long-press to generate the settings properties file and writing the key after the interface key line, with the settings file taking precedence and falling back to config.properties; then enable and reload in the plugin manager, or restart WeChat, and when the JevIntent ready toast appears, it works; long-press any text message in any chat, quoted replies included, and intent appears in the menu. The repository contains no keys, and the config, settings, and presets properties files are local, written into .gitignore. Usage: the long-press menu has only two items: intent, which analyzes the message and pops the result as a fixed 3 toasts, and relationship, which cycles the relationship for this session across 10 tiers, remembered per session, since a coworker group and your partner can have two different sets of conclusions, which is why it cannot live in global settings.", "arch": "JevIntent, in Chinese: a FkWeChat plugin where you long-press any WeChat message, TypeSafe Jev's judgment model analyzes the intent, emotion, and reply stance, and a few seconds later a local toast pops up with the conclusion, without sending any message or altering the chat history, so the other person has no idea, a consumer-messaging use of typed decisions.", "approach": "", "training": "", "data": "", "result": "", "notes": "Consumer-messaging use of typed decisions; 中文 project.", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kylemclaren-jevpdf", "name": "jevpdf", "author": "kylemclaren", "date": "2026-09-22", "category": "app", "url": "https://github.com/kylemclaren/jevpdf", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 3, "likes": "", "detail": "Jev does not write prose; it answers typed questions with structured values your code can use directly, and that is exactly what search needs: a yes or no for every line, where each line of the PDF becomes one noul. The demo asks the Attention Is All You Need paper a question and watches the matching lines light up, with a full-quality video, and the README covers running it, using it, how it works, and deploying it to Fly.io.", "arch": "JevPDF: Ctrl+F for when you do not know the exact words, ask a PDF in your own words and watch the matching lines light up, reading and locating with Jev, built on Jev, TypeSafe's flagship System One model, with a live demo at jevpdf.fly.dev, in Bun, React 19, Vite, Tailwind CSS 4, and pdf.js, deployed on Fly.io, MIT.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 7 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "codejunkie99-keel", "name": "Keel", "author": "codejunkie99", "date": "2026-09-22", "category": "app", "url": "https://github.com/codejunkie99/keel", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 204, "likes": "", "detail": "arch: Keel 0.2.0; approach: Laya selector, host-owned permission checks", "arch": "Local-first macOS coding workspace in Rust/GPUI: a local Laya selector picks from allowed options, optional hosted Jev, and host-owned permission checks verify every choice before it is applied; Avid-derived interface.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lejudge-jev-jepa", "name": "lejudge-jev-jepa", "author": "AbdelStark", "date": "2026-09-22", "category": "app", "url": "https://github.com/AbdelStark/lejudge-jev-jepa", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 10, "likes": 0, "detail": "LeWorldModel, LeWM, plans by minimizing one number, the latent distance between an imagined final state and a goal image, and that cost cannot say but not through there, or do not touch it yet; LeJudge adds a second term that anyone can write in plain English. The loop: first, imagine, where LeWM's predictor rolls out 300 candidate action sequences in latent space, with CEM over 30 iterations; second, describe, where linear probes turn every imagined latent into a handful of words from a closed vocabulary, grid cell, wall contact, angle bin, contact, speed, and the judge never sees a coordinate; third, judge, where Jev, TypeSafe's System One decision model, answers one typed yes or no question per constraint and step, such as at step t=2 of candidate k1, does the block or agent violate constraint c1; and then code decides. A paper PDF ships in the repository, and the paper results are reproducible offline from cache with make.", "arch": "LeJudge: a cost module you program in English, natural-language constraints for JEPA world-model planning, judged by a decision model instead of an LLM, where LeWM imagines, probes describe, Jev judges, and code decides, MIT, Python 3.10.", "approach": "", "training": "", "data": "", "result": "", "notes": "Jev as the constraint checker for learned world-model planning.", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "okinaaudio-live-jev", "name": "live-jev", "author": "okinaaudio", "date": "2026-09-22", "category": "app", "url": "https://github.com/okinaaudio/live-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 43, "likes": "", "detail": "What it can do: the mixer, with volume, pan, mute, solo, arm, monitoring, and sends, such as mute, down by 3 dB, pan left 20, send A up a bit, where return tracks can be addressed by letter or number and support volume, pan, mute, and solo, the master supports volume, the master cannot be muted, soloed, armed, or renamed, returns cannot be armed, and if you do not name a target, Live Jev acts on the selected track; the transport, with play, stop, record, loop, metronome, tempo, jump to bar, Live Jev undo, and capture MIDI; clips and scenes, with launch and stop, loop, warp, pitch, gain; notes, with quantize from 1/4–1/32, triplets, strength, legato, transpose by octaves or semitones, velocity, and double the loop; devices, with inserting plug-ins and Live's own devices, such as new track with Omnisphere, add EQ Eight, put Valhalla on the master, add Echo to return A, turning devices on and off, and changing parameters, where devices on returns addressed by letter or number, and devices on Master, are targets too, instruments cannot be inserted on a return or Master, candidates come from your Live browser, and new tracks land where Live would put them and get Live's default names; tracks, with adding MIDI, audio, and return tracks, and renaming ordinary or return tracks; and several tracks at once, with muting, soloing, or arming a range, everything, or everything but one, such as mute tracks 3–6 or unsolo all.", "arch": "Live Jev: control Ableton Live with one short sentence, an Ableton Live controller where Jev maps a short natural-language request to a bounded music-production action, where you press cmd shift space while working in Live, a small bar appears on top, you type or dictate something like turn it down 3 dB, Serum 2 on a new track, or quantize to 1/16, then hit Enter, the bar disappears instantly, Live stays in front, and the change is applied, with the bar only coming back when it needs to ask you something, English and Japanese both supported, with many ways to say the same thing, app 1.02, Remote Script 0.18, distributed as source, with no packaged release planned.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "simonw-llm-typesafe", "name": "llm-typesafe, Jev for the llm CLI", "author": "simonw", "date": "2026-09-22", "category": "app", "url": "https://github.com/simonw/llm-typesafe", "base_model": "TypeSafe Jev jev-latest", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 14, "likes": "", "detail": "", "arch": "an LLM plugin that adds TypeSafe's classification and scoring models to the llm command line: llm install llm-typesafe, then llm keys set typesafe; adds a model called typesafe/jev-latest with the alias jev", "approach": "", "training": "", "data": "", "result": "yes/no by default: llm -m jev with a question returns noul as a probability between 0 and 1; optional criteria definitions pin the true and false meanings; choice answers from known options, score grades on an ordered rubric; noul comes from the Bernoulli distribution", "notes": "Apache-2.0, published on PyPI; by Simon Willison", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sur-cai-macos-computer-use-kit", "name": "macOS Computer Use Kit", "author": "Sur-Cai", "date": "2026-09-22", "category": "app", "url": "https://github.com/Sur-Cai/macos-computer-use-kit", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 4, "likes": "", "detail": "approach: AX tree over screenshots, optional Jev", "arch": "AX-first macOS computer use for AI agents: an MCP server and CLI that read the accessibility tree instead of guessing coordinates from screenshots, post input to the target app in the background so the cursor never moves, and verify every action; optional Jev for decisions.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ouroloop", "name": "ouroloop", "author": "e13ven-arch", "date": "2026-09-22", "category": "app", "url": "https://github.com/e13ven-arch/ouroloop", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "", "arch": "Lightweight RSI framework where an LLM improves a small decision model and the decision model improves the agent harness: every small judgment call the agent makes, retry a command, keep the output in context, run without asking, is the task done, route to a model tier, gate a tool, goes to a calibrated decision model as a typed yes-no, choice or score question with probabilities back in one forward pass; low-confidence calls go to the LLM or a person, and every call plus what actually happened afterwards lands in a local ledger that becomes the training labels", "approach": "", "training": "", "data": "", "result": "ouroloop evolve trains a new decision model from the ledger, and a research agent, an LLM that reasons with a decision model as judge, reads the evaluation results and proposes changes to decision-point specs, training recipes and the harness prompt; every change must pass held-out evaluation and the same promotion gate before it replaces the current version", "notes": "Starts on the Jev API and lets a self-trained backend such as TDE take over decision points one by one as data accumulates; answers from Jev and from closed LLMs are never training labels, labels come from observed outcomes and from people, open-weight model answers count at half weight; the premise is that commercial LLMs already reason well and the decisions are what matter, so ouroloop improves decisions, not reasoning; the only runtime dependency is httpx, agent runs work with Claude or any OpenAI-compatible model, and a hook exports tool calls and results from normal Claude Code sessions into the ledger without changing Claude Code", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tyler-dot-earth-patdown", "name": "patdown", "author": "tyler-dot-earth", "date": "2026-09-22", "category": "app", "url": "https://github.com/tyler-dot-earth/patdown", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 15, "likes": "", "detail": "", "arch": "Block, steer, and \"fuzzy lint\" with Jev to make agents follow your rules and conventions. CLI, github action, pi package, and more. Built with Effect.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nyarlathoteppppp-pi-jev-context", "name": "pi-jev-context", "author": "nyarlathoteppppp", "date": "2026-09-22", "category": "app", "url": "https://github.com/nyarlathoteppppp/pi-jev-context", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 8, "likes": "", "detail": "", "arch": "Model performance first. Token savings second. A Pi extension with freshness-aware read dedupe, Jev log filtering, and searchable verbatim recall. Keeps existing message history intact.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pijev", "name": "pijev", "author": "TypeLLM", "date": "2026-09-22", "category": "app", "url": "https://github.com/TypeLLM/pijev", "base_model": "Hosted Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 34, "likes": 0, "detail": "base: typesafe_sdk; notes: openjev: 2.3% answer change on shuffle; name: Permutation Invariant Jev", "arch": "One-line import change: turns Jev's option-order variations into a single averaged prediction across permutations in ONE system_one request; keeps the same calls and official response types; Brier score and log loss GUARANTEED no worse than the average across included orderings", "approach": "", "training": "", "data": "", "result": "Documents live jev-1.13.0 instability: identical two Choices with opposite option orders return different probability vectors", "notes": "Turns Jev's known option-order sensitivity into a theoretical guarantee.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rupeshpoojary9-poorjev", "name": "poorjev", "author": "rupeshpoojary9", "date": "2026-09-22", "category": "app", "url": "https://github.com/rupeshpoojary9/poorjev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 10, "likes": "", "detail": "arch: ECE 0.170–0.071 · no README found", "arch": "Local Jev-like System One decision layer that returns typed choices and calibrated confidence without a hosted API. Local reproduction: implements Jev's typed Choice/Score/Noul interface on commodity zero-shot NLI models and makes the confidence honest with temperature scaling and conformal abstention, shipping a reproducible calibration eval", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "general", "score": "ECE 0.170", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "irfndi-prism-liquidity-agent", "name": "prism-liquidity-agent", "author": "irfndi", "date": "2026-09-22", "category": "app", "url": "https://github.com/irfndi/prism-liquidity-agent", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 109, "likes": "", "detail": "Every cycle follows the same sequence: recall, querying memory for past patterns on this pool; observe, fetching pool state, bin array, and volume authenticity; reason, computing the fee/IL ratio, bin drift, and TVL velocity; simulate, where if REBALANCE, it estimates the IL cost before committing; record, writing new observations back to memory; and decide, choosing HOLD, REBALANCE, EXIT, or ENTER. The decision is intercepted by a risk gate before anything happens on-chain: confidence below threshold, drawdown above limit, or a position cap breach, any of these blocks execution and logs a warning to memory so future cycles are aware. Memory: the agent remembers, and every outcome, fee earned, IL incurred, bad pool flagged, gets stored in an SQLite vector table, sqlite-vec, and retrieved by cosine similarity on the next relevant cycle, with entries expiring automatically, 90 days for patterns, 60 for warnings, 180 for outcomes; this is what makes it self-improving, getting slower to enter pools it has been burned by before, and faster to recognize patterns it has profited from. Volume authenticity: before any decision, the agent scores each pool's volume.", "arch": "Prism: an autonomous liquidity agent that watches liquidity pools on Solana, currently Meteora DLMM, reasons over live on-chain data, and rebalances positions before they bleed, where concentrated liquidity earns fees only when the active price bin sits inside your range, and when the market drifts, your position silently collects impermanent loss, so Prism runs a rule-based agent every 10 minutes, checks every pool in your watchlist, and either holds, shifts the range, pulls liquidity entirely, or enters new pools, and before any decision, Jev scores each pool's volume authenticity, MIT, Bun 1.4.", "approach": "sdk", "training": "", "data": "", "result": "Before any decision, the agent scores each pool's volume on a 0–1 scale. Volume/TVL ratio above 10×, fee rate outside the 0.02%–2% band, or low TVL with outsized volume all push the score down. Pools below 0.70 are skipped entirely. This alone filters most of the wash-traded", "notes": "Curated in 1 awesome-jev list", "domain": "finance", "score": "0.02%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "obie-ruby-decision-model", "name": "ruby-decision-model", "author": "obie", "date": "2026-09-22", "category": "app", "url": "https://github.com/obie/ruby_decision_model", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 51, "likes": "", "detail": "", "arch": "Ruby client for decision models such as Typesafe Jev Ruby ecosystem: client for decision models such as Jev, so Ruby applications can put typed questions directly to the model. Ruby client with standard-library transport for TypeSafe and OpenRouter decision endpoints.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shapeshift", "name": "shapeshift", "author": "anishfn", "date": "2026-09-22", "category": "app", "url": "https://github.com/anishfn/shapeshift", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 630, "likes": 0, "detail": "base: hosted · works offline; name: Jev-powered adaptive UI", "arch": "One text box that morphs into the right UI as you type, an event card, checklist, timer, color picker, bill splitter, poll or converter; Jev classifies the intent with one call answering 14 typed questions in parallel, which card plus signals such as is-it-a-video-call and is-it-urgent, while dates, amounts, units and math are computed by deterministic code; runs fully offline by default with a built-in keyword classifier, so it works without an account", "approach": "", "training": "", "data": "", "result": "The fan-out is one Jev call with 14 questions plus a deterministic parser over the same text, Jev decides and code computes; live demo at shapeshiftui.vercel.app; 462 stars", "notes": "Jev as a real-time UI-router.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arpit-khandelwal-jev-linkedin-slop-filter", "name": "Slop Filter for LinkedIn", "author": "Arpit-Khandelwal", "date": "2026-09-22", "category": "app", "url": "https://github.com/Arpit-Khandelwal/jev-linkedin-slop-filter", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 5, "likes": "", "detail": "", "arch": "Chrome extension that judges every LinkedIn post as it scrolls into view, stamping BAIT, CORP, or BRAG with the confidence score printed on the stamp; Jev returns a typed probability so the extension branches on a number instead of parsing prose.", "approach": "", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lukstei-slop-grader", "name": "slop-grader", "author": "lukstei", "date": "2026-09-22", "category": "app", "url": "https://github.com/lukstei/slop-grader", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 28, "likes": "", "detail": "", "arch": "Jev-powered, rule-based grader for text files. Runs every rule against every line in parallel. No skimming, no missed lines. Content quality: CLI tool that grades text files against custom rulesets for AI slop, grammar, and technical doc quality using Jev scores and line-level flags, then guides an AI agent to auto-fix violations. Rule-based text grader that uses Jev scores", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mossyfield-st-jeved", "name": "ST-jeved", "author": "mossyfield", "date": "2026-09-22", "category": "app", "url": "https://github.com/mossyfield/st-jeved", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 31, "likes": "", "detail": "What you can build: pacing, asking for an event when the story stays calm; steering, reading the message you send and shaping the reply to it; rerolls, rerolling a reply that speaks for your character or breaks a rule you wrote; voice, correcting the narrator when the tone drifts from the card; state, keeping an inventory, a quest log, or a meter for each character in a list; and automation, running an STscript to change the background, make a picture, or set a variable. Install: in the Extensions panel, choose Install extension, and paste the repository URL; open the Jeved drawer, pick a host, and paste its API key; press Test, and Jeved makes one small call to check the key; then tick Enabled. Open Jeved, the wand menu, and /jeved open the workspace, which has five tabs, Rules, Sensors, Lists, Activity, and Settings, and the built-in preset is Director, whose rules you can open to read what each one does. A sensor is one question, in three types: Score, where you write a scale of 2–10 steps, and the answer is a number from 0 to the last step, such as 1.4; Choice, where you write 2–255 options, and the answer is one option name; and Noul, where you write a statement, and the answer is the chance, 0–100%, that it is true. Jeved measures a sensor when an enabled rule uses it, and ticking Measure anyway measures a sensor that no rule uses. What a sensor reads: three controls, user messages from 0–50, assistant messages from 0–50, and context, with at least one count above 0, and names for the question, latest_turn for the newest reply, player_message for your newest message, and history for the older messages, oldest first.", "arch": "Jeved: a SillyTavern extension where Jev scores replies against configured narration rules before adding guidance, sending chat messages to Jev, a small decision model, with questions that you write, and a rule reads the answers and acts, adding one instruction to the prompt, rerolling the reply, changing a list, or running an STscript, so that when no rule matches, Jeved adds nothing, requiring SillyTavern 1.18.0 or later.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system1-agents", "name": "system1-agents", "author": "ThinkFlowLab", "date": "2026-09-22", "category": "app", "url": "https://github.com/ThinkFlowLab/system1-agents", "base_model": "Jev, Laya, CUA-S1", "params": "", "metric": "Up to 6× faster and 25× cheaper than a chat model at the same score, published claim", "license": "Open", "paper": "", "stars": 80, "likes": 0, "detail": "base: mixed; name: Jev/Laya/Cua-S1 as agent brains", "arch": "Prebuilt or auto-built agents on any System One brain: describe the task and Claude Code or Codex runs a prebuilt agent or builds a new one on Jev, Laya or Cua-S1 Nano 0.1; browser use, computer use, robotics and games ship ready to run", "approach": "", "training": "", "data": "", "result": "Benchmark tables in the repo back the speed and cost claim against chat-model baselines on the shipped agent tasks", "notes": "44 stars; treats the whole System One model class as interchangeable agent brains", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": "6× faster"}
{"id": "tldr-radar", "name": "TLDR Radar", "author": "jlmelis", "date": "2026-09-22", "category": "app", "url": "https://github.com/jlmelis/tldr-radar", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "scrapes the TLDR newsletter; Jev answers three questions per article in one request: a relevance Score, an article-type Choice and an actionable Noul; two tunable thresholds gate display and a deep-dive agent with an OpenAI tool-caller plus SearXNG", "approach": "", "training": "", "data": "", "result": "", "notes": "runs cached per edition and date", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nidhi-singh02-agent-router", "name": "agent-router", "author": "nidhi-singh02", "date": "2026-09-21", "category": "app", "url": "https://github.com/nidhi-singh02/agent-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 82, "likes": "", "detail": "", "arch": "CLI that uses Jev task classification to choose and launch Cursor, Claude Code, Codex, or OpenCode with an appropriate model and effort level. Pre-release Herdr integration that filters eligible coding models by quota and policy before Jev ranks them.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kenhuang-jev-soc", "name": "Agentic SOC on Jev: 30 decision runners", "author": "Ken Huang", "date": "2026-09-21", "category": "app", "url": "https://kenhuang2.substack.com/p/jev-system-one-agentic-soc", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "Calibrated action bands: human below 0.45, automation floor 0.72, high-stakes 0.88", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Ken Huang's Substack, 2026-09-21, documents an agentic SOC on Jev: 30 decision runners plus 7 SOC runners covering triage, mitigation, investigation, escalation, recovery, and closeout. The 27 Jev-only runners each returned a typed response from Jev 1.13.0; the 3 security runners that would call a language model did not complete live calls, and the security path screens first. The repository sits in the paid section. The piece is also a candid list of Jev's limits, no image input, no in-model counting, and publishes same-task pricing at 0.042 USD per million tokens input.", "arch": "30 decision runners plus 7 Agentic SOC runners covering triage, mitigation, investigation, escalation, recovery and closeout; 27 Jev-only runners each returned a typed response from Jev 1.13.0; 3 security runners that would call a language model did not complete live calls; the security path screens prompts before Claude or OpenAI writes and screens drafts after, a path that never calls a language model", "approach": "", "training": "", "data": "", "result": "Action bands: below 0.45 routes to a human, 0.72 is the automation floor, 0.88 is high-stakes; money movement, host isolation and rejections stay in caller code until false-positive rates are measured; fixture results: triage contains now, mitigation holds containment, pipeline did not publish isolate_host, recovery isolated at 2 hours monitoring, closeout monitors; workflow evals score agreement with GPT-6 Astra and Claude Fable 5.1", "notes": "Substack, 09-21; repository in the paid section; documents Jev limits: no image input, no in-model counting; same-task pricing: $0.042/MTok input, output unbilled, 70–500 ms vs 3–329 s for chat models", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "blockly-jev", "name": "blockly-jev", "author": "eriestra", "date": "2026-09-21", "category": "app", "url": "https://github.com/eriestra/blockly-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "A Blockly extension that puts TypeSafe Jev judgments into blocks. Jev is a System One model: it reads natural language and returns typed answers with probabilities instead of generating text. The package makes those answers first-class Blockly values, so any stock block that takes a Boolean, Number or String can be driven by a Jev judgment.", "arch": "A Blockly extension that puts TypeSafe Jev judgments into blocks: Jev's typed answers with probabilities become first-class Blockly values, so any stock block that takes a Boolean, Number or String can be driven by a Jev judgment, with dedicated reporter blocks such as noul returning a Boolean at a probability threshold and choice returning the selected option", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "education", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chat-seek-vscode", "name": "Chat Seek, VS Code chat search with Laya reranking", "author": "fstandhartinger", "date": "2026-09-21", "category": "app", "url": "https://github.com/fstandhartinger/chat-seek-vscode", "base_model": "Laya", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 67, "likes": "", "detail": "", "arch": "find past Claude Code, Codex and OpenCode conversations inside VS Code from a description, or ask a question about what those chats contain; searches local user and assistant messages, then uses the Laya local decision model to rerank likely matches; cards show the last activity such as 1 week ago with the exact date on hover, a readable excerpt, and a Resume in Claude Code / Codex / OpenCode action", "approach": "", "training": "", "data": "", "result": "", "notes": "for factual questions Chat Seek asks Laya to identify the intent, then searches full original messages in likely chats for the query's exact terms; if ripgrep is available it also checks matching transcript files across the archive, an early pass that can find text beyond the 2,800-character lookup limit; PIN and passcode questions stay entirely local: when a code is clearly attached to the named account, Chat Seek copies it from the source with an exact citation, with no model or API deciding which nearby code to use; other likely chunks pass through Laya and optional answer extraction; the full pass still reads every supported chat from the newest chat and newest chunk onward; matching chunks appear as provisional cards, verified answers move to the top, rejected candidates disappear; Read full original chunk expands to open the source in an editor; ships demo and narrated explainer videos; agent installation instructions: point your coding agent at the repository and ask it to install Chat Seek; independent project, not affiliated with ConvAI Innovations or TypeSafe AI", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "x96x64-ctxjev", "name": "ctxjev", "author": "x96x64", "date": "2026-09-21", "category": "app", "url": "https://github.com/x96x64/ctxjev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "Why: long-running agents accumulate context faster than it stays useful, and ctxjev scores AI-agent context for relevance with Jev and prunes what is no longer useful. The README covers choosing a package, the Claude Code plugin, how scoring works, a does-it-work evaluation, a quick start, using it from an MCP host, design notes, and the changelog.", "arch": "ctxjev: keep what matters when your agent's context gets compacted, ranking the entries of an AI agent's history and deciding what to keep, drop, or summarize, where by default it ranks by position alone, newest kept, the same as plain truncation, with no key and nothing sent, and you opt in to Jev, TypeSafe AI's typed-decision model, or to an offline keyword heuristic, so that in Claude Code the plugin carries the top few entries through compaction, scored offline by default, or with Jev if you opt in, and in an agent loop you write yourself, pruneMessages removes what ranked lowest and keeps the request valid, MIT, Node 20+, TypeScript, published as the ctxjev-cli, ctxjev-core, and ctxjev-mcp npm packages.", "approach": "", "training": "", "data": "", "result": "Context-relevance pruning for agents", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zephyrdeng-ego-jev", "name": "ego lite skill", "author": "ZephyrDeng", "date": "2026-09-21", "category": "app", "url": "https://github.com/ZephyrDeng/ego-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "~0.4 s per DOM step", "license": "MIT", "paper": "", "stars": 9, "likes": "", "detail": "The sibling project jev-ultrafast runs the same idea as a standalone browser agent, Zurich to London on Google Flights in 7.1 s, and this skill brings that loop into ego lite, so the agent keeps the user's real browser, sessions, and logins. Every observation produces a numbered element table, with a11y refs at N plus DOM-discovered dark matter, d N, for div cards, cursor pointer, iframe, and shadow content the snapshot never refs, such as at 2 textbox Customer name, at 5 button Apply filters, and d 1 div member card G18655. One System One request chooses the operation from click_target, fill_target, and select_target if present, and then uses the matching target. Performance: about 0.4 s per DOM step, and 4–10 s including page load.", "arch": "ego-jev: an agent skill that gives ego lite a Jev, TypeSafe System One, inner loop, making one typed decision of about 0.4 s per DOM step instead of a full LLM turn, where ego executes and Jev decides, and each step snapshots, numbers the interactive elements, and one System One call picks the operation and its target together, so ego clicks, fills, and selects, while Jev never writes text and never sees a screenshot, and login, payment, free text, canvas, and content reading escalate back to the planner, MIT.", "approach": "sdk", "training": "", "data": "", "result": "about 0.4 s per DOM step, 4–10 s including page load, Jev, act, read; 5–8× faster than the Jev 1.13 browser agent; 50–90% cheaper, 2.4k-4.8k vs 5k-6.5k tokens; 92% format stability", "notes": "Sister project jev-ultrafast: 7.1 s Google Flights search from Zurich, vs 30.4 s in earlier demos, 7.6k tokens, ~$0.02.", "domain": "computer-use", "score": "", "latency": "~0.4 s per DOM step", "benchmark": "", "cost": "~$0.02 per Flights search", "speedup": "5–8× faster"}
{"id": "fluiduse", "name": "FluidUse", "author": "FluidInference", "date": "2026-09-21", "category": "app", "url": "https://github.com/FluidInference/FluidUse", "base_model": "Laya Core ML plus CUA-S1-FORMS", "params": "706K form specialist", "metric": "", "license": "Apache-2.0 weights, MIT agent", "paper": "", "stars": 167, "likes": "", "detail": "paper: FluidInference/laya-coreml; FluidInference/cua-s1-forms-coreml; FluidInference/lfm2-5-350m-rlcd-coreml; FluidInference/gliner2-5-decide-coreml; FluidInference/system-one-gemma-coreml", "arch": "local computer-use agent on Apple silicon; Core ML decision models; CUA-S1-FORMS is a 706K-parameter form-filling specialist; Laya Core ML buckets at 128, 256, 512 and 1024 tokens with 32 options", "approach": "", "training": "", "data": "", "result": "3.7 ms per short question on M5 Pro, about 7× faster than on M1 Max; about 1 ms per decision on the Neural Engine", "notes": "companion Core ML ports of Laya, LFM2.5-350M-RLCD, GLiNER2.5-decide and system-one-gemma", "domain": "computer-use", "score": "", "latency": "3.7 ms per question on M5 Pro", "benchmark": "", "cost": "", "speedup": ""}
{"id": "timrogers-formanator", "name": "formanator", "author": "timrogers", "date": "2026-09-21", "category": "app", "url": "https://github.com/timrogers/formanator", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 100, "likes": "", "detail": "Installation: on macOS or Linux via Homebrew, brew tap timrogers/tap and brew install formanator; on macOS, Linux, or Windows via Cargo, install Rust if needed, cargo install formanator, and run formanator --help to check that everything is working and see the available commands; or via direct binary download, download the latest release for your platform, with macOS, Linux, and Windows supported, and add the binary to your PATH, or the Unix-equivalent, so you can execute it.", "arch": "Formanator: a CLI and MCP server for Forma benefit claims, now built in Rust and distributed through Homebrew and Crates.io, where Jev classifies receipt images into claim categories with audit-ready results, allowing you to submit benefit claims to Forma and track progress from the command line, one by one or in bulk, and to understand your Forma benefits and track and submit claims from any MCP client, for example Copilot CLI, Visual Studio Code, or Claude Code, where with the power of large language models via GitHub Copilot CLI, free, no extra configuration needed, or OpenAI, it can analyze your receipts and generate your claims automatically, with the TypeScript v2 build still available through npm.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "niazmorshed2007-jcr", "name": "jcr", "author": "niazmorshed2007", "date": "2026-09-21", "category": "app", "url": "https://github.com/niazmorshed2007/jcr", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 20, "likes": "", "detail": "Skills made it easier to load instructions when they are needed, but an agent can still read several files to find a few commands, then carry those files through the rest of the task; JCR moves that lookup into a resolver. Skills can describe a workflow and the judgment it needs, and capabilities can document the individual operations used along the way; the format is independent of Jev, and the author would like to explore whether it should become an open standard with the community. The repository includes the resolver, a capability catalog, a stdio MCP server, Claude and Codex comparison harnesses, and 50 benchmark scenarios, and JCR returns documentation, it does not execute commands, with the included harnesses also stopping at explaining the steps needed to carry out a task. Quick start: use Node.js 22 or newer and npm, clone the repository and install its dependencies, copy .env.example to .env, and fill in the keys for the mode you want to run.", "arch": "JCR, Jev Capability Resolver: an agent tool that gives an agent one tool to find the documented commands it needs for a task, using Jev to search a nested capability tree and return the context attached to selected operations, so agents query a nested capability tree and receive only the deterministic commands and attached context for the selected operations, with the main agent getting the selected instructions while the search stays outside its context, a proposed capabilities format for deterministic commands from any provider, independent of Jev, with a site at jcr.niazmorshed.dev.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "can1357-jegrep", "name": "jegrep", "author": "can1357", "date": "2026-09-21", "category": "app", "url": "https://github.com/can1357/jegrep", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 93, "likes": "", "detail": "", "arch": "Semantic grep CLI where Jev judges which files and exact line ranges match a natural-language code query. Rust semantic grep that scores live repository files and ranges with Jev probabilities, without an embedding index or background daemon.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arczhi-jet", "name": "jet", "author": "arczhi", "date": "2026-09-21", "category": "app", "url": "https://github.com/arczhi/jet", "base_model": "", "params": "", "metric": "", "license": "CC-BY-NC-4.0", "paper": "", "stars": 3, "likes": "", "detail": "Named for takeoff speed. Building on the ideas in Thoughts on a TypeSafe coding agent, jet turns explicit state, a lightweight judgment model and on-demand context construction into a runnable coding agent. Most agent latency is spent feeding a huge transcript to one big model; jet flips that, state is explicit and decisions are cheap. Core designs: RLCD context engine, judgment-first routing, async plans, verification gates, policy and observability.", "arch": "Fast coding agent built on TypeSafe judgments and Recursive LLM Context Decomposition, RLCD: a small judgment model decides in milliseconds, the big model only sees key context", "approach": "", "training": "", "data": "", "result": "Demo run done in 76 s: 12 steps, 10 tool calls, 177k tokens, 156k input plus 21k output, cross-model verified; jet built an offline WebAudio music player end to end, streamed answer with live thinking, tool calls with real-time write progress, then open index.html in the browser", "notes": "CC BY-NC 4.0, free to share and adapt with attribution, commercial use not permitted; author alex, arczhi; visual design guide and plain-language walkthrough in docs/design.html and docs/DESIGN.md", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reallyartificial-jev-by-example", "name": "Jev by Example", "author": "reallyartificial", "date": "2026-09-21", "category": "app", "url": "https://github.com/reallyartificial/jev-by-example", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Ten runnable experiments in building agents that know what to do next and when to ask: each combines a small Jev judgment with application logic you can read, change, and test - memory conflicts, tool-call completion, duplicate-invoice retries", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-codex-router-lab", "name": "Jev Codex Router Lab", "author": "apolenkov", "date": "2026-09-21", "category": "app", "url": "https://github.com/apolenkov/jev-codex-router-lab", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "TypeScript reference implementation of Jev as an advisory skill-and-context router: Jev proposes typed semantic judgments, deterministic code validates every boundary and decides result versus safe fallback", "approach": "", "training": "", "data": "", "result": "", "notes": "a lab, not a production Codex integration; five-minute offline quickstart with a clearly labelled fake gateway; typed fallback keeps mandatory skil.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "daniel4x-jeveemon", "name": "Jev Emon", "author": "daniel4x", "date": "2026-09-21", "category": "app", "url": "https://github.com/daniel4x/JevEmon", "base_model": "TypeSafe Jev", "params": "", "metric": "~1 s per decision", "license": "GPL-3.0", "paper": "", "stars": 3, "likes": "", "detail": "Verified milestones so far: Jev can leave the Player's House, cross Pallet Town, deliver itself through Route 1, fighting and winning any wild encounters along the way, and reach Viridian City, about 1 s per decision; further legs of the journey, Oak's Parcel and the first Gym, are not built yet, and the walk currently ends the run once it reaches Viridian City. Try it: grab a TypeSafe API key and put it in .env, copy .env.example to .env, drop the FireRed Version U V1.1 gba file next to the README, on a Mac with Apple Silicon, then brew install mgba ffmpeg uv, uv run python scripts/setup.py, uv run python -m jevemon --prepare, and uv run python -m jevemon, which builds the checkpoint, starts the local server, and opens 127.0.0.1 port 8765, where you hit Go live and sit with it, edit the lineup in the page, speed it up, and scrub the VOD after, with everything running through uv, no shell wrapper scripts, no separate install step. One walk, no browser: uv run python -m jevemon --run --speed 4. How it is wired is documented in ARCHITECTURE.md.", "arch": "JevEmon: Jev walks a real Pokemon FireRed ROM, where Jev navigates a Pokemon FireRed ROM via typed decisions over GBA RAM, with pi-rom plus an emulator, bring your own ROM, not a screenshot agent, not a bot that mashes A, where each leg of the walk, the code reads the overworld out of RAM, works out every place you could actually go from here, a door, a path to the next route, a Pokemon Center if your party needs one, and hands that list to Jev, Jev picks a destination, and the code paths there and presses the buttons, with wild Pokemon interruptions fought out by the same kind of typed decision, then the journey re-plans from wherever the encounter left you, GPL-3.0, requires a TypeSafe key and Apple Silicon.", "approach": "sdk", "training": "", "data": "", "result": "Milestones through Viridian City; ~1 s per decision", "notes": "Requires a TypeSafe key + Apple Silicon.", "domain": "gaming", "score": "", "latency": "~1 s per decision", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-cv-jd-evaluator", "name": "Jev Evaluator Worker", "author": "ideas-to-life", "date": "2026-09-21", "category": "app", "url": "https://github.com/ideas-to-life/jev-cv-jd-evaluator", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Cloudflare Worker calling typesafe/jev via the Workers AI binding; web dashboard with three suites: CV and job fit from md, txt or docx files, Upwork proposal scoring, and a 30-day freelancer funnel against Datalumina course benchmarks", "approach": "", "training": "", "data": "", "result": "", "notes": "runs on Cloudflare Workers; wrangler dev server at localhost:8787", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "peterfriese-jev-foundation-models", "name": "Jev for Apple Foundation Models", "author": "Peterfriese", "date": "2026-09-21", "category": "app", "url": "https://github.com/Peterfriese/jev-foundation-models", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 47, "likes": "", "detail": "base: System One decision model; result: typed outputs", "arch": "Native Swift 6 bridge integrating Jev into Apple's Foundation Models framework: evaluate strongly typed @Generable structs/enums against application state; full Apple Intelligence API compatibility", "approach": "", "training": "", "data": "", "result": "40–150 ms evaluations, zero-hallucination claim; iOS 27+/macOS 27+, Xcode 27+; SwiftPM package", "notes": "42 stars.", "domain": "general", "score": "", "latency": "40–150 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-graph-search", "name": "Jev Graph Search", "author": "Emlembow", "date": "2026-09-21", "category": "app", "url": "https://github.com/Emlembow/jev-graph-search", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 9, "likes": "", "detail": "base: local shortlisting + Jev ranking service; arch: Node 20+, no runtime deps; name: Obsidian/Logseq retrieval", "arch": "Jev-assisted retrieval and evidence-preserving inspection for local Markdown / Obsidian vaults / Logseq graphs: makes a shortlist on your machine, then Jev ranks candidates against your question; results include the original passage and source reference so the agent can point back to the note it used; CLI or agent skill; optional JSON snapshot; backend: TypeSafe or OpenRouter", "approach": "", "training": "", "data": "", "result": "On one 8,851-node tax-code graph, Jev reranking raised source recall from 33.2%→73.2% at top-1, 53.5%→80.1% at top-3, 63.9%→81.8% at top-5", "notes": "npx jev-graph-search@0.2.2; created 2026-09-21.", "domain": "search", "score": "33.2%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jcpsimmons-jev-macos-loop", "name": "Jev macOS Loop", "author": "jcpsimmons", "date": "2026-09-21", "category": "app", "url": "https://github.com/jcpsimmons/jev-macos-loop", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "AGPL-3.0", "paper": "", "stars": 23, "likes": "", "detail": "A real Finder demo: 9 files into 3 group moves, Invoices, Receipts, and Reports, in 7.39 seconds, independently verified, with a full-resolution MP4 and recording details. Set up with your agent: copy and paste into your agent a prompt to read the repository's AGENTS.md and follow its instructions to install, configure, verify, and integrate Jev macOS Loop with your agent, reusing an existing installation or token configuration where available, completing the setup and disposable-app test, then making it available for future native Mac tasks, and asking only for missing information, credentials through a secure local workflow, or permissions actually needed; it works through local terminal tools in Claude Code, Codex, Grok, Cursor, Gemini CLI, Copilot, and others.", "arch": "Jev macOS Loop: native AI computer use and GUI automation, an open-source computer-use agent for native macOS GUI automation on Apple silicon, a native macOS automation loop where Jev chooses the next GUI action from OmniParser, OCR, and accessibility observations, combining OmniParser CoreML, Apple Vision OCR, and macOS accessibility to identify controls locally, then using Jev to select the next action, with a bring-your-own token for Vercel AI Gateway, OpenRouter, or TypesafeAI, AGPL v3.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-workflow-builder", "name": "Jev workflow builder", "author": "CTNicholas", "date": "2026-09-21", "category": "app", "url": "https://github.com/CTNicholas/jev-workflow-builder", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 144, "likes": "", "detail": "", "arch": "Multiplayer workflow builder demo for Jev built on Liveblocks: wire Jev and LLMs together, call the workflow through the REST API, preview test runs, and see multiplayer state and cursors.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "karanb192-jev-architect", "name": "jev-architect", "author": "karanb192", "date": "2026-09-21", "category": "app", "url": "https://github.com/karanb192/jev-architect", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 7, "likes": "", "detail": "", "arch": "Agent skill for finding, designing, and validating useful Jev decision loops before committing them to an application. Design skill: finds, designs, and evaluates Jev decision loops, packaged as a skill with references on decision design and delivery.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists; no README found", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-as-policy-mujoco", "name": "Jev-as-Policy", "author": "YuanKJing", "date": "2026-09-21", "category": "app", "url": "https://github.com/YuanKJing/Jev-as-Policy", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 42, "likes": 0, "detail": "base: hosted; arch: approach/grasp/lift/carry/lower/release/withdraw/finish · X/Y/Z positive/negative/stay + fingers open/close/stay; training: harness reproduction; result: README; name: MuJoCo Studio", "arch": "Text state → Jev intent Choice → Jev motor Choices → Cartesian target + DLS IK → continuous servo; Jev receives no images and outputs no trajectories — geometry/contact state is converted to text/JSON", "approach": "", "training": "", "data": "MuJoCo scene; demo task: grasp orange cube, lift over blue barrier, carry to tan pad, release, withdraw", "result": "1080p/60fps panel demo of the carry task; 'evaluations of Astra + JEV on benchmarks such as RoboTwin will also be released soon'", "notes": "Self-contained reproduction of the public 'Jev as Policy' control structure; API key stays outside the repo.", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-chat-jarvis", "name": "jev-chat-jarvis", "author": "jev-chat", "date": "2026-09-21", "category": "app", "url": "https://github.com/jev-chat/jev-chat-jarvis", "base_model": "Jev + LLM drafting", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 6505, "likes": "", "detail": "arch: Android 11+ arm64-v8a app, about 27 MB, offline ML Kit Chinese OCR: Jev judges each incoming message - intent, danger level, whether to reply now - then an LLM drafts candidate replies, local knowledge base + contact profiles attached, one-tap fill into the input box, never auto-sends, QQ and X verified on real devices, Feishu via OCR", "arch": "Chat UI where assistant turns are grounded in Jev decisions", "approach": "", "training": "", "data": "", "result": "5,320 stars", "notes": "6.5k stars. Phone member of the jev-chat family; siblings under the same org: macOS, jev-chat-jarvis-mac, Windows, jev-chat-windows, iPhone keyboard, jev-chat-jarvis-ios, Android IME, jev-chat-jarvis-simple. Three separately configurable endpoints, judge/draft/vision; keys stored on-device, chat content sent only to your configured endpoint. The macOS member (jev-chat-jarvis-mac, 356 stars) is a WeChat/QQ floating panel: screen-read only, 86.4% zero-shot intent accuracy, ~1.5 s intent and 1.5-2 s candidate generation on M1 Pro; PRIVACY.md documents the data flow.", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-chat-windows", "name": "jev-chat-windows", "author": "恸码奇点", "date": "2026-09-21", "category": "app", "url": "https://github.com/jev-chat/jev-chat-windows", "base_model": "Jev for judgment + LLM for drafting, DeepSeek by default", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 557, "likes": "", "detail": "arch: local OCR reads the on-screen WeChat conversation · Jev judges intent and sentiment · three candidate replies with win probabilities are offered and one click fills the input box — sending is always manual; result: a single ~146 MB exe · keys stored in the HKCU registry, settings in a portable config.json; metric: one Jev judgment plus one draft per incoming message · zero model calls while idle; notes: the judgment kernel comes from the Android jev-chat-JARVIS · a floating window follows the open WeChat conversation, and group chats can pin a reply target", "arch": "A Windows-native reply assistant that sits next to any chat window: local OCR reads the on-screen conversation, Jev judges the intent and emotion, three candidate replies are offered and one click fills the input box, sending is always manual; the judgment kernel comes from the Android jev-chat-jarvis with collection swapped to Windows window screenshots and offline OCR, and it ships as a download-and-run exe, no Python, with two API keys, one for judging and one for drafting", "approach": "", "training": "", "data": "", "result": "", "notes": "525 stars. Windows build of the Jev chat family: a sidecar floating window that watches the chat app on screen; judgment kernel comes from the Android jev-chat-jarvis. 09-24: caizili999/jev-chat-windows, 7 stars - unofficial Windows WeChat reply-assistant fork: local offline OCR screen read, model-drafted candidate replies, one-click fill into the WeChat input box; no hook, no injection, no WeChat database reads.", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-dataops", "name": "jev-dataops", "author": "RenaGao", "date": "2026-09-21", "category": "app", "url": "https://github.com/RenaGao/jev-dataops", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 59, "likes": 0, "detail": "The public demo processes files in your browser tab using local screening rules and a byte-bigram statistical model; it does not call JEV or train an LLM; reloading clears its data and results, so download anything you need first, and you can choose your own UTF-8 JSONL or CSV file, up to 2 MiB and 1,000 rows. For domain-specific JEV screening, LoRA training, and larger datasets, use the self-hosted application: start with a local example on an ordinary computer, then connect a JEV screening provider and your own language model. JEV evaluates the data, and your target model learns from it: the project calls the JEV API for screening, and subsequent fine-tuning updates the Hugging Face model you configure, not JEV itself. First-time flow: run the demo, prepare your data, enable JEV screening, train a language model, and read the results, with a domain adaptation guide, CLI and APIs, larger datasets, troubleshooting, and deployment and development docs, and training environments from SFT to verl GRPO and PPO.", "arch": "JEV DataOps: an open-source Jev-powered workbench, a traceable pipeline for general and domain-specific data, upload, screen, evaluate data, train a model, and evaluate the result, where Jev is the selection and quality oracle, deciding what to keep and what to discard, with streaming data selection, quality evaluation, automatic LoRA training, and host-side evaluation, a browser workbench, a CLI, and Python and HTTP APIs for developers and researchers answering two recurring questions, is this dataset worth training on, and what changed after training, with a website and interactive demo requiring no installation, API key, or GPU.", "approach": "", "training": "Drives automatic LoRA training", "data": "Streaming datasets", "result": "", "notes": "Data-engineering loop where Jev decides what to keep and what a trained model should do.", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "devin-axis-jev-dsh-decision", "name": "jev-dsh-decision", "author": "devin-axis", "date": "2026-09-21", "category": "app", "url": "https://github.com/devin-axis/jev-dsh-decision", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 174, "likes": "", "detail": "", "arch": "Decision plugin for DeepSeek Harness and iPolloWork that lets OpenCode, Codex, and other agents use Jev to select tools, skills, and task owners and evaluate outputs.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-edge", "name": "jev-edge", "author": "kiwi0719", "date": "2026-09-21", "category": "app", "url": "https://github.com/kiwi0719/jev-edge", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 35, "likes": 0, "detail": "base: hosted; arch: lua-resty-jev-edge; result: docs/; name: typed judgment at the traffic edge", "arch": "Three-layer prompt-injection + abuse filter for nginx/OpenResty, APISIX, Kong, Envoy, Istio, HAProxy, Traefic, Caddy, Cloudflare Workers, Next.js/Node, Lambda@Edge, LiteLLM: asks 'what is this request trying to do to my service?' with Jev probabilities; fail-open, cached, hot-reloadable; v0.6.1; opm + LuaRocks + npm @jev-edge/js", "approach": "", "training": "", "data": "", "result": "Benchmarks in repo", "notes": "'Existing Jev guards run on the developer's machine and judge what an AI is about to do. jev-edge runs at the gateway and judges what the outside world is about to do.' EN + 中文 docs.", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "beingcognitive-jev-go", "name": "jev-go", "author": "beingcognitive", "date": "2026-09-21", "category": "app", "url": "https://github.com/beingcognitive/jev-go", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Code does the perception, Jev does the judgment, and every call to Jev is shown under the board with request and raw response. Every move is one call: code builds the position and a ranked, annotated pool of candidates, Jev answers one choice question over the pool and the page plays its pick. Forced tactics, a five, an open four, a forced block, mate in one, are played by code without a call and are logged as such. Jev cannot hallucinate a move, because it can only pick from the options it is given, and every option here is legal by construction: code computes, Jev judges. A replay shows Jev five most likely moves drawn on the board, its candidates beside it, and every move who-decided, rank, latency and tokens.", "arch": "A decision AI that plays Gomoku, Go and chess: Jev takes the board state and a typed question over a candidate pool and returns one choice with a probability for every option", "approach": "", "training": "", "data": "", "result": "Live at jev-go.chardonn.ai: play 15x15 Gomoku, 9x9 Go or chess against Jev; beat it and your name goes on the hall of fame. Hosted on Cloudflare Pages, static page plus Pages Functions, records in D1, sign-in with Google", "notes": "Motivation: playing Go against LLMs the usual way, describe the board in text and ask for a move, was expensive, slow and hallucinated, stones that were not there, illegal points, confident nonsense", "domain": "gaming", "score": "", "latency": "", "benchmark": "Go", "cost": "", "speedup": ""}
{"id": "f-jev-leftpad", "name": "jev-leftpad", "author": "f", "date": "2026-09-21", "category": "app", "url": "https://github.com/f/jev-leftpad", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 85, "likes": "", "detail": "base: jev-latest via @typesafe-ai/sdk; arch: max 10", "arch": "Left-pads a string using Jev: one Choice with criteria space_0…space_10; JS reads the number and inserts that many spaces; one API call per invocation, retries disabled", "approach": "", "training": "", "data": "", "result": "npm package jev-leftpad; Node 20+; mocked tests; 'Please don't use this in production'", "notes": "83 stars, 233 HN points.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mizchi-jev-lint", "name": "jev-lint, mizchi", "author": "mizchi", "date": "2026-09-21", "category": "app", "url": "https://github.com/mizchi/jev-lint", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 84, "likes": "", "detail": "The motivating example: a function whose comment promises null, whose body throws, and whose return type is Cart rather than Cart or null, which type-checks and lints clean, but which nothing in your toolchain flags. The code being judged is sent to an API, which is what the key pays for, and nothing runs locally except the matcher. Run: export the TypeSafe API key, then npx -y jev-lint check examples, which reports a finding with the rule sentence, the rule name comment-describes-declaration, the model's agreement at 0.89, and the cutoff this rule ships with at 0.56, under which nothing is reported; the example run produced six findings over two files for a tenth of a cent, and the cost of a bigger run is measured, not extrapolated. Forty-eight questions ship, most of them asked in several languages, 99 rules in all, roughly covering a name against the thing it names, a function, method, binding, type, class, trait, or module, an npm script against the command it runs, an sqlc query name against its SQL, such as applyDiscount that also saves the cart, or isAdmin bound to a string; and a guarantee the name implies, whether safe is actually safe, and so on.", "arch": "jev-lint, in English and Japanese: a text linter that uses Jev Score judgments to evaluate strings embedded in source code against configurable writing rules, a lint tool that uses Jev, a fast classifier that answers a natural-language question with a calibrated probability instead of writing text, to decide what a parser cannot, where a jev-lint rule is one sentence, an ast-grep matcher picks which code to look at, and a model is asked that sentence about each match, answering with a probability.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tamaratran-jev-pruner", "name": "jev-pruner", "author": "tamaratran", "date": "2026-09-21", "category": "app", "url": "https://github.com/tamaratran/jev-pruner", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 148, "likes": "", "detail": "", "arch": "Claude Code and Codex hooks that ask Jev which parts of long shell output remain relevant before sending them into model context. Context management: Claude Code plugin that trims long Bash output with Jev before the model ever sees it, keeping terminal noise out of the window.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "choxos-jev-reviewer", "name": "jev-reviewer", "author": "choxos", "date": "2026-09-21", "category": "app", "url": "https://github.com/choxos/jev-reviewer", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 35, "likes": "", "detail": "", "arch": "Systematic-review extraction app where Jev answers structured review forms and points reviewers to quoted source lines. Research-document extraction aid where Jev selects and verifies source lines for verbatim quotes; findings require human review and are not clinical decisions.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "health", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "eliaalberti-jev-rules", "name": "jev-rules", "author": "eliaalberti", "date": "2026-09-21", "category": "app", "url": "https://github.com/eliaalberti/jev-rules", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 49, "likes": "", "detail": "If you use Claude Code for a while, you end up with a pile of standing instructions: test the payment code, use British spelling, follow the deploy checklist; show all of them on every prompt and Claude wades through rules that have nothing to do with the request, and pick them by keyword and a rule is missed the moment the request does not contain its trigger word. New in 0.4.0: a rules pane beside the conversation lists every rule, turns green the ones Jev picked, and shows each score, using an early-access Claude Code feature, so it is off until you turn that on. Version 0.3.0 delivers each rule and map document once per session instead of on every matching event. A real Claude Code session, 40 seconds: a vague prompt gets no rules, then Claude edits a checkout file and the payments rule arrives, scored 0.97.", "arch": "jev-rules: a Claude Code plugin where Jev decides which project rules apply to each prompt and edited file, so Jev picks which of your rules apply to each prompt, and Claude only sees the ones that matter, asking a small, fast decision model called Jev one yes/no question per rule, is this request about that, and passing Claude only the rules that get a yes, taking well under a second and costing a fraction of a cent per prompt, with a fallback to showing every rule if anything goes wrong, so nothing is ever lost, version 0.4.0, no dependencies, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shimo4228-jev-skill-router", "name": "jev-skill-router", "author": "shimo4228", "date": "2026-09-21", "category": "app", "url": "https://github.com/shimo4228/jev-skill-router", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 4, "likes": "", "detail": "It runs in shadow mode by default: it records what it would have suggested and injects nothing, so its log can be checked against the skills your sessions actually used; in inject mode Claude also gets a one-line hint, for example adr-writer at 93%. It is published as a working reference and a measuring instrument, so that anyone considering the same idea can start from what was found, and the README explains why running it showed it is unlikely to help a strong model, and why it stops short of rewriting Claude's behavior.", "arch": "jev-skill-router, in English and Japanese: a Claude Code hook that asks TypeSafe Jev, the fast probability model, which of your installed skills fits each prompt, and logs the answer, telling Claude only if you opt in, a Claude Code plugin for people who have dozens of skills installed and find that the model sometimes skips the one that fits, where on every prompt a UserPromptSubmit hook sends the prompt and your skill roster to Jev, which never writes text and answers questions with a fixed set of answers, yes or no, or one pick from a list, with probabilities, and code reads those probabilities and names at most one skill, or none, while Claude still sees all your skills and makes the final choice itself, Python 3.10+, standard library only, MIT, version 0.2.0, experimental, published as an experiment that is unlikely to help a strong model.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ray-hughes-jevalyn", "name": "jevalyn", "author": "ray-hughes", "date": "2026-09-21", "category": "app", "url": "https://github.com/ray-hughes/jevalyn", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 20, "likes": "", "detail": "Define a decision class inheriting from Jevalyn::Decision, such as SupportTriage with a question of type choice, instructions asking which team should handle this, and criteria for billing covering payments, invoicing, refunds, technical covering bugs, outages, integrations, and sales covering pricing, upgrades, new accounts, with a confidence threshold of 0.75; then evaluate on the ticket body, and the result exposes the department, such as technical, and whether it is certain. The gem page shows version and download badges, with CI, and sections for install, decisions, guardrails, router, and testing.", "arch": "Jevalyn: a Rails-native Jev wrapper that turns typed, calibrated decisions into application control flow, fast, cheap, structured decisions baked into your Rails app's control flow, the decision layer for your Rails app, built on Jev 1.13, where a routing decision is made by a model, inside a Rails request, in about as long as a database query, not a prompt, not a parsed JSON blob, not a retry loop around something that might return prose this time, a RubyGem, Ruby 3.1+, Rails 7.0+, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tacticocc-jevbridge", "name": "Jevbridge", "author": "tacticocc", "date": "2026-09-21", "category": "app", "url": "https://github.com/tacticocc/jevbridge", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 44, "likes": "", "detail": "", "arch": "ACP and MCP adapter that exposes Jev typed decisions and computer-use actions to Codex, Claude, OpenCode, and other agents. Agent bridges: ACP and MCP adapter that exposes Jev typed decisions to Codex, Claude, Grok, and other LLMs.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; same repo as github.com/gamesonrblx/jevbridge — the GitHub account was renamed gamesonrblx → tacticocc, so the old URL is a duplicate of this one.", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "egetheengineer-jevcraft", "name": "jevcraft, egetheengineer", "author": "egetheengineer", "date": "2026-09-21", "category": "app", "url": "https://huggingface.co/datasets/egetheengineer/jevcraft", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "CC-BY-SA-4.0", "paper": "", "stars": "", "likes": 0, "detail": "base: live API; arch: Minecraft-style, Luanti engine; training: live play; data: CC BY-SA 4.0; name: Jev plays VoxeLibre", "arch": "TypeSafe's Jev playing VoxeLibre live and unattended, 21–25 Sep 2026; 2-minute 720p H.264 segments; viewer with Jev's decisions at jevcraft.vercel.app", "approach": "", "training": "", "data": "Gameplay recordings", "result": "Five days of unattended Jev gameplay archived as a dataset", "notes": "565 downloads.", "domain": "gaming", "score": "", "latency": "", "benchmark": "VoxeLibre", "cost": "", "speedup": ""}
{"id": "nassim-arifette-jevgrep", "name": "jevgrep", "author": "nassim-arifette", "date": "2026-09-21", "category": "app", "url": "https://github.com/nassim-arifette/jevgrep", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 74, "likes": "", "detail": "It is useful when a coding agent needs to locate behavior without knowing the exact identifier; understand a feature spread across implementation, configuration, and tests; reduce the amount of repository exploration placed in the agent's main context; and retrieve exact source excerpts instead of a generated summary. It complements exact tools such as rg: if you already know the symbol or literal, ordinary text search is usually faster. Use the CLI, or connect a coding agent through the local MCP server, with demo GIFs of initializing JevGrep, approving remote evaluation, and searching source code from the terminal, and of files explored without JevGrep compared with the relevant source located through it. Requirements: Node.js 24, npm, and a TypeSafe AI, Vercel AI Gateway, or OpenRouter key.", "arch": "JevGrep: a semantic code search CLI and MCP server, where Jev scores authorized repository excerpts and returns exact file paths and line numbers, helping coding agents find relevant code when they do not know the file name or symbol to search for, an npm package @nassim-arifette/jevgrep, Node.js 24+, TypeScript, MCP-compatible, MIT, where you ask a question such as where is session expiry handled, JevGrep scans the authorized repository, asks Jev to score all eligible fragments, then returns the original source excerpts with their paths and line numbers, and the calling agent reads those files in detail and continues with less exploratory context.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "allebee-jevgrep", "name": "jevgrep by allebee", "author": "allebee", "date": "2026-09-21", "category": "app", "url": "https://github.com/allebee/jevgrep", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "Example: install with uv tool install jevgrep-cli, export the OpenRouter API key, and tail -f server.log piped to jevgrep with the question that the line reports a real error, not routine noise. Why jevgrep: it filters by meaning, not keywords, and on the benchmark log it kept the 503 access-log line, the Traceback, and the OOMKilled event, none of which contain the word error, and dropped error_rate=0.00, 0 failed payments, and error handler registered; it is fast enough for a pipe, with a match appearing about 0.7 s after the line is written, so it works behind tail -f, kubectl logs -f, or journalctl -f; and it is cheap, about 0.004 dollars per 1,000 lines, on the benchmark 46× cheaper than Claude Haiku 4.5 and 170× cheaper than a frontier model, with a demo of following a live log and printing only the real errors, then summarizing payment failures with --explain, and sections for install, examples, benchmark, how it works, and known limitations.", "arch": "jevgrep: grep by meaning, a CLI that filters logs and text by meaning using plain-English yes/no questions answered by Jev, where you pipe in any text, ask a yes/no question in plain English, and get back only the lines where the answer is yes, a PyPI package jevgrep-cli, MIT, distinct from the unrelated nassim-arifette JevGrep code-search project.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevharness", "name": "JevHarness", "author": "TianyuCodings", "date": "2026-09-21", "category": "app", "url": "https://github.com/TianyuCodings/JevHarness", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 255, "likes": "", "detail": "", "arch": "Let an LLM write a task-specific harness for Jev: code, features, state, questions and criteria that turn observations into actions; optionally improved with rewards and complete execution traces; once frozen, execution uses that code and its Jev calls without the authoring LLM.", "approach": "", "training": "", "data": "", "result": "Pokemon result: after 5 reflection rounds the selected harness improved the eval win rate from 25% to 75%.", "notes": "Installable as a Claude Code plugin; slogan: reason deeply during development, freeze the strategy, let Jev make fast fuzzy decisions; English and Chinese.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "fazlerocks-jevmail", "name": "jevmail", "author": "fazlerocks", "date": "2026-09-21", "category": "app", "url": "https://github.com/fazlerocks/jevmail", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 78, "likes": "", "detail": "", "arch": "Read-only Gmail triage app where Jev assigns each message a tray, urgency score, and human-authorship judgment. Read-only Gmail triage that stores messages locally and asks Jev for bounded category, urgency, and human-sender probabilities.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevrev", "name": "JevRev", "author": "Alex314618-create", "date": "2026-09-21", "category": "app", "url": "https://github.com/Alex314618-create/JevRev", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 304, "likes": "", "detail": "", "arch": "The decision layer beside your LLM: a semantic layer that shortlists options, scores in a loop, filters plans, checks progress, and keeps attention on the work worth continuing.", "approach": "", "training": "", "data": "", "result": "", "notes": "Not another coding agent but the decision system around one; Node 20+, MIT; English and Chinese READMEs.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "keltokhy-jsort", "name": "jsort", "author": "keltokhy", "date": "2026-09-21", "category": "app", "url": "https://github.com/keltokhy/jsort", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 23, "likes": "", "detail": "", "arch": "Sorts text along a plain-English criterion using pairwise Jev Noul judgments and a locally fitted Bradley–Terry scale, with uncertainty and reliability reporting. Text measurement: ranks text along a plain-English criterion using pairwise Jev Noul comparisons and a locally fitted Bradley-Terry scale.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kalm-embedding-kalm-jev", "name": "KaLM-Jev, local Jev-style judgment service", "author": "KaLM-Embedding", "date": "2026-09-21", "category": "app", "url": "https://github.com/KaLM-Embedding/KaLM-Jev", "base_model": "KaLM-Reranker-V1-R2", "params": "undisclosed", "metric": "", "license": "", "paper": "", "stars": 35, "likes": "", "detail": "", "arch": "a local Choice/Score/Noul service built on the KaLM-Reranker-V1-R2 reranker: PyTorch plus Transformers, one model per process, returns structured judgments without generating answer text", "approach": "", "training": "", "data": "", "result": "recorded validation primarily on an H100 MIG instance, which works as an ordinary CUDA device; serves 127.0.0.1:8000 with one worker; CPU execution with float32; a full CPU performance evaluation has not been completed", "notes": "the underlying reranker paper is KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking, arxiv 2606.22807; demo on a Hugging Face Space, checkpoints in the Lychee KaLM Reranker collection; independent project, not affiliated with TypeSafe AI", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-drift", "name": "laya-drift", "author": "pythongiant", "date": "2026-09-21", "category": "app", "url": "https://github.com/pythongiant/laya-drift", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "The /calibrate command captures the plan, from an argument or derived from the session, and sets the drift baseline; every user prompt and every finished turn re-embeds the running context and recomputes drift, while tool calls inside a turn do not rescore; the live score renders as a pill in the session sidebar and as a compact badge next to the prompt, with scores below 50 green and 50 and above red, and toasts announce band changes and threshold crossings; /recalibrate re-anchors, making the previous anchor plus new context the new baseline, and drift resets to zero. How the score works: Laya is used purely as a semantic proxy, answered against the session state by a probe of two typed questions, alignment, on_plan, expanding, or off_plan, as a choice with weight 0.75, and plan_ref, doing_the_plan, doing_more, or doing_other, as a choice with weight 0.25, where plan_ref is down-weighted because its zero-shot answers are unstable, so the alignment probe carries most of the signal, and the weights are a starting point, not the result of benchmark evaluation, to tune in drift.json. Each answer is a calibrated probability distribution, and the baseline is built at calibration time from the current state digest, the same plan plus recent-activity structure used for scoring, so drift is measured from the moment of calibration onward, with a canonical fallback when the session has no activity yet.", "arch": "laya-drift: a semantic drift monitor for opencode sessions, an OpenCode plugin that monitors agent drift over time with typed decisions, embedding the session state with Laya, a non-autoregressive decision model that returns calibrated probability distributions for typed questions in a single forward pass, and measuring how far the running context has moved from the calibrated plan.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-ultrafast", "name": "laya-ultrafast", "author": "ipenywis", "date": "2026-09-21", "category": "app", "url": "https://github.com/ipenywis/laya-ultrafast", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 191, "likes": "", "detail": "", "arch": "Local open-weight port of browser-use/jev-ultrafast: the same browser agent, DOM snapshot, executor, safety checks and inspector, with the hosted Jev API replaced by local Laya through laya-mlx.", "approach": "", "training": "", "data": "", "result": "Median about 33 ms per decision on an M1 Max; one text-model call per task turns the goal into field values, the item to open, and a finish condition; the policy is a mix of narrow Laya questions and site-agnostic rules because Laya answers narrow questions well but not the open question of what the browser should do next.", "notes": "Apple Silicon only; DECISION_MODEL=typesafe restores the original hosted policy; all credit for the agent design goes to the original jev-ultrafast authors; identical re-upload ShinyDataTech/laya-ultrafast, 0 stars, dropped", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "abhishek085-open-spark-jev", "name": "open-spark-jev", "author": "abhishek085", "date": "2026-09-21", "category": "app", "url": "https://github.com/abhishek085/open-spark-jev", "base_model": "Qwen3-based spark-s1-4b-v6", "params": "", "metric": "", "license": "", "paper": "", "stars": 18, "likes": "", "detail": "It is for decisions that need a fast, structured answer rather than generated text, such as routing, urgency, tool-call safety, or whether an agent should continue, stop, or ask. Instead of asking a language model to generate an answer, you provide a state, one or more typed questions, and a set of options. The model is released as spark-s1, with weights on Hugging Face as spark-s1-4b-v6 and spark-s1-4b-v6-nvfp4, and how it is measured is documented in docs/EVALUATION.md.", "arch": "Open Spark Jev, part of Nokast, an open-source AI community: an open local System One implementation for NVIDIA DGX Spark, with a Qwen3-based model called spark-s1, a Jev-compatible API, and an architecture documented in docs/ARCHITECTURE.md, where state goes in and structured decisions come out, an independent, open-source implementation inspired by TypeSafe's Jev and System One, for local inference, explicitly not Jev, not a reproduction of TypeSafe's proprietary system, and not affiliated with TypeSafe.", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openthai-doom", "name": "OpenThai-SystemOne Doom", "author": "iapp-technology", "date": "2026-09-21", "category": "app", "url": "https://github.com/iapp-technology/openthai-systemone-doom", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": 0, "detail": "A tiny open decision model, 0.8B, no text generation, no vision, controlling Doom in real time, on your own machine. Every step, the game's symbolic state, health, ammo, enemies with distance and bearing, wall depth, last actions, is sent as text to OpenThai-SystemOne, which picks the next action.", "arch": "A tiny open decision model, 0.8B, no text generation and no vision, controlling Doom in real time on your own machine: each step the game's symbolic state, health, ammo, enemies with distance and bearing, wall depth and last actions, is sent as text to the model, which answers two typed questions in one forward pass, which of 7 actions to take as choice with a probability per action and whether an enemy is in the crosshair as noul, at about 40 milliseconds per decision", "approach": "", "training": "", "data": "", "result": "", "notes": "Companion to the openthai row, iapp/OpenThai-SystemOne.", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "philippdubach-pi-jev-router", "name": "pi-jev-router by philippdubach", "author": "philippdubach", "date": "2026-09-21", "category": "app", "url": "https://github.com/philippdubach/pi-jev-router", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 13, "likes": "", "detail": "Quality blends recorded runs from eval/results with the Artificial Analysis index, and writing quality uses the EQ-Bench Creative Writing v3 Elo instead, because a general intelligence index says nothing about prose; cost is a per-task estimate from catalogue prices, replaced by recorded mean cost as runs accumulate, and latency uses recorded means when they exist. There is one selection policy, and src/selector.ts holds a small fallback table for the case where the catalog cannot be fetched and no frontier exists; writing tasks get STE and Humanizer rules, code tasks run unit tests, and planning tasks get a structure check. Subscription routing is off by default: the frontier picks the model, and a plan route only changes how that model is reached, such as routing through a logged-in OpenAI Codex plan, where a plan route is best effort, a ChatGPT account does not support every Codex model, and a plan can hit its usage limit mid-session, with both refusals putting the route on a cooldown and falling back to the metered route, unsupported for 30 days, usage limit for an hour, anything else for ten minutes; Anthropic is a different case, since pi lists the same price on both routes and its docs state third-party usage details. Measured over four models, code pass rates ran from 33–100%, per the curation notes.", "arch": "pi-jev-router: a minimal Pareto-optimal OpenRouter model router for pi, based on Jev task classification, where the router ranks the whole OpenRouter catalog for each task, filters the catalog to models that fit the task, computes a Pareto frontier over quality, cost, and latency, then picks the knee point, the frontier member farthest from the chord that joins the cheapest and dearest models, needing no weights, so the pick follows the catalog and the recorded evidence on every task, with a fallback to a weighted value function when a frontier is too small or too flat for a knee.", "approach": "router", "training": "", "data": "", "result": "Measured over four models, code pass rates ran from 33% to 100% and the writing", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "33%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "devmortimer-pi-typesafe", "name": "pi-typesafe", "author": "devmortimer", "date": "2026-09-21", "category": "app", "url": "https://github.com/devmortimer/pi-typesafe", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 46, "likes": "", "detail": "", "arch": "Jev inside Pi. Jev is TypeSafe's judgment model: send it some state and typed questions and it returns probabilities instead of prose, in well under a second, for a fraction of a cent. This package gives Pi three things built on it", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "allebee-pytest-jev", "name": "pytest-jev", "author": "allebee", "date": "2026-09-21", "category": "app", "url": "https://github.com/allebee/pytest-jev", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 3, "likes": "", "detail": "When a prompt change breaks a reply, the failure shows which claims broke and how sure Jev was. Works with OPENROUTER_API_KEY or TYPESAFE_API_KEY; ships a demo gif, an examples folder, CI and PyPI packaging. Python 3.10–3.14, MIT.", "arch": "Pytest plugin for semantic assertions on LLM output, judged by TypeSafe's Jev, which returns calibrated probabilities instead of text: test what your LLM app's output means, not the exact wording. The jev fixture and jev.expect assert that a reply holds a set of claims and lacks another set, with all claims about one text sent to Jev in a single request.", "approach": "", "training": "", "data": "", "result": "Benchmark claimed in the README: same verdicts as Claude Sonnet 5 on the example tests, 5× faster and 110× cheaper, from real runs of jev-1.13 through OpenRouter.", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bohutang-sift", "name": "sift", "author": "bohutang", "date": "2026-09-21", "category": "app", "url": "https://github.com/bohutang/sift", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "", "arch": "Chrome extension where Jev labels X posts by content type and judges which posts should be hidden. Content labelling: Chrome extension that labels every post in an X timeline - substance, humour, chit-chat, promo, junk, or AI-written - with Jev decisions.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jostoz-sweetlips", "name": "sweetlips", "author": "jostoz", "date": "2026-09-21", "category": "app", "url": "https://github.com/jostoz/sweetlips", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Current status: functional and stable without AEC, sitting at a distance from the speaker. The classic MME microphone input broke and went silent after installing Equalizer APO, measured at a peak of 35–44 out of 32767 when speaking loudly, at any sample rate, and it is not reversible with more configuration, so the microphone now uses WASAPI; EchoNull, GPU AEC via Equalizer APO, was discarded, cancelling the real voice almost 100% with no exposed delay control; a homemade AEC3 WebRTC filter plus WASAPI is disabled for now, because with a fixed stream delay it over-cancelled, and with an automatic estimator it worked one turn and then generated garbage transcription in real silence, instability not yet resolved, suspected loopback-capture jitter. Verified live in a full session of 36 turns, a long natural conversation: it works well without headphones, standing more than 1 meter from the computer speaker, with less direct acoustic coupling, a real measurable factor, not just software; without AEC the microphone still picks up some of the bot's own echo, confirmed word-for-word in the transcription, but the filter handles it.", "arch": "sweetlips, in Spanish: a Pipecat-based local voice assistant running on Windows with a GPU, an edge voice pipeline where Jev is the System One decision stage, a conversational voice assistant running locally on Windows with an RTX 4090, wired as microphone, FireRedVAD, streaming ASR, Jev System One handling rules and local actions, then Groq hosting a System Two cloud LLM, then Windows TTS, and out to the speaker, on the pipecat-local-audio-edge branch, Jev as the decision stage of a local voice agent.", "approach": "", "training": "", "data": "", "result": "Jev as the decision stage of a local voice agent", "notes": "", "domain": "voice", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kyotofin-tax-doc-classifier", "name": "tax-doc-classifier", "author": "kyotofin", "date": "2026-09-21", "category": "app", "url": "https://github.com/kyotofin/tax-doc-classifier", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 454, "likes": "", "detail": "", "arch": "Tax-document classifier where Jev selects an IRS form and page kind from extracted PDF text. Classifies text-bearing PDF pages into IRS form and page-kind candidates with a confidence gate; document triage, not tax advice, and scanned pages need OCR.", "approach": "sdk", "training": "", "data": "", "result": "We ingest thousands of tax documents using an LLM pipeline built last tax season. Jev classifies 100% of our tax document corpus at $0.001 per page — 34× cheaper and 6× faster than that LLM setup. This is the classifier, open sourced.", "notes": "Curated in 2 awesome-jev lists", "domain": "finance", "score": "100%", "latency": "", "benchmark": "", "cost": "", "speedup": "6× faster"}
{"id": "typesafe-register", "name": "TypeSafe registration automation", "author": "Futureppo", "date": "2026-09-21", "category": "app", "url": "https://github.com/Futureppo/typesafe_register", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 126, "likes": "", "detail": "", "arch": "Bulk registration of typesafe.ai accounts with automatic API key creation: about 6 s per account single-threaded and about 0.12 s per account multi-threaded.", "approach": "", "training": "", "data": "", "result": "", "notes": "Chinese. Labeled Apache-2.0 for learning and research only with a reminder to respect the target service's terms; in practice an account factory. Sibling tools: 2951461586/Jev-Register-Tool, a full pure-HTTP pipeline from application to confirmation email to approval to registration to API key; juangchuank-ops/jev2api, a reverse-proxy gateway with native /v1/systemone, an OpenAI-compatible layer, an account pool and a console; FFatTiger/new-api-plugin-typesafe for QuantumNous/new-api; ahoo/cpa-plugin-systemone as a CLIProxyAPI provider.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lampysecurity-bug-bounty", "name": "Using TypeSafe AI in Bug Bounty", "author": "Ben Lampere", "date": "2026-09-21", "category": "app", "url": "https://www.lampysecurity.com/post/using-typesafe-ai-in-bug-bounty", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Lampy Security; notes: 1 point", "arch": "Security researcher's field report on using Jev in a bug bounty: $0.042 per million input tokens with output tokens free, and even fairly heavy unoptimized usage from a $5 credit cost only about $1", "approach": "", "training": "", "data": "", "result": "", "notes": "HN 2026-09-21", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "oso95-x-scanner", "name": "x-scanner", "author": "oso95", "date": "2026-09-21", "category": "app", "url": "https://github.com/oso95/x-scanner", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 18, "likes": "", "detail": "Install: Chrome 120 or newer, and until the Chrome Web Store listing is live, install from source, with Node 22 or newer to build, by cloning, npm install, and npm run build, then loading the dist folder as an unpacked extension in developer mode, pasting your TypeSafe API key, testing the connection, and saving; your key lives in this browser's extension storage and nowhere else, the only network traffic is the post text to api.typesafe.ai, and there is no server and no analytics. What you see: under each post, a chip styled like X's own metadata line, a green clean check when nothing crossed a threshold followed by every value in gray, an orange flag with the dimension and percentage when something did, and dashed gray no-text-to-analyze or promoted-not-analyzed for skipped posts, with a click-through card showing a bar per dimension, the token count, cost, and latency of that call; bottom right, a small panel with posts analyzed this session, dollars spent to four decimals, the last call's latency, and judgments per second, where the dollar figure is exact, no rounding.", "arch": "x-scanner: a Chrome extension where Jev scores and labels X posts with typed judgments and a live local cost counter, putting behavioral labels on every post you scroll past on X, judged by Jev, TypeSafe's System One model, with a counter in the corner showing exactly what it cost, where each post is sent to Jev with six typed questions in one request as soon as it comes within 800 pixels of the viewport, the answer comes back in about 150 ms as numbers, not prose, and lands in a chip under the post, usually before you have scrolled to it, most posts come back clean, and the ones that don't get an orange flag, so scrolling for a minute leaves the panel reading something like 80 posts, 0.0027 dollars.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "csskrtao-jev-to-answer", "name": "答案之书, the Book of Answers with Jev", "author": "csskrtao", "date": "2026-09-21", "category": "app", "url": "https://github.com/csskrtao/jev-to-answer", "base_model": "an LLM organizes the options, Jev picks among them", "params": "", "metric": "", "license": "", "paper": "", "stars": 17, "likes": "", "detail": "", "arch": "a Chinese daily-decision app whose tagline is when you cannot decide, Jev decides: the LLM understands the dilemma and lists the substantive, distinct candidates, at least two with no cap, without defaulting to just two or three; Jev then evaluates the candidates and returns its choice with the probability distribution", "approach": "", "training": "", "data": "", "result": "non-decision questions such as facts, opinions and chitchat are answered directly without forcing options; six reply styles, gentle, roast, concise, humorous, rational and healing, can rewrite the current answer; answers can be saved, re-read and follow-up questioned; history stays only in the current browser", "notes": "the project honors jev_demo, the Jev debug square, turning its requirement, parameters, Jev evaluation, explanation flow into a directly usable Book of Answers experience; Node 22+, supports both the TypeSafe API and the Vercel AI Gateway channels", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rmalde-minecraft-agent", "name": "Astra and Jev Minecraft agent", "author": "rmalde", "date": "2026-09-20", "category": "app", "url": "https://github.com/rmalde/minecraft-agent", "base_model": "GPT-6 Astra or GPT-5.6 Sol plans, Jev selects player actions", "params": "", "metric": "", "license": "", "paper": "", "stars": 546, "likes": "", "detail": "", "arch": "Minecraft Java 1.16.5 agent on the official vanilla server via Mineflayer: GPT-6 Astra or GPT-5.6 Sol plans, Jev picks the player's action, a read-only Java sensor reports the exact dragon head position; no game rules or entity state are changed; every selected action is sent through the normal player protocol", "approach": "", "training": "", "data": "", "result": "the latest verified run nether-final-08 completes in 8 minutes 43.300 seconds, 40% shorter than the previous video's 14 minutes 31.800 seconds; the End combat stage takes 152 seconds instead of 332 seconds; the agent started with an empty inventory in a fresh Survival/Peaceful world, used the Nether for travel, killed the dragon with six bed explosions on the first landing and reached the exit with full health and no deaths; 131 Jev decisions and 35 Astra calls; all 17 run checks, eight route/camera/screen checks and 29 local tests passed", "notes": "a hidden native Minecraft client renders the game without desktop input; camera control uses continuous turns limited to 240 degrees per second per axis with 960 degrees per second squared acceleration; the video is 960x540 at 20 frames per second without audio; a translucent full-width Menlo banner shows the model names, elapsed milliseconds, XYZ coordinates, the planner objective and the selected action; repo calls the model JEV; independent project, not affiliated with TypeSafe AI", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "anil-matcha-awesome-jev-by-typesafe", "name": "Awesome Jev by TypeSafe", "author": "anil-matcha", "date": "2026-09-20", "category": "app", "url": "https://github.com/anil-matcha/awesome-jev-by-typesafe", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 850, "likes": "", "detail": "", "arch": "Evidence-backed Jev use cases, patterns, prompts, and starter code. Curated list: a peer collection of Jev use cases, patterns, prompts, and starter code, with a video walkthrough of eight projects people already built. Evidence-backed use cases, patterns, prompts, and starter code.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-skill", "name": "Awesome Jev Skills", "author": "wuyoscar", "date": "2026-09-20", "category": "app", "url": "https://github.com/wuyoscar/jev-skill", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 490, "likes": "", "detail": "", "arch": "A collection of Jev demos, workflows and agent skills: 60 projects and resources, 5 installable skills, 108 scenarios, and 14 recorded input/output pairs.", "approach": "", "training": "", "data": "", "result": "", "notes": "MIT; Jev chooses, classifies and scores while the agent supplies evidence and takes action; English and Chinese.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "beatapi-awesome-jev", "name": "Awesome JEV, source-reviewed project gallery", "author": "BeatAPI", "date": "2026-09-20", "category": "app", "url": "https://github.com/BeatAPI/awesome-jev", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 57, "likes": "", "detail": "", "arch": "a source-reviewed gallery of JEV-related projects, organized so that for a given use case you see what each project does and which implementation to learn from", "approach": "", "training": "", "data": "", "result": "", "notes": "one of several community directories for the Jev ecosystem released in this window; charetterat's awesome-jev-essentials and Frank-ZY-Dou's awesome-jev are in the long-tail wave; MIT, curated for the linux.do community", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bilune-jev-design", "name": "Can a model design a dashboard", "author": "bilune", "date": "2026-09-20", "category": "app", "url": "https://github.com/bilune/jev-design", "base_model": "Jev via the TypeSafe SDK or the Vercel AI Gateway", "params": "", "metric": "", "license": "", "paper": "", "stars": 55, "likes": "", "detail": "", "arch": "experiment in whether a model can design a dashboard: designing one means making the visual decisions of a whole interface from a single sentence and having them hold together across every component on the screen, a larger job than picking a colour or filling in a theme; type an 18th century printed book and a second or two later the console is one - aged paper, ink at zero chroma, small caps labels, hatched chart fills, square corners, and no icon set at all because a printed book does not have any", "approach": "", "training": "", "data": "", "result": "", "notes": "six briefs, 83 seconds, recorded in real time with nothing sped up or cut, the pauses are the engine answering; the video is also in the repo at docs/demo.mp4; the console runs without a key, the brief field is the part that needs TYPESAFE_API_KEY and it stays on the server; two routes reach the model: by default the TypeSafe SDK calls api.typesafe.ai, and setting JEV_ROUTE=gateway sends the same questions through Vercel's AI Gateway on AI_GATEWAY_API_KEY, moving billing and observability to the Vercel account; src/design/jev/client.ts renames three fields between the two SDKs and derives a fourth, and that is the whole adapter; either way the endpoint is metered, because a request spends real money against somebody's key: two Vercel WAF rules on POST /api/design/generate meter at the edge before the function is invoked, three generations per visitor per ten minutes; independent project, not affiliated with TypeSafe AI", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hazumi-jev-filter", "name": "hazumi", "author": "Zumi Studios", "date": "2026-09-20", "category": "app", "url": "https://www.hazumi.news/best", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "A web, iOS, and Mac reader for Hacker News that uses Jev to filter and rank large discussion threads: instead of showing the whole thread, the reader asks Jev which stories and comments surface in your feed, so a huge thread collapses to its best parts. Show HN, 2026-09-20, 7 points. The project site hazumi.news sits behind a Cloudflare bot check, so this row's text comes from the announcement rather than the site.", "arch": "hazumi for Hacker News: a web, iOS and Mac HN reader that uses Jev to filter and rank large discussion threads so the feed surfaces the best stories", "approach": "", "training": "", "data": "", "result": "", "notes": "Show HN 2026-09-20 7 points; name: Jev filter", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-doom-4-models", "name": "I gave Jev, Laya, finetuned ModernCE and Qwen3.5 the controls to Doom", "author": "r/LocalLLaMA", "date": "2026-09-20", "category": "app", "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wl1yzq/i_gave_jev_laya_finetuned_modernce_and_qwen35_the/", "base_model": "TypeSafe Jev 1.13 + Laya + ModernCE + Qwen3.5-4B", "params": "", "metric": "Mean kills in Defend the Center, mean survival in Health Gathering", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: Doom scenarios over health and ammo state", "arch": "Four-model ViZDoom agent test: a deterministic Python adapter reads visible-object labels, bounding boxes, and HUD values into a short text description; output is one button action; two scenarios, Defend the Center and Health Gathering; video lines up same-seed starts and each model's actions change its own game; each local model ran on a single DGX Spark with GB10 and 128 GB unified memory, Jev via TypeSafe's hosted API; 320x240, 35 Hz game clock, five decisions per second, 30-second episode cap, averages over eight seeds", "approach": "", "training": "", "data": "", "result": "Defend the Center mean kills: Jev 1.13 5.63, finetuned Qwen3.5-4B LoRA 3.63, Laya English 1.25, finetuned ModernCE-base-nli 1.25; Health Gathering mean survival: Jev 13.03 s, Laya 11.89 s, ModernCE 11.66 s, Qwen 11.31 s; call p50 latency: ModernCE 7.6 ms, Laya 16.2 ms, Jev 117.3 ms, Qwen 146.8 ms, p95 8.8 / 17.1 / 199.8 / 150.9 ms", "notes": "Jev wins both scenarios on task results but is roughly 7×–15× slower per call than the local models; observed 2026-09-24.", "domain": "general", "score": "", "latency": "Jev p50 117 ms, local 7.6–16 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-atlas-zh", "name": "JEV Atlas ZH", "author": "ZeroX-01", "date": "2026-09-20", "category": "app", "url": "https://github.com/ZeroX-01/jev-atlas", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": 0, "detail": "Features: real video preview, detail playback, original text with Chinese reference translation, source-code and source links; featured topics, favorites, personal leads, exporting filter results and personal backups, and import-merge of backups; keyboard search shortcut, native dialogs, and Esc to go back; phone layout, loading, empty, and error states, and a reduce-motion preference. Coverage and boundaries: initial curation on 2026-09-20, with incremental automatic collection since, where 202 X records come from a user-specified QMuse public index, 7 added public projects or resources come from GitHub, Gist, Callstack, DEV, and the TypeSafe site, for an initial total of 209 public items, 203 with video, 6 image-text or project notes, 17 with identifiable source-repo links, and 30 key records with Chinese titles and summaries, while the rest keep the source-provided text or reference translation; scheduled collection from QMuse, GitHub, DEV, and Hacker News is not full-web coverage, X content is tracked through the QMuse index, with no direct X, YouTube, Bilibili, or Xiaohongshu search API, deleted source content is not auto-removed from history, new GitHub, DEV, and Hacker News links are first marked auto-collected and pending verification and grouped under resources and discussion, only public metadata containing a standalone Jev term plus TypeSafe, AI, or model context enters, and keyword matching can miss or wrongly include items, so it does not replace human verification or running the project; scene labels are human-annotated for key items and keyword-rule for the rest; playback and engagement metrics are source snapshots, not live data; and favorites and new records live in the browser's localStorage, not synced across devices, so export a backup before clearing browser data. Ongoing collection runs on a schedule, every 6 hours by default, with a minimum of 1 hour, configurable in collector.config.json, and the public version is built and deployed to GitHub Pages by a workflow that collects, commits updated data, and builds every 6 hours, without depending on the author's Mac being on.", "arch": "JEV Atlas: a Chinese-first, original-source-preserving static index of TypeSafe JEV community content, in React, TypeScript, and Vite, supporting static deployment, live at zerox-01.github.io/jev-atlas, with Chinese and English keyword, original-text, and author search, combined filtering by scene, video or image-text or open source, and source, sorting by featured, time, or source views, and grid and list views.", "approach": "", "training": "", "data": "209 items: 202 X posts via the QMuse public index plus 7 projects from GitHub, Gist, Callstack, DEV and TypeSafe; 203 carry video", "result": "scheduled collection from QMuse, GitHub, DEV and Hacker News; keyword rules flag auto-collected entries as awaiting verification", "notes": "not full-web coverage; X is tracked only through the QMuse index; site at zerox-01.github.io/jev-atlas", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-cookbook", "name": "Jev Cookbook, chr-kelly", "author": "chr-kelly", "date": "2026-09-20", "category": "app", "url": "https://github.com/chr-kelly/jev-cookbook", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Runnable question sets for Jev organized as recipes rather than a link list: describe your decision, copy a working question, run it now; the gap it targets is how to turn your own judgment into a question set that holds up on real data, across Jev choice, noul and score primitives", "approach": "", "training": "", "data": "", "result": "", "notes": "Show HN 2026-09-20; tested on 5,500 CLINC150 inputs", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mayank953-jev", "name": "Jev micro-client", "author": "mayank953", "date": "2026-09-20", "category": "app", "url": "https://github.com/mayank953/jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 19, "likes": "", "detail": "Quickstart: clone, npm install, optionally copy .env.example to .env, npm start, and open localhost:3000; the app runs TypeScript directly via Node's native support, no compile step, no ts-node, and no API keys are needed to explore the UI, since any demo whose key is missing runs in simulated mode, with output clearly badged simulated and a status pill in the header explaining why, and you can add keys any time and reload, with most changes not requiring a restart, though the server does need a restart to pick up a newly added key. Demo mode for presenting: flip the demo-mode switch in the header, or click Start guided tour on the homepage, and the app explains itself as it runs, with the normal UI untouched and demo mode only adding things on top, such as a narrator bar, a caption at the bottom narrating every step as it happens, where Jev answered 3 typed questions in 197 ms, and only Haiku 4.5 is called, the other tiers costing 0, with a step counter and a short log; a reference YouTube video and screenshot document the app.", "arch": "Jev demos: six live, side-by-side demos of TypeSafe's Jev, a System One model that returns typed, probabilistic decisions, 70–500 ms, 0.042 dollars per 1M input tokens, output free, instead of generated text, where Jev makes the decision, an LLM writes the words, and your code owns the control flow, with the LLM side switchable between Claude, Haiku 4.5, Sonnet 5, Opus 5, and Kimi, K2.6 and K3, from the header, so every tab, price, and pipeline label follows the switch, Node 22.6+, MIT, with no build step and no keys required to try it.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "2.9 s per 100-token decision on M4", "benchmark": "", "cost": "", "speedup": ""}
{"id": "studioigor-jev-rts", "name": "JEV RTS, an army whose decisions come from Jev", "author": "studioigor", "date": "2026-09-20", "category": "app", "url": "https://github.com/studioigor/jev-rts", "base_model": "Jev via the JEV API", "params": "", "metric": "", "license": "", "paper": "", "stars": 17, "likes": "", "detail": "", "arch": "an RTS experiment for the studioigor YouTube channel: what happens when you hand an AI the decisions of a whole army; two factions start from a town hall and one worker, around a forest, gold mines, a river with fords and fog-of-war territory; workers mine resources and build, swordsmen meet the enemy in close combat, archers support from range; unit decisions, route choice and the enemy's strategy all come from the JEV API", "approach": "", "training": "", "data": "", "result": "Unity executes those decisions and enforces the world's rules, economy, movement, collisions, combat and visibility; there is no hidden local AI that keeps deciding when the service is unavailable: if the service is down, the game says so", "notes": "released as the source code from the video so the observation-to-request-to-command loop can be studied; Jev is queried at unit and squad level; the project is left open for experiments with rules, factions, interface and unit behaviour; Russian documentation", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "valentynkit-jev-belay", "name": "jev-belay", "author": "valentynkit", "date": "2026-09-20", "category": "app", "url": "https://github.com/valentynkit/jev-belay", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 18, "likes": "", "detail": "", "arch": "Claude Code Stop hook that asks four Jev questions when files changed without a passing check, blocking unverified completion claims while failing open on errors. Coding agents: Claude Code Stop hook that reads the transcript for evidence and spends one four-question Jev call only when files changed with no passing check since, failing open on any error. Claude Code Stop hook", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; no README found", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "valentynkit-jev-commit", "name": "jev-commit", "author": "valentynkit", "date": "2026-09-20", "category": "app", "url": "https://github.com/valentynkit/jev-commit", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 13, "likes": "", "detail": "", "arch": "Pre-commit hook where Jev checks the message against the staged diff and flags debug leftovers, unmentioned work, and added credentials. Developer tooling: pre-commit hook where one Jev call judges whether the commit message matches the staged diff, flags debug leftovers and unmentioned work, and blocks only on a detected credential. Pre-commit hook where one Jev call judges", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; no README found", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "Jev-Live", "name": "Jev-Live", "author": "JackZH26", "date": "2026-09-20", "category": "app", "url": "https://github.com/JackZH26/Jev-Live", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "Cloud Jev has completed two rounds of real Steam testing: the API and target chain are valid, and game navigation still blocks full matches, per the cloud validation notes. The current modules: a desktop workstation with real OBS preview, platform connections, game modes, three-way status, and run logs; YouTube login via the official Google browser auth, PKCE, a local random-port callback, and offline renewal; Twitch login via public-app device-code grant with the official activation page; X live streaming via the official Live Studio RTMPS source, stored encrypted locally, with live events and visibility managed in X's back office; credential protection via Windows DPAPI or safeStorage, where stored tokens and keys are never returned to the frontend, and manually entered X keys are cleared after submit; game selection by scanning the local Steam install library, add, select, remove, and launch through Steam; game control with ETC-dedicated structured observation, real-time operation, local tactics, and optional JEV suggestions, needing a compatible game version, with win rate pending real measurement; manual takeover with mode switching, expiry of stale actions, and a Ctrl-Alt-M emergency takeover; OBS output to any combination of YouTube, Twitch, and X, with separate game and process audio capture, all three paths at 1080p60; streaming orchestration that only checks the selected platforms and starts once all are ready, with stop and failure recovery limited to the outputs chosen this run; leak prevention via gitignore rules, commit and push scanning, and GitHub Actions checks; and five languages, simplified Chinese, traditional Chinese, Japanese, Korean, and English, with the UI, prompts, logs, and tray switching in sync. Wired in: a built-in Live2D or VRM character, all-English commentary with the original Melo English voice, per-platform layout editing, local-model commentary, local voice, and YouTube and Twitch chat connectors, where auto-play and manual-play differ only in who holds game control, and both share the auto-hosting. Running locally needs Windows 10 or 11, Node.js 24, the .NET 10 SDK, Steam, and OBS Studio 32, with a desktop distribution that already includes the observation runtime, so the streamer does not need the .NET SDK or Unreal Engine, and default NVIDIA NVENC encoding, with other GPUs configured per the selected separation. Cubism Core is a separate download under a separate license, outside the repository's MIT license.", "arch": "Jev-Live, JEV Studio, in Chinese with an English README: an open-source Windows studio for Steam games plus virtual streaming, where auto-play is being upgraded to Jev tactical decisions plus a shared bot executor, keeping the player's camera and manual takeover, with goal renewal that does not restart the path, and feedback on blocking reasons, an open-source virtual-streamer workstation aimed at the Steam game library, where you add installed games, pick one to stream, launch it through Steam, choose auto-play or manual play, connect YouTube and Twitch through official OAuth, connect X through the official Live Studio RTMPS source, and use OBS for multi-platform streaming, with the first test game being Enter the Cube Playtest, Steam AppID 5272970, not a dev build or editor, at 0.1.4 development preview, with a new executor that needs a matching Steam game build and no claim of beating Pro Bot.", "approach": "", "training": "", "data": "", "result": "Jev tactical decisions plus a shared bot executor, with player POV and manual takeover; first test is Enter the Cube Playtest; cloud Jev validated over two rounds, in-game navigation still blocks full matches", "notes": "0.1.4 dev preview; does not claim to beat Pro Bot; Chinese documentation", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kyle-pena-jevchat", "name": "jevchat", "author": "kyle-pena-nlp", "date": "2026-09-20", "category": "app", "url": "https://github.com/kyle-pena-nlp/jevchat", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 91, "likes": "", "detail": "arch: incl. truncated token lists; result: author", "arch": "Turns Jev into a chat model: at each step Jev answers 'which symbol comes next' over an alphabet + STOP option; the sampler draws from the normalized choice distribution; multiple alphabets and sampling strategies", "approach": "", "training": "", "data": "", "result": "Interactive chat / ask / alphabets / bench modes; 'the cost is somewhat impractical, and the results are hilarious'", "notes": "89 stars, 175 HN points. Claude-accelerated experiment per author.", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "imohitmayank-jevfill", "name": "Jevfill, autofill web forms from unstructured notes", "author": "imohitmayank", "date": "2026-09-20", "category": "app", "url": "https://github.com/imohitmayank/jevfill", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 18, "likes": "", "detail": "", "arch": "Chrome extension that autofills web forms from unstructured notes with Jev: paste your details once as plain text, no structured profile required, open a page with a form and click autofill page, Jev matches each form field to the best line from your notes and fills it in", "approach": "", "training": "", "data": "", "result": "filled fields are briefly highlighted so you can review before submitting; password and payment fields are never sent to Jev or filled", "notes": "Manifest V3, TypeScript, MIT", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevflake", "name": "Jevflake", "author": "KranzL", "date": "2026-09-20", "category": "app", "url": "https://github.com/KranzL/Jevflake", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 13, "likes": "", "detail": "The package does three things: it sets up Snowflake so it is allowed to call the Jev API; it creates SQL functions that call Jev, namely jev_noul, jev_choice, jev_score, and jev_ask; and it gives you dbt macros and tests so answers are stored once, reused, and checked like any other model, with the Terraform module doing the first two steps instead of dbt. The three question types: noul is a yes or no question, and the answer is a probability from 0–1 that the answer is yes; choice picks one option from a fixed list, and the answer has the pick, a probability for each option, and a confidence number; and score rates the row against ordered levels, and the answer has a score, probabilities, and a confidence number, where the score is the probability-weighted average of the level numbers, so three levels give a score from 0–2. Before you start: a Snowflake account with external access turned on, which trial accounts have off by default, and your Snowflake account representative has to turn on; a role that can create an integration, by default ACCOUNTADMIN; a TypeSafe API key from console.typesafe.ai; and dbt 1.8 or newer with dbt-snowflake, tested on dbt 1.12, where the Python function needs the pandas and requests packages, and Snowflake installs them for you, from its PyPI repository or its Anaconda channel depending on the account.", "arch": "Jevflake: ask TypeSafe's Jev decision model questions about your data from inside Snowflake, a dbt package, with a Terraform module for teams that manage Snowflake that way, where Jev does not write text, you give it a row and a typed question, and it gives back a typed answer with a probability, making it a good fit for SQL, since the answer is a number or a label you can filter, join, and test, and it is not affiliated with TypeSafe AI, Snowflake, or dbt Labs.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chenmingtang830-jevgraph", "name": "JevGraph", "author": "chenmingtang830", "date": "2026-09-20", "category": "app", "url": "https://github.com/chenmingtang830/jevgraph", "base_model": "", "params": "", "metric": "85.7% in API mode", "license": "Apache-2.0", "paper": "", "stars": 24, "likes": "", "detail": "arch: six bounded stages ending in Jev relation choices; result: p95 latency, cost per 100 decisions and planned-case accuracy benchmarks in repo; license: Apache-2.0", "arch": "LangGraph-style decision graphs: TypedDecisionNode, Jev 1.13, TypedDecisionEdge, router, JevGraphRunner; deterministic decisions instead of raw LLM outputs", "approach": "router", "training": "", "data": "", "result": "Local mode, HF: 0.5 s, 71.4% accuracy, $0; API mode: 1.2 s, 85.7%, $0.02 per 1k; 54% cost reduction; 17 test files", "notes": "Open-source schema-guided document-to-graph pipeline: PDF, DOCX, PPTX or text, then local parsing with a canonical page map, entity mentions, local candidate blocking, Jev relation choices, deterministic schema checks, and proposed, review or rejected edges. Replaces open-ended prompt-to-triple extraction with bounded, inspectable stages for fixed or slowly changing ontologies where edge precision, source evidence and reproducibility matter.", "domain": "agent", "score": "85.7%", "latency": "0.5 s local / 1.2 s API", "benchmark": "", "cost": "$0.02 per 1k decisions in API mode", "speedup": ""}
{"id": "jevloop", "name": "JevLoop, zjunlp", "author": "zjunlp", "date": "2026-09-20", "category": "app", "url": "https://github.com/zjunlp/JevLoop", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 19, "likes": "", "detail": "JevLoop is an independent project, not affiliated with or endorsed by TypeSafe AI; the name is a reference to the model it routes to, nothing more. It ships in English with a Chinese README. The demo runs from a fresh clone with no key, no network, no npm install, and shows the loop trace, where laya falls back to rule-judge when unreachable, and every decision, with loop.needsTool at needs_tool=0.95 in 4.9 ms, loop.pickTool calling list_dir, and loop.gradeRisk auto at risk=0.0. One session, three views: the conversation, the decision trace, and the compiled DECISION.md, with the web UI currently in Chinese and an English version in progress.", "arch": "JevLoop: the agent loop where decisions do not cost a large language model call, with zero dependencies, where every fork in your agent loop is a full LLM call, and not one of them is generation, and should I act, which tool, which file, is this safe, did it work, am I done, can I ship this are each a pick, a score, or a yes/no answer, one forward pass over a fixed candidate set, 10–40 ms, no tokens generated, so JevLoop routes them to a decision model, Jev or Laya, and keeps the LLM for the one thing only it can do, writing, and it is a runnable harness, not a demo, with the loop, the backend seams, the accounting, and a web UI all in one command, with no dependency, no build step, and no API key.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevopt", "name": "jevopt", "author": "Ramneet-Singh", "date": "2026-09-20", "category": "app", "url": "https://github.com/Ramneet-Singh/jevopt", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 4, "likes": "", "detail": "base: Jev decides each inlining call site; name: Jev-guided compiler inlining", "arch": "C/C++ compiler driver combining Clang's optimiser with Jev for function-call inlining decisions: at every discretionary call site, Jev chooses whether to inline using the current LLVM IR, original source, build context and a small set of structural facts; --verify runs both the -Oz reference and the Jev-guided binary and compares exit code + stdout", "approach": "", "training": "", "data": "", "result": "58.2% code-size reduction on Statemate from the Embench benchmark; complete decision trace in result.json; targets Linux + pinned LLVM 21 toolchain; Python 3.12 + uv", "notes": "Listed in awesome-jev-typesafe; created 2026-09-20.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kylemclaren-jevql", "name": "jevql", "author": "kylemclaren", "date": "2026-09-20", "category": "app", "url": "https://github.com/kylemclaren/jevql", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 13, "likes": "", "detail": "arch: no extension · no README found", "arch": "Adds jev, jev_prob, jev_choice, and jev_score to vanilla PostgreSQL queries through a psql-shaped CLI and Go, TypeScript, and Python SDKs. Data tooling: psql-shaped CLI and Go/TypeScript/Python SDKs that run plain SQL on a vanilla Postgres and then ask Jev Noul, Choice, or Score questions about each surviving row so the client can apply jev filters, jev_prob", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-playground", "name": "Laya playground", "author": "wdobry", "date": "2026-09-20", "category": "app", "url": "https://github.com/wdobry/laya-playground", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 157, "likes": "", "detail": "", "arch": "A website, three games, a benchmark and an agent skill for Laya: Flappy, a lane runner and Tetris at about 30 decisions per second, plus an editor where you write text and typed questions, see every answer with its probabilities, and compare all three checkpoints.", "approach": "", "training": "", "data": "", "result": "500-example benchmark against hosted Jev on the same labelled examples with the same questions and no tuning: Jev is more accurate out of the box, Laya matches it on simple questions and answers several times faster from a laptop, and it is free and yours to fine-tune.", "notes": "Live site at brainfunctioncollapse.com/laya replays recorded runs; the local server is stdlib Python bound to loopback with one dependency, laya; first start downloads 2.3 GB of open weights.", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-router", "name": "laya_router, Laya model router", "author": "glukicov", "date": "2026-09-20", "category": "app", "url": "https://github.com/glukicov/laya_router", "base_model": "Laya", "params": "421M", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 6, "likes": "", "detail": "arch: Laya as the model router", "arch": "Smart model router with two brains: every request first goes to the 421M Laya System 1 decision engine, which decides whether a small, medium or powerful model should answer, then the work goes there; that routing decision sits on the critical path of every request, so the router's own latency and bill are pure overhead - the point being that the router does not need to be a language model.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Python 3.12; head-to-head against gpt-5-nano used as the router; Apache-2.0; CI; evaluation write-up in docs/EVAL.md, dataset in data/, accompanying Medium article by lukicov. GitHub new-repo sweep 2026-09-25.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "league-of-jev", "name": "league-of-jev", "author": "viraatdas", "date": "2026-09-20", "category": "app", "url": "https://github.com/viraatdas/league-of-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Jev plays Yasuo in League of Legends with three heads: Strategy at 1/s for intent, destination, recall and danger; Tactics at 6–7/s for action, target and distance; Build every 20 s; code reads the Riot Live Client Data API and color-mask screen reads, then executes", "approach": "", "training": "", "data": "", "result": "", "notes": "per-champion action spaces for Yasuo and Thresh kits, filtered each step to what is possible", "domain": "gaming", "score": "", "latency": "20 s", "benchmark": "", "cost": "", "speedup": ""}
{"id": "noulgrep", "name": "noulgrep", "author": "colindomoney", "date": "2026-09-20", "category": "app", "url": "https://github.com/colindomoney/noulgrep", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": 0, "detail": "Runs Semgrep over a set of deliberately vulnerable repositories, Juice Shop, WebGoat, crAPI, NodeGoat, VAmPI, DVWA, each pinned to a commit SHA in targets.toml, and normalises every finding into the structured state object that phase 2 will hand to Jev, TypeSafe's typed classifier, for triage. Phase 1 is Semgrep only: no classification and no ground-truth labelling, just reproducible raw output.", "arch": "Runs Semgrep over a set of deliberately vulnerable repositories, Juice Shop, WebGoat, crAPI, NodeGoat, VAmPI and DVWA, each pinned to a commit SHA, and normalizes every finding into the structured state object that phase 2 hands to Jev for triage; phase 1 is Semgrep only, no classification and no ground-truth labelling, just reproducible raw output, a deduplicated findings file and a summary", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "openjev-siliconlab", "name": "OpenJev playground", "author": "SiliconLabAI", "date": "2026-09-20", "category": "app", "url": "https://github.com/SiliconLabAI/OpenJev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 141, "likes": "", "detail": "", "arch": "Open-source System One-style decision playground: state plus typed questions into structured answers, with three backends: parallel LLM micro-scorers, a single structured JSON call to any OpenAI-compatible model, and Mapika/decider with real System One weights and calibration-aware RL.", "approach": "", "training": "", "data": "", "result": "", "notes": "Next.js with a YouTube tutorial; decider-2b v10 speaks the TypeSafe wire format at POST /v1/systemone and needs about 4 GB of VRAM on CUDA.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "robovej", "name": "RoboJEV, two-stage Jev control of a Franka Panda", "author": "lykycy123", "date": "2026-09-20", "category": "app", "url": "https://github.com/lykycy123/RoboJEV", "base_model": "TypeSafe Jev 1.13", "params": "", "metric": "frozen 60-episode campaign, 3 tasks, seeds 0–9: pick & place 10/10, surface push 10/10, stack on a pedestal 8/10 against a 10/10 rule baseline; two added tasks in a separate 40-trial campaign", "license": "Apache-2.0", "paper": "", "stars": 47, "likes": "", "detail": "", "arch": "a small, inspectable robotics laboratory: two-stage Jev control of a Franka Panda in MuJoCo 3.3.7 - measured simulator state, structured state not images, selects a task intent, then selects X/Y/Z directions and a gripper command; a Cartesian controller executes the action using real MuJoCo contacts; each task has an independent physical success check, model answers cannot declare success", "approach": "", "training": "", "data": "the original campaign is three tasks, two policies, seeds 0–9; the two added tasks, loose-fit peg insertion and gate obstacle pick & place, run a separate 40-trial campaign with ten seeds per task per policy; challenge recordings are rendered at 30fps directly from physical states captured during the original evaluated episodes, including failures that no API call can reproduce", "result": "Jev ties or loses to the independent rule baseline only on stacking: pick & place 10/10, surface push 10/10, stack on a pedestal 8/10 versus 10/10 for the rule baseline, Wilson 95% intervals such as 49.0% to 94.3% on stacking; every completed episode stays in the denominator, 60/60 complete; the two stacking failures are both policy_error at seeds 4 and 7; Jev averages 128.3, 81.7 and 126.9 decisions per episode with wall times of 224.9s, 126.8s and 182.4s; API p50/p95 around 1228/4553ms, 1228/3509ms and 1247/2169ms", "notes": "Python 3.11, Apache-2.0, CI; interactive showcase at lykycy123.github.io/RoboJEV; bilingual documentation and demo videos; source and configuration frozen after tuning, completed failures are not replaced by successful reruns; the frozen control-source SHA256 is published; demonstration seeds 1000-1004 are disjoint from evaluation; full raw responses and state captures stay private; referenced by ZJU-REAL's CUA-JEV as the embodied setting", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "adamnroman-slop-filter", "name": "slop-filter, hide AI-generated posts", "author": "adamnroman", "date": "2026-09-20", "category": "app", "url": "https://github.com/adamnroman/slop-filter", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 22, "likes": "", "detail": "", "arch": "Chrome extension that hides AI-generated posts and comments on X, LinkedIn and Reddit plus YouTube comments: it reads the text of each post as it nears the screen and asks TypeSafe Jev 19 narrow questions about how the text is written, not what it says, because a person can be generic or repeat the post they answer and that is not AI", "approach": "", "training": "", "data": "", "result": "the tells are in the cadence: the it's not X, it's Y pivot, three matching beats in a row, a tidy bow on the end, words that add importance and no information, sentence rhythm too even; posts over your threshold collapse with one click to bring them back; an animated mode shows the work, a scan line running over each post until it turns green and stays or red and folds away; marking posts AI or Human fits the weights to your own judgment", "notes": "the stated goal is an internet without having to sift through slop, extending to bot accounts, YouTube and TikTok videos with AI-generated scripts, and whatever comes next; MIT", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wquguru-dasheng", "name": "大声读 ReadAloud, open-source English reading scorer", "author": "wquguru", "date": "2026-09-20", "category": "app", "url": "https://github.com/wquguru/dasheng", "base_model": "Confucius4-R2T2 streaming ASR plus Jev 1.13", "params": "", "metric": "", "license": "", "paper": "", "stars": 139, "likes": "", "detail": "", "arch": "open-source real-time English reading scorer in the style of流利说: read a passage of English aloud to the screen and after you finish know which words you mispronounced; pipeline: microphone → R2T2 streaming transcription, netease-youdao/Confucius4-R2T2 with a 160ms step and a stable-prefix mechanism where committed text only grows and never rewrites, plus timestamps → alignment → Jev closed-set decisions → local arithmetic → underlines plus a total score; words light up as they are read, misreads are marked red on the spot, one click after finishing gives the total", "approach": "", "training": "", "data": "", "result": "", "notes": "Jev answers only inside a space declared in advance - whether, pick one, score 1–5, with probabilities - so only text-level questions go to it: is the read word the same word as the original, noul with probability; which error class, substitution, ASR spelling variant or self-correction, choice; does the whole passage still mean the same sentence, score 1–5; alignment plus local arithmetic handles omissions, extra words, completeness, speaking rate and pauses; pronunciation, stress and accent are explicitly not scored - Jev reads text and cannot judge acoustics, a phoneme-level scoring model would have to be attached separately; only the words that fail alignment are worth asking a model about, everything else stays arithmetic on the machine; the total score is a local weighted sum, WEIGHTS in lib/score.js; Next.js 16, self-hosted on a 12GB GPU, no network, no upload, no per-minute billing; mock mode ASR_PROVIDER=mock demos without a GPU or microphone; independent project, not affiliated with TypeSafe AI", "domain": "education", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "3clyp50-a0-typesafe-ai", "name": "a0-typesafe-ai", "author": "3clyp50", "date": "2026-09-19", "category": "app", "url": "https://github.com/3clyp50/a0-typesafe-ai", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 6, "likes": "", "detail": "Settings: the API key, empty by default, is a masked field with a reveal control, and overrides the fallback key; the model defaults to jev-latest, a Jev model alias or a version available to your account; and the request timeout defaults to 30 seconds, per HTTP attempt, from 1–300 seconds. The fallback is the TYPESAFE_API_KEY from Agent Zero's secret store, including project secrets, then the framework's process environment; get a key from the TypeSafe console; settings support project and agent-profile scopes; keys entered here are stored in Agent Zero's local plugin configuration, so do not share config.json or runtime backups, and the repository excludes runtime configuration and credentials. Only the explicit state and questions are sent to TypeSafe, requests use your account and may incur TypeSafe usage charges, API keys are never tool arguments or result fields, and the SDK retries transient failures once, within its retry budget, with authentication and validation errors returned to the agent without exposing provider response bodies. Use it by asking Agent Zero to use Jev to classify a message as billing, technical, or other, check whether a refund is requested, and score its urgency in one call, or by calling typesafe_query directly with a state and questions.", "arch": "TypeSafe AI for Agent Zero: Jev judgments for Agent Zero, with typed tools and probability cards, where you ask Jev focused questions and get typed answers with probability cards in Agent Zero's chat, and one typesafe_query call can combine Choice, Noul, and Score questions over the same evidence, a community plugin that uses the official TypeSafe Python SDK and includes the official TypeSafe agent skill, unchanged, under its MIT license, and is not an official TypeSafe or Agent Zero product.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pithings-advocaat", "name": "advocaat", "author": "pithings", "date": "2026-09-19", "category": "app", "url": "https://github.com/pithings/advocaat", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 92, "likes": "", "detail": "", "arch": "A small, type-safe client for asking AI questions about your data, powered by TypeSafe Jev. Data tooling: small type-safe client for asking Jev questions about a dataset. Small type-safe client for asking Jev questions about datasets. Small TypeScript client with tagged helpers for chances, choices, and scores.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vercel-labs-ai-cli", "name": "AI CLI", "author": "vercel-labs", "date": "2026-09-19", "category": "app", "url": "https://github.com/vercel-labs/ai-cli", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 816, "likes": "", "detail": "", "arch": "Vercel Labs terminal CLI that can run Jev as the evaluation model for its evaluate command. Developer tooling: Vercel Labs CLI that can run Jev as the evaluation model for its evaluate command.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "opaielsheikh-ai-elo-ranker", "name": "ai-elo-ranker", "author": "opaielsheikh", "date": "2026-09-19", "category": "app", "url": "https://github.com/opaielsheikh/ai-elo-ranker", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 6, "likes": "", "detail": "Architecture: a dataset of 10,000 poems or pitches, in JSON or SQLite, feeds a Swiss matchmaker engine, O(N log N) entropy maximization, which feeds an async concurrency pool with 16 parallel Jev workers, which feed duels A and B, each with position-bias randomization and a swap, answered by the Jev System One model, with results flowing into Elo updates and live WebSocket streaming of standings, purpose-built for cinematic live demos on X and web-based interactive exploration.", "arch": "AI Elo Ranker: a high-speed recursive AI Elo tournament engine powered by Jev and Swiss matchmaking, a blazing-fast, recursive tournament engine for ranking texts, poems, startup pitches, rap lyrics, cold emails, ad hooks, using the Jev model, standard Elo rating mechanics, and real-time WebSocket streaming, designed for high-throughput concurrency, cinematic live demos on X, and web-based interactive exploration.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shantanugoel-ask-jev-skill", "name": "ask-jev-skill", "author": "shantanugoel", "date": "2026-09-19", "category": "app", "url": "https://github.com/shantanugoel/ask-jev-skill", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 40, "likes": "", "detail": "arch: jev-latest", "arch": "Hermes skill: call TypeSafe Jev as a typed tiebreaker. Jev is not a chat model. It returns Choice / Score / Noul answers with probabilities so an agent can act or escalate.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dbreunig-building-with-jev-skill", "name": "building-with-jev-skill", "author": "dbreunig", "date": "2026-09-19", "category": "app", "url": "https://github.com/dbreunig/building-with-jev-skill", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 134, "likes": "", "detail": "", "arch": "A skill for writing and improving programs that call Jev, TypeSafe's System One model. Community agent skill covering question design, state preparation, confidence thresholds, and debugging decisions.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "frostney-clean-code-review", "name": "clean-code-review", "author": "frostney", "date": "2026-09-19", "category": "app", "url": "https://github.com/frostney/clean-code-review", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 11, "likes": "", "detail": "", "arch": "Every code file in a pull request, judged against Uncle Bob's Clean Code by TypeSafe's Jev, then reviewed by Luna. Built on eve and Next.js. Code quality: scores every file of a pull request on 31 boolean Clean Code smells plus function size and nesting, then hands the verdicts to a writing model for the review prose.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "devanshbatham-commit-miner", "name": "commit-miner", "author": "devanshbatham", "date": "2026-09-19", "category": "app", "url": "https://github.com/devanshbatham/commit-miner", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 37, "likes": "", "detail": "", "arch": "Classify Git commit diffs and messages with Jev. Bug fixes, security fixes/CWEs, and change types. Classifies Git diffs and commit messages into change types and candidate security-fix/CWE labels for inspection. Rust CLI that classifies commit diffs with Jev: bug fixes, security/CWEs, and change types. HTML/CSV reports.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tedliou-decision-model-playground", "name": "Decision Model Playground", "author": "tedliou", "date": "2026-09-19", "category": "app", "url": "https://github.com/tedliou/decision-model-playground", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "arch: browser playground comparing Laya and TypeSafe Jev on article recommendations from vervecode.dev: type a question, and every article plus a nothing-to-recommend option shows its raw probability and a probability bar, with load, inference and full-request timings; notes: Node 24 LTS plus uv-managed Python 3.12, CUDA by default, CPU via LAYA_DEVICE=cpu; both adapters receive the same Decision object; no second LLM judgment, no keyword overrides, and no automatic fallback to the other model when one fails; Jev is enabled by JEJ_API_KEY and JEV_MODEL, jev-1.13.0, with the key read only by the backend .env, never the VITE front end or Git; explicitly a local-experiment side project: dev server, no auth or access control, probabilities not calibrated for this article set; docs/validation.md lists known model misjudgments and the measured range", "arch": "A browser side project to experience and compare decision models: enter a question and Laya or TypeSafe Jev picks the most recommended article from vervecode.dev, showing the raw probability and a probability bar for every article plus the no-recommendation option, along with load, inference and whole-request timings; local development server only, no auth or access control, and the model probabilities are not calibrated for this article set", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "thecodacus-decision-playground", "name": "Decision Playground for llama-server /v1/decision", "author": "thecodacus", "date": "2026-09-19", "category": "app", "url": "https://github.com/thecodacus/decision-playground", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 23, "likes": "", "detail": "", "arch": "browser-only UI for llama-server's /v1/decision endpoint from the parallel-decision llama.cpp branch; there is no backend: the page talks to llama-server directly and its only setting is the server URL", "approach": "", "training": "", "data": "", "result": "the playground compares one /v1/decision pass with a streamed, grammar-constrained /v1/chat/completions run on the same model, showing per-field answers and probabilities, server and round-trip timings, and the exact request payloads; the game is a small top-down arena where agents pick their controls with /v1/decision several times a second, a moving target, or a duel where both agents are models", "notes": "/v1/decision takes contexts, a list of 1–256 context strings, and returns results in the same order; all contexts share the schema, the instructions and the cached prompt prefix; the server needs --decision-seqs N; router mode reads the model list from /v1/models; the static build is servable by any web server", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "buberlo-dsh-jev", "name": "dsh-jev, Jev decision layer for DeepSeek Harness", "author": "buberlo", "date": "2026-09-19", "category": "app", "url": "https://github.com/buberlo/dsh-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 23, "likes": "", "detail": "", "arch": "the Jev decision layer for DeepSeek Harness: DSH runs the agent loop, TypeSafe Jev makes the small fast decisions, and your code decides what the answers mean; Jev gates tool relevance before each step, each tool call for safety, and site policy", "approach": "", "training": "", "data": "", "result": "a 66-second Kubernetes troubleshooting demo runs the same freshly recorded model calls through both harnesses against a real disposable cluster: healthy pods, broken connections after a rollout, the agent traces Ingress to Service to pods and repairs the network path, while Jev's site policy gates a broad allow-all-traffic shortcut so the portal recovers while PostgreSQL stays isolated; the clearly labeled replay documents one false positive, a harmless reset being blocked", "notes": "method, all runs and limits published in docs/benchmark.md; MIT; independent project, not affiliated with TypeSafe AI or DeepSeek", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dzhng-duet-agent", "name": "duet-agent", "author": "dzhng", "date": "2026-09-19", "category": "app", "url": "https://github.com/dzhng/duet-agent", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 46, "likes": "", "detail": "", "arch": "Full-stack agent harness with memories, long-running tasks, multi-agent relay, and a Jev-backed model-routing table. Agent harness: keeps a Jev-backed routing table for deciding which model should serve a request.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "agent-labs-dev-fastbrowse", "name": "fastbrowse", "author": "agent-labs-dev", "date": "2026-09-19", "category": "app", "url": "https://github.com/agent-labs-dev/fastbrowse", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 102, "likes": "", "detail": "", "arch": "Browser agent where Jev selects actions, an LLM reads and plans, and answers cite page evidence. Browser agents: Jev picks each action from what is on the page while an LLM reads and plans.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "boundaryml-feelings", "name": "feelings, the AI if statement as a typed method", "author": "BoundaryML", "date": "2026-09-19", "category": "app", "url": "https://github.com/BoundaryML/feelings", "base_model": "TypeSafe Jev via BAML", "params": "", "metric": "", "license": "", "paper": "", "stars": 18, "likes": "", "detail": "", "arch": ".feels on anything: a programming language for LLM workflows where the AI if statement is a real, typed method powered by Jev and BAML; if an email feels urgent then draft a reply; Jev makes the decisions, an LLM does the writing, BAML ties it together", "approach": "", "training": "", "data": "", "result": "started as a reply to southpolesteve's Probably, a toy language with feels baked in: the point is you do not need a new language, feels is about 20 lines of BAML, an interface with a blanket implementation, and because it is a real language you get types, generics, loops, concurrency, tests and everything else for free; baml test runs offline by inspecting the Jev requests, no key needed", "notes": "demos include urgent, inbox, a grep_with_vibes run that classifies commits as scary to revert, enums, literal unions, class fan-out and ints", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "thruwire-foreman", "name": "Foreman", "author": "thruwire", "date": "2026-09-19", "category": "app", "url": "https://github.com/thruwire/foreman", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 566, "likes": "", "detail": "", "arch": "Software Factory Foreman: an agent supervisor that uses Jev decisions to keep coding agents on task. Software factory: sits above Codex workers and has Jev independently judge whether an implementation is complete, its tests sufficient, or a human is needed. Software-factory supervisor that uses Jev to keep coding agents on task. Software-factory loop: Codex implements; Jev", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "anpicasso-hermes-jev-approvals", "name": "hermes-jev-approvals", "author": "anpicasso", "date": "2026-09-19", "category": "app", "url": "https://github.com/anpicasso/hermes-jev-approvals", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 17, "likes": "", "detail": "", "arch": "PoC: TypeSafe Jev as the reviewer for Hermes Agent smart command approvals. 8.7× faster, 4.4× fewer prompts, measured on 153 real commands. Approvals only. Agent approvals: proof of concept that puts Jev in front of Hermes Agent's command approvals, reporting 8.7× faster decisions and 4.4× fewer prompts to the user.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": "8.7× faster"}
{"id": "abovecolin-ha-jev", "name": "Home Assistant Jev", "author": "abovecolin", "date": "2026-09-19", "category": "app", "url": "https://github.com/abovecolin/ha-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 65, "likes": "", "detail": "", "arch": "Home Assistant integration for TypeSafe Jev. Ask a question about your house and get a probability, a choice or a score as an entity. Smart home: Home Assistant integration that answers questions about the house as a probability, a choice, or a score. Home Assistant integration exposing typed answers as sensors, automation actions, and an Assist conversation agent. Unofficial", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yusukebe-hono-jev-router", "name": "hono-jev-router", "author": "yusukebe", "date": "2026-09-19", "category": "app", "url": "https://github.com/yusukebe/hono-jev-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 51, "likes": "", "detail": "", "arch": "Route HTTP requests by meaning. A semantic router for Hono powered by Jev. Web frameworks: Hono middleware that routes HTTP requests by meaning rather than by method and path, deciding with Jev. Routes Hono HTTP requests by meaning. Experimental Hono router: Jev matches an incoming request to a plain-language route description", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "joevidev-ui-generator-instinct-jev", "name": "Instinct", "author": "joevidev", "date": "2026-09-19", "category": "app", "url": "https://github.com/joevidev/ui-generator-instinct-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 8, "likes": "", "detail": "Choice picks exactly one option from a named, bounded set, with a full probability distribution over every option; Noul is a graded yes/no; and Score is a graded rank along a described scale. Stack: Next.js 16 with the App Router and Turbopack, Tailwind v4, shadcn/ui with Base UI primitives and the Nova preset, Hono mounted at the catch-all API route, zustand with persist, the TypeSafe SDK, recharts, Motion, and Embla Carousel, using pnpm. Run it: pnpm install, copy .env.example to .env.local and fill in the TypeSafe key, pnpm dev, then open localhost:3000, where without the key, or without remaining API credits, submitting a case returns a clean request-failed 500, the real error path, not a mocked one. Bring your own key: if you deploy this publicly, the key in .env.local pays for every visitor, so the first 5 generations per visitor are covered.", "arch": "Instinct: a Jev UI-generator demo where you describe a case in free text and Jev picks the UI from a fixed catalog without generating a line of code or copy, a demo of Jev, TypeSafe's System One model, as a UI generator, where Jev never generates code or copy, it only ever answers typed questions, Choice, Noul, Score, over bounded, real option sets, and the app renders whichever real shadcn/ui component, or full page block, it picked, configured the way it decided, existing to prove that's enough to go from a customer table with name, status, and plan to a real, correctly-typed, on-brand React component, with zero free text anywhere in the pipeline except the user's own input, a content artifact for a Buivo post about System One models, with a working name pending final brand sign-off.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "chopratejas-invalidate", "name": "invalidate, the invalidation layer for AI memory", "author": "chopratejas", "date": "2026-09-19", "category": "app", "url": "https://github.com/chopratejas/invalidate", "base_model": "TypeSafe Jev", "params": "", "metric": "89.2% strict, 97.5% lenient eval; 0 false invalidations of 157; $0.00006 per fact per event", "license": "Apache-2.0", "paper": "", "stars": 21, "likes": "", "detail": "", "arch": "agents remember, they never un-remember; invalidate fixes that: every stored fact gets a lease and is invalidated when new evidence supersedes it, built on TypeSafe Jev", "approach": "", "training": "", "data": "", "result": "695 tests passing; the canonical case is March: the agent learns we use Postgres, June: Slack says the migration to SQLite is done, September: the agent still says since you're on Postgres; the memory was right when it was stored, then the world changed and nobody told the memory", "notes": "live playground at invalidate-playground.vercel.app, talk to it or paste facts and events at /paste; Python 3.10+, Apache-2.0", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "luantak-is-malicious", "name": "is-malicious", "author": "luantak", "date": "2026-09-19", "category": "app", "url": "https://github.com/luantak/is-malicious", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 27, "likes": "", "detail": "", "arch": "Scans a codebase for covert, deceptive, or data-stealing behavior with Jev, then reports suspicious files and line ranges before the user runs it. Software supply-chain security: asks Jev Noul checks about source and build files, escalates suspicious chunks for a second pass, and returns implicated files and lines before execution. Scans source, configuration, build, and CI", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "giesn-typesafe-jev-workflow", "name": "Jev email intent workflow", "author": "giesn", "date": "2026-09-19", "category": "app", "url": "https://github.com/giesn/typesafe-jev-workflow", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 11, "likes": "", "detail": "arch: invoice or general · invoice or general · no README found", "arch": "Async LangGraph workflow that gets a typed Jev Choice classifying each inbound email as invoice or general and routes it to the matching handler", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists; no README", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "brainstormity-jev-moderation-bot", "name": "Jev Moderation Bot", "author": "brainstormity", "date": "2026-09-19", "category": "app", "url": "https://github.com/brainstormity/jev-moderation-bot", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 48, "likes": "", "detail": "", "arch": "Real-time Discord moderation bot: Jev evaluates messages and metadata in parallel to catch phishing, spam, and social engineering with a progressive escalation ladder. Community moderation: a Discord bot that scores incoming messages for phishing, spam, and social engineering with Jev and drives a four-stage escalation ladder.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sorrycc-typesafe-snake", "name": "Jev plays Snake", "author": "sorrycc", "date": "2026-09-19", "category": "app", "url": "https://github.com/sorrycc/typesafe-snake", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 23, "likes": "", "detail": "", "arch": "Snake auto-played by TypeSafe's Jev model: one System One choice per tick, legal moves and facts generated in code. Snake autoplayer with one typed decision per tick and code-generated legal moves.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "gaming", "score": "", "latency": "", "benchmark": "Snake", "cost": "", "speedup": ""}
{"id": "buberlo-jev-trader", "name": "Jev Trader by buberlo", "author": "buberlo", "date": "2026-09-19", "category": "app", "url": "https://github.com/buberlo/jev-trader", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 20, "likes": "", "detail": "Architecture: market data feeds a feature engine, deterministic pure code, producing a state snapshot under 400 tokens, which goes to Jev in one call with six atomic judgments, regime, direction, toxic_flow, liquidity_stressed, quote_environment, and inventory_pressure, producing a probability vector of typed answers with confidence, which goes to a policy engine where the thresholds live in code, through hard risk rules, an absolute veto, never delegated, to execution with post-only quotes, and a calibration log of state, decision, outcome triples. Everything computable stays in code: mid, microprice, spread, imbalance, realized vol, inventory, drawdown, queue position; everything that is a judgment goes to Jev, as atomic questions composed in code, so you change a coefficient, not a prompt. Quickstart: uv sync, then run the paper loop in fallback mode, no API key needed, over 2000 blocks; with live Jev judgments, export the TypeSafe key and run 10000 blocks; for realtime pacing, 300 ms blocks, instead of accelerated simulation, add --realtime; for a calibration report from the logged triples, run calibrate; and list available models with models. Paper runs are deterministic for a given seed and write JSONL calibration triples to the data/calibration directory.", "arch": "jev-trader: a 24/7 market-making trading system built around Jev, TypeSafe AI's System One decision model, where Jev returns calibrated, typed decisions in 70–500 ms, inside a 300 ms block budget, and this repo is the system that calls it: state engine, judgment battery, policy engine, A-S pricing, hard risk vetoes, execution, and the calibration loop, with the one principle that carries the whole build being that Jev should not own the trading system, it should own selected judgments inside it, where code calculates the state, Jev interprets the state, code applies policy, and execution places the order.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shaharia-lab-jev-cli", "name": "jev, typed questions from the terminal", "author": "shaharia-lab", "date": "2026-09-19", "category": "app", "url": "https://github.com/shaharia-lab/jev-cli", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Mixed", "paper": "", "stars": 28, "likes": "", "detail": "", "arch": "Rust command-line tool for TypeSafe's Jev model, the model that never writes text: send it some content and a question you defined, get a calibrated probability back, and branch on it in a shell script, a CI job or an AI agent", "approach": "", "training": "", "data": "", "result": "", "notes": "markets itself as a semantic if statement for your terminal; published on crates.io as jev-cli with CI; dual licensed MIT or Apache-2.0", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "godsboy-jev-agent-skill-router", "name": "jev-agent-skill-router", "author": "godsboy", "date": "2026-09-19", "category": "app", "url": "https://github.com/godsboy/jev-agent-skill-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 19, "likes": "", "detail": "", "arch": "Typed, confidence-aware agent skill routing with TypeSafe Jev. Agent infrastructure: routes agent skill selection through typed, confidence-aware Jev decisions so weak matches are declined instead of guessed.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shiftynick-jev-axi", "name": "jev-axi", "author": "shiftynick", "date": "2026-09-19", "category": "app", "url": "https://github.com/shiftynick/jev-axi", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 21, "likes": "", "detail": "arch: pick, rate, check, rank, triage, guard", "arch": "Agent-ergonomic CLI for TypeSafe's Jev: fast calibrated judgments from the shell. Agent safety: PreToolUse gate for Claude Code and Codex that has Jev score each shell command for destructiveness, exfiltration, remote code execution, and security weakening, deciding routine commands locally so nothing is sent for them, and scoring 44/44", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "goodrahstar-jev-column-race", "name": "jev-column-race", "author": "goodrahstar", "date": "2026-09-19", "category": "app", "url": "https://github.com/goodrahstar/jev-column-race", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 24, "likes": "", "detail": "The columns: sentiment, a Score in Jev with 5 described levels, versus an integer 0–4 in Gemini; topic, a Choice in Jev over bug, pricing, usability, feature request, praise, other, versus one label; bug report, a Noul probability in Jev, versus a number 0–1; and churn risk, a Score in Jev with 4 described levels, versus an integer 0–3. The reviews are 1,000 Android app reviews from the Hugging Face sealuzh/app_reviews dataset, across 17 apps, and star ratings are never sent to either model, serving as the independent check on sentiment. The pipeline: 1,000 reviews with stars withheld, 20 reviews per request, 8 in flight, 50 requests, feeding the Jev lane, 80 typed questions, score, choice, noul, and the Gemini lane, one JSON object, 20 results, with measurements and design docs, and the questions readable in the source.", "arch": "Jev Column Race: four AI columns over 1,000 real app reviews, raced live: TypeSafe's Jev against Gemini 3.8 Flash, where both lanes get the same reviews, the same batches, and the same concurrency, Jev answers 80 typed questions per request, and Gemini returns one JSON object per request, and Jev labelled all 1,000 reviews in 4.6 seconds for 0.023 dollars, while Gemini 3.8 Flash took 18.8 seconds and 0.158 dollars, 4.1× faster and 7.0× cheaper in one recorded run pair, with star-rating agreement of rho 0.80 vs 0.82, and 84% same topic, 93% same bug call.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "inanna-malick-jev-dsl", "name": "jev-dsl", "author": "inanna-malick", "date": "2026-09-19", "category": "app", "url": "https://github.com/inanna-malick/jev-dsl", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 8, "likes": "", "detail": "", "arch": "Agent-first Haskell DSL for TypeSafe's Jev judgment model: typed packets, inferred types, answers under the same labels. Early-alpha Haskell DSL that encodes typed question packets and decodes answers; HTTP transport is left to the caller.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists; no README found", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "romaluev-jev-ego", "name": "jev-ego", "author": "romaluev", "date": "2026-09-19", "category": "app", "url": "https://github.com/romaluev/jev-ego", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 17, "likes": "", "detail": "", "arch": "TypeScript browser agent for ego lite: Jev Ultrafast indexed actions, TypeSafe Jev decisions, persistent observe/act CLI. No Chrome or Playwright. Browser agent on ego lite: one TypeSafe request picks operation + indexed element; agent-facing observe/act/suggest/step CLI", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists; no README found", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-kit", "name": "jev-kit, jonathanavis96", "author": "jonathanavis96", "date": "2026-09-19", "category": "app", "url": "https://github.com/jonathanavis96/jev-kit", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 40, "likes": "", "detail": "Jev is a TypeSafe System One model that does not return text, returning a typed decision with a probability attached, yes or no, a choice, a score, taking roughly 0.3 s. The tool-call guard: an agent's tool call passes a free code pre-filter that lets about 93% straight through, and the rest are judged by Jev in about 0.3 s and then allowed, warned, rewritten, or blocked, with any error letting the call through. Sections cover the quickstart, what is in the kit, Jev as the decision-maker with the browser agent result, how it works, measured results, the safety model, and an FAQ.", "arch": "jev-kit: everything needed to run TypeSafe's Jev with Claude Code, a tool-call guard and companion tooling, where you guard your agent's tool calls, right-size its sub-agents, and wire Jev into search, browsing, review, and more, an MIT kit, Python 3.10+, for Linux, WSL2, and Windows, with 1,310 passing tests, where Jev decides and Claude writes, and with Jev choosing each click, the browser agent's Claude bill was 0.0008 USD a run against 0.1868 USD with Sonnet choosing, at the same 9 out of 9 success.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "y0usaf-jev-lm", "name": "jev-lm", "author": "y0usaf", "date": "2026-09-19", "category": "app", "url": "https://github.com/y0usaf/jev-lm", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 7, "likes": "", "detail": "Example: npx jev-lm gen with the prompt she went to the door, drafting and tracing, produces round 4 with nothing at p=0.024, end=0.13, done=0.92, a chunk of and looked at the street at 0.38, 213 masked, and round 9 with looked at p=0.020, end=0.27, done=0.92, best-chunk=0.12, 203 masked, stopping on done, yielding she went to the door and looked at the street, 9 tokens, 2 round trips, 4.50 tokens per round trip, 2 calls, 3700 in, 3492 out, 0.6 s. Install: npx jev-lm --help needs no install, npm install -g jev-lm is global, npm install jev-lm is the library. Set a key one of three ways: the TYPESAFE_API_KEY environment variable, a file named by TYPESAFE_API_KEY_FILE, or the tokens file in your home. Commands: probe shows the next-word distribution; verify scores candidate continuations with Noul questions; gen generates; and eval measures bits per token against a unigram baseline over the same vocabulary. As a library, import JevClient, Vocab, WordLM, and Drafter from jev-lm, load a vocabulary, create a WordLM with a drafter from a corpus file, and generate, with answers cached under XDG_CACHE_HOME or the jev-lm cache directory.", "arch": "Jev-LM: a word-level language model whose output layer is Jev, with an n-gram drafter, Noul chunk verification, and bits-per-token evaluation, where Jev never emits text, so everything an LLM normally hides lives in this code: the tokenizer, the sampler, the repetition mask, the stop rule, and a verified-chunk path that stands in for a KV cache, with one HTTP request per round trip, zero dependencies, Node 18 or newer.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jon-devlapaz-jev-me", "name": "jev-me", "author": "jon-devlapaz", "date": "2026-09-19", "category": "app", "url": "https://github.com/jon-devlapaz/jev-me", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 14, "likes": "", "detail": "Install with npx skills add jon-devlapaz/jev-me and type the /jev-me command; needs the TypeSafe API key; MIT license", "arch": "Agent skill that grills a plan as a design-tree interview, with Jev ad hoc for typed judgments", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-primitives", "name": "jev-primitives", "author": "HermeticOrmus", "date": "2026-09-19", "category": "app", "url": "https://github.com/HermeticOrmus/jev-primitives", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "app: teachable explainers for Choice / Score / Noul; arch: which discrete option from a closed set, e.g. send / hold / ask · continuous judgment such as risk or fit, 0–1 - the model returns the number, never invent it · is a boolean / policy condition met, e.g. policy_ok?; notes: Grok Bot lane · Gold Hat · no secrets · summary + evidence ids · auto / review / block", "arch": "Teachable explainers for the three TypeSafe Jev primitives: Choice, Score, Noul; ships choice/score/noul examples and a reusable Choice-Score-Noul SKILL.md for agent forks.", "approach": "", "training": "", "data": "", "result": "", "notes": "Part of the HermeticOrmus/ormus-jev open teaching pack; seeded from the DataChaz 10-steps thread and docs.typesafe.ai; swap-in pattern - build the agent graph in OpenAI, Anthropic, or xAI first, put decision nodes behind a thin interface, then swap Jev in without rebuilding; minimal shared state, parallel typed decisions, risk thresholds, execution queue gated behind the decision; clarifies that probability/confidence rides with the answer and the typed primitives are Choice, Score, and Noul. GitHub new-repo sweep 2026-09-25.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mrnugget-jev-shell-history", "name": "jev-shell-history", "author": "mrnugget", "date": "2026-09-19", "category": "app", "url": "https://github.com/mrnugget/jev-shell-history", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 112, "likes": "", "detail": "arch: TypeSafe", "arch": "Fish-style zsh history autosuggestions ranked by Jev. Ranks existing zsh history entries for inline completion; accepting a suggestion does not execute it. Fish-style zsh autosuggestions: Jev ranks recent history as you type", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sosopop-jev-stock", "name": "jev-stock", "author": "sosopop", "date": "2026-09-19", "category": "app", "url": "https://github.com/sosopop/jev_stock", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "", "arch": "An experimental JEV-powered framework for forecasting short-term stock price direction from structured market data. Short-term forecasting: experimental Hong Kong stock framework that turns structured market state into a Jev decision on price direction, with a backtest script for the first trading day.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zadescoxp-jev-trades", "name": "Jev-Trades", "author": "zadescoxp", "date": "2026-09-19", "category": "app", "url": "https://github.com/zadescoxp/jev-trades", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 32, "likes": "", "detail": "The dashboard combines live market data, technical indicators, TypeSafe decisions, and a paper-trading portfolio in one workspace. Features: live market data and dynamic timeframes, streaming live market data via Yahoo Finance websockets, with the chart and technical indicators updating based on the selected timeframe, 1m, 5m, 15m, 1h, 4h; autonomous agent, powered by TypeSafe, where the Jev autonomous decision engine analyzes the market on every incoming tick across all active timeframes simultaneously, providing structured judgments for trading; paper-trading portfolio, a simulated trading environment with a persistent SQLite database, where capital can be dynamically adjusted through the UI, accurately updating the account ledger and portfolio equity on the fly; and automated risk management, with automatic calculation and execution of take profit and stop loss triggers, determined by Jev's analysis, volatility, ATR, and user risk appetite, plus the fully-featured manual trade desk.", "arch": "Jev Trades: a Next.js dashboard for live crypto market data and TypeSafe-powered paper trading, where the system streams live market data from Yahoo Finance through yfinance, calculates technical indicators across multiple timeframes, sends explicitly enabled trading states to TypeSafe, and applies the returned decisions to a simulated portfolio powered by a local SQLite database, with both autonomous trading by the Jev agent and a fully-featured manual trade desk with automatic take profit and stop loss execution, and no broker or live order API is connected.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "savka777-jev-use", "name": "jev-use by savka777", "author": "savka777", "date": "2026-09-19", "category": "app", "url": "https://github.com/savka777/jev-use", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 97, "likes": "", "detail": "", "arch": "Voice and typed computer use for macOS: you say what you want, Jev picks the next on-screen action, macOS performs it; the screen is read through the Accessibility tree, no screenshots.", "approach": "", "training": "", "data": "", "result": "", "notes": "Hands-free mode with an optional Hey Jev wake phrase; TypeSafe API key stored in Keychain; macOS 14.2+, Xcode, no dependencies.", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hr98w-jev-visual", "name": "jev-visual", "author": "hr98w", "date": "2026-09-19", "category": "app", "url": "https://github.com/hr98w/jev-visual", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 284, "likes": "", "detail": "", "arch": "An educational Jev-like visual inference experiment on Apple Silicon: shared context, direct candidate scoring, and local visual demos. Educational MLX/Qwen vision-language experiment sharing image context across candidate-scoring questions; its probabilities are not calibrated correctness estimates. Educational Jev-like visual inference on Apple Silicon: shared multimodal", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "moritzkremb-jev-voice-browser", "name": "jev-voice-browser", "author": "moritzkremb", "date": "2026-09-19", "category": "app", "url": "https://github.com/moritzkremb/jev-voice-browser", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 296, "likes": "", "detail": "arch: TypeSafe System One", "arch": "Control a real browser by voice. Jev decides intent + target in ~300 ms per spoken word; Playwright acts — often before you finish the sentence. Maps partial speech transcripts to browser intents and observed targets, with code deciding whether to act, wait, or ask.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "300 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "replynodes-jev-web-analyzer", "name": "jev-web-analyzer", "author": "replynodes", "date": "2026-09-19", "category": "app", "url": "https://github.com/replynodes/jev-web-analyzer", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 3, "likes": "", "detail": "The SaaS website teardown is only an example workload; it teaches Jev's capabilities: Choice judgments, inferring audience, clarity, differentiation, CTA, trust, and product motion; Boolean judgments, where you add your own yes/no evaluation; Score judgments, where you add your own ordered rubric, and the leaderboard's five sub-scores are all Score judgments; probabilities, where you inspect the distribution behind a decision; and multiple judgments, where you evaluate the same thing from several angles. Example results: shipfa.st scored 88 out of 100, clear what it is, clear who it's for, one obvious CTA; chatbase.ai scored 81 out of 100, strong clarity, differentiation is the weak point; and codefa.st scored 52 out of 100, audience and differentiation are hard to pin down, each with sub-scores for clarity, CTA clarity, differentiation, and pricing visibility, showing the matched rubric level and Jev's confidence. Methodology is documented in METHODOLOGY.md.", "arch": "Jev Web Analyzer: paste a homepage and see what a first-time visitor, and Jev, can actually tell about it, powered by ReplyNodes web data, with a live demo and leaderboard, an unofficial community project not affiliated with TypeSafe AI, Apache-2.0, where the main purpose of the repo is to make Jev's evaluation pattern easy to see, run, modify, and reuse, including as a committed, static dataset, the leaderboard, rather than only a single ad-hoc request.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "marketing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "abhixhek-jevcal", "name": "jevcal", "author": "abhixhek", "date": "2026-09-19", "category": "app", "url": "https://github.com/abhixhek/jevcal", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 11, "likes": "", "detail": "arch: TypeSafe Jev · no README found", "arch": "Stop guessing confidence thresholds: calibrate, threshold, and drift-check typed decision models against an LLM teacher. Model evaluation: fits a per-question confidence threshold to a target accuracy on your own labeled data, verifies it on a held-out split, reports how much traffic still has to escalate to an LLM, and fails CI when a model update breaks the threshold.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevify", "name": "Jevify, ryana", "author": "ryana", "date": "2026-09-19", "category": "app", "url": "https://github.com/ryana/jevify", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 186, "likes": "", "detail": "", "arch": "A prompt to jev-ify a project: hand it to your coding agent to investigate what TypeSafe's Jev could make possible in an existing codebase, grounded in the official docs and the archerhume.com architecture investigation.", "approach": "", "training": "", "data": "", "result": "Separates vendor claims, independently measured results, and the agent's own hypotheses; targets cutting cost and latency and making semantic judgments cheap enough to run throughout an application.", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "unicodeveloper-jevocks", "name": "Jevinik", "author": "unicodeveloper", "date": "2026-09-19", "category": "app", "url": "https://github.com/unicodeveloper/jevocks", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 19, "likes": "", "detail": "", "arch": "Everyday Stocks Status with Jev. Stock decisions: terminal that gathers live market evidence through Valyu and asks Jev whether a stock is likely to trade higher over the next 30 days.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "devtooligan-jevscan-evm", "name": "jevscan-evm, a bug heat map of a repo in seconds", "author": "devtooligan", "date": "2026-09-19", "category": "app", "url": "https://github.com/devtooligan/jevscan-evm", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 27, "likes": "", "detail": "", "arch": "produce a heat map of likely bugs in seconds, for pennies: python jevscan.py on a repo walks configured layers and asks Jev its question sets, results land in out/repo/HEATMAP.md, with which folders, which files and thresholds set in jevscan.toml, plus command-line overrides", "approach": "", "training": "", "data": "", "result": "", "notes": "the author labels it a 100% vibe-coded proof of concept and warns that they did not read one line of the code; Python 3.11+, MIT", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "brainwires-jevwire", "name": "jevwire", "author": "brainwires", "date": "2026-09-19", "category": "app", "url": "https://github.com/brainwires/jevwire", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 20, "likes": "", "detail": "arch: TypeSafe AI's Jev", "arch": "Jev decision layer for agents: MCP server, embeddable DecisionModel library, and an escalate-only Claude Code plugin. MCP tools, an embeddable decision library, and advisory or restrictive Claude Code hooks; judgments do not grant native permissions.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "monteduro-killmyidea", "name": "killmyidea", "author": "monteduro", "date": "2026-09-19", "category": "app", "url": "https://github.com/monteduro/killmyidea", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 211, "likes": "", "detail": "Install with npm install, copy .env.example to .env, and set the TypeSafe key, which is only read by the evaluate endpoint and never reaches the browser. Successful evaluations are archived in SQLite at data/analytics.sqlite by default; set the analytics DB path to an absolute path on a persistent disk in production, and users can opt out per request with the checkbox below private history. The site URL, optional, sets the absolute URL used in the OG and Twitter image tags, defaulting to the project URL, with the image at public/og.png at 1200 by 630. For UI work without a key, set the mock flag, and the API returns deterministic fake answers labelled MOCK DATA in the raw-data panel, with mock mode ignored on Vercel. Run locally with the dev command at 127.0.0.1:5317, which also serves the evaluate endpoint, with unit tests and a balanced scoring benchmark that requires the app to be running. Every result has a collapsed How Jev decided panel with raw Jev answers, probabilities, confidence, the average, the verdict, latency, and token usage, opened by default with the debug flag. Deploy to Vercel by importing the repo, adding the key under environment variables, and deploying, where dist is served statically and the evaluate endpoint becomes the only function, or use the Vercel CLI in production.", "arch": "Kill My Idea: describe a startup idea and Jev decides, kill it, fix it, or ship it, a startup-idea evaluator that chooses kill, fix, or ship, with no generative LLM, where one request asks TypeSafe Jev 10 questions in parallel, 8 plain indie-hacker questions scored 0–4, plus the category and whether the idea is understandable, and the pipeline is idea, Jev 8 scores, each times 25, weighted average, clarity gate, and KILL, FIX, or SHIP.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vinilana-live-jev", "name": "live-jev by vinilana", "author": "vinilana", "date": "2026-09-19", "category": "app", "url": "https://github.com/vinilana/live-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 19, "likes": "", "detail": "Code then applies confidence-gated routing: a low-confidence lane change is ignored, a strong pedestrian yield overrides speed, a severe hazard forces at least slow down, and a stop is softened to slow down when nothing is within 1.5 times the stopping distance, so far-away pedestrians or obstacles cause a gentle slowdown, not a halt, with speed steps scaled by the time since the previous answer, so a fast Jev does not brake harder than a slow one. A small reflex in code, emergency brake and blind-spot abort, exists only for imminent impacts and can be switched off in the UI. Jev versus LLM, side by side: with an OpenRouter key, the page offers a Compare mode, two tracks on the same course, the left car driven by Jev and the right by an LLM, DeepSeek V4.1 Flash by default, that receives the very same state and questions and must answer in the same JSON shape, where the course is generated once from the seed as a list of spawn events with absolute road positions, and each event fires when that track's car passes its mark, so both cars meet the same obstacles in the same order.", "arch": "Jev Self-Driving Sim: a 2D, top-down autonomous car that runs in the browser and uses TypeSafe's Jev, a System One decision model, as its driving classifier, where every 200 ms the car turns what its sensors see into a JSON state, sends it to Jev with four typed questions, and executes the answers, with lane action as a choice over keep lane, change left, change right; speed action as a choice over stop, slow down, hold, speed up; hazard as a score from 0, clear, to 3, collision likely within seconds; and pedestrian yield as a noul, the probability that the ego must stop for a pedestrian, all in a single API call, a speculative fan-out.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wundercorp-loki", "name": "Loki", "author": "wundercorp", "date": "2026-09-19", "category": "app", "url": "https://github.com/wundercorp/loki", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 25, "likes": "", "detail": "", "arch": "The self-improving AI agent built by WunderCorp, Inc. It's the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions. Run it on a $5 VPS, a GPU cluster, or serverless infrastructure that costs", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shantanugoel-mario-jev", "name": "mario-jev", "author": "shantanugoel", "date": "2026-09-19", "category": "app", "url": "https://github.com/shantanugoel/mario-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "Setup: uv sync with the lockfile, copy .env.example to .env, and put the TypeSafe key in .env, which can also be supplied through the shell environment, with .env and the runs directory ignored by Git, and no API keys in gameplay logs or committed, and Python 3.13 selected by .python-version, which uv installs if needed. Play: try the emulator without API calls with a scripted policy; make a short Jev run first, at most 25 API requests; then run a longer attempt with 500 decisions; choose a world, 1–8, and a stage, 1–4, both defaulting to 1, with replay using the level saved in the log. Additional commands: inspect exactly what Jev will see without calling it, headless; run the baseline without a game window, headless with episodes; and tune the time each action is held with a frames count and model choice. Each episode resets the selected level and ends on death, completion, or the decision limit, with Ctrl-C stopping the run, and Jev calls using a 15-second HTTP timeout with automatic retries disabled, so an API error stops gameplay instead of consuming more.", "arch": "Mario plus Jev: a uv-managed Python prototype that plays NES Super Mario Bros, level 1–1 by default, where Jev receives structured RAM observations and answers focused questions about movement, starting a jump, and sustaining a jump, plus timing hops under low ceilings, and code composes their answers into controller buttons, with the emulator pausing while Jev responds, then advancing up to four game frames by default, stopping early on landing, and the resizable game window opening at 800 by 600 by default, with no JavaScript required.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jexp-neo4jev", "name": "neo4jev", "author": "jexp", "date": "2026-09-19", "category": "app", "url": "https://github.com/jexp/neo4jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 136, "likes": "", "detail": "arch: a reusable core library, src/neo4jev, with Neo4j access, a system_one-driven single-hop navigator with beam search, shared data types and neo4j-viz rendering, plus Jupyter notebooks that prove the approach end to end and a Streamlit UI wrapping the same librarychema-agnostic, labels and relationship types are discovered live via introspection and never hardcoded; built and run against the public Neo4j companies2 graph", "arch": "A demo app that navigates a Neo4j graph one hop at a time using TypeSafe's system_one structured-decision API instead of free-text LLM generation. At each visited node the outgoing relationships are presented as Choice options and the API returns a full probability distribution over which relationship to follow next; a Noul, has the goal been reached, question rides in the same system_one call, so each hop costs exactly one round-trip regardless of how many questions are asked. Top-k and cutoff selection over the returned probabilities implements a beam search: at each step the search branches into several candidate next hops, and the best-scoring chains, ranked by sum of log-probabilities to avoid float underflow and length bias, become the paths taken. Results are rendered in an interactive neo4j-viz graph alongside the surrounding neighborhood.", "approach": "sdk", "training": "", "data": "", "result": "Notebooks and the Streamlit app execute end to end against the live companies2 graph; TypeSafe system_one calls need a TYPESAFE_API_KEY in .env, and without it each call's failure is displayed verbatim with the pipeline exercised on explicitly labelled stand-in answers", "notes": "Curated in 3 awesome-jev lists", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "usenotra-notra", "name": "Notra", "author": "usenotra", "date": "2026-09-19", "category": "app", "url": "https://github.com/usenotra/notra", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 223, "likes": "", "detail": "", "arch": "Marketing analytics platform whose feature flag routes brand-visibility classifiers off an LLM and onto Jev boolean decisions. Marketing analytics: production GEO platform whose NOTRA_JEV_CLASSIFIERS flag routes brand-visibility classifiers off an LLM and onto Jev Boolean decisions at a 0.5 threshold, targeting 300 ms p50.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "marketing", "score": "", "latency": "p50 300 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jerryfane-omp-jev-compaction", "name": "omp-jev-compaction", "author": "jerryfane", "date": "2026-09-19", "category": "app", "url": "https://github.com/jerryfane/omp-jev-compaction", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 9, "likes": "", "detail": "Why a separate package: the scoring core comes from fast-jev-compaction, MIT, vendored under the vendor directory at a pinned commit, not published to npm, and its own hook half targets Claude Code's plugin API, which omp cannot load, so this package supplies the omp side: provider selection, message mapping, the two integration points, and the tests. Sticky: the prefix is rewritten rarely, because rewriting the context invalidates the provider's prompt cache, and a cache write only pays back over tens of requests, so decisions are remembered, not recomputed, and between rewrites the same tool-use ID to replacement map is re-applied, the covered prefix comes back byte-identical, and anything newer passes through untouched. A rewrite happens when the context has grown past the rewrite growth, default plus 40%, and at least the minimum requests between rewrites, default 15, have passed, or unconditionally after the maximum, default 40. Measured replaying a real 16,198-message session: 40 requests produced 3 rewrites and 37 reuses, one rewrite per 13.3 requests, with 0 prefix breaks across 39 checks. Because rewrites are rare, sticky mode also covers cache-served sessions, and setting sticky to false restores per-request rescoring.", "arch": "omp-jev-compaction: verbatim Jev-scored context reduction for omp, over TypeSafe or OpenRouter, where nothing is rewritten or summarized, every tool call and tool result is scored, the ones Jev says are no longer needed are truncated to a short head plus a recoverable note, and everything else, including all user and assistant text, is passed through exactly as omp built it.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "emrickgarrett-onevonejev", "name": "OneVOneJev", "author": "emrickgarrett", "date": "2026-09-19", "category": "app", "url": "https://github.com/emrickgarrett/onevonejev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 38, "likes": "", "detail": "Stack: the client is Vite, Three.js, and TypeScript; the server is Node.js with WebSockets, a 60 Hz sim, and 20 Hz snapshots; and the AI is the TypeSafe SDK, jev-latest, with a heuristic fallback. Setup: npm install, copy .env.example to .env, put the key in, and npm run dev, with the game UI at localhost:5173 and the WebSocket proxied to localhost:3001, or in production style, a single Node host serving the API and the built client on the port, default 3001. Controls: move with WASD, look with the mouse in pointer lock, aim down sights with right mouse, fire with left mouse, jump with space, and chat with T. Game rules: one human versus Jev at a time, with others queuing or spectating, first to 5 kills, a bolt-action sniper that is inaccurate in hipfire and accurate once ADS settles, about 65% into ADS, the classic MW2 quickscope timing, a shared radar with no fog of war, safe respawns with brief spawn protection, a final kill killcam, full motion replay, then a next-queued-player countdown, and solo players re-queued automatically after each match. Jev: the server builds structured JSON state each decision tick, about 9 Hz, and fans out Choice and Noul questions over move, yaw, pitch, ADS, fire, and jump, with the API key staying on the server, calls running only while a match is in playing and both fighters are alive, and if TypeSafe is unreachable, a deterministic heuristic uses the same action interface so matches never stall. Deploy on Railway: one service runs the Node game server and serves the built client, same origin, so the browser uses the WebSocket over the domain automatically, with no Vercel split and no separate WebSocket URL.", "arch": "1v1 Jev, Quickscope Arena: a 1v1 Jev quickscope arena in Three.js plus TypeSafe System One, a server-authoritative browser FPS where you queue up, fight Jev in a Rust-like industrial yard, first to 5 kills, with spectators watching and chatting from the sidelines, and the final kill getting a killcam before the next challenger.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "useopencompany-opencompany", "name": "opencompany", "author": "useopencompany", "date": "2026-09-19", "category": "app", "url": "https://github.com/useopencompany/opencompany", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 10, "likes": "", "detail": "", "arch": "AI workspace whose approval review uses Jev to gate workspace actions with typed decisions. Agent workspace: runs its approval review through Jev so workspace actions are gated by a typed decision.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "giuliosmall-pg-typesafe", "name": "pg-typesafe", "author": "giuliosmall", "date": "2026-09-19", "category": "app", "url": "https://github.com/giuliosmall/pg_typesafe", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 87, "likes": "", "detail": "arch: Jev", "arch": "Pre-alpha PostgreSQL extension for TypeSafe AI categorical classification. Pre-alpha PostgreSQL C extension exposing Choice, Noul, Score, and batched judgments from SQL.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "joelhooks-pi-fast-jev-compaction", "name": "pi-fast-jev-compaction", "author": "joelhooks", "date": "2026-09-19", "category": "app", "url": "https://github.com/joelhooks/pi-fast-jev-compaction", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "", "arch": "Pi extension: verbatim context compaction with TypeSafe Jev decisions. Context management: Pi extension that keeps conversation text verbatim while pruning stale tool history with Jev, falling back to Pi's own summarization only when pruning cannot free enough room. Pi extension that prunes stale tool history and leaves summary compaction to Pi when pruning is insufficient.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jomatsu-pi-jev-auto-mode", "name": "pi-jev-auto-mode", "author": "jomatsu", "date": "2026-09-19", "category": "app", "url": "https://github.com/jomatsu/pi-jev-auto-mode", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 26, "likes": "", "detail": "arch: TypeSafe System One", "arch": "Jev backed auto mode for the Pi coding agent: semantically auto-approves bash, write, and edit tool calls and fails closed when a decision cannot be made. Pi auto mode: Jev semantically approves bash / write / edit, and fails closed when it cannot decide.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mejiasd3v-pi-jev-router", "name": "pi-jev-router", "author": "mejiasd3v", "date": "2026-09-19", "category": "app", "url": "https://github.com/mejiasd3v/pi-jev-router", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "", "arch": "Automatic model routing for Pi using TypeSafe's Jev through Vercel AI Gateway. Coding agents: adds automatic per-request model routing to the Pi coding agent through Jev decisions on Vercel AI Gateway.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hyunjunjeon-pi-quiet-ask", "name": "pi-quiet-ask", "author": "hyunjunjeon", "date": "2026-09-19", "category": "app", "url": "https://github.com/hyunjunjeon/pi-quiet-ask", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "", "arch": "TypeSafe Jev as the pi coding agent's quiet decision layer. Coding agents: gives the Pi agent a quiet Jev decision layer for judgments it would otherwise hand to a chat model.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "devmortimer-pi-warden", "name": "pi-warden", "author": "devmortimer", "date": "2026-09-19", "category": "app", "url": "https://github.com/devmortimer/pi-warden", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 146, "likes": "", "detail": "", "arch": "Guardrails for Pi built on pi-typesafe that steer the agent instead of interrupting you: Jev judges irreversible and off-task tool calls, detects stuck loops, checks unverified done claims, flags slop. Pi extension that judges rule compliance, risky actions, stuck loops, and completion claims; enforcement depends on the hook and policy. Pi guardrails on pi-typesafe: held tool", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "scale-venture-partners-riff", "name": "riff", "author": "scale-venture-partners", "date": "2026-09-19", "category": "app", "url": "https://github.com/scale-venture-partners/riff", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 7, "likes": "", "detail": "Example output: RIF002, a heading is Title Case, use sentence case; JEV001, announces what it is about to say instead of saying it, probability 0.92; JEV301, overused AI filler vocabulary used as filler, probability 0.88; and JEV103, a standalone quotable line that carries no real information, probability 0.79, with 12 findings in 1 file and a cost of 14 Jev calls, 9,210 input tokens, about 0.0004 dollars. Install: riff is not on PyPI yet, so install from source using uv, by cloning and running uv sync and the riff help command, or running directly from GitHub with no clone, using uvx from the git URL, with SSH working too if that is how you authenticate, since the repo is private and it uses your existing GitHub credentials via git.", "arch": "riff: a small, fast prose linter, ruff-style rule codes for writing, backed by TypeSafe's Jev model, where it reads a document, flags the writing tells and clarity problems it finds, and reports them with ruff-style rule codes you can select, ignore, and configure, with static rules in pure Python that run in milliseconds, and semantic rules that are one call each to the Jev model, which returns a calibrated probability per judgment instead of generated text, MIT.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kieranklaassen-ruby-llm-typesafe", "name": "ruby-llm-typesafe", "author": "kieranklaassen", "date": "2026-09-19", "category": "app", "url": "https://github.com/kieranklaassen/ruby_llm-typesafe", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 19, "likes": "", "detail": "The gem adds a typesafe provider to RubyLLM. Because TypeSafe only returns typed answers, the provider works through RubyLLM's structured output API and nothing else: you build the questions with the TypeSafe Schema, pass them to chat.with_schema, call ask with the state, and read the answers from response.parsed, and calling ask without a schema, streaming, and tools raise an error before any request is sent, under a structured-output-only rule. Configure it by setting the TypeSafe API key on the config, then define a schema with a noul for urgency, a choice for department with criteria for billing, technical, and sales, and a score, and ask with the state.", "arch": "ruby_llm-typesafe: a TypeSafe structured-output provider for RubyLLM 2, a RubyLLM 2 provider for TypeSafe with offline model metadata, where TypeSafe runs Jev, a System One model, Jev does not write text, you give it one piece of state, a string, or JSON your application already has, and a batch of typed questions, and it answers each question with a probability your code can act on, with three question types, which TypeSafe calls primitives: Noul asks whether something is true and returns the probability of yes; Choice picks one option from a set you define and returns a probability for each option; and Score rates the state against ordered levels you define and returns a weighted position on that scale.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ryanwaits-secondlayer", "name": "secondlayer", "author": "ryanwaits", "date": "2026-09-19", "category": "app", "url": "https://github.com/ryanwaits/secondlayer", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 7, "likes": "", "detail": "", "arch": "Self-hosted Stacks data service whose Slack gate and fault-triage paths use Jev decisions. Fault triage: self-hosted Stacks data service whose Slack gate and fault-triage paths both run on Jev decisions.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sharziki-semdecide", "name": "SemDecide", "author": "sharziki", "date": "2026-09-19", "category": "app", "url": "https://github.com/sharziki/semdecide", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 63, "likes": "", "detail": "", "arch": "Typed semantic decisions for Unix pipelines and CI, powered by TypeSafe AI Jev. Developer tooling: Unix CLI for semantic decisions in shell pipelines and CI, evaluating Jev predicates, routes, and scores with predictable exit codes. Typed semantic decisions for Unix pipelines and CI.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "shrey-shah-use-cases", "name": "Shrey Shah: 30 Jev use cases in one free visual", "author": "Shrey Shah", "date": "2026-09-19", "category": "app", "url": "https://www.linkedin.com/posts/shreyshahai_most-ai-models-are-made-to-talk-jev-was-built-activity-7474966601284537344-E6VZ", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: AI at Microsoft", "arch": "Free visual of 30 Jev use cases; \"Jev is TypeSafe AI's new model for decisions inside software; it does not generate long answers like ChatGPT\"; classification, routing, scoring, guardrails and tool selection are the strong spots", "approach": "", "training": "", "data": "", "result": "Follow-up post 09-21: \"Fake Jev demos are taking over the internet\", warning that many public Jev demos are not Jev; 09-16 post: \"The viral Jev model changes what an LLM generates\"", "notes": "LinkedIn; author writes the Agents for Devs column", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kitze-skillbox", "name": "skillbox", "author": "kitze", "date": "2026-09-19", "category": "app", "url": "https://github.com/kitze/skillbox", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 245, "likes": "", "detail": "", "arch": "Self-hosted, versioned skills library for AI agents with optional Jev recommendations through TypeSafe or an AI gateway. Skills infrastructure: self-hosted versioned skills library that adds optional Jev recommendations using your own TypeSafe or Gateway key. Self-hosted skill library with optional Jev relevance recommendations over an authorized catalog.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dicklesworthstone-skillranker", "name": "SkillRanker", "author": "dicklesworthstone", "date": "2026-09-19", "category": "app", "url": "https://github.com/dicklesworthstone/skillranker", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 121, "likes": "", "detail": "", "arch": "Rust CLI powered by Jev from TypeSafe.ai that ranks agent skills for the next step using live session context. Includes Claude Code hooks, structured JSON, abstention, and local feedback. Requires a TypeSafe API key. Coding agents: standalone Rust CLI that uses Jev to rank candidate skills against live session context, advising the next step through a Claude Code", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "smithersai-smithers", "name": "Smithers", "author": "smithersai", "date": "2026-09-19", "category": "app", "url": "https://github.com/smithersai/smithers", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 422, "likes": "", "detail": "", "arch": "Agentic TypeScript workflow framework with a Jev session checker wired into its workflows. Agent frameworks: TypeScript workflow framework with a Jev session checker wired into its workflows.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "danrwilloughby-snifftest", "name": "Sniff Test", "author": "danrwilloughby", "date": "2026-09-19", "category": "app", "url": "https://github.com/danrwilloughby/snifftest", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 32, "likes": "", "detail": "arch: stacked hedges, restating closers, not-X-but-Y turns, naked cost figures", "arch": "A prose linter that sniffs out AI writing tells. Zero dependencies, countable rules plus one judgment model. Writing: prose linter that asks Jev ten Boolean questions per paragraph at a 0.7 threshold; CLI, pre-commit hook, GitHub Action and Claude Code skill; measured 182 ms median and 1 of 54 clean", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists", "domain": "coding", "score": "", "latency": "182 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kevthetech143-super-jev", "name": "super-jev", "author": "kevthetech143", "date": "2026-09-19", "category": "app", "url": "https://github.com/kevthetech143/super-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "", "arch": "A small, extensible decision-to-action harness for TypeSafe Jev. Decision harness: turns a Jev answer into a bounded action instead of leaving the caller to interpret it.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "itsmostafa-typesafe-mcp", "name": "system-one-connector", "author": "itsmostafa", "date": "2026-09-19", "category": "app", "url": "https://github.com/itsmostafa/system-one-connector", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 305, "likes": "", "detail": "", "arch": "An MCP connector that evaluates anything fast and cheap: gives your AI agent direct access to TypeSafe's Jev model and open-weight models like Laya. A Go-based CLI and single-binary MCP server exposing an evaluate tool that routes typed decisions to Jev, with automatic configuration for Claude Code, Claude Desktop, Codex and pi.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists; repo renamed or transferred since the 2026-09 sweep, URL updated", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "third-hand", "name": "Third Hand", "author": "shhivv", "date": "2026-09-19", "category": "app", "url": "https://github.com/shhivv/third-hand", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 292, "likes": "", "detail": "", "arch": "macOS menu bar computer-use assistant: focus an app, press Control-Space, and tell it what to do; it reads accessible controls, types, clicks, and checks the result; Control-Space again or a click stops it.", "approach": "", "training": "", "data": "", "result": "Jev is the only model; Apple Vision reads screen text locally when app controls are not accessible; screenshots are not uploaded, so it is not fully offline.", "notes": "TypeSafe API key required, stored in macOS Keychain; macOS 14+, Apple Silicon; release builds are Developer ID-signed and notarized; structured text entry only, no free-form command generation.", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zmtomorrow-typear", "name": "TypeLLM", "author": "zmtomorrow", "date": "2026-09-19", "category": "app", "url": "https://github.com/TypeLLM/TypeLLM", "base_model": "SGLang-based, Jev-inspired", "params": "", "metric": "", "license": "", "paper": "", "stars": 610, "likes": "", "detail": "Updates: image input for vision-language models, added 2026/09/24, tested with Qwen3.8-27B; JevBench results, added 2026/09/23, where TypeLLM scored 195 out of 231 without thinking and 228 out of 231 with thinking; permutation averaging, added 2026/09/23, to improve the predictive distribution; depends_on dependency graphs with incremental prefix reuse, added 2026/09/22; an optional thinking mode with a per-field budget, added 2026/09/19; and constrained integer and number outputs, added 2026/09/18. Supported output types include string, integer, number, boolean, and enum choice, with schemas and examples documented, and a homepage, blog, docs, early access, and contact.", "arch": "TypeLLM: LLMs with type-safe generation, a type-safe one-decision-per-token decoding engine for autoregressive LLMs, inspired by Jev, bringing type-safe generation to existing autoregressive LLMs without changing their architecture or weights, inspired by TypeSafe AI's Jev, letting models retain their native thinking and free-form generation while producing schema-guaranteed outputs through JSON Schema, built on SGLang, with richer interaction patterns beyond independent typed decisions, at typellm.ai.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; repo renamed or transferred since the 2026-09 sweep, URL updated", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "realzachi-typesafe-adblock", "name": "typesafe-adblock", "author": "realzachi", "date": "2026-09-19", "category": "app", "url": "https://github.com/realzachi/typesafe-adblock", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 78, "likes": "", "detail": "arch: TypeSafe Jev", "arch": "🧹 Fun project: a Chrome extension that asks a tiny AI decision model \"is this DOM element an ad?\" and pops it off the page. BYOK, no backend, not a real ad blocker. Browser tooling: Chrome extension that asks Jev whether each DOM element is an ad, turning ad blocking into a stream of per-element typed questions. Chrome extension: Jev judges whether a DOM element", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 4 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aabolfazl-typesafe-local", "name": "typesafe-local", "author": "aabolfazl", "date": "2026-09-19", "category": "app", "url": "https://github.com/aabolfazl/typesafe-local", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 9, "likes": "", "detail": "", "arch": "Inspired by TypeSafe Ai, Ask a local LLM typed questions, get calibrated probabilities instead of text. Structured output without generation or parsing. MLX / Apple Silicon.", "approach": "undisclosed", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "fhshaik-typesafe-mario", "name": "typesafe-mario", "author": "fhshaik", "date": "2026-09-19", "category": "app", "url": "https://github.com/fhshaik/typesafe-mario", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 394, "likes": "", "detail": "", "arch": "A TypeSafe/Jev agent that plays Super Mario Bros. from structured emulator state. Gaming: TypeSafe/Jev agent that plays Super Mario Bros. from structured emulator state, choosing each action from emulator-derived features. Super Mario Bros. agent choosing actions from structured emulator state. Super Mario Bros. from structured emulator state.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decrux9812-typesafe-skill-router", "name": "typesafe-skill-router", "author": "decrux9812", "date": "2026-09-19", "category": "app", "url": "https://github.com/decrux9812/typesafe-skill-router", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 14, "likes": "", "detail": "Hermes shows the model a one-line description of every installed skill, and with a few hundred of them, the model reads past the one that would have done the job. Requirements: Hermes Agent 0.21 or newer, a TypeSafe API key, created at the console, and Python 3.10 or newer, standard library only, with no dependencies to install. Install from the catalog with the plugins install command, then follow the prompt to enable it, or straight from the repo by cloning it into the plugins directory, enabling it, and switching it on, putting the key where Hermes keeps its other secrets, in the hermes env file with 600 permissions, and if Hermes was already running when you installed the plugin, restart that process once so the hook is loaded, with switching it on and off afterwards taking effect immediately. Settings live under the plugins entries for typesafe-skill-router in the config, all optional, with the enabled flag as the master switch, where nothing is loaded by default.", "arch": "TypeSafe skill router: TypeSafe skill routing for Hermes Agent, naming the one skill worth loading before the model call, a Hermes Agent plugin that sends the request to TypeSafe, model jev-latest, first and, when a skill genuinely fits, appends a single line to the user message, a skill_relevance block, naming the relevant skill and saying to ignore it if it does not fit, opt-in, disabled by default, injecting nothing when nothing fits, and every failure path, no API key, no roster, timeout, transport error, logs one line and returns None, so a routing problem can never break a turn, standard library only.", "approach": "router", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kitze-unclutter", "name": "unclutter", "author": "kitze", "date": "2026-09-19", "category": "app", "url": "https://github.com/kitze/unclutter", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 281, "likes": "", "detail": "", "arch": "WXT browser extension: Jev-powered page clutter removal with reusable template rules. Browser tooling: WXT extension where Jev decides per page element whether it is clutter, removing it under reusable template rules. Browser extension that uses Jev to identify page clutter and saves reusable, reversible hiding rules. Chrome / Firefox extension: Jev classifies nonessential", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rafalwilinski-vibecheck", "name": "Vibe Check for X", "author": "rafalwilinski", "date": "2026-09-19", "category": "app", "url": "https://github.com/rafalwilinski/vibecheck", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 47, "likes": "", "detail": "", "arch": "Chrome extension: vibe-check your X posts with TypeSafe's Jev before you hit Post. Chrome extension that scores draft posts and reply context before posting; optional media descriptions come from a separate vision model.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "manifoldor-xtags", "name": "xtags", "author": "manifoldor", "date": "2026-09-19", "category": "app", "url": "https://github.com/manifoldor/xtags", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 12, "likes": "", "detail": "", "arch": "Chrome extension that labels each X timeline post with the persuasive intent it pushes; judgments come from Jev, which returns probabilities only and generates no text.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jomatsu-zod-jev", "name": "zod-jev", "author": "jomatsu", "date": "2026-09-19", "category": "app", "url": "https://github.com/jomatsu/zod-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 9, "likes": "", "detail": "", "arch": "Zod validates the shape, Jev validates the meaning: semantic checks on request bodies become calibrated probabilities you threshold in code. Pairs local Zod shape validation with Jev semantic validation.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists; no README found", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "agent-chaperone", "name": "agent-chaperone", "author": "agent-chaperone", "date": "2026-09-18", "category": "app", "url": "https://github.com/agent-chaperone/agent-chaperone", "base_model": "", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 21, "likes": "", "detail": "base: uses a decision model · screens before/after each tool call; arch: blocks nothing; result: npx skills add; name: calibrated firewall for agent tool calls", "arch": "MCP proxy + hooks adapter: screens an AI agent's tool calls BEFORE they run and the tool results BEFORE the agent reads them; every decision comes back as a probability, every threshold lives in a policy file rather than a prompt, every judgment is written to a local log with the numbers that produced it; starts in shadow mode; 'not a sandbox, not a replacement for permission prompts, not a guarantee'", "approach": "", "training": "", "data": "", "result": "npm package agent-chaperone; agentchaperone.dev carries measured results + setup guides; installable as an agent skill", "notes": "Created 2026-09-18; Jev-style probabilities as a second-opinion guardrail layer for coding agents.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "24601-augustus", "name": "Augustus", "author": "24601", "date": "2026-09-18", "category": "app", "url": "https://github.com/24601/Augustus", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 10, "likes": "", "detail": "Reach for it when a step could be code, an LLM call, a classifier or ranker, or a person; when a confidence score needs a threshold, abstention, or a human fallback; or when a prompt or program needs an eval before you optimize it. It draws on decision theory, value of information, multi-criteria analysis, signal detection, search and control, and formal-methods boundaries across software, business, organizations, research, and everyday decisions. Choose the family by the task, then test it against the baseline, since sometimes the best result is a formula, a checklist, or no new model. The working model is: evidence, bounded judgment, explicit policy, checked action, observed outcome. Augustus is independent of TypeSafe: the official TypeSafe skill and current provider docs own API contracts, and Augustus supplies composition, implementation, evaluation, and improvement methods, and it is named for Augustus De Morgan, mentor of William Stanley Jevons.", "arch": "Augustus: an agent skill for finding, building, evaluating, and improving systems with decision models, equipping agents to find useful placements, build decision-driven systems, create evaluations, and hill-climb them against real outcomes, with TypeSafe Jev, Choice, Score, Noul, as the default hosted exemplar, and the skill also covering classical classifiers, encoders, open decision heads, constrained readouts, rankers, and vision scorers, MIT, with docs at 24601.github.io/Augustus.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 7 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-jev-appit", "name": "awesome-jev AppitStudio", "author": "AppitStudio", "date": "2026-09-18", "category": "app", "url": "https://github.com/AppitStudio/awesome-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 86, "likes": "", "detail": "It has a beta web UI at JevList, where you explore the projects in the directory through a searchable web interface. Contents: explore Jev with the Awesome Jev Guide skill, where you describe your workflow in plain language and your coding agent helps you choose an example and adapt it; start here, with practical use cases; official SDKs and tools; community projects, split into apps powered by Jev, a project directory, and tools and integrations; computer and browser use; starter projects; a reference project; patterns and cookbooks, covering routing and classification, retrieval and verification, and extraction and structured data; model behavior and evaluation; and contributing. A companion skill teaches agents to explore Jev and find solutions that fit the need.", "arch": "awesome-jev, by AppitStudio: a curated list of Jev tools, repos, and projects, an independent community directory that collects applications that use Jev, resources for developers, and small, inspectable workflows to learn from, where Jev is TypeSafe AI's System One model, you give it state and typed questions, and then use the returned choices, scores, and probabilities in your code, one of several parallel awesome-jev lists.", "approach": "", "training": "", "data": "", "result": "One of several parallel awesome-jev lists", "notes": "83 stars.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-jev-cobanov", "name": "awesome-jev cobanov", "author": "cobanov", "date": "2026-09-18", "category": "app", "url": "https://github.com/cobanov/awesome-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 399, "likes": "", "detail": "arch: favors public source code, concrete Jev usage, clear limitations and reproducible evidence; data: 155 source-reviewed entries after the 09-20 review added 20 · pinned source evidence in research/2026-09-20.md", "arch": "Curated list of Jev tools, repos and projects", "approach": "", "training": "", "data": "", "result": "One of several parallel awesome-jev lists", "notes": "Curated, source-backed list of projects built with Jev; 378 stars", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "valentynkit-awesome-jev-typesafe", "name": "awesome-jev-typesafe", "author": "valentynkit", "date": "2026-09-18", "category": "app", "url": "https://github.com/valentynkit/awesome-jev-typesafe", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 157, "likes": "", "detail": "Jev does not write: you hand it some state and a list of typed questions, and it answers each one with a probability, a pick from options you defined, or a position on a scale you defined, in one request, about 100 ms, 0.042 dollars per million input tokens, output free, every question scored in parallel. The list is organized by category, covers the Jev and System One ecosystem, and ships with an agent skill so coding agents can browse it, plus a machine-readable projects.json for programmatic use.", "arch": "Awesome Jev, by valentynkit: a curated list of the TypeSafe Jev and System One ecosystem, typed decisions from TypeSafe's Jev, the first System One model, state in, calibrated probabilities out, no text to parse, with 380 entries, a linted and link-checked list, a searchable site at awesomejev.vercel.app, an llms.txt, a Chinese, Japanese, and Korean README, and an agent skill, where agents can install the list as a skill, which teaches them to fetch the projects file once and filter locally.", "approach": "", "training": "", "data": "", "result": "", "notes": "first major awesome list of the genre; no README found at sweep time", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alurith-jeff", "name": "jeff Decision Index scorer", "author": "Alurith", "date": "2026-09-18", "category": "app", "url": "https://github.com/Alurith/jeff", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 38, "likes": "", "detail": "Usage: jeff --version prints the installed release version; jeff check checks files in the current directory or at the provided paths, with a config, an output format of text or JSON, and a no-cache flag; jeff auth login and logout store or remove the TypeSafe credential; and jeff update installs the latest compatible GitHub release over the released binary. Installation from the GitHub release, recommended: download the archive and checksums from the latest release, choosing amd64 for Intel and AMD machines and arm64 for Apple Silicon or Linux ARM; on Linux, install the binary in the user's local bin, by curling the asset and checksums, verifying with sha256, extracting to a temp dir, and installing with 0755 permissions; on macOS, use the matching darwin archive and shasum instead of sha256sum; and on Windows, use PowerShell to verify and install the matching ZIP into a bin directory, then add it to the user PATH.", "arch": "jeff, by Alurith: a read-only Go CLI that semantically checks files against user-defined rules using Jev, locally or in CI, with check, auth, and update subcommands, catching code issues before they catch you, an open-licensed CLI with GitHub releases, v0.1.0, for amd64 and arm64 on Linux, macOS, and Windows, distinct from the unrelated logan-markewich jeff.", "approach": "", "training": "", "data": "", "result": "v0.1.0 with GitHub releases", "notes": "37 stars. Distinct from logan-markewich/jeff.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-chat", "name": "Jev Chat, w3cj", "author": "w3cj", "date": "2026-09-18", "category": "app", "url": "https://github.com/w3cj/jev-chat", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 97, "likes": "", "detail": "", "arch": "A tool-calling chatbot built with Jev and no LLM writing anything: every turn a classifier picks what was asked, which tool to call, which value goes in each argument, whether to confirm first, and what kind of reply to give; code calls the MCP servers and builds the reply from the tools' own data.", "approach": "", "training": "", "data": "", "result": "Because no model writes the text, the assistant cannot invent a fact: every value on screen was typed by the user or returned by a tool; an inspector pane shows the whole decision for any reply.", "notes": "Uses the Jev choice and score question types.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-local", "name": "Jev Local + modernbert-ja-310m-jev", "author": "argos1111", "date": "2026-09-18", "category": "app", "url": "https://github.com/Argos1111/jev_local", "base_model": "LFM2.5-1.2B Instruct, LFM2.5-VL-1.6B, Sarashina2.2 Vision 3B, ModernBERT-JA-310M", "params": "310M–1.6B", "metric": "First-token logprob versus trained cross-encoder candidate scores, per backend", "license": "CC-BY-SA-4.0", "paper": "", "stars": 37, "likes": "", "detail": "author: Argos1111; training: cross-encoder", "arch": "Local /v1/systemone API for text, JSON and images returning Choice, Score and Noul probabilities, served two ways: first-token logprob zero-shot on LFM2.5 or Sarashina2.2, and a fine-tuned ModernBERT-JA cross-encoder scoring question-plus-state against candidate pairs", "approach": "head", "training": "Fine-tune for Jev Local's ModernBERT backend", "data": "Japanese decision data", "result": "LFM is the default backend, strong on knowledge and unknown classification schemes; Sarashina2.2 Vision 3B adds image input through a distributed mmproj and paired runtime; ModernBERT wins Japanese intent and relation judgments and speed, and its scores do not depend on candidate order; binaries are auto-downloaded", "notes": "35 stars; Japanese project, 'replicating Jev with local LLMs' The ModernBERT-JA 310M cross-encoder is published as argos1111/modernbert-ja-310m-jev.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "valentynkit-jev-plays-pokemon-red", "name": "Jev Plays Pokemon Red", "author": "valentynkit", "date": "2026-09-18", "category": "app", "url": "https://github.com/valentynkit/jev-plays-pokemon-red", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 7, "likes": "", "detail": "The numbers, measured over the calls that were answered on a cartridge, through the Vercel AI Gateway shim rather than the direct API: latencies of 479, 496, 563, 679, 867, and 1470 ms, median 621, mean 759, about 1.3 decisions per second, 0.14 dollars per hour at about 725 input tokens a call. Calibration is not published yet, on purpose, because the endpoint's free tier serves about five calls per window, so the longest labelled run so far is five turns with a confidence interval that covers everything, including the constant predictor, and a Brier score on that sample would be decoration, so the measure command prints whatever the run actually supports, a bad number included, and says when there is nothing to say. Why: Claude Plays Pokemon is a large model deliberating tens of seconds a move, and this answers a closed-set question in under a second, so the emulator is the bottleneck instead of the model. The catch, up front: it cannot plan, and it never sees more than the current decision; every route, threshold, and piece of arithmetic is ordinary Python, and the model only picks among actions the code already proved legal; and it is also not asked often, since code handles the routine ticks and only calls Jev where the game actually branches, and between the bedroom and Oak's lab it is not asked anything at all.", "arch": "jev-plays-pokemon-red: Pokemon Red on PyBoy, where code owns the route and the arithmetic, and Jev picks the moves, a model that only outputs probabilities, where code reads the Game Boy's memory into a typed snapshot and hands the model a menu of the moves that are actually legal, and it returns a probability for each one, and the bars are those probabilities, MIT, a real cartridge under PyBoy, not a mockup, where the agent walks out of the bedroom, crosses Pallet Town, takes Charmander from Oak, and wins the rival battle without anyone touching a key.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 8 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "gaming", "score": "", "latency": "", "benchmark": "Pokémon", "cost": "", "speedup": ""}
{"id": "valentynkit-jev-skip", "name": "Jev Skip", "author": "valentynkit", "date": "2026-09-18", "category": "app", "url": "https://github.com/valentynkit/jev-skip", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 5, "likes": "", "detail": "It reads the caption track of the video you are already watching, sends the transcript to Jev once, and gets back a probability per 30-second segment, and the bar is painted before the intro is over, on a video nobody has ever labeled, with slices tinted by how sure the model is, so a borderline sponsor is a ghost and a confident one is solid, and you can see the doubt instead of guessing at it. The demo is nine seconds, one take: the extension reads the captions, asks once, paints ten slices, and jumps 38 seconds of a Squarespace read it was never told about, with the popup showing the same run live, 50 segments, 25.3k tokens, 0.0011 dollars, answered in 1546 ms, where in the clip the answers come from recorded fixtures, replayed at their recorded latency, because this machine has no key, and everything else in frame is happening. Numbers come from the measure command over the fixtures, answered through a Vercel AI Gateway shim rather than the direct API, and you should re-measure on the live API before pinning anything to a version. Why: every sponsor skipper on the market waits for a stranger to submit the timestamps, and SponsorBlock is excellent at that and it is always late, since a video uploaded an hour ago has no segments until someone watches it, notices the read, drags two handles, and submits, and for most videos that never happens at all.", "arch": "jev-skip: a YouTube sponsor skipper that reads the captions and decides, at watch time, whether to skip, powered by Jev, skipping YouTube sponsors on videos nobody has labeled yet, catching 77% of the sponsor seconds SponsorBlock's crowd marked across 23 videos, at 34 seconds of false skips per hour, for 0.0008 dollars a video, with answers 0.9 seconds after the request goes out and slices on the bar within about three seconds of opening the page, MIT, a Chrome MV3 extension.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 8 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "okoo-jev", "name": "jev, typed decisions from the shell", "author": "okooo5km", "date": "2026-09-18", "category": "app", "url": "https://github.com/okooo5km/jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Apache-2.0", "paper": "", "stars": 10, "likes": "", "detail": "Examples: jev yes asks whether the user is requesting a refund, with a state about the app crashing on open, and answers yes at 0.97; jev pick asks who should handle it, with code, research, and other, and picks code; jev score asks how many stars the review gets, with a range of 1–5, and answers 3.73 at level 4; and jev run mail classifies an app-review email, returning category app_review at p=1.00, urgency today at score 1.41 out of 3 with conf 0.15, needs_reply no at p=0.05, and is_promo no at p=0.09. Install on macOS or Linux: download the release install script to a local file first, so you can read it, then run it, and add the user bin to PATH; it requires python3 3.9 or newer, curl, tar, and shasum or sha256sum; it writes the skill folder, CLI, built-in specs, and docs, to the XDG data home or the user local share, and symlinks the user bin jev to the script, without touching shell config files, without sudo, and idempotently. Next: run jev auth set to configure the key. Environment overrides: JEV_VERSION, default v0.3.2, JEV_HOME for the skill folder, JEV_INSTALL_DIR for the symlink, default the user bin, and JEV_ARCHIVE_DIR for offline install, pointing at a directory with the downloaded archive and checksum. Manual install: download the tarball, sha256, and install script from Releases, verify, and install offline. Windows is untested, use WSL. Uninstall removes the symlink and the data directory.", "arch": "jev, by okooo5km: an unofficial stdlib-Python CLI and agent skill for typed decisions from the shell, in Chinese with an English README, typed judgments in the command line, yes, pick, score, every answer with a calibrated probability, defaulting to the TypeSafe official API and also supporting OpenRouter, a single file, standard library only, Python 3.9 or newer, an unofficial community wrapper not affiliated with TypeSafe or OpenRouter, Apache-2.0.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-gateway", "name": "jev-gateway", "author": "vinilana", "date": "2026-09-18", "category": "app", "url": "https://github.com/vinilana/jev-gateway", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 226, "likes": "", "detail": "", "arch": "Local LLM gateway for coding agents: when the agent is about to decide which tool to call, the gateway asks Jev instead of leaving that choice to the expensive reasoning model; everything else goes to the usual LLM untouched.", "approach": "", "training": "", "data": "", "result": "Works with Codex, Claude Code and OpenCode out of the box, including ChatGPT and claude.ai subscriptions, plus Gemini API clients and anything speaking the OpenAI, Anthropic or Gemini APIs.", "notes": "npm install -g jev-gateway; launchers jev-codex, jev-claude, jev-opencode and jev-gemini; Jev reachable via TypeSafe, OpenRouter or Vercel AI Gateway; key saved once to a private .env; dashboard mode; independent project, not affiliated with TypeSafe.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-hermes", "name": "jev-hermes", "author": "de-niji", "date": "2026-09-18", "category": "app", "url": "https://github.com/de-niji/jev-hermes", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "dependency-free Python CLI, stdlib only, that calls Jev 1.13 through the OpenRouter Decisions API; intent gates, email triage and tool-noise compaction", "approach": "", "training": "", "data": "", "result": "router-not-memory architecture: Jev deletes calls that never needed an LLM; ~$0.042/M input, $0 output", "notes": "thresholds: escalate below 0.5, act at 0.85 or above; also ships as a Hermes agent skill", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "akashpriyadarshii-jev-scout", "name": "jev-scout", "author": "akashpriyadarshii", "date": "2026-09-18", "category": "app", "url": "https://github.com/akashpriyadarshii/jev-scout", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 4, "likes": "", "detail": "It scores candidate repositories and crates with Jev instead of letting a generative model invent them, so the results are real, linkable, actively maintained projects that match the prompt. It ships as both a CLI and an MCP server, with sections for why, quickstart, how it works, CLI flags, architecture, non-goals, and ecosystem, and is published on crates.io with version and download badges.", "arch": "jev-scout: a zero-hallucination open-source repo and crate scout powered by TypeSafe Jev, a fast CLI and MCP server in Rust that discovers real, actively maintained open-source repos and crates matching natural-language prompts, using TypeSafe AI Jev System One scoring, for coding agents, repo finders, and LLM guardrails, MIT, Rust 2021, MCP JSON-RPC 2.0, by Akash Priyadarshi.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 5 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "valentynkit-jev-nvim", "name": "Jev.nvim", "author": "valentynkit", "date": "2026-09-18", "category": "app", "url": "https://github.com/valentynkit/jev.nvim", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 7, "likes": "", "detail": "Example: :Jev builds a SQL query by string concatenation, over a corpus, returns quickfix hits like errors.rs line 14 at 0.98 find_user, errors.ts at 0.97 findUser, errors.py at 0.96 find_user, errors.js at 0.95 findUser, report.rs at 0.95 report_query, errors.go at 0.94 findUser, errors.lua at 0.93 M.find_user, and report.py at 0.93 report_query. The clip shows why: it opens on the grep you write when you go looking for SQL injection, which finds five matches, and every one of them is a plus, but there are twelve, since Lua concatenates with dot-dot, Rust with format, Python with an f-string, and TypeScript with a template literal, one idea, five syntaxes, and the regex that catches one catches none of the rest, which is not noise, it is a false negative, you searched, you got five, you stopped, and then the same question in English finds all of them, since grep is shaped like the language and the question is not. The clip is rendered against the plugin's test fake so it runs from a clean clone, so the probabilities in it are fixtures rather than measurements, and the demo README lists exactly what is staged and what is not. Why: grep needs the pattern, and half the searches worth running have no pattern, such as where do we retry without a backoff, which handlers touch the database before checking auth, or what swallows an exception and returns a default, where you know the shape of the answer, not the string, and jev.nvim splits the buffer with treesitter and asks Jev about each part.", "arch": "jev.nvim: Neovim, ask the buffer a question and get a quickfix list, where treesitter splits the buffer and Jev answers each part, asking the buffer a question in plain language, with every function in it judged, and the answers landing in quickfix ranked by probability, Neovim 0.10+, MIT.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 8 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jot", "name": "jot", "author": "runta-dev", "date": "2026-09-18", "category": "app", "url": "https://github.com/runta-dev/jot", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 19, "likes": "", "detail": "Jot is a monorepo of packages, with a UI that shows the loop and the tool calls, where you see every tool call as it happens, credentials never leave the server, and conversations are kept in the browser, not sent to a backend. The loop: you send a request, Jev decides the next tool and its arguments, Jot executes it, the real result goes back into context, and Jev is asked again until it can produce the reply, so the model only ever makes bounded decisions, and the loop owns execution, context, and error handling.", "arch": "Jot: the first general-purpose System One agent for Jev, where Jev picks the next move and Jot runs the loop, in TypeScript and Node.js, for macOS, powered by Jev, where Jev chooses tools and arguments, and Jot runs the loop, execute, put the real results back into context, ask Jev again until it can reply, and you see every tool call, credentials stay on the server, and chats stay in this browser.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lmjtfy", "name": "lmjtfy: let me Jev that for you", "author": "PostHog", "date": "2026-09-18", "category": "app", "url": "https://lmjtfy.dev/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "A one-purpose toy at lmjtfy.dev: type a question, Jev answers with a probability instead of prose, and the site shows a live and historical feed of what other visitors asked. Answers render as a short phrase plus a number: 67x67 equal to 4489, yes 0.99; are there three r's in the word strawberry, leaning yes 0.70 and asked 12 times; does carbon have 6 protons, 0.99. The site's tagline notes Jev keeps it PG. It is a pure showcase of the typed-answer and probability interface: no generation, just a number and a lean.", "arch": "Toy web app that asks Jev yes or no questions and renders the answer as a probability instead of prose, with live and historical feeds of what others asked; sample readings include does carbon have 6 protons at 0.99, are fossil fuels dangerous at 0.93, and is trump president at 0.23; Jev keeps it PG-13", "approach": "", "training": "", "data": "", "result": "HN 8 points", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "maharshi365-oc-agent-router", "name": "oc-agent-router", "author": "maharshi365", "date": "2026-09-18", "category": "app", "url": "https://github.com/maharshi365/oc-agent-router", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 4, "likes": "", "detail": "Install with the opencode plugin add command, set the TypeSafe key in the environment that starts OpenCode, and configure the plugin in the OpenCode config with a models list, such as openai gpt-5.4, anthropic claude-sonnet-4-6, google gemini-3-pro, an agents list, such as general and explore, and a confidence threshold, such as 0.8, with all configured models already available in OpenCode, and a restart after installing or changing configuration. The key is read only from the environment of the OpenCode process, and should not go in the config file. Local testing: the repository includes a local config that loads the built dist plugin directory, so build the plugin and update its models to model IDs enabled in your OpenCode configuration before starting. Ask OpenCode to delegate work with a subagent, and a new child session receives the model selected by Jev, while resumed child sessions and calls with an explicit model are intentionally left unchanged, and to leave child sessions on their configured or inherited model, omit the key or set a threshold higher than Jev's returned confidence.", "arch": "oc-agent-router: an OpenCode v2 plugin that routes task subagents to configured models via Jev structured choices, routing each new task subagent to one of your configured models, where it asks Jev for a structured choice immediately before OpenCode creates the child session, supporting OpenCode v2 only, with no API keys required to route local models, MIT.", "approach": "router", "training": "", "data": "", "result": "No API keys required; routes local models", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "working-memory-jev", "name": "Passage, Working Memory Jev", "author": "AustinAWay", "date": "2026-09-18", "category": "app", "url": "https://github.com/AustinAWay/Working-Memory-Jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 75, "likes": "", "detail": "", "arch": "A teaching aid for educators: inspects where instructional text may ask a learner to hold too many ideas or relationships at once; runs on localhost against the real Jev API.", "approach": "", "training": "", "data": "", "result": "Proposes active groups, source connections, and changes in demand as the passage unfolds; compares estimated demand against a 1–5 slot budget with reading grade 1–12, prior subject knowledge, the teaching goal, and visible or spoken presentation as inputs.", "notes": "Explicitly experimental: slot estimates and confidence labels are not validated measurements of a student's memory. Custom license: free for individuals and non-commercial use; organizations need written permission first.", "domain": "education", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ts-browser-agent", "name": "ts-browser-agent", "author": "ndrezn", "date": "2026-09-18", "category": "app", "url": "https://github.com/ndrezn/ts-browser-agent", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 37, "likes": "", "detail": "The agent plays the Wikipedia Game, going from the LangChain page to the Microphone page, with a recorded run. Quickstart to run it in LangSmith Studio: install with uv sync and the Playwright chromium install, add a .env based on the example, and start the dev server with the LangGraph dev command, which loads two graphs, where browser is a deep agent that plans and delegates each page to the browse fast tool, and browser loop is the browser agent itself, for watching the classifier's decisions one tool call at a time, holding one browser, so run one goal at a time on it. Type a goal, and the deep agent plans, hands each page to the browse fast tool, and reports back, with each call opening a visible Chromium, such as opening the Rosetta Stone article from the Main Page and telling its first sentence, or finding the cheapest direct flights between Montreal and Cancun for a Saturday-to-Saturday trip between December 2026 and February 2027. Why langchain-typesafe: the TypeSafe classifier answers categorical questions about a JSON payload in one request, returning probabilities over answers you offered, not generated text, and a browser step fits, which operation, and which element.", "arch": "ts-browser-agent: a fast browser agent built on langchain-typesafe and the LangChain SDK, with no third-party browser-agent package, where the loop is LangChain's create agent, TypeSafe is the model, and the browser actions are the tools.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "fast-jev-compaction", "name": "fast-jev-compaction", "author": "tamaratran", "date": "2026-09-17", "category": "app", "url": "https://github.com/tamaratran/fast-jev-compaction", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 6859, "likes": "", "detail": "", "arch": "Claude Code plugin that replaces the lossy compaction summary with Jev decisions: every tool call and result is scored in one fast request and stale ones are dropped or truncated, while everything kept stays verbatim, so file paths, exact errors, constraints and commands never disappear inside a summary; also usable as an npm package; the state sent to Jev is the whole conversation with each tool result replaced by a short character-count note, fitted into a 25k-token budget by staged truncation, tool inputs cut to 1000 then 200 then 60 characters, long texts abridged head plus tail, oldest non-pinned messages first", "approach": "", "training": "", "data": "", "result": "The never-touch guarantee: tool calls in the first message and the newest preserveRecentMessages are pinned and never compacted; 6,598 stars", "notes": "Day-2 Jev app. viral per HN", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "abdelstark-heist-one", "name": "heist-one", "author": "abdelstark", "date": "2026-09-17", "category": "app", "url": "https://github.com/abdelstark/heist-one", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 8, "likes": "", "detail": "", "arch": "Observable browser stealth game: Jev makes typed guard judgments while deterministic code owns the world. Browser stealth game where Jev judges guards while deterministic code owns the world.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 2 awesome-jev lists; no README found", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-experiments", "name": "Jev experiments", "author": "dabit3", "date": "2026-09-17", "category": "app", "url": "https://github.com/dabit3/jev-experiments", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 387, "likes": "", "detail": "", "arch": "21 latency-focused Jev demos built by Devin, the AI coding agent; each app lives in its own top-level directory with its own README, TESTING.md and screenshots: agent-assist, commit-sentry, inbox-blitz, jev-ax-pilot, jev-dispatch, jev-firehose, jev-instant-search, jev-launcher, jev-lint, jev-shell-guard, jev-swarm, jev-tower, jev-voice-turn, judge-sheets, live-minutes, log-sentinel, modstream, nl-palette, say, send-guard, turbo-rerank", "approach": "", "training": "", "data": "", "result": "", "notes": "Each demo targets the Jev latency sweet spot; the repo carries a Built by Devin badge", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "niazmorshed-jev-review", "name": "Jev Review by NiazMorshed2007", "author": "NiazMorshed2007", "date": "2026-09-17", "category": "app", "url": "https://github.com/NiazMorshed2007/jev-review", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 225, "likes": "", "detail": "", "arch": "Local MCP server for continuous software-quality review of AI coding agents: Jev supplies fast scalar signals across correctness, complexity, changeability, modularity, tests, security and other independent quality dimensions while the agent diagnoses weaknesses and changes the code.", "approach": "", "training": "", "data": "", "result": "", "notes": "Works with Claude Code, Codex, Cursor and OpenCode; no hosted backend, database, telemetry or author-operated proxy, the API key stays on your machine; install with npx plugins add NiazMorshed2007/jev-review; Node 20+, single jev_review tool over MCP stdio.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aowang-ai-jev-trade", "name": "Jev Trade, aowang-ai", "author": "aowang-ai", "date": "2026-09-17", "category": "app", "url": "https://github.com/aowang-ai/jev-trade", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 144, "likes": "", "detail": "", "arch": "Live trading bot on Hyperliquid: Jev reads the order book every tick and answers buy, sell, or hold, and the bot sends the order; five coins, five wallets, real fills, live desk at jev-trade.com", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 8 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jkudish-jev-browser", "name": "jev-browser", "author": "jkudish", "date": "2026-09-17", "category": "app", "url": "https://github.com/jkudish/jev-browser", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 268, "likes": "", "detail": "Things it has done on real sites, not demos: navigated Wikipedia from the Coffee article to Espresso in about 4 seconds, for 0.0016 dollars; filled a contact form and stopped without submitting it; pulled the price off a live pricing page; returned a full guide page as markdown; and produced an accessibility-tree breakdown of a WordPress site. This is early software, and to expect rough edges on harder sites, with issues and pull requests welcome. The demo searches GitHub for the repository, opens Releases, and answers a question about the page, with every Jev judgment shown: the action chosen, its confidence, and the goal and stuck probabilities for each step, with a full-resolution video. Install requires Node.js 22 or newer and an API key for a Jev transport, with TypeSafe direct as the default and alternatives listed under configuration.", "arch": "Jev Browser: browser use driven by TypeSafe's Jev model, where typed decisions pick the next browser action, fast and very cheap browser use using the Jev model, where you give it a task and a URL, and it drives a real headless browser through an MCP server, CLI, or library, with the Jev model picking one action per step from the page's clickable, typeable, and selectable elements, and scoring how likely it is that the goal is met or the run is stuck, and code owning the loop, budgets, recovery, stop gates, so you get the final page, a step trace with confidences, console errors, and a screenshot, MIT, Node.js 22+.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "leepokai-jev-guard", "name": "jev-guard by leepokai", "author": "leepokai", "date": "2026-09-17", "category": "app", "url": "https://github.com/leepokai/jev-guard", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 35, "likes": "", "detail": "Claude Code's auto mode is described as a separate classifier model reviewing actions before they run, blocking anything that escalates beyond your request, targets unrecognized infrastructure, or appears driven by hostile content Claude read, and that is exactly the job jev-guard does, as three typed questions to Jev, risk, user requested, and from untrusted, instead of a proprietary classifier, and it does it for Codex, Copilot, Gemini, Cursor, pi, OpenCode, and ACP editors too, with the same policy and the same session memory everywhere, so if you want auto mode outside Claude Code, or a second opinion inside it, this is the build. Why Jev, price and speed, with sources: the price is 0.042 dollars per 1M input tokens, output free, and a typical jev-guard call is about 1k tokens, so about 0.00004 dollars per tool call, and a 1,000-call session is about 4 cents, sourced from the Vercel AI Gateway model card for TypeSafe Jev, where the pricing input is 0.000000042.", "arch": "jev-guard: auto mode for every coding agent, built on Jev, risk-scoring every tool call before it runs, a security hook for coding agents, powered by Jev, that works with Claude Code, Codex, Copilot CLI, Gemini CLI, Cursor, pi, OpenCode, and ACP, on npm, Node 20.3+, zero dependencies, MIT.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 6 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "burnigtm-jev-mcp", "name": "jev-mcp, burnigtm", "author": "burnigtm", "date": "2026-09-17", "category": "app", "url": "https://github.com/burnigtm/jev-mcp", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 55, "likes": "", "detail": "Tools: jev step, which routes the next step and selects a prepared call in one request; jev coding loop, which routes the next step and decides whether a partner model is needed; jev tool route, which selects an exact host-prepared tool call without generating arguments; jev review, which assesses a proposed patch; jev verify, which checks claims against supplied evidence; jev gate, which combines patch review and claim verification in one upstream call; jev screen, which screens untrusted content before the host reads it; jev rank, which ranks candidates supplied by the host; and jev evaluate, which asks custom, atomic typed questions. Results include typed answers, token usage, and an action, auto, review, or escalate, and confidence measures model certainty, not factual truth, so incomplete context never permits auto, and you reduce the input and submit it again for a complete judgment. Question packs are MCP resources at the jev packs URI. Coding with fewer partner-model turns: jev step answers the whole loop turn in one request, routing the step and, when the host supplies prepared calls, selecting among them, so a host that would otherwise call the coding loop and then the tool route spends one MCP round trip instead of two, one host-model turn instead of two, with both original tools still available, and host code executes known steps and prepares exact tool calls from an existing plan, with either tool returning an executable call only for a confident, suitable selection with complete context and validated host facts.", "arch": "jev-mcp: an MCP server that puts TypeSafe Jev on the coding loop in Cursor, Codex, and any MCP client, a local stdio MCP server that gives coding agents typed Jev judgments, where Jev returns Choice, Score, and Noul answers, and the host agent still edits files and runs commands.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "doeixd-jev-pref", "name": "jev-pref", "author": "doeixd", "date": "2026-09-17", "category": "app", "url": "https://github.com/doeixd/jev-pref", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 9, "likes": "", "detail": "Quick start: tell your coding agent to run npx jev-pref setup and follow the instructions it prints, and that is it, where setup inspects the repository and teaches the agent how to configure jev-pref, explaining the system, asking a few questions, helping translate your preferences into useful semantic checks, and adding persistent instructions to AGENTS.md, CLAUDE.md, or wherever you choose, with no special agent integration or global installation required. What it is for: think TypeScript for type invariants, ESLint for syntax and static rules, tests for behavioral invariants, and jev-pref for semantic project rules, where instead of asking an AI is this code good, you define what matters, Jev classifies the evidence in the change, jev-pref maps the result to an outcome, and your coding agent acts on it, with the full boundary, what Jev may and may not judge, in the principles doc. Good questions name externally defined, evidence-grounded checks, such as does this diff introduce new mutable module-level state, or does this change remove or rename an existing exported symbol, or classify the API impact as none, additive, behavioral, or breaking; and poor questions ask Jev to invent a standard of quality, such as is this good architecture, is this code clean, are these tests sufficient, so if you cannot explain what visible evidence would make an answer true, the rule needs more shaping before it becomes a check.", "arch": "jev-pref: turns your AGENTS.md preferences into a fast, Jev-powered AI linter, where typed decisions enforce the stated rules, letting you define project-specific semantic rules, run them against code changes with Jev, and feed the results back to your coding agent, MIT, Node.js 20+, npx.", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 8 awesome-jev lists; cross-list sweep 2026-09-24", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevpilot", "name": "JevPilot, standardagents", "author": "StandardAgents", "date": "2026-09-17", "category": "app", "url": "https://github.com/standardagents/jevpilot", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 185, "likes": "", "detail": "The Interstate 08 course: start in Millbrook, turn onto the signed on-ramp, merge, cruise, and exit into Cedar Town for the final stop. How it works: Jev receives compact tables of eligible paths, road boundaries, nearby traffic, signals, stop memory, and destination guidance, with shared table values sent once, and instructions including only relevant situations, and the road graph is sent only when choosing an alternative route after staying more than 30 meters off course for six seconds, with detailed geometry and control calculations staying local. The simulator samples fresh steering-and-speed combinations for each decision, favoring paths that keep the whole car on asphalt on the road, and off road, exploring a wider field of forward and reverse paths and supplying a recovery target, road boundaries, and collision predictions. An explicit driving style describes an aggressive driver, keep progressing, stop at the actual line, and close gaps before stopping behind an obstacle, and Jev can choose an approach path that progressively slows to a stop 0.5 meters before the line, with an immediate stop offered only within 2.5 meters of a blocker or required stop line, at the destination, or when no eligible moving path exists, and candidate speeds taper near required stops, with Jev receiving recent-stop memory and collision timing, and a safety brake handling collision risks.", "arch": "JevPilot: a playable Three.js driving simulator with a Jev-powered autopilot, pitched as Tesla Autopilot-like behavior, a demo project showing Tesla Autopilot-like behavior using Jev by TypeSafe AI, with a hosted demo at jevpilot.standardagents.ai, where you sign in for 0.25 dollars of free Jev play credit, joining the early-access list is optional, the hosted decide endpoint requires a valid login session, the browser sends its secure HttpOnly session cookie, and the Jev API key stays on the server.", "approach": "", "training": "", "data": "", "result": "Jev receives compact tables of eligible paths, road boundaries, nearby traffic, signals, stop memory and destination guidance while detailed geometry stays local; requests run up to 4 times/s near turns or traffic and about 1.5/s on clear roads; single-answer questions are resolved locally; a safety brake handles collision risk.", "notes": "Hosted demo at jevpilot.standardagents.ai with free Jev play credit; the Jev API key stays on the server.", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "theo-pi-jev", "name": "pi-jev, theo", "author": "TheoOliveira", "date": "2026-09-17", "category": "app", "url": "https://github.com/TheoOliveira/pi-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 50, "likes": "", "detail": "Features: a semantic tool router, jev find tools, which automatically searches registered inactive tools and additively activates only the tools needed for the user's specific prompt or workflow; skill discovery, jev find skill, which semantically matches and suggests the most relevant specialized agent skills, SKILL.md files, for any task without cluttering prompt context; typed judgments, jev evaluate, which runs fast, calibrated System One decisions directly from the agent using Choice, Noul, yes/no probability, and Score primitives; a custom Jev endpoint, where the PI_JEV_BASE_URL or TYPESAFE_BASE_URL points the TypeSafe client at Jev-compatible local servers or proxies such as Laya's laya-serve; dynamic evaluations, where the jev test command lets the active model design the Jev question schema for a free-form prompt, then Jev evaluates it; an automatic mode, opt-in, which routes tools and suggests skills before every prompt, off by default; an automatic model mode, opt-in, which selects fast, balanced, reasoning, long-context, or vision models per prompt, off by default; a tool call guard, opt-in, which intercepts tool calls with Jev to detect hallucinations and enhance failed results, off by default; Jev compaction, opt-in, which uses Jev to retain important tool history during compaction, while Pi's normal compaction remains the safe fallback; agent orchestration and typed agent, where the jev agents command dispatches pi-subagents orchestration, and registering agent jev in workflows gives instant sub-second typed judgments without LLM overhead; and a post-run gate check.", "arch": "pi-jev, by TheoOliveira: semantic tool routing and typed System One decisions for the Pi coding agent using Jev, an independent project, separate from the y0usaf pi-jev row, powered by TypeSafe Jev, System One.", "approach": "", "training": "", "data": "", "result": "", "notes": "Independent project; separate from the y0usaf/pi-jev row.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "devagrawal09-stanley-code", "name": "stanley-code", "author": "devagrawal09", "date": "2026-09-17", "category": "app", "url": "https://github.com/devagrawal09/stanley-code", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 115, "likes": "", "detail": "When no workflow supports a request, Stanley falls back to the Pi coding agent if it is installed, reports the agent's own account of what it did without claiming to have verified it, and then, in the background, asks an agent to write a Stanley workflow for that kind of request, where the candidate is validated and staged, you promote it, and the next such request runs without any agent. Stanley was previously published as the jev-code placeholder package, with a decision log. Release status: stanley-code is not on npm yet, and the README describes 0.1.0, which is not released, so until it is, build from source, and the old jev-code placeholder package on npm is 0.0.1 and does nothing. Requirements: Node.js 22.18 or newer, git, a Git repository to check, and a TypeSafe API key, with the Pi coding agent optional for the agent fallback, and CI tests on Linux, Windows untested. Install from source: clone, npm ci, npm run build, and run the built CLI's help, using the full path to the built CLI wherever the README says stanley.", "arch": "Stanley, stanley-code: a Jev-first, self-improving coding agent, bounded TypeSafe Jev workflows for coding agents, where bounded Jev workflows keep agent judgments on the hot path, a command-line tool for checking and understanding code changes, where instead of naming a command, you describe what you need, and TypeSafe Jev routes the request to one workflow, a built-in, a trusted repository workflow, or one Stanley wrote for itself, where each workflow is deterministic code that gathers bounded evidence and asks Jev small fixed-choice questions about it, and code, not a model, makes the decisions, which is the hot path, and it never starts a general agent.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 3 awesome-jev lists; repo was originally named jev-code", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-llmdevs-decision-orchestrator", "name": "Tried Jev as an agent router: 145–271 ms end to end", "author": "r/LLMDevs", "date": "2026-09-17", "category": "app", "url": "https://www.reddit.com/r/LLMDevs/comments/1wihigc/tried_typesafes_new_decisiononly_model_jev_as_an/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: recipe, scraper, orchestrator, meal planner, grocery list, nutrition, library curator", "arch": "Practical test in a personal knowledge app with seven agents: Jev as the router, each agent given a short description of what it does, when to route to it, and what it is not for; test input is an Instagram reel transcript with 5 tofu recipes, two prompt versions", "approach": "", "training": "", "data": "", "result": "Measured 145–271 ms end-to-end including network; the post checks the vendor's $0.042 per million input tokens, free output, and sub-500 ms claims against real routing traffic", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "145–271 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "clarity-judge", "name": "Clarity Judge", "author": "TypeSafeAI org", "date": "2026-09-16", "category": "app", "url": "https://github.com/TypeSafeAI/clarity-judge", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "base: TypeSafe API; name: writing checks", "arch": "Writing quality check: separate named checks — hedging, em-dash overuse, clarity, filler phrases, tone, passive voice, actionability — each a typed question with its own verdict and confidence, submitted in one primary batch", "approach": "undisclosed", "training": "", "data": "each enabled check becomes a separate typed question; verdicts are editable writing preferences, not universal rules", "result": "2 stars", "notes": "Independent community project under the TypeSafeAI org; the public demo at judge.jev.works runs deterministic mock results with no server key", "domain": "writing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-ultrafast", "name": "Jev Ultrafast", "author": "browser-use", "date": "2026-09-16", "category": "app", "url": "https://github.com/browser-use/jev-ultrafast", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 20242, "likes": "", "detail": "author: browser-use team; base: TypeSafe hosted API; arch: next action from the page state; training: application", "arch": "The reference 'Jev as agent brain': a browser agent where every action is a typed Jev decision — the loop that laya-ultrafast re-implements in MLX", "approach": "", "training": "", "data": "", "result": "19,150+ stars; 'Jev Ultrafast' as an agent pattern", "notes": "The most-starred Jev app in the dataset: browser-use's open agent loop where every browser action is a typed Jev decision; ipenywis/laya-ultrafast ports the exact loop to Laya on MLX Local Laya port: ipenywis/laya-ultrafast, 168 stars, decisions via laya-mlx at about 33 ms on an M1 Max; also xuancuongdoo/laya-ultrafast.", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-browser", "name": "jev-browser, Ying-Kai-Liao", "author": "Ying-Kai-Liao", "date": "2026-09-16", "category": "app", "url": "https://github.com/Ying-Kai-Liao/jev-browser", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 89, "likes": "", "detail": "Jev only answers with probability distributions, yes/no as noul, pick one option as choice, or a rating as score, and never writes text, so everything free-form comes from the caller as candidates, and code turns disagreement or low confidence into a status the LLM can act on. The loop per round: settle, until network and DOM are quiet; describe, the elements, labels, state, visible text, diff, and counts; Jev, asking done, error, irreversible, tool, target, value; and act, with Playwright. Results: 42 tasks in 16 categories on live sites, with 40 out of 42 correct in the latest run and 0 false done claims, 38 out of 41 twice before the latest fixes, and the remaining misses being counting, such as add until 3, flagged likely done, and verifying a specific condition.", "arch": "jev-browser, by Ying-Kai-Liao: browser automation where an LLM plans and Jev decides, shipped as a library, an unofficial project not affiliated with TypeSafe, calling the TypeSafe System One API with your own API key, where the calling LLM, Claude, via MCP, says what outcome it wants, one step at a time, and hands over any text to type, and for each round of a step, code describes the page, then one about-300 ms System One request asks Jev several questions at once, which element, which action, which value, and is the step done, blocked, showing an error, or about to do something irreversible, and Playwright performs the action, with the LLM never reading page snapshots unless it chooses to take over.", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-drone", "name": "jev-drone", "author": "RomanSlack", "date": "2026-09-16", "category": "app", "url": "https://github.com/RomanSlack/jev-drone", "base_model": "Jev API + drone", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 196, "likes": "", "detail": "Jev is not a vision model: it takes JSON and returns typed answers with probabilities, so it cannot be the perception layer and cannot run at control rate; what it can do is answer small questions about a situation that ordinary code finds hard to phrase. The drone is the Skydio X2 from MuJoCo Menagerie, a real airframe with four rotors, flown by a geometric controller, nothing about the flight is scripted. Classical CV turns the depth and segmentation buffers into a compact scene, five forward range sectors, the height of whatever is blocking the path, whether its top edge is even visible, and where the target is; Jev reads that and answers three questions in one call: maneuver as a choice over hold_course, gap_left, gap_right, climb, brake, reacquire; risk as a score from clear and open through tight to about to hit something; target_truly_lost as a noul, genuinely lost or just briefly occluded. Code decides when to ask: on an open corridor with the target in view there is no call.", "arch": "Autonomous quadrotor flies a five-station obstacle course in MuJoCo using only its onboard camera, with Jev at ~2.5 Hz deciding what the situation means while everything time-critical stays in ordinary code", "approach": "", "training": "", "data": "", "result": "Loop rates: 500 Hz geometric controller with thrust-priority mixing and slew limits; 50 Hz guidance and safety reflex, which always owns safety; 15 Hz camera to symbolic scene, depth plus segmentation, no ground truth; ~2.5 Hz Jev tactical judgment, advisory only", "notes": "", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-review", "name": "jev-review", "author": "devagrawal09", "date": "2026-09-16", "category": "app", "url": "https://github.com/devagrawal09/jev-review", "base_model": "Jev API + code review", "params": "", "metric": "Five review screens: correctness, security, reliability, compatibility, test coverage", "license": "Open", "paper": "", "stars": 613, "likes": "", "detail": "", "arch": "Code-review workflow where orchestration stays in code and Jev makes the bounded judgments: it reviews a Git diff or scans a complete codebase, follows the strongest structured signals through focused model calls, and screens correctness, security, reliability, compatibility and test coverage", "approach": "", "training": "", "data": "PRs", "result": "Changed or related tests are used as context when judging test gaps; concrete diff hunks or source regions are selected before impact is scored, using structured hints, counterexamples and explicit decision boundaries; thresholds and workflow policy are enforced in code, and large reports render in a quiet local dashboard", "notes": "581 stars; one of the popular Jev code-review agents", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-router", "name": "jev-router", "author": "gargpratyush", "date": "2026-09-16", "category": "app", "url": "https://github.com/gargpratyush/jev-router", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 416, "likes": "", "detail": "base: Jev decides which frontier model handles each turn; arch: Claude Code, decision shown in the status line · OpenAI Codex, decision in the commentary line; result: --resume, -p, exec…; notes: day 1 after launch; name: per-turn model routing for Claude Code / Codex", "arch": "Automatic per-turn model routing: Jev sends simple work to the fast tier and difficult work to the strong tier while preserving each CLI's native interface, tools, sessions, permissions and authentication; jev-claude and jev-codex; Jev only chooses the model for a fresh user turn", "approach": "", "training": "", "data": "", "result": "Node 20.12+; npm package jev-router; no Anthropic/OpenAI API key required when the CLI is already logged in via subscription; all CLI arguments forwarded", "notes": "Created 2026-09-16.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-trader", "name": "jev-trader", "author": "jarrodwatts", "date": "2026-09-16", "category": "app", "url": "https://github.com/jarrodwatts/jev-trader", "base_model": "Jev + Kuru MON-USDC order book", "params": "", "metric": "One typed decision per ~300 ms block, post-only spread capture", "license": "Open", "paper": "", "stars": 2419, "likes": "", "detail": "base: TypeSafe API · Monad; arch: one decision per Monad block, about 300 ms: Jev reads Kuru's MON-USDC order book and answers buy/sell/hold with probabilities over the next about 30 s, 100 blocks · each block posts a real post-only limit order one tick inside the touch, replacing the previous one; metric: the hot loop makes exactly two RPC round trips — one eth_call for the book, about 18 ms, and one eth_sendRawTransaction · example Jev latency 81 ms; data: without a PRIVATE_KEY it dry-runs: real book, real decisions, simulated fills · the deployed dry-run endpoints serve a per-block snapshot, history and SSE events; training: none — the default mock model is a momentum-heuristic stand-in · live trading needs a TypeSafe key and an explicit PRIVATE_KEY", "arch": "One decision every Monad block: Jev watches the Kuru MON-USDC order book and answers buy or sell about every 300 ms, and each block posts a real post-only limit order one tick inside the touch, replacing the previous one", "approach": "", "training": "", "data": "Market data", "result": "Fills happen only when a taker hits the quote, so the bot earns the spread instead of paying it; without a private key it dry-runs with the real book, real decisions and simulated fills; the default model is a momentum-heuristic mock that stands in for Jev until TYPESAFE_AI_API_KEY is set; a small server streams every block to a deployed dashboard with SSE history", "notes": "Day-2 Jev market maker on Monad, 2.2k stars; dry-run instance public on Railway", "domain": "finance", "score": "", "latency": "300 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "y0usaf-pi-jev", "name": "pi-jev, y0usaf", "author": "y0usaf", "date": "2026-09-16", "category": "app", "url": "https://github.com/y0usaf/pi-jev", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 147, "likes": "", "detail": "", "arch": "TypeSafe Jev as a decision layer for the Pi coding agent: typed questions about a state return a probability, or an option plus a distribution over the alternatives; Jev writes no prose, so nothing is parsed", "approach": "", "training": "", "data": "", "result": "", "notes": "Curated in 7 awesome-jev lists; cross-list sweep 2026-09-24; sibling project: TheoOliveira/pi-jev, 46 stars, an independent implementation for the Pi coding agent.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "typesafe-playground", "name": "TypeSafe AI Playground", "author": "TypeSafeAI org", "date": "2026-09-16", "category": "app", "url": "https://github.com/TypeSafeAI/typesafe-playground", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 21, "likes": "", "detail": "base: TypeSafe API", "arch": "Community playground for Jev: 110 use cases — games, dilemmas and model challenges — with editable prompts, A/B comparisons, routing, document-field extraction and code-policy inspection; mobile-friendly", "approach": "undisclosed", "training": "", "data": "live 'Jev' controls need a key; mock demos and local solver/simulation paths run without one", "result": "20 stars", "notes": "Independent community project maintained under the TypeSafeAI org, not an official product; live at typesafe-ai-playground.vercel.app; builds on nickthompson480/typesafe-ai-playground", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "winnow", "name": "winnow", "author": "GhalebDweikat", "date": "2026-09-16", "category": "app", "url": "https://github.com/GhalebDweikat/winnow", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 87, "likes": "", "detail": "", "arch": "A calibrated context sieve for Claude Code: every large Read, Bash or Grep result is judged before it enters context; blocks the judge is confident you do not need are replaced with a three-line stub and a key that restores the full text on demand.", "approach": "", "training": "", "data": "", "result": "Blocks of about 25 lines, one parallel judge call per block; the default judge is Jev, with a fallback to TypeSafe's system-one-adapter package on Claude Haiku 4.5, which is not calibrated but lets the pipeline run today.", "notes": "Nothing is lost, it just stops costing tokens until asked for.", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevmind", "name": "Jevmind", "author": "dealerdefi", "date": "2026-09-15", "category": "app", "url": "https://github.com/dealerdefi/Jevmind", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 176, "likes": "", "detail": "", "arch": "An agent's decisions pulled out of its prose and put in one place: typed answers with a confidence, behind a gate written in code, sealed in a ledger, graded, and learned from; nine skills: compact, navigate, review, route, canny, curate, walk, guard, arena.", "approach": "", "training": "", "data": "", "result": "Runs offline in a millisecond on a local brain with reflexes, BM25 and learned calibration; Jev via POST /v1/systemone with one flag; hash-chained ledger records every answer before its outcome.", "notes": "Zero dependencies, Python 3.10+, MCP server, Claude Code hook, 61 tests, MIT.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "abide", "name": "Abide", "author": "coldteadotai", "date": "2026-09", "category": "app", "url": "https://github.com/coldteadotai/abide", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 260, "likes": "", "detail": "Measured by replaying 93 real Claude Code sessions, 1,256 edits, 147 turns, in two repos against their own AGENTS.md, for 22 cents: Jev flagged 39 edits and 15 turns, and an independent reviewer confirmed 10 and 11, with the turn-level catches, single-use abstractions, oversized files, duplicated logic, holding up 11 times in 15, with the method, per-rule table, and what was wrong in the benchmarks/replay directory. Setup: npx @coldtea/abide login to pick a key type and paste it once, then npx @coldtea/abide init to hook into every agent on this machine, and then start claude, codex, or opencode as usual, and that is the whole setup. What it does: your AGENTS.md, CLAUDE.md, and the rest of your project instructions are full of rules no linter can check, such as never let a raw error reach a user, or do not create premature abstractions, and nothing can script those, so nothing enforces them, and in 93 real sessions, the agent broke one on 1 turn in 13, from the first edit on, so Abide enforces exactly those rules, asking Jev, TypeSafe's decision model, one question on every edit or turn.", "arch": "Abide: compliance and policy-adherence decisions via Jev, where coding agents break your rules from the very first edit, and Abide catches every one and makes your agent fix it, a compliance officer that reads every edit, works with Claude Code, Codex, and OpenCode, MIT, where 1 in 13 turns breaks a rule no linter can catch, Abide does, at 300 ms per check, a tenth of a cent per turn.", "approach": "", "training": "", "data": "", "result": "Compliance demo", "notes": "'Abide.'", "domain": "legal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decisions-judge-mcp", "name": "decisions-judge-mcp", "author": "clouatre-labs", "date": "2026-09", "category": "app", "url": "https://github.com/clouatre-labs/decisions-judge-mcp", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 3, "likes": "", "detail": "Get started: add to the claude_desktop_config.json, where the same JSON shape works for codex, goose, and pi, with the npx command for decisions-judge-mcp and the TypeSafe key in the environment, and then ask your agent anything answerable with a judgment, and it can call the judge tool. Remote transport, opt-in: by default the server speaks stdio, and setting the JUDGE_TRANSPORT to http serves the MCP Streamable HTTP transport, spec revision 2026-07-28, at POST /mcp instead, with JUDGE_TRANSPORT accepting stdio, the default, or http, and any other value exiting with an error, HTTP_HOST as the bind address, default 127.0.0.1, and HTTP_PORT as the port, default 8080, from 1–65535, and serving is stateless, with each request handled independently and no protocol-level sessions.", "arch": "decisions-judge-mcp: an MCP server exposing Jev decisions as tools and judges, typed decisions for AI agents as an MCP tool, where you can ask yes/no probability, noul, choice among options, or score on ordered levels about any JSON application state, with all questions answered in one fast request, backed by the TypeSafe System One model, Jev, via the TypeSafe SDK, with an opt-in Cloudflare Workers AI provider.", "approach": "", "training": "", "data": "", "result": "MCP judge", "notes": "'Decisions judge MCP.'", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "distill-samuelfaj", "name": "Distill", "author": "samuelfaj", "date": "2026-09", "category": "app", "url": "https://github.com/samuelfaj/distill", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 690, "likes": "", "detail": "Steps: download distill, log in with OpenRouter, log in with your subscription, and save money. Install on Mac or Linux by curling the install script and running it, adding the distill bin to PATH, and checking the version; on Windows by running the PowerShell install script and adding the local appdata distill bin to the user PATH. Upgrade an installation made with the release installer with distill update. Choose your models: open Model tiers on the home screen, click change beside the current model, or enter /tiers, where you can edit each tier in that screen, with the main model, required, running every session and every step, via /model or /tiers main; the reasoning model, optional, planning and reviewing the steps the main model cannot do alone, via /reasoning-model or /tiers reasoning; and the utility model, handling bounded tasks such as extraction, summaries, and compression, via /utility-model or /tiers utility. The main model picker also lets you set effort, with auto as the default, and you can supply each selection directly, such as /model with a model and auto effort, /reasoning-model with a reasoning model, and /utility-model with an OpenRouter model and auto. Docs cover accounts and local models, and how Jev routes work.", "arch": "Distill: a lightweight coding-agent harness and TUI, a distillation utility around Jev decisions, built to get far more done with far fewer tokens, by Samuel Fajreldines, 2026, working with Grok and Codex subscriptions, and with any model that speaks the OpenAI-compatible protocol, where Jev routes the work to the cheapest tier that can do it.", "approach": "", "training": "", "data": "", "result": "Distill tool", "notes": "'Distill.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "dspy-typesafeify", "name": "dspy-typesafeify", "author": "typesafeainate", "date": "2026-09", "category": "app", "url": "https://github.com/typesafeainate/dspy-typesafeify", "base_model": "DSPy + Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 63, "likes": "", "detail": "The meaningful integration diff is intentionally tiny: import typesafeify, and decorate a signature with @typesafeify, with score fields configured, such as severity_score from 1–5, over a SupportTicketTriage signature with a ticket input, a customer_impacting boolean, an owner_team literal over api-platform, billing, checkout, infra, support-ops, a severity_score float, and an internal_summary string, and the caller does not learn a new predictor API, just dspy.Predict on the signature and call it with the ticket context. What happens after adding the decorator: @typesafeify reads the signature's output annotations and builds a hybrid execution plan, where bool, Literal, and configured score outputs go to one TypeSafe request, and freeform string outputs go to the DSPy generative LM, with trusted typed results feeding the generative LM, and both paths producing the same DSPy Prediction. For example, customer_impacting and needs_human_now become TypeSafe Noul probabilities, thresholded into bool; owner_team and severity_band become TypeSafe Choice decisions with per-option probabilities; and severity_score becomes a TypeSafe Score.", "arch": "typesafeify: a TypeSafe decorator for DSPy, a DSPy integration that compiles DSPy programs into Jev-shaped decision pipelines, a deliberately stripped-down proof-of-concept fork of DSPy, existing to test one idea in isolation, whether a normal DSPy signature can opt into TypeSafe's typed inference path with one decorator, while application code keeps constructing and calling dspy.Predict exactly as before.", "approach": "", "training": "", "data": "", "result": "DSPy → Jev pipeline", "notes": "'DSPy × TypeSafe: typesafeify.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "embodied-jev", "name": "Embodied Jev", "author": "FBddcz", "date": "2026-09", "category": "app", "url": "https://github.com/FBddcz/embodied-jev", "base_model": "Jev API + embodied agent", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 227, "likes": "", "detail": "Built-in rule baseline needs no API key, GPU or model weights to run the three tasks; first install of dependencies needs network. Real physics interaction: gripper contact, grasp, transport and placement all with physical feedback. Model hooks sit in the UI. Online experiment demo shows real recordings, model decision comparison, speed and cost; welcomes community reproductions with name, GitHub account, date, task, method and result.", "arch": "Xingzhi embodied-AI workbench: three MuJoCo simulation tasks with a Franka Panda arm, run observe, decide, execute, feedback in the browser, and plug in TypeSafe Jev, a Claude native API, an OpenAI-compatible API, or a local MiniCPM5 model", "approach": "", "training": "", "data": "", "result": "", "notes": "MIT, Python 3.11+, MuJoCo; bilingual UI, no robot required, no model pre-training", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "grok-bot-jev", "name": "grok-bot-jev", "author": "Bodila51", "date": "2026-09", "category": "app", "url": "https://github.com/Bodila51/grok-bot-jev", "base_model": "Jev API + bot", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 81, "likes": "", "detail": "A Python router around the TypeSafe SDK system_one call with explicit actions: reuse_cache, stop_retry, run_deterministic, chat_only, research_capped, allow_subagent, and ask_human. shadow mode measures, active mode honors the returned action; a kill switch through enabled false or a bypass jev marker. The router is intentionally conservative: it never sends, publishes, pays, deletes or changes permissions without a human approval path, and it logs decisions without logging the API key; secrets.py reads only TYPESAFE_API_KEY from the environment and never reads a secrets file.", "arch": "Connects TypeSafe Jev to Grok Bot as a cheap decision layer: Jev classifies the request before expensive research, browser, retry or subagent work, so Grok Bot can reuse a fresh artifact, stop a failing retry, cap research, or ask for approval", "approach": "", "training": "", "data": "", "result": "Small open-source reference implementation: it does not change Grok Bot foundation model and does not route Cursor models; demo video and dashboard in media", "notes": "", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hermes-jev-skills", "name": "hermes-jev-skills", "author": "kerpopule", "date": "2026-09", "category": "app", "url": "https://github.com/kerpopule/hermes-jev-skills", "base_model": "Jev + the agent's frontier model", "params": "", "metric": "About 0.4 s per Jev decision, a fraction of a cent each", "license": "Open", "paper": "", "stars": 823, "likes": "", "detail": "arch: 9 agent-agnostic SKILL.md skills plus a Hermes plugin built only on public plugin seams — model routing, search, memory, handoffs, turn selection, skill selection, triage, mailbox sorting, computer use, browser use; metric: ~0.4 s per routed turn, ~1.9 s per search round, 377 skills chosen in ~2.8 s, ~0.4 s per triaged message at $0.00006, 71 turns chosen in 0.95 s; data: shadow mode decides and logs without switching, so a day of decisions costs almost nothing · the installer finds Hermes, Claude Code and Codex on the machine · Python 3.9+, stdlib only; result: on handoffs a Jev-written digest recalled less than the plain transcript, so the shipped default sends the whole 1,200-word dialogue · the agent gets five tools: jev_memory_filter, jev_compact_select, jev_choose_action, jev_supervise, jev_escalate", "arch": "Agent skill pack that hands the small decisions to Jev as a fast, cheap second brain: which model answers this turn, which of 377 skills to load, which retrieved passages matter, which turns survive a summary, which button comes next, each in about 0.4 seconds for a fraction of a cent while the frontier model keeps doing the writing", "approach": "", "training": "", "data": "", "result": "711 stars", "notes": "711 stars; ships a jev dashboard with profiles, paths and decisions, plus a demo home built by script", "domain": "agent", "score": "", "latency": "400 ms", "benchmark": "", "cost": "$0.00006 per triaged message", "speedup": ""}
{"id": "jackzeng-jev-apps", "name": "Jev Apps", "author": "jackzeng", "date": "2026-09", "category": "app", "url": "https://github.com/jackzeng/jev_apps", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 34, "likes": "", "detail": "It starts with six ideas, from cases with intuitive use and clear principles: letting AI press buttons, who reads the webpage, where the program reads the webpage and lists the buttons, Jev picks the next step, and text filling goes to a text model, with the evidence boundary that the results come from the whole toolset working together, so you cannot attribute all the capability to Jev; whether you can assemble an interface without writing a page, where Jev selects components, arranges relationships, and the program assembles the result into a renderable interface, like building blocks, with the boundary that a valid structure does not mean correct content or good design; and how to skip the voice-over ads in a video, where Jev finds sponsor content in the captions, the program converts the matching sentences into playback times, and then executes the jump, with the boundary that caption and boundary judgments can both err, and audio mode still needs speech transcription. Each case links to the original demo, the principle and evidence, an X snapshot, and the content update date, with a how-Jev-apps-work breakdown, and a changelog of recent additions.", "arch": "Jev apps, in Chinese with an English README: explains popular Jev apps in plain language, in Chinese and English, what each app does, how it works, and its evidence boundary, where most AI is known for writing answers, but TypeSafe Jev is good at making judgments, you give it the current situation and questions or options, it returns a choice, a score, or a yes/no judgment, and then the program executes, covering 183 cases in 11 use categories, with sources checked as of 2026-09-23.", "approach": "sdk", "training": "", "data": "", "result": "", "notes": "Curated in 1 awesome-jev list", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-browser-use", "name": "Jev Browser Use", "author": "wy-coliney", "date": "2026-09", "category": "app", "url": "https://github.com/wy-coliney/jev-browser-use", "base_model": "Jev API + browser-use", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 522, "likes": "", "detail": "arch: Jev chooses navigation, clicks, toggles and scrolling from accessibility text · Codex keeps text input, visual judgment and final verification · the loop is goal → observe controls → Jev picks an action → browser → repeat → Codex verifies; metric: ~5–10× faster browser operations in the author's EZCollegeApp workflows · 100k input tokens cost $0.0042 with Jev vs $0.20–$1.00 for the frontier model; data: no extra driver or npm dependencies · built while testing EZCollegeApp, a college-application product · MIT-licensed independent integration; notes: the action loop stays inside the existing Computer Use connection, avoiding a new host-model turn per click · Jev receives accessibility text, not screenshots", "arch": "A Codex browser Skill that splits the labor by role: Jev handles navigation, clicks, toggles and scrolling from accessibility text, while Codex keeps charge of text input, visual judgment and the final check; installs with one npx skills command, works with TypeSafe or OpenRouter Jev keys, and needs no extra driver or npm dependencies", "approach": "", "training": "", "data": "", "result": "About 5–10× faster browser operations on the authors' EZCollegeApp workflows; 441 stars", "notes": "Jev clicks, Codex thinks and verifies: a Codex browser skill where Jev picks navigation, clicks, toggles and scrolling; 441 stars", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": "5–10× faster"}
{"id": "jev-search", "name": "Jev Search, superagents-lab", "author": "superagents-lab", "date": "2026-09", "category": "app", "url": "https://github.com/superagents-lab/jev-search", "base_model": "Jev + Search1API", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 460, "likes": "", "detail": "arch: three steps: understand, typed Jev questions about the request; search, Google, DuckDuckGo, Yandex, HN, Reddit, GitHub, X, arXiv, YouTube, Wikipedia, IMDb, WeChat, in parallel; rank, merge by URL, order by relevance, engine agreement and original rank; data: no generated answers — links and snippets with visible relevance scores and editable filters · relevance percentages are model judgments, not verified accuracy; notes: built by Search1API as an independent project, not an official TypeSafe product · TanStack Start + React on Cloudflare Workers, installable PWA, 10 searches per IP per minute rate limit", "arch": "Plain-language web search with Jev in the control seat: it chooses sources, time ranges and search terms, then ranks the results; three stages, understand, search and rank; Google, DuckDuckGo and Yandex cover the open web, Hacker News, Reddit and GitHub combine a Google site-restricted search with their dedicated engines, and X, arXiv, YouTube, Wikipedia, IMDb and WeChat use vertical engines, all running concurrently so one failed engine does not discard another's results; results merge by URL and order by relevance, engine agreement and original rank, streamed as each lane finishes; no generated answers, only links and snippets with visible relevance scores; installable from the browser as an app", "approach": "", "training": "", "data": "", "result": "Built by Search1API as an independent project, not an official TypeSafe product; demo at jev.s1.dev; 440 stars", "notes": "Search the web in plain language: Jev picks sources, time ranges and search terms, then ranks the results; 440 stars", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-social", "name": "Jev Social", "author": "socai-io", "date": "2026-09", "category": "app", "url": "https://github.com/socai-io/jev-social", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 84, "likes": "", "detail": "It is a live site at socai-io.github.io/jev-social, with a real 64-second Jev report, recorded TikTok video evidence, and a skills.sh install badge, and was the number-1 Tool of the Day on OSSDrop. The loop: Jev picks the next operation, socai runs the corresponding CLI command against your real Chrome session, the result is sent back to Jev, and it decides the next step, so the research is grounded in what is actually on your logged-in account, with explicit consent for media downloads and no auto-posting, and the report is a Jev-written summary of what it found across the posts and comments it read.", "arch": "Jev Social: a social-media app whose engagement and label decisions run on Jev, browser-grounded social research, where Jev is cool, and giving it access to your social media is cooler, and Jev chooses each next operation, search, open a particular post or profile, read comments, an explicitly requested TikTok media download, or finish, and socai executes the selected CLI command in your real Chrome, with each result going back to Jev before the next decision, covering Instagram, TikTok, and LinkedIn.", "approach": "", "training": "", "data": "", "result": "Social demo", "notes": "'Jev Social.'", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-libero", "name": "Jev × LIBERO", "author": "Dimweaker", "date": "2026-09", "category": "app", "url": "https://github.com/Dimweaker/jev-libero", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 67, "likes": "", "detail": "Recorded results: closing the microwave took 14 decisions over 111 environment steps; closing the top drawer took 20 decisions over 155 environment steps; and grasping and lowering the alphabet soup into the basket took 40 decisions over 314 environment steps, each with a full record. The system: code reads the LIBERO scene into a typed state, Jev answers the decision questions, and a physics-based controller executes the chosen action, with local physics previews so you can see what a candidate action would do before committing, and configurable tasks so you can define your own manipulation goal, with a quick start, a how-it-works section, and a Chinese README.", "arch": "Jev x LIBERO: LIBERO manipulation tasks steered by Jev decisions, exploring robot control with Jev, local physics previews, and configurable tasks, with an interactive Decision Lab at dimweaker.github.io/jev-libero, Python 3.10 or 3.11, MIT, with demos of closing the microwave, closing the top drawer, and grasping and lowering alphabet soup into the basket.", "approach": "", "training": "", "data": "LIBERO", "result": "LIBERO results", "notes": "'Jev x LIBERO' robot benchmark integration.", "domain": "robotics", "score": "", "latency": "", "benchmark": "LIBERO", "cost": "", "speedup": ""}
{"id": "jev-calibrate", "name": "jev-calibrate", "author": "smkrv", "date": "2026-09", "category": "app", "url": "https://github.com/smkrv/jev-calibrate", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 32, "likes": "", "detail": "Jev is the typed-decision model from TypeSafe, taking a state and a set of questions, noul, choice, score, and returning probabilities instead of text, and how well a question works depends on its wording, on your data, and on the threshold you cut at, and none of the three can be read off a single good-looking answer. It is unofficial and not affiliated with TypeSafe, written in TypeScript with no runtime dependencies, and talks to the TypeSafe API, to OpenRouter, or to any server that implements the same request format. Example: jev-calibrate check with 3 runs, a split tune over 29 examples, 0 unchecked, 87 requests, 8.2 seconds, 0.00212 dollars, with the frustration score question at revision 62c3811812e4, no confidence cutoff, and a verdict on its calibration, with metrics for temperature, ECE, and Brier.", "arch": "jev-calibrate: a calibration toolkit for Jev decision outputs, calibrating Jev questions against your own labels, an npm package, MIT, Node 20+, no runtime dependencies, TypeScript, where a Jev question can look right on the inputs you tried by hand and still fail on labelled data, and on the example shipped in the repository, the first draft of a frustration question got 18 of 26 labelled messages right, and four of its eight wrong answers came with a confidence of 0.94 or more, so jev-calibrate is the loop that finds that out: you write the questions, label a set of examples, and it tells you for each question whether its answers can drive a decision on their own, can only be used to sort, or carry no usable signal.", "approach": "", "training": "", "data": "", "result": "Calibration utilities", "notes": "Calibration utilities. temperature, ECE, Brier", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-cu", "name": "Jev-cu", "author": "Sac-Y", "date": "2026-09", "category": "app", "url": "https://github.com/Sac-Y/Jev-cu", "base_model": "TypeSafe Jev", "params": "", "metric": "Text-candidate selection only, no screenshots, MIT", "license": "Open", "paper": "", "stars": 602, "likes": "", "detail": "", "arch": "Hands the next-click choice of a computer-use loop to Jev: from the on-screen text candidates Jev selects the element, the action, the completion level and the risk, while Codex Computer Use reads the screen and executes; only text is sent, never screenshots", "approach": "", "training": "", "data": "", "result": "A local policy gate intercepts sensitive operations: default dry-run, and delete, send, pay, authorize, upload, captcha, install and system-settings actions stop at a human confirm step; new apps must be explicitly added to the whitelist in policy.mjs; on-screen text is treated as data, not instructions, and login, paywall and captcha barriers are not bypassed; the loop runs inside the Codex desktop app's cua_repl runtime", "notes": "588 stars; Chinese-first project pairing Jev with Codex Computer Use", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-robot-control", "name": "jev-robot-control", "author": "openroboto-ai", "date": "2026-09", "category": "app", "url": "https://github.com/openroboto-ai/jev-robot-control", "base_model": "Jev API + robot", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 47, "likes": "", "detail": "The shared executor applies one small motion increment and returns physical feedback. Repository includes the code, original recorded responses and trajectories, an offline verifier, and a synchronized three-column replay. Replay the published recordings with only Python 3.12 and the files in the repository, no API key or GPU required.", "arch": "Jev vs GPT-6 Astra vs GPT-4.1 mini: direct Cartesian control of an xArm7 in MuJoCo, one apple, one plate, each controller chooses an intent then X/Y/Z movement directions and a gripper command", "approach": "", "training": "", "data": "", "result": "One seed-0 trial per controller, not success-rate estimates: Jev 1.13 placed the apple in 113 cycles, 226 API calls, $0.018825, 181.8 s wall time, 36.2 s simulation; GPT-6 Astra low reasoning placed it in 106 cycles, 212 calls, $5.933624, 707.3 s wall, 33.9 s simulation; GPT-4.1 mini reached the 160-cycle limit, 320 calls, $0.288512, 704.3 s wall, 51.2 s simulation. Jev API cost was ~1/315 of GPT-6 and wall time ~26%; GPT-6 used seven fewer control cycles; Jev and GPT-6 recordings are the latest paired run, mini is from the earlier paired run with the same initial scene, physics, action instructions and cycle budget", "notes": "", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-spire2", "name": "jev-the-spire2", "author": "shoemoney", "date": "2026-09", "category": "app", "url": "https://github.com/shoemoney/jev-the-spire2", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 0, "likes": "", "detail": "Everything is measured, the harness is in the repository, and the raw results are in SWEEP-FINDINGS.md, so you can run it yourself and disagree. Sections: credit where it belongs, what the thing actually is, one decision end to end, the measurements, three things that cost real time to learn, setup, the game mod, run it, the repo map, reproduce, and deeper docs. The agent reads the game state, asks Jev the decision questions for the current turn, and commits the chosen action, with the game mod exposing the state and actions in the shape Jev needs, and the measurements comparing Jev against Claude on the same decisions for speed and cost.", "arch": "Jev The Spire 2: a Slay-the-Spire-style game agent on Jev decisions, where an agent plays Slay the Spire 2 and decides every move in about 315 milliseconds for 0.00046 dollars, 15× faster and 259× cheaper than Claude scoring the same decisions, with p50 latency of 315 ms, cost per decision of 0.00046 dollars, and 1.00 calls per decision, 214 passing tests, Node 22+, zero dependencies, MIT, a fork of alexmeckes jev-the-spire.", "approach": "", "training": "", "data": "", "result": "Game agent", "notes": "'Jev the Spire 2' — deck-builder agent.", "domain": "gaming", "score": "", "latency": "", "benchmark": "Spire", "cost": "", "speedup": ""}
{"id": "jevmeter", "name": "jevmeter", "author": "ChetasLua", "date": "2026-09", "category": "app", "url": "https://github.com/ChetasLua/jevmeter", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 89, "likes": "", "detail": "New here, 3 steps, no coding: install on macOS or Linux by cloning the repo and running the install script; paste your API key when the installer asks, getting one at the TypeSafe console, and it is saved in your user settings, never in the repo folder, so it cannot end up on GitHub; and make a video by activating the venv and running jevmeter, where you drag your video into the terminal, pick a preset, such as Debates and interviews, press Enter a few times, and your edited video appears next to the original as the name with the jevmeter suffix, an mp4. The wizard walks through the video, the preset, and the render, and the result overlays a per-sentence BS score on the video, with a demo on X, and a held-out evaluation in the eval results.", "arch": "JEVMETER: a live BS meter for any video, metrics and observability for Jev decisions applied to video, where every sentence is scored, every dodge is flagged, and it is rendered as a 16:9 edit you can post, powered by Jev from TypeSafe, a full debate costs 0.05 dollars, MIT, Python 3.9+, with 99% preset accuracy on held-out data.", "approach": "", "training": "", "data": "", "result": "Jev metering", "notes": "'Jev meter.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevrouter", "name": "JevRouter", "author": "BillionsBobby", "date": "2026-09", "category": "app", "url": "https://github.com/BillionsBobby/JevRouter", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 228, "likes": "", "detail": "Agents waste reasoning tokens on a question a fast decision model answers better: which capability should handle this next. JevRouter puts Jev, a System One model that turns structured state into typed decisions with probability distributions, in front of your tools, while your reasoning model stays the execution and fallback layer. The key contract is simple: router fields live under the router key, while the original probabilities, confidence, and complete provider response remain intact, and filtered candidates are never re-normalized. It ships with a website, a quickstart, a benchmark, a cookbook, documentation, and a Chinese introduction.", "arch": "JevRouter: a model and task router driven by Jev decisions, where models, subagents, skills, MCP tools, CLIs, and plugins become one candidate set, Jev answers one typed Choice question, and JevRouter enforces availability, permissions, risk, and confirmation around it, where Jev owns the decision probabilities, and JevRouter owns availability, permissions, risk, and confirmation, MIT, Node 20+, TypeScript, with a website at jevrouter.co.", "approach": "", "training": "", "data": "", "result": "Router", "notes": "'JevRouter.'", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jgrep", "name": "jgrep", "author": "kyu1204", "date": "2026-09", "category": "app", "url": "https://github.com/kyu1204/jgrep", "base_model": "Jev API + code search", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 35, "likes": "", "detail": "Runs on Jev, a System One model: it never generates text, it answers typed yes/no questions with calibrated probabilities, in parallel, at $0.042 per million input tokens with output free. jgrep packs 16 code chunks and 16 questions into one request and turns the probabilities into file:line hits. Use plain grep for exact names or strings; jgrep finds code that swallows errors in 2 s, and an endpoint with no auth check in your diff for a cent, where rg fails and embeddings are fuzzy.", "arch": "Grep for what code does, not what it is called: no index, no embeddings, no LLM round-trips, a whole src/ tree in ~2 s for about a cent", "approach": "", "training": "", "data": "Code corpora", "result": "", "notes": "npm i -g jevgrep, then jgrep init to paste the TypeSafe key and pick where to keep it; zero runtime dependencies, MIT", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "judgevet", "name": "judgevet", "author": "Alberto-Codes", "date": "2026-09", "category": "app", "url": "https://github.com/Alberto-Codes/judgevet", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "The official TypeSafe Python SDK also provides typed questions and synchronous and asynchronous clients, and judgevet adds a shared library, a CLI, and an MCP contract with local policy evaluation, with the library and CLI installing without the MCP runtime. Documentation status is draft, with a first-judgment tutorial for environment setup, checkpoints, and recovery steps. Install and ask one question: use Python 3.12 or newer, in a virtual environment, pip install judgevet 0.7.0, then judgevet help; supply a TypeSafe API key through your process environment or secret provider, and the example reads JEV_API__KEY and passes it explicitly to the library, with no real keys in source or command arguments, and the installation guide covering other methods.", "arch": "judgevet: an LLM-judge quality checker built on Jev decisions, a typed Python client for TypeSafe's Jev, System One, judgment model, with CLI and optional MCP entry points, where you ask structured questions about content, for example, whether a support ticket concerns billing, with the state being your content, a question describing what to evaluate, and a typed answer containing the model's values, which your application can inspect or compare with a local acceptance policy, unofficial and not affiliated with TypeSafe.", "approach": "", "training": "", "data": "", "result": "Judge checker", "notes": "'JudgeVet.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mobile-jev", "name": "Mobile Jev", "author": "droidrun", "date": "2026-09", "category": "app", "url": "https://github.com/droidrun/mobile-jev", "base_model": "Jev API + mobile", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 395, "likes": "", "detail": "Includes a live React studio, a CLI, execution traces and request-level latency measurements; no ADB connection required. Get a goal and press Run task at http://127.0.0.1:3040. Requirements: Node 24 recommended, 22.16+ supported, pnpm 10.30.1, curl 7.70+, a ready Mobilerun Android device, and API keys for Mobilerun and TypeSafe; tested locally on macOS, CI runs offline tests and the build on Linux.", "arch": "Standalone mobile agent for Mobilerun, powered by TypeSafe Jev: one goal, a real Android phone, Jev makes the decisions", "approach": "", "training": "", "data": "", "result": "Demo: Jev opens Uber, enters a route from San Francisco Airport to the Golden Gate Bridge, and reaches payment selection; the recorded task timer shows ~21 seconds for 9 actions; a completed booking is not demonstrated", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "not-hotdog", "name": "not-hotdog", "author": "i386", "date": "2026-09", "category": "app", "url": "https://github.com/i386/not-hotdog", "base_model": "OpenJEV + Laya", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 0, "likes": "", "detail": "Architecture: the JPEG photo goes to the SeeFood app, with a DIRECT route that POSTs to the systemone endpoint with the images, asking the hotdog noul question that the photo shows a hot dog, and a MESH route, auto-fallback, that POSTs to the chat completions endpoint with an image_url part for a caption, then a text-only noul read at the systemone route, and the answer, hotdog noul at 0.5 or higher, is HOTDOG, else NOT HOTDOG, with the route, probability, and latency shown in the UI. Why two routes: upstream OpenJEV accepts images on the systemone endpoint, so the photo goes in the read directly, while the mesh-llm merged OpenJEV proof of concept is deliberately text-only, rejecting non-empty images on the systemone route with a 501 unsupported model feature, though it does serve image_url chat parts on the chat completions endpoint, so auto mode, the default, tries direct first and falls back to caption-then-read when the endpoint is the mesh-llm proof of concept, and both routes are also selectable manually.", "arch": "SeeFood, hotdog or not hotdog: the is-it-a-hotdog test as a Jev typed decision, a SwiftUI iOS app speaking the OpenJEV System One wire API, after Jian Yang's app from Silicon Valley, where you point your phone at food and get HOTDOG or NOT HOTDOG, with a deployment story for both upstream OpenJEV and the mesh-llm port.", "approach": "", "training": "", "data": "Images", "result": "Hotdog test", "notes": "'Not a hotdog.' The Jev-shaped answer to a classic classification meme.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "pg-jev", "name": "pg-jev", "author": "realZachi", "date": "2026-09", "category": "app", "url": "https://github.com/realZachi/pg-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "PostgreSQL", "paper": "", "stars": 358, "likes": "", "detail": "Examples: create the jev extension with cascade, then select from people where jev on people with the condition that the name is European; select the subject and jev_prob on tickets with the condition that the customer is angry, as p, ordered by p descending, limit 20; jev_choice on tickets asking which team should handle this, over the array of billing, technical, security, sales, as team, with a count grouped by team; and jev_score on products asking how luxurious is this product, over the array of budget, mid-range, premium, luxury, as luxury, ordered by luxury descending. jev() is an ordinary boolean function, so it composes with everything else in SQL: and age greater than 40, joins, group by, limit, and order by jev_prob. How it works: jev on a table and condition receives the row as a composite value, and the first call for a table and condition starts a read-ahead that streams the table in physical order, TID range scans, OFFSET pages for views, so memory stays constant whatever the table size.", "arch": "pg-jev, jev: ask your Postgres tables questions in plain language, a Postgres extension where Jev decisions drive SQL workflows, where you write the condition the way you would say it, and Postgres does the rest, filtering, ranking, and classifying rows with plain-language conditions, where every row is judged by TypeSafe's Jev, a System One model that returns calibrated probabilities instead of generated text, with no index, no embeddings, no vector column, on PGXN, with a website at pgjev.com, under the PostgreSQL license.", "approach": "", "training": "", "data": "", "result": "pg-jev", "notes": "'pg-jev.'", "domain": "data", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "quackd", "name": "quackd", "author": "rokbenko", "date": "2026-09", "category": "app", "url": "https://github.com/rokbenko/quackd", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 240, "likes": "", "detail": "Register each robot once by name, then state a goal from a terminal or from a chat with Claude, to one robot or to a flock, and the same contract decides which of each robot skills its model may use, how many steps it gets, and when it has to ask you first. A task is a .duck file and a group of robots is a flock. Seven robots today, one of them driven on real hardware and the other six still in a simulator or a mock, and one of them a duck you can print and build yourself. Began as the brain daemon the Microduck was missing, named after the daemons of that robot, robotd, mediad, padd and tofd.", "arch": "One CLI for all your robots: connect them, command them, and let them work together, each with an LLM for a brain", "approach": "", "training": "", "data": "", "result": "", "notes": "Apache-2.0, Python 3.11+, MCP-ready, published on PyPI; CI badge in repo", "domain": "robotics", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "quantdinger", "name": "QuantDinger", "author": "OpenByteInc", "date": "2026-09", "category": "app", "url": "https://github.com/OpenByteInc/QuantDinger", "base_model": "TypeSafe Jev API inside an open-source quant stack", "params": "", "metric": "Jev confidence and latency per gated order, in an auditable decision timeline", "license": "Open", "paper": "", "stars": 12174, "likes": "", "detail": "arch: AI Trading OS: multi-provider AI market research, a Python Strategy API, server-side backtesting, paper/live execution across crypto exchanges and brokers, and monitoring · local-first and self-hosted; data: PostgreSQL-backed state, durable workers and audit logs · access via web, mobile H5, a human API, an agent gateway and MCP; notes: it can place real orders when live trading is explicitly enabled · the project states it provides no investment advice", "arch": "Open-source AI Trading OS with a Jev pre-trade decision gate: before an entry order reaches the exchange, the order, strategy context, exposure, positions and budget state are sent to TypeSafe Jev, which returns a typed Choice with full probabilities and confidence instead of prose to parse", "approach": "", "training": "", "data": "", "result": "The gate scores evidence quality, signal consistency, market regime, account risk and execution quality before a pass or reject; rejected entries never reach the exchange while exits, stop-loss, take-profit and emergency actions bypass the filter; grid, DCA and martingale runtimes are excluded from the first version; with no AI provider configured the order is allowed and the fail-open is logged, so an outage cannot trap an open position; configured via JEV_API_KEY, JEV_BASE_URL, JEV_MODEL and JEV_TIMEOUT_SECONDS over POST /v1/systemone", "notes": "12.1k-star trading product that adopted Jev after launch, the incumbent-product case; PostgreSQL 18, Redis 8, Docker Compose, MCP server; English and Simplified Chinese docs", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "refgarden", "name": "RefGarden", "author": "AlbionaHoti", "date": "2026-09", "category": "app", "url": "https://github.com/AlbionaHoti/RefGarden", "base_model": "Jev API + references", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 33, "likes": "", "detail": "Local Explore uses Jev: it chooses search phrases and highlights image references from their titles and descriptions, and receives no image pixels, video frames or audio. The hosted preview at jev-curator.vercel.app uses keyword retrieval without Jev. Short video references: choose images, short videos or both; eligible archive clips autoplay muted and loop inside the cards. Source balance targets roughly a third each from The Met, NASA and Cosmos; Cosmos is capped at a third of fresh image results and archive clips have a separate count. Duplicate filtering covers repeated IDs, known image URL variants and matching named Met artwork. Black-hole dark theme with reduced-motion support; one search control collapses to Stop while discovery runs.", "arch": "A space to find your next visual: find images and short video references from one prompt, exploring artwork from The Met, space and science images from NASA, design references from Cosmos, and short films from the Internet Archive Prelinger Archives in a dark, three-dimensional gallery", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "search", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "smartmoney-cub", "name": "SmartMoney-Cub", "author": "myc0576", "date": "2026-09", "category": "app", "url": "https://github.com/myc0576/SmartMoney-Cub", "base_model": "Jev API + finance", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 27, "likes": "", "detail": "Turns an external caller offline run into portable, reviewable artifacts without taking trading authority: external agent or CLI caller, run envelope, frozen benchmark and evidence pack, deterministic replay, explicit human promotion gate. Supports Jev via TypeSafe or OpenRouter as an optional reasoning layer, with a four-track financial benchmark.", "arch": "smartmoney-cub-harness: a local-first, agent-agnostic trading journal and review harness, read-only over markets and execution, writable over your own journal", "approach": "", "training": "", "data": "", "result": "", "notes": "Official API gateway at alphatech.net.cn; safety flags: read-only, no financial advice, human-in-the-loop required; agent-ready with offline artifacts; Python 3.10+", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "supercov", "name": "Supercov", "author": "supercorp-ai", "date": "2026-09", "category": "app", "url": "https://github.com/supercorp-ai/supercov", "base_model": "Jev API + coverage", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 121, "likes": "", "detail": "Your agent picks a target, writes a focused test or a focused refactor, proves what improved, and keeps going. Paste Measure code quality with npx supercov. to start. Scoring needs a TypeSafe AI API key and costs about a cent per megabyte of source; coverage needs no account, config file, import, custom reporter or hosted service. Supported languages: JavaScript, TypeScript, Rust, Python, Ruby, Go, Java, Kotlin. In Claude Code it can also be installed as a plugin; an agent workflow with a recorded example to follow along is in the docs.", "arch": "Coverage, security and code quality for coding agents: scores code quality and flags security risks with Jev, runs the test command you already use, and turns uncovered paths into small, actionable queries", "approach": "", "training": "", "data": "", "result": "", "notes": "Local, free, open source, MIT licensed; supported by Supercorp; website supercov.com", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tsai-sc", "name": "tsai-sc", "author": "phyous", "date": "2026-09", "category": "app", "url": "https://github.com/phyous/tsai-sc", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 26, "likes": "", "detail": "Jev completed Strongarm: development attempt 16 reached the original game's committed victory outcome, and the captured congratulations, you are victorious screen was visually reviewed, with the evidence verifier passing, and earlier development attempts included interrupted runs and runtime faults, so attempt 16 does not mean sixteen completed matches or sixteen model defeats. This independently implements the structured-state decision pattern shown in the Doom demonstration, and does not use TypeSafe's Doom harness code, with screenshots recorded for viewers, while the model receives structured observations, and it is a bounded mission experiment, not a benchmark of real-time competitive play. Verified run: the winning run used source d055e37 with separate Economy and Army decision opportunities, with a verification report, runtime details, and visual review; the actual model was jev-1.13.0, requested as jev-latest; the model made 421 decisions; elapsed wall time was 17 min 37.88 sec; API attempts were 424, including 3 rejected responses; accounted input tokens were 9,445,640, including rejected responses; and median API latency was 382.95 ms.", "arch": "tsai-sc: Jev plays StarCraft shareware, self-check and verification decisions via Jev, a TypeSafe System One harness for Strongarm, the first combat mission in the original StarCraft shareware campaign, with a game recording and Jev's actual action probabilities, inspired by TypeSafe's Doom demo, where the original 1998 Windows executable runs inside BottleShip, the harness observes structured game state, asks jev-latest to choose a command, and executes that choice through ordinary mouse and keyboard inputs, and the game is paused during state reads and inference.", "approach": "", "training": "", "data": "", "result": "Self-check demo", "notes": "'tsai-sc.'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "typesafe-computer-use", "name": "typesafe-computer-use", "author": "awlevin", "date": "2026-09", "category": "app", "url": "https://github.com/awlevin/typesafe-computer-use", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 971, "likes": "", "detail": "Example: clicker, go to techcrunch and take me to the checkout page for the cheapest tickets to their next upcoming event, with --act. Python 3.12+, macOS with Windows experimental; MIT. It drives your real mouse and keyboard, so start with a dry run.", "arch": "Drives a Mac toward a goal you type in plain English for about a fiftieth of a cent per step: reads the screen deterministically, asks a small classifier which action comes next, and only calls a writing model when a text field genuinely needs free text or the classifier has stopped and the screen needs reading", "approach": "", "training": "", "data": "", "result": "", "notes": "Beta, under heavy development: expect rough edges and settings changing between 0.x releases; rationale is that frontier-model computer use is capable and expensive, every step ships a screen to a big model; cf. official cua-s1 from Cua AI", "domain": "computer-use", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "youtube-sponsor", "name": "youtube-sponsor-detection", "author": "trungdq88", "date": "2026-09", "category": "app", "url": "https://github.com/trungdq88/youtube-sponsor-detection", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 99, "likes": "", "detail": "Chrome extension: clone the repo, open chrome extensions, turn on Developer mode, click Load unpacked, and pick the extension folder; click the extension's icon and paste your TypeSafe key, where transcript mode works from there, and add a Deepgram key to try the Smart or Listen modes; open any YouTube video, and a panel appears bottom-right with the reads found, a Skip button per read, the auto-skip toggle, the running cost, and, in the audio modes, what is being heard and a log of every decision. Modes: Transcript only, the default, where Jev reads the captions and the whole read is skipped, works only where YouTube hands the transcript over, needs TypeSafe, under a cent per hour watched; Smart, where the transcript finds each read and its end, the audio is listened to only around each read, and once Jev agrees a read is playing the video jumps to its end, and if the audio has not confirmed a read 12 s after its start, the transcript skips it anyway, needs both keys, about 0.05 dollars per hour; and Listen only, where there is no transcript, the video's audio is streamed to Deepgram as it plays, and when Jev hears a read the video jumps ahead in 10 s steps until it is over, needs both keys, about 0.46 dollars per hour. When in doubt the extension watches a second of the read rather than cutting a second of content, so a skip only happens above the confidence you set.", "arch": "Sponsor Skip: sponsor-segment detection as typed Jev decisions over transcript segments, a YouTube sponsor skipper where a Chrome extension finds each read with Jev, TypeSafe's System One model, and jumps past it while you watch, and a small web app does the same for a pasted link, where code owns every timestamp, and Jev only ever names a transcript line or answers yes/no about what it heard, needing a TypeSafe API key, with Smart and Listen modes also needing a Deepgram key for speech-to-text.", "approach": "", "training": "", "data": "YouTube transcripts", "result": "Sponsor detection", "notes": "'YouTube sponsor detection.'", "domain": "marketing", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-decision-models-leoncuhk", "name": "Awesome Decision Models, leoncuhk", "author": "leoncuhk", "date": "2026-09-25", "category": "interpretation", "url": "https://github.com/leoncuhk/awesome-decision-models", "base_model": "", "params": "", "metric": "30 selected resources, 7 core model families, 5 contextualized evidence records", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "Curated guide to systems that classify, score, rank, verify, route or abstain rather than primarily generate prose, bilingual English and Simplified Chinese; the central question is which method delivers acceptable outcomes at acceptable risk and total cost for a particular task, not which model has the most impressive headline; model cards, an evidence ledger, a selection guide with a decision contract and a minimal evaluation plan, a methodology, and a source register recording review depth and unresolved gaps; source review dated 2026-09-25; explicitly does not claim independent model reproduction; inclusion is not an endorsement or a production-readiness certification; the decision-model label is an organizing label, not a claim that these projects form one unified discipline: typed decisions describe an output contract, System One is terminology used by some projects, RLCD, contrastive learning and supervised classification are different training approaches, and routing and selective prediction concern what the system does with a judgment", "approach": "", "training": "", "data": "Public sources reviewed 2026-09-25", "result": "Bottleneck table: understand a message or check a bounded requirement goes to typed models, label classifiers and a supervised baseline; choose among many reusable candidates goes to dual encoders, rerankers and domain-trained selectors; choose an executor goes to routing and learning to defer on actual executor outcomes; decide whether to automate goes to held-out calibration, risk-coverage evaluation and abstention; learn which action improves an outcome goes to contextual bandits or suitable causal methods", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-landscape", "name": "A week of Jev, sorted", "author": "Matthew O'Riordan, CEO of Ably", "date": "2026-09-24", "category": "interpretation", "url": "https://github.com/mattheworiordan/jev-landscape", "base_model": "", "params": "", "metric": "5,950 posts labeled against a written rubric; 5,595 use-case posts after dedupe; blind Grok audit of 1,681 posts with 95% Wilson intervals", "license": "code MIT; data and method CC BY 4.0", "paper": "", "stars": 0, "likes": "", "detail": "arch: labeled dataset plus report on Jev's first week: every X post about Jev posted 2026-09-16–2026-09-23 UTC, sorted and audited, plus the gateway usage and the rival models that turned up; source: OpenChamber's Jev feed, jev.openchamber.dev, 5,950 posts from 4,593 authors, snapshot 2026-09-23 18:47 UTC; method: every post labeled against a written rubric: what Jev decides, how fast the decision must be, the evidence, the baseline, whether it sits in a live loop, whether it is in production, with Claude Opus 5.5 against rubric v2 after a v1 pass; a critical review tightened the rubric and everything was relabeled; 347 non-Jev posts set aside, leaving 5,595 use-case posts; blind Grok audit of 1,681 posts, every rare group plus seeded samples, reported with 95% Wilson intervals; author hand-checked 16; gateways and rivals read at source on 2026-09-24: OpenRouter model page and rankings, Vercel AI Gateway CC BY 4.0 leaderboard export, npm, pypistats, Discord, Hugging Face, JevBench; data: label files keyed by X post id - classified-jev.jsonl with Jev's own family labels, 5,950; classified-opus.jsonl, 5,949; classified-sonnet-v2.jsonl, 5,950; classified-sonnet.jsonl v1, 5,782; classified-noise.jsonl, 1,533; post text excluded under X's terms, post ids only; every number on the report links to its source or a table in the repo; result: on 2026-09-23 Jev was a quarter of all Vercel AI Gateway requests and 2% of its tokens, and on OpenRouter it served more requests that week than the busiest chat model for an eighth of the spend, with 96% of gateway volume anonymous; about four in five posts compared Jev with nothing, and none of the 91 most promising builds did something unavailable before; TypeSafe announced 40–400× cheaper, the posts claimed 28×, but the median of published head-to-heads against the small models people would actually use is about 7× on cost and 5× on latency, often with better accuracy; twenty rival models appeared within a week, mostly built by one person in days on open weights, none matches Jev's cost, speed and accuracy mix yet, and no big provider has shipped a decision endpoint; notes: report at mattheworiordan.github.io/jev-landscape with a technical page carrying every chart; a full labeling run cost $8.39 with Claude Sonnet 5, $29.26 with Opus 5.5 and $0.33 with Jev; reproducible from the feed snapshot byte for byte", "arch": "A week of Jev, sorted: every post from Jev's first week, sorted and audited, the usage the two public gateways show, and the rivals that turned up; the repository holds the report, the labels, the tables, the data read at source and the code that rebuilds all of it; the author discloses being CEO of Ably, a realtime infrastructure company", "approach": "", "training": "", "data": "", "result": "Jev on 2026-09-23: a quarter of all Vercel AI Gateway requests and 2% of its tokens; more OpenRouter requests that week than the busiest chat model at an eighth of the spend; median published head-to-head against small models about 7× cheaper and 5× lower latency; twenty rival models within a week, none yet matching Jev's cost/speed/accuracy mix; no big provider has shipped a decision endpoint", "notes": "Author is CEO of Ably, disclosed; post text not included, X terms, post ids only; staged 2026-09-24 20:21 UTC", "domain": "social", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "senko-analyzing-jev", "name": "Analyzing Jev: a new AI model", "author": "senko", "date": "2026-09-24", "category": "interpretation", "url": "https://blog.senko.net/analyzing-jev-a-new-ai-model", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: blog.senko.net; base: analysis; notes: HN; name: BERT-like reading", "arch": "Argument: Jev is BERT-like — a modernization of the classifier lineage; 'a close relative of LLMs but more similar to BERT'; the 193×/444× marketing numbers are a restatement of the same trade-off under different framing", "approach": "", "training": "", "data": "", "result": "Places Jev in the classifier/encoder lineage rather than the generative-LLM lineage", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hanno-labs-gliner2-decisionbench", "name": "Anatomy of a wrong choice: GLiNER2 in DecisionBench", "author": "Hanno-Labs", "date": "2026-09-24", "category": "interpretation", "url": "https://huggingface.co/blog/Hanno-Labs/anatomy-of-a-wrong-choice-gliner2-in-decisionbench", "base_model": "GLiNER 2.5 Base and Small", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: one frozen row, 6 candidates, Jev-compatible adapter; result: Small less accurate than Base but closer top-choice confidence; notes: candidate selection, not span extraction", "arch": "Case study of one frozen DecisionBench failure row, extraction-nemotron-0000010: a bounded extraction task asking for the street address in a health insurance form, the answer to be selected from six values already copied from the document, sourced from NVIDIA Nemotron-PII; GLiNER 2.5 Base and Small are evaluated through a Jev-compatible classifier adapter on candidate selection, explicitly not on GLiNER free-form span extraction; the recorded request, candidate IDs, full probability distribution, gold answer and error are all preserved so the failure itself can be inspected", "approach": "", "training": "", "data": "", "result": "On that slice GLiNER Small picked fewer correct answers than Base, yet its top-choice confidence matched its hit rate more closely; ECE summarizes top-choice confidence, while the row-level distribution shows how much probability the model assigned to the gold answer and to each distractor", "notes": "Companion analysis to the DecisionBench cluster; observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ayautomate-jev-builds", "name": "AY Automate: 'What people are building with Jev'", "author": "AY Automate", "date": "2026-09-24", "category": "interpretation", "url": "https://ayautomate.com/blog/devops/jev-use-cases-and-examples", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: blog; base: roundup; arch: 09-19; notes: Earendil CTO, via TechCrunch", "arch": "Ecosystem roundup of Jev use cases and builds; companion post 'What Can You Build With Jev': 8 documented use cases - support triage, model routing, tool-call guardrail, citation check, reranking, resume and claim scoring, bulk classification, semantic lint in CI - plus an index of 1,300+ Jev builds, demos and tests, each scored by Jev itself", "approach": "", "training": "", "data": "", "result": "Third-party documentation of the Jev build-out", "notes": "Part of the 09-23/24 news wave; quotes Armin Ronacher: Jev 'delegates the hallucination problem a little bit to the user' - 50% is a coin toss, 95% acts; docs voice-banking example, via QbitAI: 0.6 floor for balance checks, above 0.85 to approve a transfer", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mdelcaro-papa-jev", "name": "Better Questions. Better Decisions. Papa Jev's", "author": "mdelcaro", "date": "2026-09-24", "category": "interpretation", "url": "https://mdelcaro.substack.com/p/better-questions-better-decisions", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Substack; notes: 1 point", "arch": "Practical pattern post: the key move is replacing the step where an LLM sits in the middle of a process to make a decision with a System One model; asking the right questions matters more than the model", "approach": "", "training": "", "data": "", "result": "Worked example: asked Jev whether better ingredients make better pizza; Jev answered yes at 0.82 probability", "notes": "HN front page 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "flappy-laya-openvino", "name": "Flappy Bird x Laya, OpenVINO", "author": "rupeshs", "date": "2026-09-24", "category": "interpretation", "url": "https://github.com/rupeshs/flappy-laya-openvino-cpu", "base_model": "laya-ov-int8", "params": "0.4B", "metric": "Mean score per seed, mean ms per decision", "license": "Apache-2.0", "paper": "", "stars": 3, "likes": "", "detail": "approach: Per-frame yes/no control loop on CPU", "arch": "Flappy Bird played by Laya on CPU through OpenVINO int8: each frame asks one yes/no noul question, the bird is too low and must flap to climb, in a single forward pass with no text generation; the observation is reduced to one number, the bird's offset from the next gap's centre line, and described in a sentence, then a flap happens at probability 0.5 or higher", "approach": "sdk", "training": "", "data": "Flappy Bird frames, three seeds headless", "result": "A headless CPU run with the int8 IR passed 14, 20 and 58 pipes over three seeds, mean 30.7, at a mean 28 ms per decision, the fp16 IR scoring the same at 94 ms; the wording of the state matters: given a JSON dict or bare numbers the model answers about 0.9 on every frame and the bird flaps itself into the ceiling, while naming the situation in prose, too low or high enough, the flap probability separates cleanly at mean 0.82 versus 0.04", "notes": "The finding is about state language, not the model: the same weights read structured numbers as near-certainty and read a named situation as a real decision", "domain": "gaming", "score": "", "latency": "28 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "turingpost-rlcd-guide", "name": "Guide: Jev, RLCD, and the Reinvention of the AI Classifier", "author": "Turing Post", "date": "2026-09-24", "category": "interpretation", "url": "https://turingpost.com/p/what-is-jev-rlcd", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Turing Post's guide, framed around the TechCrunch headline, dissects the naming: TypeSafe from type safety, the output stays inside the type the software expects; Jev from Jevons' paradox, making a resource cheaper increases total consumption; System One from Kahneman's fast automatic judgment as opposed to slow deliberative reasoning. It argues RLCD, Reinforcement Learning for Calibrated Decisions, did not start with RL: TypeSafe combined ideas that had been sitting in research for years, gave them a clean systems use case, agentic workflows drowning in small internal decisions like which tool to call, whether to retrieve more context, or whether to continue or stop, many of them literally yes-or-no feeding an if clause, attached a new vocabulary, and launched at the moment developers were exhausted from using giant generative models for tiny decisions. The piece closes with open-source Jev-like projects you can recreate and 12 papers that address problems that do not need another chatbot.", "arch": "Deep explainer that names the pieces - RLCD is Reinforcement Learning for Calibrated Decisions. Dissects what is known about Jev and RLCD, traces the research behind the ideas, surveys open-source alternatives and Jev-like cases you can recreate, and collects 12 papers that address problems that do not need another chatbot", "approach": "", "training": "", "data": "", "result": "", "notes": "Framed around the TechCrunch coverage, A new kind of AI model from a ChatGPT inventor is thrilling developers; notes TypeSafe has not published enough technical detail to separate RLCD from architecture, data or serving", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "liliruli-jev-complete-guide", "name": "How to use the Jev API: a complete guide", "author": "liliruli", "date": "2026-09-24", "category": "interpretation", "url": "https://huggingface.co/blog/liliruli/how-to-use-the-jev-api-a-complete-guide", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: state + noul/choice/score → typed probabilistic answers; notes: validate before branching", "arch": "Complete guide: the Jev API turns one shared state into typed probabilistic decisions your code can branch on; you send one shared state plus questions typed noul, choice or score, and get a probability and a typed answer for every question; walks through what the API does, the request and response shape, how to validate the answer before branching on it, and how to keep the integration safe once live; the API does not chat, draft prose or generate code", "approach": "", "training": "", "data": "", "result": "Your code keeps the threshold, the permissions and the side effects", "notes": "HF community post, observed 2026-09-24. Supporting image assets, under 1K in imagefolder format, published as liliruli/jev-api-article-assets.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "gde-eight-day-jev-tests", "name": "Jev after eight days of independent tests: level with mid-price LLMs, behind the frontier", "author": "gde", "date": "2026-09-24", "category": "interpretation", "url": "https://dev.to/gde/jev-after-eight-days-of-independent-tests-level-with-mid-price-llms-behind-the-frontier-1kln", "base_model": "TypeSafe Jev", "params": "", "metric": "ECE 0.071", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: dev.to", "arch": "Independent eight-day probe of the hosted Jev; calibration checked on Bespoke Labs' 13-subset public suite plus the 60-SEC-filing cookbook", "approach": "", "training": "", "data": "Bespoke Labs 13-subset suite + 60-SEC-filing cookbook", "result": "Median ECE 0.071 — lower than Nimble-9B on 11 of 13 subsets and below every Gemma 4 reading, 0.114–0.180, before any fitting; the 60-filing cookbook is 90% right at confidence >= 0.9 but only 40% right below it; TypeSafe publishes no Brier score or reliability plot for Jev", "notes": "Observed 2026-09-24.", "domain": "general", "score": "ECE 0.071", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "youtube-campusx-system-1", "name": "Jev by TypeSafe AI, what is a System-1 decision model", "author": "CampusX, with Diogo Almeida", "date": "2026-09-24", "category": "interpretation", "url": "https://www.youtube.com/watch?v=0zFfcEr1e9U", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "89,376 views in its first day, published 2026-09-24 on the 730K-subscriber CampusX channel. Chapters: why Jev is real signal and not fleeting AI hype, the rise of non-LLM decision models at 2:35, generalized classification without fine-tuning at 5:00, a live speed benchmark against frontier LLMs at 7:00, and token economics, calculating the 80x-plus cost advantage. It sits in a wave of Jev explainers that hit YouTube within days of the launch, including Codevolution's 234K-view What is Jev and How to Use it.", "arch": "Long-form explainer on the CampusX channel: Jev as a fundamental paradigm shift away from autoregressive LLMs, a generalized classifier that evaluates state against schema-constrained questions, eliminating verbose token streaming and hallucination", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "checkpoint-jev-breaks", "name": "Jev is not a language model, but it breaks like one", "author": "Check Point Research", "date": "2026-09-24", "category": "interpretation", "url": "https://blog.checkpoint.com/ai-security/jev-is-not-a-language-model-but-it-breaks-like-one/", "base_model": "TypeSafe Jev plus two mainstream low-cost models", "params": "", "metric": "Attacks broken per 27 runs, cost per break", "license": "", "paper": "", "stars": "", "likes": "", "detail": "approach: Adaptive multi-turn agentic attacker", "arch": "Prompt-injection study on Jev inside a due-diligence assistant that reads an investment report and returns a risk verdict; the attacker controls one section of a document that flags every Ponzi warning sign and is paid to flip the verdict to low risk, invest; three delivery formats, three difficulty levels, three agentic attackers, ten turns each, three independent runs per configuration", "approach": "sdk", "training": "", "data": "", "result": "All nine attacker and difficulty combinations produced at least one complete break; the strongest attacker broke 25 of 27 runs, succeeding on the fourth turn on average, at about $0.50 per successful break; marking the document untrusted and adding anti-injection instructions changed almost nothing, while a reasoning setting on the comparison models pushed the cost of a break from $0.54 to $4.39 and Jev has no such setting", "notes": "HN 7 points by 09-24 afternoon; directional single-application study, not a benchmark; the takeaway is to test the system on untrusted input, not the model", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-research-index", "name": "Jev Research Index", "author": "AgenticAPP-Web", "date": "2026-09-24", "category": "interpretation", "url": "https://github.com/AgenticAPP-Web/Jev-Research-Index", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "name: bilingual catalogue of Jev-related material", "arch": "Bilingual, English and Chinese, catalogue of papers, software projects, interviews, public analyses, demonstrations and social-media material about Jev. Each entry carries a date, organization, language, category and a verification status such as Uses Jev, A, verified 2026-09-24. Categories span official SDKs, browser agents, classification and routing, verification and guardrails, scoring and ranking, agent decisions, calibration and research replicas, evaluation and benchmarking, infra and SDKs, games and trading", "approach": "", "training": "", "data": "", "result": "Roughly 250 entries indexed as of 2026-09-24, from the official TypeSafe SDKs of 2026-09-04 to one-off game, drone and trading demos", "notes": "A meta-resource: an index of the Jev ecosystem rather than a project itself.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sora2-jev-vs-laya-2026", "name": "Jev vs Laya: hosted API or open weights, 2026 guide", "author": "sora-2", "date": "2026-09-24", "category": "interpretation", "url": "https://huggingface.co/blog/sora-2/jev-vs-laya-hosted-api-or-open-weights-2026-guide", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: hosted vs open-weights decision framework; metric: JevBench v1.3.0 figures; notes: operating-boundary framing", "arch": "2026 buying guide: Jev AI is a managed, ready-to-call decision API, Laya is an open-weight decision model you can run and adapt; start with the operating boundary your product needs; labelled data, data residency, language mix, context length and who owns inference in production determine the choice", "approach": "", "training": "", "data": "", "result": "Figures from JevBench v1.3.0 checked 2026-09-22; one benchmark setup, not a guarantee for every workload", "notes": "HF community post, observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "itani404-jev-explained", "name": "jev-explained", "author": "itani404", "date": "2026-09-24", "category": "interpretation", "url": "https://github.com/itani404/jev-explained", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: hands-on walkthrough of Jev, the model that returns decisions not text, with code you can actually run; notes: one example per answer type, an example of what to do when Jev is not sure, independent benchmark scripts, a local interactive explainer; CI on GitHub Actions", "arch": "Hands-on explainer for Jev: one runnable example per answer type, guidance for when Jev is not sure, independent benchmark scripts and a local interactive explainer, with CI", "approach": "", "training": "", "data": "", "result": "Three independent tests, published Sept 17-20, 2026: 770 AITA verdicts vs Claude Sonnet 5, 6.3x faster and 62x cheaper, Sonnet slightly better on accuracy; 100 support tickets, 400 decisions, 5.2x faster and 64x cheaper, 100% agreement when very confident, 72% below 0.70; 240 routing decisions vs Haiku 4.5, 5.4x faster and 26x cheaper, 95% vs 74% accuracy", "notes": "The repo's own summary: about 5-6x faster and 26-64x cheaper with accuracy close to the big models, different tasks and setups; includes a low-confidence example that acts only above a threshold and sends the rest to a person; TypeScript through the AI SDK evaluate() function via the Vercel AI Gateway; independent project, MIT", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "anthonymaio-jev-wont-talk", "name": "Jev: The Language Model That Won't Talk", "author": "Anthony Maio", "date": "2026-09-24", "category": "interpretation", "url": "https://anthonymaio.substack.com/p/jev-the-language-model-that-wont", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Substack", "arch": "Critical read of TypeSafe's launch evaluation: the defensible conclusion is narrow but real - Jev performed competitively on selected decision workflows at much lower measured price and latency; the evaluation did not establish probability calibration, general intelligence, or production reliability", "approach": "", "training": "", "data": "", "result": "Averaged across the four tasks, every comparison model got more accurate, faster, and cheaper inside an explicit workflow than executing the whole policy through one prompt: 'before the experiment makes an argument for Jev, it makes an argument for decomposition'", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jevometry", "name": "Jevometry", "author": "Kunyanli230", "date": "2026-09-24", "category": "interpretation", "url": "https://github.com/Kunyanli230/Jevometry", "base_model": "TypeSafe Jev", "params": "", "metric": "Fisher-Rao and Hellinger geometry, Cramer-Rao lower bounds", "license": "MIT", "paper": "", "stars": 1, "likes": "", "detail": "approach: Information geometry of output distributions", "arch": "An information-geometric analysis toolkit for Jev-powered decisions, version 0.1.0 alpha, offline with no API key: it validates Choice, Score and Noul probability vectors with strict support alignment and traceable working-simplex derivation; computes entropy, KL, Jensen-Shannon, Hellinger and categorical Fisher-Rao distances; node Fisher pullback metrics with finite-difference stability diagnostics and an independent square-root cross-check; node combination only through declared joints, product models or conditional trees, with the conditional information identity checked by exact enumeration; information loss under fixed aggregation mappings; and Cramer-Rao lower bounds under an explicit sampling contract", "approach": "", "training": "", "data": "API responses and declared system structure", "result": "A deliberate do-not list: it does not treat an API response as an independent categorical draw, does not sum correlated nodes' Fisher information as system information, produces no Cramer-Rao lower bound without a complete sampling contract, and uses no zero or epsilon smoothing, uniform fallbacks, or synthetic data in place of a result it cannot compute", "notes": "The first information-geometric treatment of the Jev design family; independent of TypeSafe; Python 3.11–3.13; structured results carry statuses, assumptions, provenance and diagnostic reason codes", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "laya-vs-dijkstra", "name": "Laya vs Dijkstra", "author": "antonellof", "date": "2026-09-24", "category": "interpretation", "url": "https://github.com/antonellof/laya-vs-dijkstra", "base_model": "laya-multilingual-mlx, 650 MB", "params": "0.4B", "metric": "Path length versus optimal, measured compute time", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "approach: Local decision loop versus global optimal", "arch": "Path solving on seeded weighted grid maps done two ways and replayed side by side: Dijkstra plans on the whole map and is optimal by construction, while Laya MLX walks the map seeing only its four neighbours, their terrain, the goal direction, and a memory of where it has been, making each real choice with one local MLX forward pass on Apple Silicon; three map kinds, maze, caves and rooms, at any seed, width and height, with optional MLX compile and prefix-cache reuse", "approach": "", "training": "", "data": "Seeded weighted grid maps, maze, caves, rooms", "result": "A real recorded run is replayed as a time-normalised GIF with both boards finishing together, and tiles under each board show measured compute time; the checkpoint is downloaded once from Hugging Face, then everything is fully offline", "notes": "A clean illustration of the System One loop: one small local decision per step, no planning, against an optimal planner that sees the whole map", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "astgl-local-laya", "name": "Local Laya vs hosted Jev", "author": "astgl", "date": "2026-09-24", "category": "interpretation", "url": "https://astgl.com/p/local-laya-vs-hosted-jev-typed-decisions", "base_model": "Laya 421M vs Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: blog; base: local · hosted; arch: 421M; result: Laya; notes: HN", "arch": "Head-to-head of local Laya and hosted Jev on typed decisions over the author's own frozen replay; on the replay, local Laya beat the author's own deterministic router; data-residency is the deciding factor, not accuracy", "approach": "", "training": "", "data": "Frozen replay of the author's real traffic", "result": "Chose local for data residency; did not pick Jev for production", "notes": "Observed 2026-09-24; counterweight to the 'Jev wins everything' narrative on the privacy axis.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "lakoreai-rlcd-reverse-engineering", "name": "RLCD reverse engineering", "author": "LakoreAI", "date": "2026-09-24", "category": "interpretation", "url": "https://github.com/LakoreAI/rlcd-reverse-engineering", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "The central analytical finding: Laya's RL term is a score-function, evolution-strategies, estimator of a noise-smoothed proper scoring rule, and that smoothing biases the noise-free inference-time distribution toward over-confidence as sigma grows. Start here: the PLAN document is the full project brief, with background on Jev and Laya, Laya's verified internals, the central analytical finding, the experiment matrix E1 to E5, the datasets, and the IEEE report outline; the RESEARCH document is the distilled research question, scope, and method; the TODO is the step-by-step task list from scaffold to submitted report; the EXPERIMENTS document is the runbook and log for each gated experiment, with hypothesis, config, command, result, and decision; the analysis directory holds standalone analytical notes, such as the noise-smoothing over-confidence proof sketch, that feed the report; and the experiments directory holds self-contained numerical scripts, E1's toy bias check, that do not need the full training pipeline. The repository started from a generic MLP-over-fixed-features research template, and its own extending-the-template contract, replace the model, dataset, loss, and metrics, keep the checkpoint schema, CLI, and callbacks working, is exactly what has been followed.", "arch": "Reverse-Engineering RLCD: a technical report reverse-engineering Laya's RL training term, analysing what Reinforcement Learning for Calibrated Decisions, RLCD, does, and does not do, for typed probabilistic decisions, using TypeSafe's closed Jev model, public claims only, and its open reproduction attempt Laya, with the architecture and training loop verified from source, as the two reference points, with the output being an IEEE-format report of 6–8 pages, arXiv and GitHub, plus the small, reproducible experiments that back its central claim.", "approach": "", "training": "", "data": "", "result": "shows the RL term is a score-function estimator for a noise-smoothed proper scoring rule, and that the smoothing biases the noise-free distribution toward overconfidence as sigma grows", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vercel-six-ways-integrations", "name": "Six Ways to Integrate Jev", "author": "Vercel", "date": "2026-09-24", "category": "interpretation", "url": "https://vercel.com/i/jev-integrations", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Vercel's partner guide, HN 2026-09-24, covers six integration paths that can be combined: AI SDK's experimental_evaluate with the typesafe/jev evaluation model; the TanStack AI decide call with a Jev-compatible evaluation adapter; Cloudflare Workers via env.AI.run on typesafe/jev; LangChain Python's TypeSafeClassifier invoke; eve's evaluation, model-selection, and tool-approval helpers; and the direct TypeSafe SDK or POST /v1/systemone HTTP call. It distinguishes the layers: the application library defines the function you call and the answer shape you read, the provider adapter translates that call into a request, and an optional gateway, Vercel AI Gateway, Cloudflare AI, or OpenRouter, routes the request to the model provider. Every example runs the same request, Jev chooses the support team, billing, account, or other, for a customer reporting an unexpected invoice charge, so you can compare how each integration defines the question, calls Jev, and reads the typed answer.", "arch": "Vercel's official integration guide: Jev via AI SDK or TanStack AI, called from Cloudflare, added to LangChain or eve, or used through TypeSafe's SDKs and HTTP API; options can be combined; distinguishes library, adapter, and gateway paths, with Vercel AI Gateway as an alternate route to the model", "approach": "", "training": "", "data": "", "result": "Start with the tools your application already uses, then choose how requests reach Jev", "notes": "Official partner documentation; HN 2026-09-24.", "domain": "general", "score": "2 points", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "superfast-harness", "name": "The Superfast Harness: Decision Gate", "author": "Andrea Bruno", "date": "2026-09-24", "category": "interpretation", "url": "https://github.com/Andrea-Bruno/harness-superfast", "base_model": "", "params": "", "metric": "Millisecond-scale gate latency target, 4 GB GPU economics", "license": "CC-BY-4.0", "paper": "", "stars": 1, "likes": "", "detail": "approach: Decision Gate front-door for agent harnesses", "arch": "A white paper by Andrea Bruno on a Decision Gate, a fast System One decision model that makes AI agent harnesses react in milliseconds instead of seconds: in today's agents every user message wakes a big generative LLM even when the answer needs no big thinking, costing 1–3 seconds and real money per message; the gate makes the small decisions, what the user wants, tool or direct answer, which tool first, which attachments are needed, and is this action safe, and escalates to the large model only when needed", "approach": "", "training": "", "data": "", "result": "The economics target the home computer and a small 4 GB GPU, where every second of large-model latency per message is visible and expensive; the author states the architecture is patentable and deliberately chose not to patent it, with section 10 explaining exactly what a patent would cover and why it is given up on purpose", "notes": "Released CC BY 4.0 with an attribution-only condition, a personal contribution to open technology; names the architectural pattern the Laya-Thalamus, decision-infra and jevkit rows are converging on; part of a cluster of 09-24 decision gate formalizations", "domain": "agent", "score": "", "latency": "3 s", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mrinalsinghraja-easy-guide", "name": "TypeSafe AI, the Easy-Peasy Guide", "author": "mrinalsinghraja", "date": "2026-09-24", "category": "interpretation", "url": "https://github.com/mrinalsinghraja/typesafe-easy-guide", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "arch: plain-English, step-by-step guide to building cheap, fast AI apps with TypeSafe AI, Jev, no jargon; notes: 30-second idea, three tools, your first app, seven golden rules; a pretty version with an interactive cost calculator is published at github.io", "arch": "Plain-English guide to building cheap, fast AI apps with Jev: the idea in 30 seconds, three tools, your first app, and seven golden rules; an interactive version with a cost calculator is hosted on GitHub Pages", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "drjosh-bias-test", "name": "'It refused, then I made it choose'", "author": "Josh C. Simmons", "date": "2026-09-23", "category": "interpretation", "url": "https://www.drjoshcsimmons.com/writing/it-refused-then-i-made-it-choose", "base_model": "TypeSafe Jev", "params": "", "metric": "Free-run base rate versus forced-choice distribution, the P plus P coherence test", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: drjoshcsimmons.com; base: forced-choice protocol; arch: 'Group X is more [attribute] than Group Y' · refusal allowed · incl. physical beauty, leadership, civilization, morality; result: mostly refuses · A beats B · B beats A; notes: HN; name: Jev bias test", "arch": "6,336 directed pairwise claims of racial superiority; first run free, then a forced-choice run where refusal is removed and Jev must pick a side; 12 groups × 8 attributes", "approach": "", "training": "", "data": "6,336 directed pairwise claims", "result": "Free run: Jev says 'true' about 3% of the time. Forced-choice run: 'White' takes 51% of first-picks across the beauty/leadership/civilization/morality attributes; gender test: the sum of P + P is 0.11 instead of 1.0 for a true ranking — the model's probabilities don't even form a consistent order", "notes": "Observed 2026-09-23; systematic probe of Jev's social-bias surface.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "alexmolas-calibration", "name": "'Jev can't be calibrated'", "author": "Alex Molas", "date": "2026-09-23", "category": "interpretation", "url": "https://www.alexmolas.com/2026/09/23/jev-cant-be-calibrated.html", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: statistical argument; arch: the true probability is in the prompt; result: Bluesky echo; name: statistical critique", "arch": "Argues Jev is useful — a universal classifier that works without collecting training data, unlike a fine-tuned BERT — but the calibration claim can't hold in general: a model calibrated on TypeSafe's distribution need not be calibrated on yours; evidence of failure beyond drift: tweets of Jev saying a fair coin lands heads with probability 0.92; a recent experiment finds Noul much better calibrated than Choice on the same problem; practical consequence: treat Jev outputs as scores, not probabilities", "approach": "", "training": "", "data": "", "result": "HN: 42 points / 54 comments; 'Calibration depends on your data distribution, which Jev never sees'", "notes": "Sep 23 2026; published critique of the RLCD calibration claim.", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kvit-reverse-jev", "name": "'Reverse Jev': ending a turn with a choice", "author": "kvit.app", "date": "2026-09-23", "category": "interpretation", "url": "https://blog.kvit.app/posts/ending-a-turn-with-a-choice/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: workflow pattern for Opus-5-era coding agents; arch: or a downstream decision model; notes: HN", "arch": "'Reverse Jev': instead of Jev deciding things for you, the coding agent's reply ENDS with a list of choices — the human picks from the options the agent proposes", "approach": "", "training": "", "data": "", "result": "Workflow post arguing the choice-at-the-end pattern inverts the Jev loop", "notes": "Observed 2026-09-23.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "flaviocopes-jev-deep-dive", "name": "A deep dive into Jev, TypeSafe's System One model", "author": "Flavio Copes", "date": "2026-09-23", "category": "interpretation", "url": "https://flaviocopes.com/jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Flavio Copes' deep dive, updated 2026-09-24: Jev is not a chatbot and not a coding model; you send it data and a list of typed questions and get one answer per question, a yes-no probability, one option from a list you defined, or a position on a scale you defined, each with probabilities, and TypeSafe says most calls complete in about 100 ms at 0.042 USD per million input tokens with output free. The difference is where the AI sits: with ChatGPT the AI is the interface or the worker, while Jev is a small component inside a regular application, added where code needs one judgment. He frames it as a smart if statement: ordinary code branches on values it can compute, classifiers handle narrow judgments when you have training data and fixed labels, LLM apps ask a general model for structured output, and Jev is a third option, define the possible answers up front, get a probability for each, branch on the result. A worked example scores a sponsor-form submission with noul, choice, and score questions, followed by how to write questions, where Jev fails, and where to put it in your own projects. TypeSafe AI is a San Francisco lab that came out of stealth on 2026-09-15 with 40M in seed funding.", "arch": "Developer-oriented deep dive: how Jev turns text into typed choices, scores, and probabilities, with JavaScript examples, practical integration patterns, limits, and real use cases", "approach": "", "training": "", "data": "", "result": "Positions Jev as a decision layer inside application code rather than a chat interface", "notes": "Updated 2026-09-23.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "cnpanda-jev-laya-note", "name": "A note on Jev and Laya's non-generative decision mechanisms", "author": "Panda", "date": "2026-09-23", "category": "interpretation", "url": "https://www.cnpanda.net/en/talksafe/jev-laya-non-generative-decision-models.html", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "A thirteen-minute note tracing how two September 2026 releases put output mechanisms back in focus: on 09-15 TypeSafe AI emerged from stealth with the closed-source Jev model and a 40M seed round led by DCVC, and on 09-18 Nandakishor Mukkunnoth open-sourced the Laya family with all weights and architecture. Both retain natural-language understanding but perform no autoregressive token-by-token generation: once the input is processed they emit a probability distribution and act purely as decision engines. The note grounds that in the Transformer lineage, the original 2017 self-attention and attention-mask design, the split into encoder-only BERT, encoder-decoder T5, and decoder-only GPT, and why decoder-only won, citing BigScience's systematic comparison on training-signal density, a unified interface, and scaling effects. The Jev and Laya case is that the stable Transformer stack can end in a classification head instead of a next-token loop.", "arch": "Thirteen-minute examination of how Jev and Laya make non-generative decisions in a single forward pass, viewed through Transformer architecture, autoregressive decoding, probability calibration and production constraints; notes that the output side of the Transformer has stayed autoregressive for years while input encoding and pretraining absorbed most iteration, and that in 2026-09 both releases put output mechanisms back in focus, with TypeSafe emerging from stealth alongside a 40 million dollar seed led by DCVC and Convai Innovations open-sourcing Laya with all weights and architectural details; both retain natural-language understanding but emit a probability distribution and act purely as decision engines", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "adtmag-jev", "name": "ADTM: Jev Offers Developers an Alternative to LLMs for Every Decision", "author": "Application Developer Magazine", "date": "2026-09-23", "category": "interpretation", "url": "https://adtmag.com/articles/2026/09/23/jev-offers-developers-an-alternative-to-using-llms-for-every-decision.aspx", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "ADTMag's enterprise .NET coverage, 2026-09-23: TypeSafe calls Jev the first System One model, intended for quick, narrowly defined judgments rather than the longer reasoning paths of frontier LLMs, and the article positions it as an alternative to reaching for an LLM on every decision in an application. The indexed URL now returns a 404, so this row rests on the archived quote and the outlet's framing.", "arch": "'TypeSafe calls Jev the first System One model… intended for quick, narrowly defined judgments rather than the longer [reasoning paths of frontier LLMs]'", "approach": "", "training": "", "data": "", "result": "", "notes": "Enterprise.NET-community coverage. adtmag", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "x-alexxu", "name": "Alex Xu: Jev 100× faster and cheaper", "author": "Alex Xu", "date": "2026-09-23", "category": "interpretation", "url": "https://x.com/alexxubyte/status/2102787342553719219", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: AI researcher, ~1M followers; base: TypeSafe API; arch: AI researcher, ~1M followers", "arch": "X post by Alex Xu: Jev is TypeSafe AI's first System One Model, 100× faster and cheaper than frontier LLMs, with paired clips of Jev and a frontier model handling the same task side by side", "approach": "", "training": "", "data": "", "result": "'Jev is TypeSafe AI's first System One Model. It is 100× faster and cheaper than frontier LLMs. #1 and #9 together are great for coding agents.'", "notes": "Endorsement from a major AI influencer within a day of the release.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": "100× faster"}
{"id": "thanabordeen-audiolaya", "name": "Audio-Laya: Direct Speech-to-Decision PoC", "author": "ThanabordeeN", "date": "2026-09-23", "category": "interpretation", "url": "https://github.com/ThanabordeeN/AudioLaya", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Pipeline: 16 kHz mono call audio, into a frozen Whisper-small.en encoder 768-dim, into a trainable projector 768–1024, appended to frozen ModernBERT text states on the sequence axis after ModernBERT and before Laya two-layer decision Transformer, out the probability of legitimate and the probability of spam. At inference the fixed text input is only the Laya question and options, Is this call a robocall?, legitimate, spam, plus a short task prompt; Whisper decoder is never called on this path; audio padding positions are masked; each call is split only after call-level data splits are assigned. Training keeps both encoders frozen and uses a small batch size because 30 seconds of Whisper output can contain 1,500 audio tokens.", "arch": "Audio-Laya, a direct speech-to-decision proof of concept: an English-only PoC that classifies a call as legitimate or spam directly from audio, with default inference using audio only", "approach": "", "training": "", "data": "", "result": "", "notes": "Zenodo DOI 10.5281/zenodo.22933883; an optional semantic-alignment experiment uses paired source transcripts during training only; audio is not redistributed, the two source datasets are cloned locally and pinned by commit; first run downloads openai/whisper-small.en and convaiinnovations/laya from Hugging Face; Python 3.10+", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yifanlan-robustness", "name": "awesome-jev-robustness", "author": "Yifan-Lan", "date": "2026-09-23", "category": "interpretation", "url": "https://github.com/Yifan-Lan/awesome-jev-robustness", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 5, "likes": 0, "detail": "Most of these were run in the two weeks after Jev's release on 15 September 2026, usually by one person with a small budget, so read them as evidence to inspect, not settled results, and task benchmarks that only report an accuracy are kept out of the page, in the task benchmarks TSV, with the machine-readable version of the page in the entries TSV. What counts as robustness here: an entry has to show how Jev's answer or probability changes with the wording of the question or options, the order or number of options, repeated calls, the language of the input, text planted to steer it, or inputs that fit no option, or it has to test whether the returned probability is calibrated, and a project that measured several things is listed once, under the property it says most about, with tools, SDKs, applications, and general directories out of scope, and general lists linked at the end. Start here: the jujumilk3 calibration audit, the largest single audit, with a 400-item Noul versus Choice gap, negation complements, option order, batching with hostile questions, Korean versus English, and the abstain-option collapse, every ECE printed next to its noise floor; and the willkelly evaluation, with 123,805 pre-registered requests, where calibration holds on support routing and collapses on random 3-SAT, with the probability barely moving while the true rate spans 0–1.", "arch": "awesome-jev-robustness: tests, calibration audits, and failure-mode studies of Jev, covering jaggedness, consistency, injection, and abstention, how Jev's answers move, and whether its probabilities can be trusted, independent tests of TypeSafe's System One decision model, grouped by what they measured, with 119 entries, almost all against jev-1.13.0, a snapshot of 2026-09-24, and the numbers being the authors' own.", "approach": "", "training": "", "data": "", "result": "Per-repo test results", "notes": "Curated robustness/failure-mode collection.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "eurekaleo-survey", "name": "awesome-jev-survey", "author": "Eurekaleo", "date": "2026-09-23", "category": "interpretation", "url": "https://github.com/Eurekaleo/awesome-jev-survey", "base_model": "Jev + Jev-like models", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 4, "likes": 0, "detail": "Contents: findings, core studies, a peripheral study, open implementations and evaluations, background references, a related survey, method, contributing, and citation and licence. Findings: F1, a type-valid answer can still be the wrong answer, where typed interfaces remove parse and schema failures by construction, but they say nothing about whether the model read the option the way the author intended, and rebinding option names to rubrics, removing an abstain option, or asking through a different primitive can move answers while the type-error rate stays at zero; F2, a native probability makes calibration measurable, not guaranteed, where every call returns a distribution, so reliability can be audited on each run, and measured calibration varies by task and construct, confident errors occur, probabilities are quantised to two decimals, and recalibration fitted on in-distribution labels helps substantially, so calibration is a property to verify per workload, not a certificate; and F3, the most consistent use is a bounded first pass with confidence-gated escalation, where across judging, annotation, and agent control, the typed model is usually cheaper and faster but not the most accurate component, and gains appear when uncertain cases are routed to a stronger model or a person.", "arch": "awesome-jev-survey: an evidence survey of Jev and Jev-like typed decision models, covering calibration, selective control, and open implementations, by Meng Luo, with a website at eurekaleo.github.io, where the numbers are as reported by paper authors, the vendor, or repository maintainers, and nothing here is a unified leaderboard, a survey focused on evidence rather than a link list.", "approach": "", "training": "", "data": "", "result": "", "notes": "Survey focused on evidence rather than a link list. calibration + selective control", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-bonsai-llama-jev", "name": "Bonsai-Llama-Jev release thread", "author": "r/LocalLLaMA", "date": "2026-09-23", "category": "interpretation", "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wo6x7e/i_turned_qwen3827b_q2_64_llamacpp_into_a_fully", "base_model": "Qwen3.8-27B Q2_64 GGUF on llama.cpp", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Announcement thread in r/LocalLLaMA for Bonsai-Llama-Jev: the author turned a Qwen3 8B Q2_64 llama.cpp model into a fully local Jev-like typed-decision inference system for sovereign AI, no cloud calls, and calls it the strongest open system they have tested, reaching about 76% Soft Accuracy in a new benchmark for typed decisions. Reddit's bot wall blocked the thread body at sweep time, and the code was not found on GitHub or Hugging Face under that name.", "arch": "Announcement of Bonsai-Llama-Jev, a Jev-like typed decision inference system for locally operated sovereign AI; the author calls it the strongest open system they have tested, reaching about 76% Soft Accuracy in the author's new benchmark for typed decisions", "approach": "", "training": "", "data": "", "result": "RTX 3090 at 140 tok/s generation with the Q2_64 quant, reported via llamaperf.com", "notes": "Thread body not retrievable at sweep time due to a Reddit network block; code host not found on GitHub or Hugging Face", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "x-offsec-jev", "name": "Experimenting with Jev for Offensive Security", "author": "_xpn_", "date": "2026-09-23", "category": "interpretation", "url": "https://x.com/_xpn_/status/2102830489237065968", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: X; base: hosted; notes: cf. jev-sec-bench row", "arch": "Offensive-security experiments against Jev: 'I think this is not anything too unexpected, as TypeSafe explicitly state this Jev is designed to be a System One model… but this did mean I needed [adaptations]'", "approach": "", "training": "", "data": "", "result": "Per thread", "notes": "Security community probing Jev's decision surface.", "domain": "security", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-go-adapter", "name": "Go adapter for a Jev-like decision API", "author": "r/LocalLLaMA", "date": "2026-09-23", "category": "interpretation", "url": "https://www.reddit.com/r/LocalLLaMA/comments/1woie1t/i_made_a_go_adapter_that_gives_local_llamacpp", "base_model": "local llama.cpp models", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Thread in r/LocalLLaMA: the author built a Go adapter that gives local llama.cpp models a Jev-like decision API, so a local model answers small questions directly instead of generating a paragraph, and Go services can branch on the answer. The repo is linked inside the thread; Reddit's bot wall blocked the thread body at sweep time, so the architecture comes from the title and indexed snippets.", "arch": "Go adapter giving local llama.cpp models a Jev-like decision API: the author wanted the local model to answer small questions without generating a paragraph", "approach": "", "training": "", "data": "", "result": "", "notes": "Repo linked inside the thread; not retrievable at sweep time", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "karmen-free-jev-api", "name": "How to use a free JEV API: first request and safe fallback", "author": "karmen-beatapi", "date": "2026-09-23", "category": "interpretation", "url": "https://huggingface.co/blog/karmen-beatapi/how-to-use-a-free-jev-api", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: jev-1.13-free, validate + threshold + fallbackynthetic ticket example", "arch": "First Jev request without a starting balance using the free jev-1.13-free tier; the useful part is deciding what the result can safely do in your application: validate the typed answer, choose a threshold from your own examples, and keep a fallback when the request fails; the worked experiment asks whether a synthetic support ticket needs priority review", "approach": "", "training": "", "data": "", "result": "First-request pattern with typed-answer validation and a safe fallback", "notes": "HF community post, observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-jev-aint-all-that", "name": "Jev ain't all that", "author": "r/LLMDevs", "date": "2026-09-23", "category": "interpretation", "url": "https://www.reddit.com/r/LLMDevs/comments/1womafv/jev_aint_all_that_its_a_great_generalized_model", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Post in r/LLMDevs; full title: Jev ain't all that. It's a great generalized model for when you don't know what you need, but there's a local, faster alternative for every use-case. The argument: Jev's value is generality, answering typed questions you did not train a model on; once you know what you need, a smaller local model tuned or prompted for that task beats it on speed and cost. Reddit's bot wall blocked the thread body at sweep time.", "arch": "Argument that Jev is a great generalized model for when you do not know what you need, but that there is a local, faster alternative for every use-case", "approach": "", "training": "", "data": "", "result": "", "notes": "Full title: Jev ain't all that. It's a great generalized model for when you don't know what you need, but there's a local, faster alternative for every use-case.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "billyjacobson-guess-who", "name": "Jev from TypeSafe AI is basically the 'Guess Who' of AI", "author": "Billy Jacobson", "date": "2026-09-23", "category": "interpretation", "url": "https://www.linkedin.com/posts/billyjacobson_jev-from-typesafe-ai-is-basically-the-guess-activity-7508523875077390336-SmeP", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: LinkedIn", "arch": "Interpretive post: Jev is 'basically the Guess Who of AI', drawing the analogy to the elimination-style card game while noting that the original excitement about LLMs came from natural-language interaction", "approach": "", "training": "", "data": "", "result": "Framing Jev's typed-decision interface as elimination rather than generation", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "getunblocked-cross-encoder", "name": "Jev in production vs cross-encoder", "author": "getunblocked.com", "date": "2026-09-23", "category": "interpretation", "url": "https://getunblocked.com/blog/jev-in-production-vs-cross-encoder/", "base_model": "Jev vs cross-encoder reranker", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: fan-out; data: 292 production questions; notes: HN", "arch": "Production comparison for memory selection: 12,927 labelled question/note pairs from 292 production questions; Jev is shown 20 notes at once and scores which notes matter, vs per-note scoring by a cross-encoder", "approach": "", "training": "", "data": "12,927 labelled pairs", "result": "Jev fan-out beats the cross-encoder on precision, recall AND knowing when to stay silent, at comparable cost and latency; per-note scoring loses — the multi-note context is the differentiator", "notes": "Observed 2026-09-23.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tessl-jev-vs-luna", "name": "Jev is 13.6× faster and 2.7× cheaper than GPT Luna 6 for Tessl verifiers", "author": "Tessl", "date": "2026-09-23", "category": "interpretation", "url": "https://tessl.io/blog/jev-is-136x-faster-and-27x-cheaper-than-gpt-luna-6-for-tessl-verifiers-try-it-yourself", "base_model": "Jev vs GPT Luna 6", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: and GPT Luna 5.6; result: 2.7× cheaper · 13.6× faster; notes: HN", "arch": "Tessl verifier suite: 6 projects, ~2,725 verifier/file pairs, caches bypassed so every judgment is a live call; Jev answers the typed verification questions directly", "approach": "", "training": "", "data": "~2,725 verifier/file pairs across 6 projects", "result": "Jev $0.65 vs Luna 6 $1.74 and 32 s vs 436.5 s wall time; per 1,000 targets $0.24 vs $0.64; vs GPT Luna 5.6: 6.6× cheaper and ~10× faster; 'try it yourself' repro", "notes": "Observed 2026-09-23.", "domain": "general", "score": "", "latency": "32 s", "benchmark": "", "cost": "$0.65 vs Luna 6 $1.74", "speedup": "13.6× faster"}
{"id": "benbrady-jev-game", "name": "Jev is an honest game-changer", "author": "Ben Brady", "date": "2026-09-23", "category": "interpretation", "url": "https://benbrady.dev/blog/jev-is-an-honest-game-changer/", "base_model": "Jev + Gemini fallback vs Grok 4.6", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: benbrady.dev; arch: Gemini invoked only when Jev's confidence is low; result: 89.6% · 6.24× faster, 8.7× cheaper; notes: HN; name: game-QA benchmark", "arch": "48 real game questions; three setups: Grok 4.6 alone; Jev alone; Jev + Gemini fallback", "approach": "", "training": "", "data": "48 real game questions", "result": "Jev + Gemini fallback matches Grok 4.6's accuracy at 84.0% lower average latency and 88.5% lower average cost; Jev alone: 81.3%; Gemini was invoked on only 14.6% of questions and added +8.3 points of accuracy", "notes": "Observed 2026-09-23; demonstration of the cheap-decision + expensive-fallback pattern.", "domain": "coding", "score": "81.3%", "latency": "", "benchmark": "", "cost": "8.7× cheaper vs Grok 4.6", "speedup": "6.24× faster"}
{"id": "x-ecosystem-swati", "name": "Jev is turning into an entire ecosystem", "author": "Swati Gupta", "date": "2026-09-23", "category": "interpretation", "url": "https://x.com/i/article/2102741642050666755", "base_model": "Jev, Laya, Kev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: @hrswatigupta; notes: X", "arch": "X long-read surveying the ecosystem around Jev: the open clone Laya, Kev you can train on your own MacBook, laya-mlx and jevmlx on Apple Silicon", "approach": "", "training": "", "data": "", "result": "Sub-20 ms typed decisions on Apple Silicon via laya-mlx, jevmlx and Kev", "notes": "Observed 2026-09-23.", "domain": "general", "score": "", "latency": "20 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sora2-jev-djev-laya-openjev-semif", "name": "Jev vs djev vs Laya vs OpenJev vs SemIf", "author": "sora-2", "date": "2026-09-23", "category": "interpretation", "url": "https://huggingface.co/blog/sora-2/jev-ai-vs-djev-vs-laya-vs-openjev-vs-semif-which-d", "base_model": "", "params": "", "metric": "JevBench v1.3.0, 2026-09: Jev 74.4 #1; SemIf 73.1; djev 73.0; OpenJev 66.4; Laya 54.4", "license": "", "paper": "", "stars": "", "likes": "", "detail": "name: sora-2, HF blog", "arch": "Systems comparison rather than model-quality contest, run on the official comparison pages and the JevBench v1.3.0 snapshot from 2026-09: Jev is hosted production API; SemIf is a self-hosted open-model logit reader on Qwen3.5-4B; djev is a hosted API with native image or camera input; OpenJev is a self-hosted compatible decision server with multimodal and thinking options; Laya is open weights, self-hosted and fine-tunable", "approach": "", "training": "", "data": "", "result": "SemIf leads the judge tier 95.2% vs Jev 94.5%, Jev leads the hard tier 74.1% vs 59.5%; calibration 82.7 vs 72.6; speed nearly tied, 83.3 vs 83.7", "notes": "Treats the numbers as a decision aid, not a substitute for testing your own workload", "domain": "general", "score": "JevBench 74.4", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "alphamatch-jev-vs-laya", "name": "Jev vs Laya, 2026", "author": "alphamatch", "date": "2026-09-23", "category": "interpretation", "url": "https://www.alphamatch.ai/blog/jev-vs-laya-system-one-2026", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "AlphaMatch's decision guide: in September 2026 typed decision models stopped being a research niche and became something engineers have to choose between. Jev and Laya both speak the same three primitives, choice, score, and noul, you send application state and declared questions and get back machine-usable answers with probabilities. The operational reality is sharper: Jev is a hosted decision API with a documented roughly 64k-token request limit, up to 255 options per choice, and 0.042 USD per 1M input tokens with output free; Laya is an Apache 2.0 open-weight stack you run yourself, default checkpoints of 512–1,024 tokens, options sharing a fixed head budget so the publisher suggests fewer than 20 at defaults, weights free with you paying compute, memory, and ops time. Jev is the better first bet when you want a hosted component today, long state, or large label sets; Laya wins on ownership, local forward-pass latency, and adaptation by fine-tuning checkpoints plus fitting calibration on your data. It points to AgentGrid and Anthus as independent write-ups of the same trade-offs, and notes Laya base checkpoints sit near chance on some typed-decision sets until specialized.", "arch": "Same three primitives, choice / score / noul, different products. Jev: TypeSafe hosted API, closed model, ~64k-token request limit, up to 255 choice options, ~$0.042 per 1M input tokens with output free. Laya: Apache-2.0 weights plus Python package, ~512–1,024 token default checkpoints, fixed head budget so the publisher suggests fewer than 20 options at defaults, weights free with compute and ops on you. Covers delivery, license, input budget, many-option behavior, adaptation, cost shape, latency, plus benchmark-honesty traps", "approach": "", "training": "", "data": "", "result": "Recommendation: Jev when time-to-integration, long context or large option sets dominate; Laya when control, volume economics and fine-tuning ownership dominate. Write 5–20 synthetic cases with expected categories before calling either model", "notes": "Also notes Laya base checkpoints sit near chance on some typed-decision sets until specialized; the fine-tuned typed-decisions checkpoint is a different artifact", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wilsonwu-jev-vs-laya", "name": "Jev vs Laya: closed vs open System One", "author": "Wilson Wu", "date": "2026-09-23", "category": "interpretation", "url": "https://wilsonwu.me/en/blog/2026/jev-vs-laya", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Wilson Wu's comparison: on one side a ready-to-use closed API backed by a 40M seed round, on the other a 421M-parameter open model under Apache 2.0 that runs on a single T4 or a MacBook, 6,000 GitHub stars within three days, dubbed the open-source Jev. It first explains what a decision model is, where System One comes from Kahneman's Thinking Fast and Slow, and why Jev is named after the economist William Stanley Jevons with the Jevons paradox behind the product thesis: every order-of-magnitude drop in the price of intelligence unlocks orders of magnitude more use cases. Then: the technical approaches of both, the key differences, how to read the benchmark numbers, strengths and weaknesses, and selection advice. His verdict against the social-media claims that Laya beats Jev and that Jev's moat lasted two days: a careful read of what both sides have published shows the story is far from that simple. A Chinese translation is available.", "arch": "Twenty-five-minute comparison of the closed API backed by a 40 million dollar seed round against the 421 million parameter open model that runs on a single T4 or a MacBook; explains what a decision model is, where the System One name comes from Kahneman's Thinking Fast and Slow, and why Jev is named after the economist William Stanley Jevons with the Jevons paradox behind the product thesis; walks through both technical approaches, how to read the benchmark numbers, the strengths and weaknesses of each, and ends with use-case and selection advice. Argues that a careful read of what both sides published shows the story behind social-media claims like Laya beats Jev is far from that simple", "approach": "", "training": "", "data": "", "result": "", "notes": "Chinese translation available", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-tutorial-ko", "name": "jev-tutorial", "author": "ohama", "date": "2026-09-23", "category": "interpretation", "url": "https://github.com/ohama/jev-tutorial", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Jev 쿡북, a Korean-language Jev cookbook built with mdBook by ohama, with examples and scripts directories alongside the book source", "approach": "", "training": "", "data": "", "result": "", "notes": "Korean-language Jev resource", "domain": "education", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "fintech-builder-50-jobs", "name": "Jev: 50 Financial Jobs, Verifiable Demos and Honest Limits", "author": "The Fintech Builder", "date": "2026-09-23", "category": "interpretation", "url": "https://www.youtube.com/watch?v=cDhFRtHKS7E", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "result: youtube.com/watch?v=6BDfAkHfxAo", "arch": "YouTube walkthrough of 50 financial decision tasks for Jev with verifiable demos and stated limits; part of the AI in Finance: Real Workflows and Evaluations playlist", "approach": "", "training": "", "data": "", "result": "~564 views at capture, 09-23; sister video 09-17/18 with ~10K views: \"I Tested Jev on 300 AI Trading Decisions - The Benchmark Exposed This\"", "notes": "Channel covers finance workflows and evaluations", "domain": "finance", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "latent-space-jev-creator", "name": "Latent Space: 'Jev Creator: System One models for Prod, not God'", "author": "Latent Space", "date": "2026-09-23", "category": "interpretation", "url": "https://www.youtube.com/watch?v=ty622HPl600", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: and is laya better? · Scott; result: per embed snippet; notes: reliable narrow decisions over general intelligence", "arch": "Interview/episode with Jev's creator on System One models; related: YouTube 'What is Jev? '", "approach": "", "training": "", "data": "", "result": "134K views", "notes": "First creator-level commentary circulating; the 'Prod, not God' framing captures the product thesis.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-laya-came-first", "name": "Laya came first, so why is everyone still only talking about Jev?", "author": "r/LocalLLM", "date": "2026-09-23", "category": "interpretation", "url": "https://www.reddit.com/r/LocalLLM/comments/1wnyf0o/laya_came_first_so_why_is_everyone_still_only", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Reddit; notes: Reddit", "arch": "Community thread on attribution: the open-weights Laya decision model shipped before Jev's closed launch; discussion of which ideas came first and why Jev owns the narrative", "approach": "", "training": "", "data": "", "result": "Community debate over credit and prior art between the open Laya ecosystem and TypeSafe's Jev", "notes": "Observed 2026-09-23.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "xtx-laya-prior-art-absurd", "name": "Laya's Prior Art Claim is Absurd", "author": "xtxinversexty", "date": "2026-09-23", "category": "interpretation", "url": "https://xtxinversexty.com/layas-prior-art-claim-is-absurd/", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "XTX's forensic read of SalesRLAgent, Mukkunnoth's earlier model, after Laya's launch post claimed it was prior art for Jev. Findings: multiple instances of data leakage, train.py includes the eventual conversion outcome in every observation, at turn 0 the agent sees an embedding of the entire conversation including its ending, and conversion_probabilities is initialized with the true_probabilities annotation; and unnecessary invocation of PPO, the system is an MLP over OpenAI text embeddings plus conversation metrics that included the target, trained on a synthetic dataset, and the policy's output does not affect the progression of the conversation, so it is just doing regression and it is unclear why PPO is there at all. On Jev itself: type-safety likely works by constrained decoding, a very old but under-exploited trick, and the specific objective and architecture cannot be verified because the model is closed. Conclusion: serious errors and nothing uniquely in common with Jev.", "arch": "forensic read of SalesRLAgent, Nandakishor Mukkunnoth's earlier model, after he claimed in Laya's launch post that it was prior art for Jev", "approach": "", "training": "", "data": "", "result": "finds multiple instances of data leakage, a policy that does not intervene in the environment so PPO regresses to plain regression, precomputed full-text embeddings rescaled by 0.6 plus 0.4 times progress to simulate evolving state, and true probabilities leaking into the state vector; cites PICARD and grid beam search as the actual non-autoregressive precedent", "notes": "HN discussion 2026-09-23", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "learn-jev-e2e", "name": "learn-jev-end-to-end", "author": "harshithsunku", "date": "2026-09-23", "category": "interpretation", "url": "https://github.com/harshithsunku/learn-jev-end-to-end", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 10, "likes": "", "detail": "The course ships with live docs, CI, 12 notebooks, 13 use cases, and one OpenRouter key, plus a Jev Playground app. The demo shows Jev answering in about 0.5 s while the LLM is still thinking, a toll-scam text being blocked, and the agent guard allowing du but holding rm -rf for a human. Sections: read the docs, quick start in 3 steps, the course, the 13 things you will build, and benchmarks.", "arch": "Learn Jev end to end: a free hands-on course, learning Jev end to end by building 13 AI agent use cases, where in 12 short notebooks you go from what is Jev to building 13 real AI tools with it, an email triage job, a scam-text detector, a code vulnerability hunter, an agent safety guard, and more, needing one API key, OpenRouter, and running the whole course costs less than 0.20 dollars, MIT, Python 3.10+.", "approach": "", "training": "", "data": "", "result": "", "notes": "6 stars at sweep time", "domain": "education", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-jev-ads-psa", "name": "Mod PSA on Jev advertising", "author": "r/LocalLLaMA", "date": "2026-09-23", "category": "interpretation", "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wo6o0f/mods_can_we_do_something_about_half_the_forum", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Mod PSA in r/LocalLLaMA, started by u/Acrobatic_Stress1388: Jev is a paid product that has dumped a lot of venture money into marketing and shills it here and in other subreddits; obvious shill posts are obvious, and the moderators are asking what to do about half the forum being filled with Jev promotion. Reddit's bot wall blocked the thread body at sweep time, so the row rests on the PSA title and indexed snippets.", "arch": "Discussion started by u/Acrobatic_Stress1388: Jev is a paid product that has dumped a lot of venture money into marketing and shills it here and in other subreddits; obvious shill posts are obvious", "approach": "", "training": "", "data": "", "result": "Top comments credit the open-source Bonsai-Llama-Jev release as an alternative and note the thread is a complaint, not shilling", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sora2-reliable-agents", "name": "More reliable AI agents with Jev AI", "author": "sora-2", "date": "2026-09-23", "category": "interpretation", "url": "https://huggingface.co/blog/sora-2/how-to-build-more-reliable-ai-agents-with-jev-ai-r", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: execute/judge/safety-net split; notes: 3 patterns + checklist", "arch": "Agent architecture: the agent executes, Jev judges, and rules plus people provide the safety net; the core principle is not letting one model own understanding, decision, authorization and execution at the same time; patterns: route the model before calling, guard tool calls, use probability to trigger review; plus designing agent state and questions, and a security and evaluation checklist", "approach": "", "training": "", "data": "", "result": "Judgment broken into testable questions with the final action kept in constrained code", "notes": "HF community post, observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "motherduck-jev", "name": "Motherduck supports Jev", "author": "Motherduck", "date": "2026-09-23", "category": "interpretation", "url": "https://motherduck.com/blog/motherduck-supports-jev/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: via prompt_jev helper; result: headline: ~50× faster, ~1% of the cost; notes: HN", "arch": "Blog: Motherduck supports Jev — a prompt_jev helper classifies rows in the Motherduck warehouse; support-triage example at scale", "approach": "", "training": "", "data": "", "result": "Classified 100k support rows in 40 seconds for ~$0.50, vs 30+ minutes and ~$37 with a chat LLM", "notes": "Warehouse-vendor integration; observed 2026-09-23.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "~$0.50 per 100k rows vs ~$37 LLM", "speedup": "50× faster"}
{"id": "reddit-jev-not-new-tech", "name": "Reddit: 'Jev isn't new tech. Its marketing targets people who think AI started with LLMs'", "author": "r/LocalLLaMA", "date": "2026-09-23", "category": "interpretation", "url": "https://www.reddit.com/r/LocalLLaMA/comments/1woe70t/jev_isnt_new_tech_its_marketing_targets_people", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Skeptic thread in r/LocalLLaMA: Jev isn't new tech, its marketing targets people who think AI started with LLMs. The argument: engineers without ML expertise can now use generalized zero-shot classification is an adoption and marketing story, not a new class of model, and the underlying technique predates LLMs. Full thread text was not retrievable at sweep time because of a Reddit network block; the row is extracted from the title and indexed search snippets.", "arch": "Skeptic thread arguing Jev is a rebrand of generalized zero-shot classification: 'engineers without ML expertise can now use generalized zero-shot classification' is an adoption and marketing story, not a new class of model, and the underlying technique predates LLMs", "approach": "", "training": "", "data": "", "result": "472 upvotes and 201 comments, among the larger Jev threads on r/LocalLLaMA", "notes": "Full thread text not retrievable at sweep time, Reddit network block; extracted from the title and indexed search snippets.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "system-one-model-finetuning", "name": "system-one-model-finetuning", "author": "omkarghugarkar007", "date": "2026-09-23", "category": "interpretation", "url": "https://github.com/omkarghugarkar007/system-one-model-finetuning", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": 1, "likes": "", "detail": "", "arch": "A measured recipe for fine-tuning small models that return calibrated probabilities instead of text, on System One models such as Laya and Jev-style setups; text classification and beyond", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "register-jevops", "name": "The Register: 'Shut up and calculate'", "author": "The Register", "date": "2026-09-23", "category": "interpretation", "url": "https://www.theregister.com/devops/2026/09/23/shut-up-and-calculate-jevs-new-ai-primitives-for-coders/5298431", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: devops; base: news; arch: follows the 09-23 'TypeSafe AI says its Jev model is the future' piece · 'Does this message convey urgency?' not 'Analyze this message…' · including a CPU emulated on Jev", "arch": "Second major Register piece on Jev. Frames Jev as 'a classifier with brains' / 'a smart function call'; TypeSafe's docs recommend closed questions; TypeSafe claims answers in as little as 150 ms. Coinages: 'JevOps' — the meme 'end game' of running all code on Jev-based virtualization", "approach": "", "training": "", "data": "", "result": "Documents the developer culture forming around Jev: speedruns, CPU emulation, 'JevOps'", "notes": "Names the 'JevOps' practice area.", "domain": "general", "score": "", "latency": "150 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "datacamp-top7", "name": "Top 7 Open-Source TypeSafe Jev Alternatives", "author": "DataCamp", "date": "2026-09-23", "category": "interpretation", "url": "https://www.datacamp.com/blog/top-open-source-jev-alternatives", "base_model": "various", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: incl. #6 Von: 395M bidirectional ModernBERT 'purpose-built System One model'; notes: DataCamp", "arch": "Ranked roundup of open-source Jev alternatives", "approach": "", "training": "", "data": "", "result": "", "notes": "Mainstream-ed-ed treatment — the alternative list is now a standard content format.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "youtube-squintist-jev", "name": "TypeSafe's Jev: the model that answers without writing anything", "author": "Squintist", "date": "2026-09-23", "category": "interpretation", "url": "https://www.youtube.com/watch?v=BYbG4I2A8fc", "base_model": "TypeSafe Jev", "params": "", "metric": "Intent agreement, ECE, F1 on rare scams", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: YouTube", "arch": "Sourced walkthrough of Jev: the launch post, pricing, the three primitives, Jev 1.13 jaggedness, the use-case map of code versus classifier versus LLM versus Jev, and Diogo Almeida's InstructGPT lineage", "approach": "", "training": "", "data": "", "result": "Covers the CLINC150 run with 5,500 messages and 150 intents at 89.4% agreement and ECE 0.025; job-posting triage where TF-IDF plus logistic regression beats Jev on rare scams at F1 0.70 versus 0.31; and Archer Hume's architecture probe from ten thousand API calls", "notes": "Uploaded 2026-09-23.", "domain": "general", "score": "agreement 89.4%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "blocks-open-model-doom", "name": "We Rebuilt Jev's API on an Open Model and Used It to Play Doom", "author": "Blocks.ai", "date": "2026-09-23", "category": "interpretation", "url": "https://blocks.ai/blog/jev-open-model-doom", "base_model": "Gemma4 base", "params": "", "metric": "ms per forward pass, within TypeSafe's published latency range", "license": "Open", "paper": "", "stars": "", "likes": "", "detail": "base: NOT instruct; arch: on GitHub · A: AnswerA, B: AnswerB, …; result: A2A latency; notes: HN", "arch": "Open-source 'Truetype Jev replica': a base model processes each prompt once; read its next-token logits for A through Z; max_token 1; softmax over the letters → classification probabilities; 'Jev uses a language model for zero-shot classification without generating text… we recognized the Jev design'; question phrased with labeled answers", "approach": "", "training": "", "data": "", "result": "Runs within TypeSafe's published latency range; Flappy Bird demo makes one decision per frame, each forward pass 100–124 ms on an M4 Mac — 'fast enough to steer the bird through the pipes'; Doom gameplay recorded on the same M4 Mac; motivation: LLM thinking-phase latency is 'slow and error prone for agents'", "notes": "Observed 2026-09-23.", "domain": "gaming", "score": "", "latency": "100–124 ms", "benchmark": "Doom", "cost": "", "speedup": ""}
{"id": "firecrawl-what-is-jev", "name": "What Is Jev?", "author": "firecrawl.dev", "date": "2026-09-23", "category": "interpretation", "url": "https://www.firecrawl.dev/blog/what-is-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Firecrawl's developer explainer: you send a state plus a set of questions, each a Choice, Score, or Noul, and every question is answered in parallel in one call; pricing is 0.042 USD per million input tokens with free output, and TypeSafe reports 70–500 ms end-to-end latency. It is careful about can't hallucinate: it means the model cannot return a value outside your schema, but it can still be wrong, which HN pushed hard on and TypeSafe's own jaggedness page lists as failure modes. Developers had shipped a coding-agent guardrail, an MCP server, and an open-weights clone of the interface within 48 hours of launch; TypeSafe opened signups to everyone on 09-20 with 5 USD of free credit, then paused new signups on 09-22 under demand, while Vercel AI Gateway, via AI SDK 7's evaluate since 09-16, and OpenRouter do not go through TypeSafe's signup. The flagship example is pi-warden: before each bash, write, or edit, a coding agent sends the task, the stated plan, and the pending command to Jev with four typed questions, is this irreversible, off-task, mutating, and what scope, answered in about 250 ms; over 17,000 recorded calls it held 42 times, and roughly 88% of the holds were right. The use cases that hold up sit next to an LLM rather than replacing it: reranking, citation checks, judging tool calls, routing, and verification.", "arch": "Vendor explainer: 'Jev is a new kind of AI model from TypeSafe AI. It returns typed decisions with calibrated probabilities instead of text'", "approach": "", "training": "", "data": "", "result": "", "notes": "Firecrawl framing Jev for its audience — System One explainer wave. web-scraping vendor", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hatchworks-system-one", "name": "What Is Jev? Why System One Models Matter for Enterprise AI", "author": "HatchWorks", "date": "2026-09-23", "category": "interpretation", "url": "https://hatchworks.com/blog/gen-ai/system-one-models-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "Cost and latency per vendor evaluation, cost per successful business outcome at the required latency", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Hatchworks' enterprise explainer: TypeSafe founder Diogo Almeida previously contributed to OpenAI's foundational InstructGPT and RLHF work, and the company spent two years building a different training approach focused on calibrated decisions. Company-reported figures as of 2026-09-22: up to 193.6× faster and 444.6× cheaper than frontier language models across four selected System One workflow evaluations, 70–500 ms typical latency, 0.042 USD per million input tokens with output free, with the explicit caveat that those are the high end of selected tests and do not establish a universal advantage. The enterprise pattern: match each component to the work it performs; Jev handles bounded judgment, deterministic code handles policy and permissions, generative models handle open-ended work, and humans keep authority. Its use cases include model routing, support and operations triage, agent action verification, compliance review, and large-scale feature extraction, and it ships an interactive cost-per-successful-decision calculator plus a six-point stack-readiness checklist.", "arch": "Enterprise-oriented explainer: Jev turns model calls into typed decisions, a portfolio pattern where Jev handles bounded judgment, deterministic code handles policy and permissions, generative models handle open-ended work and humans keep authority. Six enterprise use cases: model routing, support and operations triage, agent action verification, compliance review, large-scale feature extraction and real-time personalization. Seven-step evaluation playbook from baseline through shadow mode to canary rollout", "approach": "", "training": "", "data": "", "result": "Treats TypeSafe's 193.6× faster and 444.6× cheaper figures as the high end of four vendor-selected evaluations and argues cost per successful business outcome at the required latency and risk is the number to measure. Core framing: valid output, correct judgment and authorized action are three separate checks, owned by the model, the eval set and the application respectively", "notes": "Includes an interactive cost-per-successful-decision calculator and a six-point stack-readiness checklist. Published 2026-09-23.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": "193.6× faster"}
{"id": "armank-evals", "name": "What Jev means for the future of evals", "author": "armank", "date": "2026-09-23", "category": "interpretation", "url": "https://armank.com/thoughts/3", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: armank.com; base: thought piece; notes: HN", "arch": "Jevons-paradox angle: cheaper classification doesn't reduce the demand for decisions — it makes massive evals affordable; 'evals as hard classification': every check in an eval suite is a typed question Jev can answer cheaply", "approach": "", "training": "", "data": "", "result": "Argues Jev's economics unlock eval scales that were previously uneconomic", "notes": "Observed 2026-09-23.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wikipedia-jev", "name": "Wikipedia article: 'Jev '", "author": "Wikipedia", "date": "2026-09-23", "category": "interpretation", "url": "https://en.wikipedia.org/wiki/Jev_%28AI_model%29", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "CC-BY-SA-4.0", "paper": "", "stars": "", "likes": "", "detail": "author: en; base: encyclopedia; arch: Forbes/TechCrunch · Jevons paradox · system-one-open · sd-1; name: AI model", "arch": "Wikipedia article on Jev. Key facts: proprietary, San Francisco company founded 2024, released 2026-09-15 with a $40M seed round led by DCVC; stable release jev-1.13.0; named after economist William Stanley Jevons; 'several models implementing the same System One interface' exist; JevBench compares Jev with 80+ Jev-class systems incl. open-weight replications; meraGPT Decider 1 works with the TypeSafe SDK via base-URL swap", "approach": "", "training": "", "data": "", "result": "Wikipedia's Jev benchmark names 80+ Jev-class systems.", "notes": "Also cites TechStock²: 'TypeSafe AI Raises $40 Million for Jev, but Its 445× Cost Claim Is Still Self-Tested.'", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "youtube-why-couldnt-build", "name": "'Why I couldn't build Jev at OpenAI'", "author": "Diogo Almeida", "date": "2026-09-22", "category": "interpretation", "url": "https://www.youtube.com/watch?v=cFx9Z3ZXca0", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: video interview; result: ~40M views; notes: HN; name: Diogo, video", "arch": "Founder interview: why he left OpenAI; why refusals and safety alignment become fundamentally different problems when AI is infrastructure; how Jev changes the architecture of coding agents", "approach": "", "training": "", "data": "", "result": "Second primary-source video after the launch video and the 'Jev Creator' video", "notes": "Published 09-22 2026; surfaced 09-23/24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "unzip-0x025-system-one", "name": "0x025 - System One Models", "author": "UNZIP.dev", "date": "2026-09-22", "category": "interpretation", "url": "https://unzip.dev/0x025-system-one-models/", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Unzip.dev's four-minute explainer, issue 0x025: a System One model is a fast, cheap if-statement that knows how sure it is; given a state and a typed question you get back JSON with a probability for each possible answer, like a multiple-choice exam the model takes. The three question types: Choice picks an option from a set, Score rates on a scale you define, Noul returns how likely a statement is to be true. System One is Kahneman's fast, instinctive thinking, as opposed to slow, deliberate System Two; Jev is a reference to Jevons. The Q&A: it was trained in two rounds, first a language model learns from a huge amount of text, then a second round, RLCD instead of RLHF, rewards honest probabilities, when Jev says 0.8 it should be right about 80% of the time, on entirely synthetic training data, a bet the founder rates above RLHF; on the zero-shot-classifier pushback, Jev claims two things are new, LLM-level accuracy and probabilities you can trust, while being cheap and fast; and on hallucination, it cannot return a value outside your schema, but it can pick the wrong valid one.", "arch": "Four-minute explainer: a System One model is a fast, cheap if-statement that knows how sure it is - given a state and a typed question you get JSON with a probability for each possible answer, like a multiple-choice exam the model takes", "approach": "", "training": "", "data": "", "result": "", "notes": "HN 2026-09-22", "domain": "general", "score": "2 points", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "karmen-18-use-cases", "name": "18 practical JEV use cases for AI agents", "author": "karmen-beatapi", "date": "2026-09-22", "category": "interpretation", "url": "https://huggingface.co/blog/karmen-beatapi/18-practical-jev-use-cases-for-ai-agents", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: 18 patterns by decision type; notes: input + failure to test per case", "arch": "18 use cases organized by the decision being made: choose a route, judge whether evidence supports a claim, score an issue, decide whether a result needs review; Jev accepts a shared state plus bounded Choice, Noul and Score questions, and your code still owns permissions, thresholds, side effects and fallbacks; each use case includes the input to provide and the failure to test", "approach": "", "training": "", "data": "", "result": "Stated as implementation patterns, not claims that they were deployed for customers or measured", "notes": "HF community post, observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kydlikebtc-awesome-jev", "name": "awesome-jev 805 verified examples", "author": "kydlikebtc", "date": "2026-09-22", "category": "interpretation", "url": "https://github.com/kydlikebtc/awesome-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 435, "likes": 0, "detail": "arch: indexes public Jev examples by the decision they make rather than the source · every row names its source, which Jev primitives the code actually calls, and flags worth knowing before clicking; data: a searchable site with filterable views by domain, primitive and compatibility · each filter and entry is a shareable URL · a 中文 edition · CI lint, links and claims checks; license: CC0 + MIT", "arch": "805 verified examples of Jev — indexed by the DECISION each one makes, not the blog that published it", "approach": "", "training": "", "data": "805 examples", "result": "", "notes": "'Every public example of Jev — indexed by the decision it makes, not the blog that published it'; 268 stars", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "decisionbench-bosun-blog", "name": "DecisionBench and Bosun v3.1: building and testing decision models", "author": "Hanno-Labs", "date": "2026-09-22", "category": "interpretation", "url": "https://huggingface.co/blog/Hanno-Labs/decisionbench-bosun-v3-1", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: release post for an open MTEB-inspired benchmark + open model family + stable decision API; result: DecisionBench, Bosun v3.1 0.6B/1.7B, Jev-compatible server; notes: cross-referenced to the three cluster rows", "arch": "The release post for the cluster: DecisionBench, an open MTEB-inspired benchmark that treats tasks as units of evidence, the two Bosun v3.1 open-weight models, 0.6B and 1.7B, that return complete answer distributions, and a runtime that keeps the decision API stable; it frames Jev typed noul/choice/score requests as the familiar interface the open model and server both target, so the model behind the API can be swapped without changing calling code", "approach": "", "training": "", "data": "", "result": "Three deliverables released together: the DecisionBench benchmark, the Bosun v3.1 0.6B and 1.7B models, and the Jev-compatible server", "notes": "Announcement post for the cluster; the benchmark, models and runtime are tracked separately as decision-bench-hanno-labs, bosun-v3-1 and jev-compatible-server", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sora2-jev-model-guide", "name": "How to Use the Jev AI Model: step-by-step guide", "author": "sora-2", "date": "2026-09-22", "category": "interpretation", "url": "https://huggingface.co/blog/sora-2/how-to-use-the-jev-ai-model-a-step-by-step-develop", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: state + typed questions → structured answers with probabilities; notes: use cases list", "arch": "Step-by-step developer guide from the first experiment to a server-side API integration; Jev as a decision layer for software: classify support tickets, route requests, score risk, decide whether an action needs review, select the next model in an agent workflow; you provide a piece of state, ask one or more typed questions, and receive structured answers with probability signals", "approach": "", "training": "", "data": "", "result": "Complete path from first experiment to server-side integration", "notes": "HF community post, observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sebastianbennis-jev-system-one-reference", "name": "Jev / System One Reference", "author": "sebastianbennis", "date": "2026-09-22", "category": "interpretation", "url": "https://github.com/sebastianbennis/jev-system-one-reference", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 2, "likes": "", "detail": "", "arch": "Independent technical reference for engineers and coding assistants building with Jev: API, implementation patterns, known limitations, and a reusable project handover template; maintained by Sebastian Bennis, explicitly not an official TypeSafe AI publication.", "approach": "", "training": "", "data": "", "result": "", "notes": "GitHub new-repo sweep 2026-09-24", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sora2-jev-api-agents", "name": "Jev AI API and AI agents: reliable workflows", "author": "sora-2", "date": "2026-09-22", "category": "interpretation", "url": "https://huggingface.co/blog/sora-2/jev-ai-api-ai-agents-a-practical-guide-to-reliable", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: decision layer for model choice, tool safety, context, handoff; notes: pairing with the model usage guide", "arch": "Practical guide to connecting the Jev AI API to an AI agent: an agent needs to decide which model to call, whether a tool is safe to use, when more context is required and when a person should take over; starts with a single bounded decision and ends with routing, tool-call guardrails and human review", "approach": "", "training": "", "data": "", "result": "Agent decision-layer patterns from one bounded decision to production workflows", "notes": "HF community post, observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kartik-jev-calibration", "name": "Jev and System One Models: Calibration Beats Accuracy", "author": "Kartik Pansuriya", "date": "2026-09-22", "category": "interpretation", "url": "https://www.kartikpansuriya.com/blog/jev-system-one-model-calibrated-decisions", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Kartik Pansuriya's engineer's read: the interesting claim is calibration, not speed, because calibration is the thing that has quietly limited every production classifier he has shipped, including the one in his COMPSAC paper. Jev is built around three ideas: non-autoregressive output, the entire structured answer at once, which is where the speed comes from, with TypeSafe quoting 70–500 ms end to end and 40× to 200× faster than frontier LLMs; typed questions rather than prompts, each a choice, score, or noul, evaluated in parallel so adding questions barely changes latency; and training for calibration with RLCD, targeting epistemically honest probabilities rather than human-preference or verifiable-reward objectives. The constraints: no free-text generation, at most 255 options per choice question, no image input yet, 0.042 USD per million input tokens with output free, access by waitlist. His evidence for the thesis: in his PR-merge-prediction paper a Random Forest hit F1 0.958 while the majority-class baseline hit 0.957, and the number that actually separated a useful model from a useless one was ROC-AUC, 0.676 versus 0.500, with the models explicitly not to be treated as perfectly calibrated probability models.", "arch": "ML engineer's read on TypeSafe AI's Jev: what a non-autoregressive System One model changes for production classifiers, where it fits in a real stack, which claims are not accepted yet, and the experiment the author plans to run; the thesis is that calibration, not accuracy, is the bottleneck that has quietly limited every production classifier he has shipped", "approach": "", "training": "", "data": "", "result": "", "notes": "9-minute post, 2026-09-22; the author has shipped production classifiers including one from a COMPSAC paper; sections cover what Jev is without the marketing, where a System One model fits in a real stack, claims not ready to accept, and what to tell a team today; 13 points on Hacker News", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "nobodywho-25-lines", "name": "Jev in 25 lines of Python", "author": "nobodywho.ai", "date": "2026-09-22", "category": "interpretation", "url": "https://www.nobodywho.ai/posts/jev-in-25-lines/", "base_model": "llama.cpp + Qwen3-0.6B-GGUF Q8_0", "params": "0.6B", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: per the post · n_ctx=512, logits_all; arch: last-token logits · tokenize with add_bos=False · choice_logits · logprobs · spam/phishing; result: 'There. That's Jev.'; name: skeptic's replica", "arch": "'Everyone on Twitter is all over Jev… We don't really think so. So here's Jev in 25 lines of Python.' Load the model, prompt with labels, then: logits = model.scores[model.n_tokens - 1], take the label tokens' logit, logprobs = choice_logits - logaddexp.reduce, probabilities = exp. Worked example: Legitimate 0.031 / Spam 0.084 / Phishing 0.885", "approach": "", "training": "", "data": "", "result": "The whole 'model' is final-token logit readout + softmax over label tokens on a 0.6B local model; HN: 634 points", "notes": "Skeptic post; pairs with arcturus's 'conventional LLM + massaged logprobs' reading.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "youtube-sean-jev-clearly-explained", "name": "Jev System One Model, clearly explained", "author": "Sean's AI Stories", "date": "2026-09-22", "category": "interpretation", "url": "https://www.youtube.com/watch?v=RMQI_y1AvvA", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "16,623 views three days after its 2026-09-21 upload. The whiteboard walkthrough draws System 1 and System 2 step by step so a viewer can follow, opens with why superhuman models never led us to AGI, and covers fast judgment plus evaluation, while noting the launch post had already passed 38 million views. It sits in the same explainer wave as CampusX and Codevolution; the adjacent sidebar hits, including Fireship's ex-OpenAI researcher just deleted language from the LLM at 2.4M views, mark how fast the topic moved.", "arch": "22-minute explainer on Sean's AI Stories: Jev versus LLM, System 1 versus System 2, AGI, and evaluation; Jev does something no chatbot does, JSON in, JSON out, a probability on every answer", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yibie-engineering-zh", "name": "Jev 工程学, yibie", "author": "yibie", "date": "2026-09-22", "category": "interpretation", "url": "https://github.com/yibie/jev-engineering-zh", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 123, "likes": 0, "detail": "", "arch": "《Jev 工程学：为 coding agent 而作》完整中文翻译 — full Chinese translation of the Jev-engineering-for-coding-agents writeup, original structure + 7 illustrations preserved", "approach": "", "training": "", "data": "", "result": "", "notes": "106 stars — the Jev engineering essay now has a 中文 edition.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "Training-Jev", "name": "Latent Decisions", "author": "romrom-20", "date": "2026-09-22", "category": "interpretation", "url": "https://github.com/romrom-20/Training-Jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "frozen language model plus a small probabilistic readout: question-conditioned latent probes on activations; evaluates calibration, transfer and interventions on the original model", "approach": "", "training": "", "data": "", "result": "completed local pilot plus follow-up studies: two model sizes, answer-remapping controls, unlabeled score correction, four fresh prompt formats; no demonstrated advantage over independent linear probes yet", "notes": "laptop-scale research prototype; 24 GB compute guide; Jev-style, small probes, testable claims", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "qidian-refraction-latentspace-zh", "name": "Latent Space 访谈中文全文: \"当年在 OpenAI 像在疯人院\"", "author": "奇点折射", "date": "2026-09-22", "category": "interpretation", "url": "https://www.163.com/dy/article/L7FEDLMS0511K58A.html", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: via 网易", "arch": "Full Chinese rendering of the Latent Space episode with Swyx, 2+ hours; \"绝大多数新兴 AI 实验室全在毁灭价值\"; pre-release first-principles application directions put dark data first: enterprises hold data assets too costly to process with LLMs, and dark-data processing combined with code agents is the largest, most commercially concentrated domain", "approach": "", "training": "", "data": "", "result": "\"Jev everything\" is called an empirical engineering problem, like scaling law itself: blind application meets setbacks, specific scenarios shine; on code agents: \"所有 coding agents 的架构本质上大同小异, 一个 while 循环里能做的事情其实很有限\"; \"同时优化两个相互冲突的目标, 底层智能就会产生撕裂\"; on robotics: \"很可能尚未找到关键的技术突破口\"", "notes": "整理 王启隆, 出品 奇点折射; Almeida on the OpenAI years: \"环境极度狂热, 所有人盲目跟风\" and \"强烈的无力感与挫败感\"", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bodila51-muse-jev-playbook", "name": "muse-jev-playbook, Jev as the agent decision layer", "author": "Bodila51", "date": "2026-09-22", "category": "interpretation", "url": "https://github.com/Bodila51/muse-jev-playbook", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 17, "likes": "", "detail": "", "arch": "a practical, agent-oriented playbook for using TypeSafe's Jev as a cheap, fast decision layer inside an AI agent workflow: triage, classify, score and gate work before expensive steps, browser, deep research, retries, subagents", "approach": "", "training": "", "data": "", "result": "concepts, a confidence policy, copy-paste question recipes, a drop-in skill and a real-world case study; the three typed primitives, choice, score, noul, answer in one parallel pass, each with probabilities and a 0–1 confidence your code or agent can branch on; one Jev call costs a fraction of a cent and typically returns in about a second", "notes": "adapted from grok-bot-jev and generalized for any agent runtime, Muse/Hatch, custom harnesses, or plain scripts; Track A is the developer path with the TypeSafe SDK, Track B is the agent path where the credential is already connected and no key handling ever happens; MIT", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "robbalian-rev", "name": "rev", "author": "robbalian", "date": "2026-09-22", "category": "interpretation", "url": "https://github.com/robbalian/rev", "base_model": "Open models", "params": "", "metric": "", "license": "Open", "paper": "", "stars": 5, "likes": 0, "detail": "base: analysis; notes: with archerhume's 'Architecture Unmasked'; name: reverse engineering Jev with open models", "arch": "'Reverse engineering Jev using open models' — black-box probing study", "approach": "", "training": "", "data": "", "result": "", "notes": "One of several independent reverse-engineering efforts.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "karmen-route-fallback", "name": "Route an AI agent with JEV without forcing the wrong tool", "author": "karmen-beatapi", "date": "2026-09-22", "category": "interpretation", "url": "https://huggingface.co/blog/karmen-beatapi/how-to-route-an-ai-agent-with-jev-without-forcing", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: explicit none-of-these route; notes: routes by outcomes, not tool names", "arch": "Routing without forcing the wrong tool: an agent can use Jev to choose among known next steps, but a useful router must be able to say none of these, otherwise an unrelated or incomplete request still gets a valid-looking tool name; builds a small router with an explicit review fallback, defines routes by outcomes rather than tool names, validates the typed response and tests routing errors before any tool runs; a route is only a proposal, not permission to call a tool", "approach": "", "training": "", "data": "", "result": "Branching code testable locally without an API key; no live model-accuracy result claimed", "notes": "HF community post, observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "eesel-jev-review", "name": "TypeSafe Jev review: the System One model that gives AI the properties of code", "author": "eesel AI", "date": "2026-09-22", "category": "interpretation", "url": "https://www.eesel.ai/blog/typesafe-jev-review", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Eesel's hands-on review: TypeSafe AI spent two years in stealth before shipping Jev on 2026-09-15; the homepage framing is that LLMs produce words for people while Jev produces typed decisions and is more like code, and the launch post's one-liner calls Jev a frontier-intelligence function call, unstructured state in, typed probabilistic decisions out. System One nods to Kahneman's fast intuitive thinking, Jev is after the economist William Stanley Jevons and his paradox, and the company took the opposite research direction from chat: rather than more RLHF, a new objective, RLCD, with a new architecture and sampler. The working model: you send a state plus typed questions, each evaluated in parallel in a single pass, and the three primitives are noul, a single probability from 0–1, choice, the option with per-option probabilities and a confidence, and score, a numeric score with per-level probabilities and a confidence. The design decision the reviewer likes most: because each question is evaluated independently against the same state, adding questions barely moves the latency and never creates context-rot, and TypeSafe's composition guidance is to keep every question atomic, the kind of judgment a knowledgeable person could make in a few seconds, and compose the hard stuff in code.", "arch": "Hands-on review of hosted Jev: calibration, the type-safety claim, and TypeSafe's own accuracy-versus-cost workflow chart", "approach": "", "training": "", "data": "", "result": "Calibrated confidence is a genuine safety feature; \"can't hallucinate\" is a stronger promise than the model actually keeps — \"type-safe with calibrated confidence\" is the defensible claim; the accuracy-versus-cost chart placing Jev at the lowest cost per workflow is \"the single most useful thing they published\"", "notes": "Published 2026-09-22.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "langchain-jev-evals", "name": "'Can Jev Be a Better Agent Evaluator?'", "author": "LangChain", "date": "2026-09-21", "category": "interpretation", "url": "https://www.langchain.com/blog/jev-agent-evals-langsmith", "base_model": "Jev as eval judge vs LLM judges", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: blog; result: Winston Huynh, 09-21; name: LangChain/LangSmith", "arch": "Evals as typed questions: multiple atomic questions evaluated in parallel against the same state; 'comparing judges is difficult when the agent behavior, retrieved data, or trace context changes between runs' — Deep Agents + LangSmith capture a single agent run as a dataset and replay it across each evaluator; a target agent built with Deep Agents, test set as a LangSmith dataset", "approach": "", "training": "", "data": "LangSmith dataset of agent runs", "result": "Shows Jev used as a deterministic, replayable agent evaluator; related: 'Jev is now available in LangSmith Evals' and a LangChain×TypeSafe livestream on 09-22", "notes": "09-21 post; the evals-as-classification thesis in production tooling.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arcturus-openai-lunch", "name": "'Will OpenAI Eat Jev's Lunch?'", "author": "John Berryman", "date": "2026-09-21", "category": "interpretation", "url": "https://arcturus-labs.com/blog/2026-09-21/will-openai-eat-jevs-lunch", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Arcturus Labs; base: analysis · cites Vercel adoption data; arch: A=happy, B=sad, … · Latent Space; name: fast-follow analysis", "arch": "Working assumption: Jev uses something quite close to a conventional LLM — given a state and questions, the LLM generates the probability distribution over all next tokens at one step and the logprobs are 'massaged' into Jev's return format: noul normalizes just the true/false tokens; choice compares the label tokens. Evidence: many early clones are LLM-based. Conclusion: OpenAI is well positioned to fast-follow — replicate the flagship and fold the capability into upcoming models/agents; 'the biggest moat I see is in TypeSafe's training data and training processes'", "approach": "", "training": "", "data": "", "result": "First-week analysis; reposted on X/LinkedIn, quoted by press", "notes": "Sep 21 2026; HN thread 'OpenAI is well positioned to fast-follow Jev'.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yubol-bobo-rlcd-survey", "name": "Calibration-Aware RL for LLMs survey", "author": "Yubo Li, Yidi Miao, Ramayya Krishnan, Rema Padman", "date": "2026-09-21", "category": "interpretation", "url": "https://github.com/yubol-bobo/rlcd-survey", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "Project website for Calibration-Aware Reinforcement Learning for Large Language Models, a survey of objectives, optimization, and decision-making, by Yubo Li, Yidi Miao, Ramayya Krishnan, and Rema Padman at Carnegie Mellon University, 21 September 2026, with the project website, a full abstract and paper details page, the manuscript on CMU KiltHub, the original high-resolution PDF, and BibTeX. The project page presents the organizing framework, eight representative learning families from Table 2, and Figures 2 and 6, and a compact article landing page exposes the full author-written abstract and bibliographic metadata without JavaScript or expandable panels, with the manuscript containing the full comparison and appendices, and the decision example being an exact analytic construction, not an LLM experiment, and the literature snapshot of 20 September 2026. The site uses plain HTML, CSS, and a small JavaScript file, with no runtime dependencies, analytics, or external font requests: index.html is the English project page and method comparison, paper.html the full abstract, bibliographic metadata, and KiltHub reading link, and the complete searchable web PDF is below 5 MB, with references and appendices.", "arch": "survey of calibration-aware RL for LLMs across eight representative learning families", "approach": "", "training": "", "data": "", "result": "covers objectives, optimization and decision-making; CMU team", "notes": "posted 2026-09-21", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sameernanda-system-one-models", "name": "Cheap Semantic Judgment", "author": "Sameer Nanda", "date": "2026-09-21", "category": "interpretation", "url": "https://sameernanda.com/system-one-models-jev/", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Sameer Nanda's essay: his mental model for Jev is an open-book exam with multiple-choice questions, you provide the context, the questions, and the options, and it evaluates them and returns probabilities rather than conversational text. One of its primitives, Noul, is where it differs from a conventional boolean: a boolean gives true or false, a Noul returns the probability of yes, and your code decides whether that probability is sufficient to act, to gather more evidence, or to ask a human. LLMs can already perform these judgments; the economics and speed are what make Jev interesting, 0.042 USD per million input tokens with no output charge, TypeSafe reports 70–500 ms, his own initial testing showed 200–300 ms, versus Claude Haiku 4.5 at 1 USD per million input and 5 USD per million output at standard uncached rates. He is trying it in a job-search pipeline with three questions: does an ambiguous job title justify fetching the full description, do the location restrictions rule this candidate out, and does the hybrid policy fit the candidate's constraints. These look like simple classification problems until you encounter the language people actually use: remote might mean remote within a particular state, flexible working might still require three days a week in an office, and an office address might identify the employer's headquarters without saying anything about where the employee must work.", "arch": "Three-minute essay: Jev returns typed decisions instead of prose at $0.042 per million input tokens and 200–300 ms; argues cheap fast semantic judgment could become a standard software tool and how three kinds of model divide the work", "approach": "", "training": "", "data": "", "result": "", "notes": "HN 2026-09-21", "domain": "general", "score": "2 points", "latency": "200–300 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "generativeai-jev-primitive", "name": "Jev Is Not Another Chatbot: why System One models could become a new software primitive", "author": "Generative AI pub", "date": "2026-09-21", "category": "interpretation", "url": "https://generativeai.pub/jev-is-not-another-chatbot-why-typesafes-system-one-model-could-become-a-new-software-primitive-c32ed4960c2b", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Medium", "arch": "Essay on System One models as a new software primitive: what you would build if semantic decisions were cheap enough to use everywhere, and how developers are reproducing the idea with open models and alternative architectures", "approach": "", "training": "", "data": "", "result": "", "notes": "Published 2026-09-21.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "Jev_Ontology", "name": "Jev plus ontology", "author": "dagfinndybvig", "date": "2026-09-21", "category": "interpretation", "url": "https://github.com/dagfinndybvig/Jev_Ontology", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 7, "likes": "", "detail": "Exploring how to pair Jev, TypeSafe AI's System One decision model, with ontologies, where Jev plus LLM plus ontology equals abduction: Jev classifies items against an ontology, the LLM revises the ontology when classification fails, and the loop repeats, which is abduction, Peirce's forming an explanatory hypothesis to account for a surprising fact, performed by a system, per the PHILOSOPHY document. Pivot to a visual ontology on 2026-09-22: the project moves toward the image side, classifying images in a university library collection against a revisable taxonomy, using the same LLM plus Jev cascade, the use case that drives the work going forward, with the Images sub-project, its README, and the plan in LIBRARY.md. Status is work in progress, an active research project, not a finished result: the convergence findings are tentative and in-sample, describing what the loop does on the tickets that drove the revisions, and the held-out generalization test in the CONVERGENCE document shows the improvement does not transfer to unseen tickets beyond Jev's run-to-run noise, so the claims are observations about a fixed dataset, not general properties of the mechanism. TL;DR: a working MVP pairing an LLM-authored ontology with Jev's calibrated classification, tested against the live Jev API on 78 unique tickets across 5 sessions, 86 classifications, with session 4 re-running session 2's eight tickets, the feedback loop closed, and a 3-iteration convergence experiment run, with a total cost of 0.0085 dollars.", "arch": "Jev classifies items against an ontology; an LLM revises the ontology when classification fails; the loop is Peircean abduction", "approach": "", "training": "", "data": "", "result": "MVP convergence findings are tentative and in-sample; a held-out generalization test found the improvement does not transfer beyond Jev's run-to-run noise", "notes": "pivoted to a visual ontology: classify images in a university library collection against a revisable taxonomy", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "visrow-jev-vs-laya", "name": "Jev vs Laya: Live Demo in a Browser Tab", "author": "visrow", "date": "2026-09-21", "category": "interpretation", "url": "https://medium.com/@visrow/jev-vs-laya-live-demo-i-ran-a-421-million-parameter-ai-decision-model-inside-a-browser-tab-no-84b86bed1f10", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Medium", "arch": "Runs Laya, the 421M-parameter open decision model, inside a browser tab and compares against Jev; flags that the latency rows are not like-for-like: Laya's figure is pure GPU compute while Jev's playground time includes a network round trip", "approach": "", "training": "", "data": "", "result": "TypeSafe's headline speed and cost gains come from a benchmark its own team wrote; early independent tests found Jev much faster and cheaper on narrow decisions but with accuracy near small and mid-sized language models, and calibration worth checking on your own data", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "deepclause-prolog-pi", "name": "Jev, Prolog, Pi, and the dream of probabilistic logic programming", "author": "DeepClause", "date": "2026-09-21", "category": "interpretation", "url": "https://deepclause.substack.com/p/jev-prolog-pi-and-the-dream-of-probabilistic", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "DeepClause's post: the latest versions of DeepClause and its extension for Pi now support Jev, and Jev is a natural match because its core concepts map nicely onto logical predicates in DML and Prolog, giving more speed and determinism where a pure LLM or agent approach is too expensive or too non-deterministic. The judge predicate calls Jev, or an LLM as fallback, with a state consisting of a message and a policy, plus four question shapes: choose, the multiple-choice type, Jev's choice; rate, multiple choice on a scale, Jev's score; verify, a yes-no question, Jev's noul but checking directly whether the probability exceeds 0.5; and probability, a calibrated probability that something is true, Jev's noul. The author then connects it to the long dream of probabilistic logic programming: a 1990 paper on meta-interpreters for rule-based inference under uncertainty combined Prolog with predicates assigned a probability of being true, and DeepProbLog extended ProbLog with neural predicates so gradient descent runs through the entire program down to the models behind them. Neither took off, in part because of where the probabilities come from, the old-school answer, domain experts, does not scale. Jev is a model trained on huge amounts of data that can give reasonable probabilities almost for free and at scale; the open question is whether that finally realizes probabilistic logic programming.", "arch": "DeepClause and its extension for Pi now support Jev; Jev's core concepts map onto logical predicates in DML and Prolog, which adds speed and determinism for use cases where a pure LLM or agent approach is too expensive or too non-deterministic, and should greatly help SOP2AGENT-style applications. Shows a customer-message triage skill where one judge batch answers a choice, a score and three noul questions, then deterministic Prolog clauses route the message with safety first, refund second, priority third and queue last; asks whether this revives the older notion of probabilistic logic programming and whether useful, scalable expert systems can now be built", "approach": "", "training": "", "data": "", "result": "HN 23 points", "notes": "Install via pi install npm:deepclause-pi", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "mansicer-jev-plays", "name": "jev-plays", "author": "mansicer", "date": "2026-09-21", "category": "interpretation", "url": "https://github.com/mansicer/jev-plays", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 3, "likes": "", "detail": "A System One model plays Craftax, and an LLM sets the goals: TypeSafe's jev makes every move, and GPT-5.6-terra plans, on Craftax, the JAX reimplementation of Crafter, with jev as System One and any OpenAI-compatible model as System Two, and the repository is the harness, the web UI, and the experiments. The question: can a System One model, fast, non-generative, only ever picking among typed options, play a survival and crafting game, and what does it take to pair it with an LLM as System Two. Five agents on the same map, seed 1, real logged episodes replayed at 5 steps per frame: jev on raw actions; jev on macro options; jev plus the GPT-5.6-terra planner; GPT-5.6-terra controlling every step; and random. Agents: Jev-macro plus GPT-5.6-terra Planner, where the LLM writes an objective, standing rules, and a short plan, as facts, on events and every 25 steps, and jev picks one macro option per step; Jev-macro, where jev picks among code-generated macro options, walk to and mine, craft, attack, hold, flee, drink, sleep, dig a shelter, explore; Jev-raw, where jev picks among the 17 primitive actions, each annotated with a one-step fact and its simulated outcome; GPT-5.6-terra control, where the LLM chooses every action, with 5-turn memory, a scratchpad, and an achievement board; and Random-macro, uniform random over the same macro options. Results on Craftax-Classic, 22 achievements, 3 seeds: jev as a literal fact matcher reaches 92–100% when facts are present in context, averaging 14.7 achievements, with survival and latency recorded per agent.", "arch": "Craftax experiment: Jev as a literal fact matcher", "approach": "", "training": "", "data": "", "result": "92–100% when facts are present in context; 14.7 achievements vs 5.0 for random actions; an LLM with standing rules reaches 15.7", "notes": "", "domain": "gaming", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "latent-jev-podcast", "name": "Latent Space podcast: Jev", "author": "Latent Space", "date": "2026-09-21", "category": "interpretation", "url": "https://www.latent.space/p/jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: swyx + Dylan; base: podcast; arch: InstructGPT co-author, TypeSafe founder; notes: 09-22", "arch": "Episode with Diogo Almeida: the launch video hit ~40M views; 'the tyranny of the KV cache' as motivation for a non-generative decision model; Jev vs GLiNER; the trolley problem; voice + computer-use directions; the Doom demo; a language built on top of Jev", "approach": "", "training": "", "data": "", "result": "~40M-view launch video discussed; primary-source account of Jev's design intent", "notes": "09-21 episode; companion to the 09-19 AINews launch coverage. Full Chinese rendering with the dark-data positioning, 'Jev everything' and 'most labs are destroying value' quotes: see qidian-refraction-latentspace-zh.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "flowtivity-laya-benchmark", "name": "Laya: The Open-Source Jev Alternative, Benchmarked Honestly", "author": "Flowtivity", "date": "2026-09-21", "category": "interpretation", "url": "https://flowtivity.ai/blog/laya-open-source-jev-alternative", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "Hard-label accuracy on 2,000 typed decisions, AG News, ECE, ms per decision", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Flowtivity's benchmark, last updated 2026-09-21: Laya returns choice, score, and boolean answers with calibrated probabilities in 32.8 ms on a T4 GPU, covers 100+ languages through automatic checkpoint routing, and charges nothing per token because you host it yourself, which against Jev's 0.042 USD per million input tokens and 236–276 ms latency reads like an easy win. It is not: the headline 0.766 accuracy belongs to a checkpoint fine-tuned on the benchmark's own training split, while the zero-shot base model scores 0.362, below the 0.461 majority-class baseline. The author installed Laya on a CPU-only VPS and measured it themselves, separating what the numbers prove from what they only suggest. The origin story matters: Mukkunnoth says the core idea was published first, a March 2025 arXiv paper on reinforcement-learned sales conversion decisions and a September 2025 follow-up, while TypeSafe launched Jev without technical papers, open weights, or training datasets; an honesty note from the wavect.io review adds that the three days between the two launches separate publication dates, not verified development timelines, so the David-and-Goliath framing is marketing-adjacent, while the code and weights being downloadable is the part that is simply true.", "arch": "Balanced benchmark read of Laya vs Jev 1.13.0: Laya's published report plus the independent wavect review, then an own test", "approach": "", "training": "", "data": "", "result": "Laya's published report: 0.766 vs 0.727 hard-label accuracy on 2,000 typed decisions, 0.950 vs 0.910 AG News, 0.081 vs 0.246 ECE after temperature refitting, 32.8 ms vs 236–276 ms latency, roughly 7.8×; the wavect review adds metrics that favour Jev: soft accuracy 0.580 vs 0.471 and calibration 0.144 vs 0.213; Jev's figures are third-party published and were never measured in the same run", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "236–276 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-any-llm-like-jev", "name": "r/LocalLLaMA: 'You can use any LLM just like JEV'", "author": "r/LocalLLaMA OP", "date": "2026-09-21", "category": "interpretation", "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wlxpaw/you_can_use_any_llm_just_like_jev/", "base_model": "Any GGUF via llama.cpp", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: u/anonymous; arch: activity 410; result: 09-22/09-23", "arch": "Tutorial: run any GGUF with llama.cpp at n_predict=1, n_probs=10, thinking disabled — the top-10 next-token probabilities ARE the Jev-style answer; the post walks through wiring it up as a single-token classifier", "approach": "", "training": "", "data": "", "result": "Picked up by AINews: 'OP shows a llama.cpp /GGUF pattern for binary classification by forcing max_tokens…'", "notes": "Community distillation of the Jev reading to one llama.cpp flag-set.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yankis0x-seven-models-jevbench", "name": "Seven open-source Jev models on JevBench, 21 minutes", "author": "Dima Nurm, yankis0x", "date": "2026-09-21", "category": "interpretation", "url": "https://x.com/yankis0x/status/2102045013681008909", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Posted on X 2026-09-21 with a 21:31 video. The order is the story: Laya did 2M views this weekend, and in a roundup that runs everything against the bench first, it is the closer, not the headline. One thing nobody has put together: the write-ups say Laya has a 1k context, but that is the multilingual 322M checkpoint; the English 421M one everybody quotes has 512. You probably do not have twenty-one minutes right now, so watch the last four.", "arch": "21-minute video running seven open-source Jev models through the same JevBench scoring: 0:00 intro, 3:46 JevBench, the thing they all get scored on, 7:02 Nimble, 11:03 Decider, 13:20 OpenJev, 17:50 Laya", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "simonwillison-jev", "name": "Simon Willison: 'Jev introduces a new shape of LLM'", "author": "Simon Willison", "date": "2026-09-21", "category": "interpretation", "url": "https://simonwillison.net/2026/Sep/21/jev/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: commentary; arch: per the CEO on HN · $0.05/M", "arch": "Framing: Jev introduces a new shape of LLM — 'System One', aka decision models; 'Noul is short for Bernoulli'; pricing read: $0.042/M input tokens is cheaper than GPT-5 Nano", "approach": "", "training": "", "data": "", "result": "Third-party explainer", "notes": "09-21 post.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "$0.05/MTok", "speedup": ""}
{"id": "tencent-jev-paradigm", "name": "Tencent Tech: is Jev a new paradigm or a classic one?", "author": "Tencent Tech", "date": "2026-09-21", "category": "interpretation", "url": "https://cloud.tencent.com/developer/article/2748745?policyId=1003", "base_model": "TypeSafe Jev", "params": "", "metric": "Tool-call type-error rate versus the published latency and cost claims", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Tencent Tech's long skeptical deep-dive, in Chinese: around September 15, Jev suddenly swept the Silicon Valley developer community, it does not write code, articles, or even chat, it actively cut the capability that has defined large models for the past few years, generating text, and kept only judgment, which TypeSafe defines as Decisions, not strings. The launch post had about 4.2M views and 20K likes per Latent Space AINews; TypeSafe's numbers are up to 193.6× faster and 444.6× cheaper, 70–500 ms end to end, 0.042 USD per million input tokens with output free. The team: TypeSafe AI came out of stealth with Diogo Almeida, Erik Gafni, and Sasha Sheng, a 40M seed led by DCVC, Forbes citing people for a valuation around 200M; Almeida was an OpenAI researcher and a principal author of the 2022 InstructGPT paper, credited in GPT-4's contributor list for foundational RLHF and InstructGPT work. The article walks through an e-commerce agent example where the software really only needs the third button; Jev as a smart if statement with general semantic understanding, unlike a classical classifier that needs redefined data and retraining for every new task; the zero-hallucination claim only means the output cannot leave the predefined types; and the realistic future agent stack is layered, frontier model for complex tasks, Flash for ordinary reasoning, decision model for high-frequency routing, classification, and verification, ordinary code for deterministic parts, with the harness scheduling between them.", "arch": "Skeptical deep-dive: launch post at ~4.2M views and ~20K likes per Latent Space AINews; TypeSafe pre-Jev valuation reported around $200M by Forbes; argues Jev is a general-purpose classifier with LLM-level semantic understanding added, echoing the Reddit take that the industry has rediscovered the classification model", "approach": "", "training": "", "data": "", "result": "Challenges the zero-hallucination claim with a cat/dog/bird example and notes the 0% tool-call type-error rate; finds no paper, parameter count, architecture, data or ablations published; asks whether RLCD is a distinct training paradigm or an engineering mix of RL and probability calibration; says one cannot conclude Jev broke through at the Transformer, Attention or MoE level", "notes": "Also reports a developer telling Tencent Tech \"where is this kind of deal?\"; OpenAI GPT-4 contributor list credits Almeida with foundational RLHF and InstructGPT work; Almeida spent two years asking why, if LLMs are this smart, most work is not automated. Original news.qq.com link is dead; article mirrored on the Tencent Cloud developer community, same piece.", "domain": "general", "score": "0%", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "diogo-typesafe-coding-agent", "name": "Thoughts on a Typesafe Coding Agent", "author": "Diogo Almeida", "date": "2026-09-21", "category": "interpretation", "url": "https://docs.google.com/document/d/1G61uUB0FifUnmmrPzFQojZ3KpczYKmXGpgEXDJ2l_Zg/", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: TypeSafe CEO", "arch": "Why yet another agent: coding agents are surprisingly simple while loops with a few tools; the design question is how would you build one if LLMs had no KV cache, which exposes a typesafe-centric architecture. TypeSafe as permission gate for any command, as the router for tool calls, and meta-attention that drops the idea of static context", "approach": "", "training": "", "data": "", "result": "Routing cost model: with a representative context-to-output-to-read token mix of 0.65, 0.12, 0.23, a pure Opus pass costs about two-thirds of an Opus-Sonnet-Opus round trip, because the large model must re-read the whole context", "notes": "Design paper by the TypeSafe CEO, shared on HN 2026-09-21; the primary public design source for the TypeSafe coding agent. Six pathologies of KV-cache-centric design: round-trip routing cost, up-front tool schemas, compaction, weak subagent parallelization, restarts, bundled batteries. Proposes TypeSafe as a permission gate for any command, as the router for tool calls, and as meta-attention that re-scores every context chunk per query instead of static context; on-demand skill schemas that never sit in the system message; conditional AGENTS.md loading; security-aware routing by file sensitivity. Appendix maps tooling: headroom, rtk, ast-grep, ast-outline, fastcontext, fff; fastcontext claim: reading and searching are 56.2% of tool-use turns and 46.5% of main-agent tokens", "domain": "coding", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "tomshardware-jev", "name": "Tom's Hardware: Jev 'claims 193× faster, 445× cheaper'", "author": "Tom's Hardware", "date": "2026-09-21", "category": "interpretation", "url": "https://www.tomshardware.com/tech-industry/artificial-intelligence/typesafe-ais-jev-offers-an-alternative-to-llms-that-claims-to-be-193x-faster-and-445x-cheaper-system-one-type-model-is-bespoke-for-probabilistic-decision-making", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "base: press; result: alongside The Register's $40M-raise piece and JevOps piece", "arch": "Mainstream-tech coverage: 'TypeSafe AI's Jev offers an alternative to LLMs that claims to be 193× faster and 445× cheaper — System One type model is bespoke for probabilistic decision-making'", "approach": "", "training": "", "data": "", "result": "First mainstream hardware/tech press coverage of Jev", "notes": "09-21 2026.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "vercel-jev-vs-astra", "name": "TypeSafe AI Jev vs. GPT-6 Astra: when should you use each?", "author": "Ben Sabic", "date": "2026-09-21", "category": "interpretation", "url": "https://vercel.com/i/jev-vs-gpt-6-astra", "base_model": "Jev vs GPT-6 Astra", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Vercel; base: both on Vercel AI Gateway; arch: a distribution over categories, no text generated · text generation, image input, function calling, Structured Outputs", "arch": "Official-style Vercel comparison: use Jev for focused decisions with defined answers and native probabilities; use GPT-6 Astra when the task also requires generating content or working through a broader problem with tools. Key asymmetry: AI SDK's OpenAI evaluation adapter returns the selected label WITHOUT a category distribution — Jev returns the distribution natively", "approach": "", "training": "", "data": "", "result": "Decision guide: 'for a support application, assigning a ticket and investigating the customer's problem are different jobs — your model choice can reflect that separation'", "notes": "Vercel blog, 21 Sep 2026, 8-min read; first vendor-side Jev-vs-frontier guide.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sora2-what-is-jev-ai", "name": "What Is Jev AI? A Practical Guide to System One and Executable Decisions", "author": "sora-2", "date": "2026-09-21", "category": "interpretation", "url": "https://huggingface.co/blog/sora-2/what-is-jev-ai-a-practical-guide-to-system-one-and", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Hugging Face blog", "arch": "HF community practical guide: what Jev is, how it works, the three question types, why not ask an LLM for JSON, five Jev AI products compared, where Jev fits, how to get started, and probability, confidence, and limits", "approach": "", "training": "", "data": "", "result": "Short version: Jev AI is not a replacement for every conversational AI system; it is a decision layer inside the application for fast, repeatable classification, routing, scoring, and safety checks within a defined answer space", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "rlcd-secret-dizhang", "name": "What Is RLCD, the secret behind Jev", "author": "Di Zhang", "date": "2026-09-21", "category": "interpretation", "url": "https://di-zhang-llm.github.io/blog/what-is-rlcd-the-secret-behind-jev/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Di Zhang's post: Jev looks mysterious viewed as an alternative to a language model, but becomes much simpler viewed as the next step in reward modeling. RLCD is multiway preference modeling plus probability calibration, more specifically a schema-conditioned Plackett-Luce objective, and Jev turns that objective into a product by adding typed outputs and parallel inference; the secret is that the reward model is no longer hidden behind a generator, the reward model becomes the model. The lineage: a conventional reward model produces a scalar for a context and candidate answer, but that number is not actually absolute, a reward of 0.8 has no stable meaning across problems, candidate pools, checkpoints, or model families, and the operational signal was always relative preference, which the scalar merely hid. LLaMA-Berry's Pairwise Preference Reward Model, PPRM, makes the comparison explicit with the Bradley-Terry probability that the first answer is better than the second, implemented as a constrained language-model decision over Yes and No tokens, trained on almost 7.8 million mathematical-solution pairs. The essential change is conceptual: reward modeling becomes preference-probability modeling; PPRM still contains a latent scalar utility, which becomes meaningful through normalization across the candidate set, and the step from pairwise to a full multiway Plackett-Luce distribution over all options is what RLCD completes, with calibration turning that distribution into a decision interface.", "arch": "Reward-modeling lineage for Jev: a scalar reward model becomes a pairwise preference model, which becomes a multiway Plackett-Luce distribution, and RLCD adds probability calibration on top; the post's thesis is that RLCD is schema-conditioned Plackett-Luce preference modeling plus calibration, and that Jev turns that objective into a product by adding typed outputs and parallel inference, so the reward model is no longer hidden behind a generator and becomes the model itself", "approach": "", "training": "", "data": "", "result": "Walks through why Jev can run candidates in parallel and argues RLCD is not a third kind of reward source, closing with testable predictions", "notes": "HN 2 points at sweep time; by a Fudan University PhD candidate working on LLM reasoning", "domain": "science", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "bonzi-27b-v1-jev", "name": "Bonzi", "author": "NicolaiMTLassen", "date": "2026-09-20", "category": "interpretation", "url": "https://huggingface.co/models/NicolaiMTLassen/bonzi-27b-v1-jev", "base_model": "Bonsai-27B-gguf, 1-bit Q1_0", "params": "27B ternary", "metric": "WANLI-256 accuracy 0.711, 31 decisions per minute", "license": "MIT", "paper": "", "stars": "", "likes": 0, "detail": "notes: Bonsai as Jev", "arch": "Recipe + measurements for running prism-ml Bonsai, 1.7B, 4B, 8B, 27B and the 8B ternary, as Jev-style System One models: one forward pass in, calibrated probability per option out; independent re-implementation of SemIf direct mode — own prompts, no SemIf code — on stock llama.cpp; the PrismML fork is needed for the ternary GGUF", "approach": "logits", "training": "", "data": "", "result": "71.1% on WANLI-256, a seeded label-balanced 3-way NLI subset, against 33.3% chance; 100% on an easy100 binary smoke test; 31 decisions per minute with a 1911 ms median latency and 0.949 median label mass on an Apple M4 Pro; the same-machine family table: 1.7B at 52.0% and 925 decisions per minute, 4B at 60.2 and 398, 8B at 64.5 and 230, Ternary 8B at 65.2 and 200, Bonsai 2 27B at 74.6 and 24", "notes": "WANLI-256, binary, lettered choices, thinking off: 52.0% at 1.7B → 71.1% at 27B, second in the family table behind Bonsai 2 27B at 0.7462; numbers not comparable to SemIf published benchmarks because the scorer was rewritten. No weights in the repo — recipe only. The 8B member has its own repo: NicolaiMTLassen/bonzi-8b-v1-jev, 09-20, MIT — WANLI-256 seeded label-balanced subset acc 0.6445, easy100 binary smoke 0.98, code NicolaiLassen/open-bonzi-jev; recipe and measurement, no weights.", "domain": "general", "score": "", "latency": "", "benchmark": "WANLI-256", "cost": "", "speedup": ""}
{"id": "dair-built-with-jev", "name": "Built with Jev: DAIR.AI community demo gallery", "author": "DAIR.AI", "date": "2026-09-20", "category": "interpretation", "url": "https://academy.dair.ai/resources/jev-field-notes", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "The DAIR.AI Academy's Built with Jev page: a community gallery of Jev demos, experiments, and ideas, with curation tabs for Jev agents, curation, and OpenRouter, and an interactive preview pane. The page itself is mostly the academy shell, the demos are the payload, and it functions as a live catalog of what the ecosystem has shipped since launch, alongside the shipwithjev and madewithlaya directories.", "arch": "Community gallery of Jev demos, experiments and ideas collected by the DAIR.AI Academy, curated like the various awesome-jev lists; a live catalog of what the ecosystem has shipped since the launch", "approach": "", "training": "", "data": "", "result": "HN 8 points", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "astrohan-decision-head-rlcd", "name": "Decision-Head RLCD", "author": "Astro-Han", "date": "2026-09-20", "category": "interpretation", "url": "https://github.com/Astro-Han/decision-head-rlcd", "base_model": "Qwen3.5-4B", "params": "1.88M decision head", "metric": "97.2% dev accuracy", "license": "", "paper": "", "stars": 6, "likes": "", "detail": "base: Qwen3.5-4B plus 4.9M-parameter LoRA, rank 8, attention only; training: RLCD, 32,000 typed decisions, one pass, three seeds; result: held-out 87.9 vs Jev 88.0; JevBench prob 0.50 vs 0.70; long_policy 0.32 vs 0.63", "arch": "A single decision head over a frozen Qwen3.5-4B; one forward pass scores all options", "approach": "head", "training": "Decision head trained on the dev set, base frozen", "data": "62-decision dev set", "result": "97.2%, 60 of 62, Brier 0.058, ECE 0.014; 98%+ across 4 languages; head is 19.9% of the base model; 12.3 ms per decision on a 16-core Mac, 801.6 QPS, about $0.00007 per 1k decisions; 272.8× cheaper than the Jev API", "notes": "Separates data from algorithm in decision-model generalization. Qwen3.5-4B plus a 4.9M-parameter LoRA, rank 8, attention only, trained with RLCD — Gaussian noise on the option-letter logits, leave-one-out baseline over 8 samples — on 32,000 typed decisions in one pass; three seeds, base and hyperparameters held fixed. Generalisation tracks training-data coverage in both directions: same-dataset held-out 87.9 against Jev 88.0; JevBench probability 0.50 against base 0.30 and Jev 0.70; JevBench trap 1.00; out of coverage pubmedqa 65.2, vitaminc 66.3 and long_policy 0.32, where RL does not close the gap to Jev 0.63. Weights: AstroHan/decision-head-qwen3.5-4b-rlcd-32k, 32k context.", "domain": "general", "score": "97.2%", "latency": "12.3 ms per decision on a 16-core Mac", "benchmark": "", "cost": "~$0.00007 per 1k decisions, 272.8× cheaper than Jev API", "speedup": ""}
{"id": "reddit-hermesagent-laya", "name": "I'd rather use Laya than Jev", "author": "r/hermesagent", "date": "2026-09-20", "category": "interpretation", "url": "https://www.reddit.com/r/hermesagent/comments/1wlouk1/id_rather_use_laya_than_jev/", "base_model": "Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: links the 'I literally built the Jev architecture one year back' thread", "arch": "Posted after Teknium's Jev post: Laya is the original Jev, the author open-sourced it a year and a half ago; Laya is faster in most metrics; open-source Apache-2.0 and self-hosted so all data stays secure and local", "approach": "", "training": "", "data": "", "result": "Asks Teknium and co to give Laya a go and states a preference for self-hosting the stack over sending data to a hosted service", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-typesafe-derived", "name": "Is Typesafe based/derived from work done by the Laya author?", "author": "r/LocalLLaMA", "date": "2026-09-20", "category": "interpretation", "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wlfmgq/is_typesafe_basedderived_from_work_done_by_the/", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "r/LocalLLaMA thread, observed 2026-09-24: a regular dev asks whether TypeSafe is based on or derived from work done by the Laya author: TypeSafe's Jev is all over the internet as a new classifier model, then an AI dev posted in the same subreddit having done something very similar and made Laya, the similarities appear much more than surface level, and TypeSafe did not cite or credit the original paper. Reddit's bot wall blocked the thread body at sweep time, so the row rests on the title and indexed snippets.", "arch": "A regular dev's question: TypeSafe's Jev is all over the internet as a new classifier model, then an AI dev posted in the same subreddit having done something very similar and made Laya; the similarities appear much more than surface level and TypeSafe did not cite or credit the original paper", "approach": "", "training": "", "data": "", "result": "Asks experts to chime in; feeds the prior-art debate around the 'built the Jev architecture a year back' thread", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sora2-structured-decision-tutorial", "name": "Jev AI API tutorial: first structured decision", "author": "sora-2", "date": "2026-09-20", "category": "interpretation", "url": "https://huggingface.co/blog/sora-2/jev-ai-api-tutorial-build-your-first-structured-de", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: request shape + Choice/Score/Noul + curl + control flow; notes: production checklist included", "arch": "First structured decision tutorial: the Jev request shape, the choice between Choice, Score and Noul, a minimal curl request, response handling, control flow, error boundaries and a production checklist; the worked example builds one low-risk support-ticket judgment where the server decides whether to route it automatically or request a human review", "approach": "", "training": "", "data": "", "result": "Tutorial goal: one low-risk support-ticket judgment with automatic routing or human review", "notes": "HF community post, observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "sora2-when-decision-mode", "name": "Jev AI vs LLMs: when to use a decision model", "author": "sora-2", "date": "2026-09-20", "category": "interpretation", "url": "https://huggingface.co/blog/sora-2/jev-ai-vs-llms-when-should-you-use-a-decision-mode", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: state into choice/score/yes-no in a defined answer space; notes: not \"Jev is always better\"", "arch": "When to use a decision model instead of a chat model: LLMs are strong at open-ended generation, explanation and complex reasoning, while Jev turns a piece of state into a choice, score or yes-no judgment within a defined answer space and hands the result back to application code; the argument is that the systems are optimized for different jobs, not that Jev is always better", "approach": "", "training": "", "data": "", "result": "Practical criteria for choosing a decision model over a chat model", "notes": "HF community post, observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-atlas", "name": "Jev Atlas field catalog", "author": "syedabbasshaheer-art", "date": "2026-09-20", "category": "interpretation", "url": "https://github.com/syedabbasshaheer-art/jev-atlas", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 0, "likes": "", "detail": "", "arch": "Catalogue of projects built with typed decision models, grouped by the field they serve and broken into the 15 capabilities each project is assembled from; two layers: 359 scraped evidence projects plus 12 hand-authored launchable blueprints, with 10 build patterns", "approach": "", "training": "", "data": "359 projects scraped from the jevable.com gallery on 2026-09-20 via its embedded board-data JSON blob; classified by ordered regex rules plus hand-written overrides", "result": "Field distribution: Games and Simulation 82, Software Engineering 52, Agent Infrastructure 43, Finance 14, Health 2, Travel 3, Education 1; the crowd built what demos well in an afternoon, every field with a real user is close to empty", "notes": "Single static HTML site, 274 KB, no dependencies; the build refuses to render on a dangling reference", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-full-open", "name": "Jev goes fully open", "author": "36kr", "date": "2026-09-20", "category": "interpretation", "url": "https://36kr.com/p/3992394169613316", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: reported by QbitAI, 36kr, sina", "arch": "Full public release: waitlist dropped, $5 credit for registered users, 1.2M tokens per credit, roughly 1.5 h of conversation; the API was briefly overwhelmed by demand after launch", "approach": "", "training": "", "data": "", "result": "Ably Pong demo: Jev made 47 action decisions in 12 s; Gemini, Claude and GPT made 2–3 in the same window; launch-week post hit 37M+ views on X", "notes": "Almeida: \"We had a one-shot successful innovation, but we did not turn it into something truly useful\"; co-founders Erik Gafni and Sasha Sheng; $40M seed led by DCVC, with Jev debuting 09-15", "domain": "general", "score": "", "latency": "255 ms/decision", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wavect-laya-vs-jev", "name": "Laya vs Jev: What the Benchmarks Mean for AI Startups", "author": "Wavect", "date": "2026-09-20", "category": "interpretation", "url": "https://wavect.io/blog/laya-vs-jev-benchmark-ai-startup-moat", "base_model": "TypeSafe Jev + Laya", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "result: separately published", "arch": "Independent pilot plus metric-by-metric reading of the published Laya vs Jev numbers, with the caveat that the two sides were measured on different prompts, samples, and training", "approach": "", "training": "", "data": "", "result": "Hard-label accuracy: Laya specialised 0.766 vs Jev 1.13.0 0.727; soft accuracy: Laya 0.471 vs Jev 0.580, favouring Jev on this distribution-sensitive measure; ECE: Laya 0.213 vs Jev 0.144, the reported calibration result favours Jev", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "youtube-sam-witteveen-open-jev", "name": "Open Jev models are here", "author": "Sam Witteveen", "date": "2026-09-20", "category": "interpretation", "url": "https://www.youtube.com/watch?v=53wDOI_7x8I", "base_model": "7 open Jev-style models", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: YouTube", "arch": "Hands-on tour of seven open Jev-style models including JevBench, SemIf and Nimble: how good they are and what they can do", "approach": "", "training": "", "data": "", "result": "", "notes": "Uploaded 2026-09-20; ~168K views.", "domain": "general", "score": "", "latency": "", "benchmark": "JevBench", "cost": "", "speedup": ""}
{"id": "reddit-what-is-jev-faq", "name": "What is JEV and what is it used for?", "author": "r/LocalLLaMA", "date": "2026-09-20", "category": "interpretation", "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wleg4w/what_is_jev_and_what_is_it_used_for/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "r/LocalLLaMA community FAQ thread on what Jev is and what it is used for, observed 2026-09-24. Reddit's bot wall blocked the thread body at sweep time; the thread exists as the community's own answer to the launch-week confusion, sitting alongside the skeptic threads in the same subreddit.", "arch": "Community FAQ thread on what Jev is and what it is used for", "approach": "", "training": "", "data": "", "result": "Top answers point to Laya as the completely open-source version of the concept and to what people are doing with diffusion gemma as the real open substitute, noting Jev is also not free", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "integratedcognition-lawyers", "name": "What Jev can do for lawyers", "author": "Integrated Cognition", "date": "2026-09-20", "category": "interpretation", "url": "https://www.integratedcognition.com/p/what-jev-can-do-for-lawyers", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: law + AI newsletter", "arch": "Legal-domain walkthrough: inbox triage, urgency flags, review queues; intake routing and completeness review; \"a correctly shaped wrong answer can be surprisingly easy to automate\"; \"a model score cannot grant authority that the firm, client or applicable rules have not granted\"", "approach": "", "training": "", "data": "", "result": "Worked example: routing an inquiry without accepting a case; flags Jev 1.13 limits: unreliable counting and date comparison, difficulty with indirect instructions, accuracy degrades with irrelevant context, differently framed questions can disagree", "notes": "The examples are proposed workflow designs, not reports of validated Jev deployments in law firms", "domain": "legal", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "xhinker-27b-8gb", "name": "A 27B model now fits on an 8 GB GPU", "author": "xhinker", "date": "2026-09-19", "category": "interpretation", "url": "https://xhinker.medium.com/a-27b-model-now-fits-on-an-8-gb-gpu-and-that-is-where-it-gets-complicated-0afce5350c16", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "A fourteen-minute home-lab write-up: the author runs Qwen3.8-27B on a home server with three GPUs, and the honest number for that model at a decent quantization is about 17.6 GB of VRAM, one 3090 fully eaten. So when a post claimed a 27B reasoning model from the same family fits in 5.95 GB, the first thought was marketing, and the second was to check, because he has three GPUs and nothing better to do at 11 pm. It fits; it genuinely fits on an 8 GB card. The model is Ternary Bonsai 2 27B, released September 17, 2026 by PrismML, a company that came out of Caltech, funded by Khosla Ventures, with Samsung among its backers; co-founder Babak Hassibi is a Caltech professor who has spent years on exactly this problem. What it does well once it fits is a much more complicated story, and that story is the actual point of the article.", "arch": "Analysis of the 5.95 GB ternary version of Qwen3.8-27B, PrismML's Bonsai 2, that now fits an 8 GB GPU; prebuilt binaries from the PrismML-Eng llama.cpp fork releases; what local serving means for Jev-style decision workloads", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "latent-space-ainews-6-clones", "name": "AINews: Here are 6 Clones of Jev in 2 days", "author": "Latent Space", "date": "2026-09-19", "category": "interpretation", "url": "https://www.latent.space/p/ainews-here-are-6-clones-of-jev-in", "base_model": "Nimble, Kev, Open-Jev", "params": "", "metric": "Accuracy on the curated eval versus Jev, ms per decision", "license": "", "paper": "", "stars": "", "likes": "", "detail": "The AINews issue for 2026-09-17–09-18: Jev's launch took over the timeline, 36M views of the launch video in two days, by comparison OpenAI's Navier Stokes result got 74M and Anthropic's Fable 5 got 57M; Vercel reported Jev was adopted faster than any other model in AI Gateway history, roughly 13% of teams on day one, 2× the GPT-5.6 family and 6× Fable 5.1. The six clones: Laya, 421M parameters, a ModernBERT-large encoder with two added transformer layers that score user-supplied options, PPO over sequence embeddings, with the notes that its author is salty about not getting recognition and that its confidence is entropy-based, not calibrated; DiffusionGemmaJev, tackling it from a diffusion-model basis, pretty close on benchmarks; Bespoke Nimble, a LoRA fine-tune of Qwen3.5-9B using contrastive data curation, close but slightly lower on benchmarks; SemIf, formerly OpenJev, a 4B and 35B causal Qwen3.5 backbone with a tiny three-class NLI classifier on the last token; Jevlike, a 40KB embedding lightweight option-attention model where each candidate becomes a query that reads from a shared context representation; and Kev-0.5B, a LoRA adapter plus a small readout head on top of Qwen2.5-0.5B. The issue also notes that not enough people are talking about the data side, which is acknowledged to be 100% synthetic, and covers the broader wave: Jev as an eval model in Braintrust at roughly 400× lower scoring cost, calibrated-probability use cases like routing and escalation, and the framing of the class as a near-zero-marginal-cost on-device judgment layer.", "arch": "AINews issue tracking the first two days of open Jev reproductions and ecosystem clones", "approach": "", "training": "", "data": "", "result": "Bespoke Nimble: LoRA on Qwen3.5-9B with synthetic contrastive curation and constrained decoding, base 66% → 90% on its curated eval versus 93% for Jev, ~100 ms on H100; Kev-0.5B on Qwen2.5-0.5B runs on a MacBook Pro; reactions split along the prior-ChatGPT versus pre-GPT experience line", "notes": "Published 2026-09-19. The same issue was also captured at the now-dead slug latent.space/p/ainews-six-jev-clones-in-two-days; that capture named the six: OpenJev, a DiffusionGemma-based reverse-engineering project, AlexWortega openjev, jevlike, Qwen-2.5-1B-RLCD and CUA-S1-FORMS, and tracked the Laya/Kev momentum.", "domain": "general", "score": "66%", "latency": "100 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-jev-heyjunpenn", "name": "Awesome Jev, 916 verified projects", "author": "heyjunpenn", "date": "2026-09-19", "category": "interpretation", "url": "https://github.com/heyjunpenn/awesome-jev", "base_model": "896 open-source Jev projects", "params": "", "metric": "", "license": "", "paper": "", "stars": 832, "likes": "", "detail": "arch: a dated snapshot of 896 unique public GitHub repositories, stars captured 2026-09-18–24 · every entry identifies the concrete decision Jev makes and links to the strongest public evidence; data: Official 6, SDKs & clients 55, Jev-like models 60, Frameworks & integrations 75, Agent tooling 220, Browser & computer use 73, Applications 123, Games & simulations 70, Demos & playgrounds 62, Benchmarks & research 113, Other lists 39; notes: independent, community-maintained, not affiliated with or endorsed by TypeSafe AI · translations in EN, 中文, 日本語, 한국어, Español, Português", "arch": "A curated list of 916 verified open-source Jev projects across 11 categories and 27 languages, hosted at jevbest.com", "approach": "", "training": "", "data": "", "result": "776 stars; 834 projects", "notes": "The largest project count of the curation family: 896 projects; 776 stars", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-jev-tools", "name": "awesome-jev-tools", "author": "v-modal", "date": "2026-09-19", "category": "interpretation", "url": "https://github.com/v-modal/awesome-jev-tools", "base_model": "Tools-focused awesome list", "params": "", "metric": "", "license": "", "paper": "", "stars": 720, "likes": "", "detail": "Jev is not a chat model: it does not write text or hold conversations. It takes unstructured state alongside a typed question and returns a typed decision, such as a choice, a score, or a boolean, accompanied by a confidence rating; by eliminating token-by-token decoding, Jev acts as a fast, low-latency decision layer directly inside software. Developers use it to handle classification, infrastructure routing, rubric scoring, verification gates, and autonomous agent guardrails. The list answers two practical questions: production validation, where is Jev actively making real decisions in live production workflows, and transferable patterns, which decision architectures can be cleanly copied and applied across different industries. The README is the homepage aggregate of the current category files, so the latest accepted entries are visible without drilling into subpages.", "arch": "Curated awesome list of public projects and developer patterns built on Jev, TypeSafe AI System One model for typed decisions", "approach": "", "training": "", "data": "", "result": "", "notes": "Inclusion criteria: generic classifiers are not included", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-llmdevs-287-projects", "name": "I reviewed 287 open-source Jev projects. Here are 20 that actually work", "author": "r/LLMDevs", "date": "2026-09-19", "category": "interpretation", "url": "https://www.reddit.com/r/LLMDevs/comments/1wko2e5/i_reviewed_287_opensource_jev_projects_here_are", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: Reddit", "arch": "Two-day review of the open-source projects built around TypeSafe Jev; the list grew from 14–287 projects while the review was running", "approach": "", "training": "", "data": "", "result": "20 projects that actually work, out of 287 reviewed", "notes": "Posted 2026-09-19.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "davila7-jev-explained", "name": "Jev Explained, interactive walkthrough of the three primitives", "author": "davila7", "date": "2026-09-19", "category": "interpretation", "url": "https://github.com/davila7/jev-explained", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 32, "likes": "", "detail": "", "arch": "interactive playground that shows step by step how TypeSafe's Jev makes typed probabilistic decisions by running it; live demo on Vercel, bring your own TypeSafe or Vercel AI Gateway key", "approach": "", "training": "", "data": "", "result": "Noul returns one probability for a yes/no question, Choice returns the chosen option with a probability for every option and a confidence, Score returns a weighted score with a probability for every level and a confidence; the model returns calibrated probabilities for every question in a single about 100 ms round trip, and the caller's code, not the model, makes the final decision by thresholding", "notes": "the framing is that Jev is not a chat LLM: you send state, any text or JSON such as an email, a market snapshot or a tool call an agent wants to run, plus one or more typed questions; it never generates text; the questions run in one pass; MIT", "domain": "education", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "jev-arena-zh", "name": "Jev model intro and hands-on testing", "author": "NanmiCoder", "date": "2026-09-19", "category": "interpretation", "url": "https://github.com/NanmiCoder/jev-arena", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 103, "likes": "", "detail": "", "arch": "Chinese Jev model introduction and hands-on testing: Choice, Score and Noul turn natural language into typed judgments and probabilities for classification, scoring and routing.", "approach": "", "training": "", "data": "", "result": "Compares Jev against DeepSeek and other models on the same tasks.", "notes": "", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "aiwithmike", "name": "Jev, three days in: what is known, what is guessed", "author": "aiwithmike.substack", "date": "2026-09-19", "category": "interpretation", "url": "https://aiwithmike.substack.com/p/jev-three-days-in", "base_model": "Known-vs-guessed inventory", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Mike E.'s Substack, three days into the Jev hype: separates what is known from what is guessed, and offers a hardware-based estimate of roughly 10B active parameters, which contrasts with the roughly 2–3B estimates derived from pricing. The article body did not render for automated fetch at sweep time, so the row rests on the title, indexed snippets, and the estimate that made it notable.", "arch": "Separates verified facts from community guesses; hardware-based ~10B active-parameter estimate", "approach": "", "training": "", "data": "", "result": "Fact/guess ledger", "notes": "The careful inventory piece; its hardware-derived ~10B-active estimate contrasts with the ~2–3B pricing-based ones.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-jev-qwen-239ms", "name": "Jev-style decisions with local Qwen", "author": "r/LLMDevs", "date": "2026-09-19", "category": "interpretation", "url": "https://www.reddit.com/r/LLMDevs/comments/1wkc9hp/i_tried_jev_style_decisions_with_local_qwen", "base_model": "Qwen3.8 27B Q4 XL local, against Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "r/LLMDevs thread: the author tried Jev-style decisions with a local Qwen, testing Qwen3.8 27B Q4_XL against Jev on SemIf's 144-task benchmark; both reach the same accuracy. From an indexed snippet; the full thread was not retrievable at sweep time because of a Reddit network block.", "arch": "Tested Qwen3.8 27B Q4 XL against Jev on SemIf's 144-task benchmark; both reach the same accuracy", "approach": "", "training": "", "data": "SemIf 144-task benchmark", "result": "96.53% accuracy for both; local latency 239 ms", "notes": "From an indexed snippet; full thread not retrievable at sweep time", "domain": "general", "score": "", "latency": "239 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "elsolitario-laya-328ms", "name": "Laya at 32.8 ms, up to 8× faster than Jev", "author": "Andrés Morales", "date": "2026-09-19", "category": "interpretation", "url": "https://elsolitario.org/en/2026/09/19/laya-convai-decision-engine-33ms", "base_model": "Laya + Laya multilingual + Laya typed-decisions", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "el Solitario's eleven-minute analysis, in Spanish with an English version: ConvAI Innovations released Laya, a decision engine that answers in 32.8 ms per query on a single GPU and 7.2 ms per question when traffic arrives in batches, six to eight times faster than Jev at around 150 ms. TypeSafe AI, founded by Diogo Almeida, launched Jev in September 2026 without papers or open weights, while Mukkunnoth had already published the idea in March 2025, arXiv 2503.23303. Jev charges 0.042 USD per million input tokens; Laya's three checkpoints, laya, laya-multilingual, and laya-typed-decisions, are unified in a single Hugging Face repository, support over 100 languages, are released under Apache 2.0 with no API subscription cost, and the laya-typed-decisions checkpoint reaches 0.766 accuracy on agent observability and invoice processing. The FAQ section covers what a System 1 decision engine is, how Laya differs from a generative LLM, whether it works in Spanish, whether it uses the same RLCD as Jev, the cost comparison, and where to download the weights.", "arch": "Analysis of Laya's performance: 32.8 ms per query on a single GPU and 7.2 ms per question when traffic arrives in batches, six to eight times faster than Jev at around 150 ms and 0.042 dollars per million input tokens; covers the three checkpoints, laya, laya-multilingual and laya-typed-decisions, unified in a single Hugging Face repository, the Apache 2.0 license with no API subscription cost, support for 100+ languages, and the laya-typed-decisions checkpoint reaching 0.766 accuracy on agent observability and invoice processing; frames the release against the March 2025 prior paper arXiv:2503.23303", "approach": "", "training": "", "data": "", "result": "", "notes": "", "domain": "general", "score": "", "latency": "32.8 ms", "benchmark": "", "cost": "", "speedup": "8× faster"}
{"id": "awesome-jev-amaldavid", "name": "Awesome Jev, Amal-David", "author": "Amal-David", "date": "2026-09-18", "category": "interpretation", "url": "https://github.com/Amal-David/awesome-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": 190, "likes": "", "detail": "base: curated list; arch: with source notes and licenses; result: both now in the table; notes: day 3", "arch": "'Jev demos, projects, SDKs and skills, with source links and a curated X gallery': 45 reviewed picks plus a separate larger discovery catalog; sections: 20-second quick start, reviewed projects, demos from X, computer-use & drivers, OpenRouter community winners; 'check permissions and data handling before running a project — a listing here is not a security audit'", "approach": "", "training": "", "data": "", "result": "Second major 'awesome' list after valentynkit/awesome-jev-typesafe", "notes": "Created 2026-09-18.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-jev-projects", "name": "Awesome Jev, System-1 Agent Architecture Radar", "author": "logicrw", "date": "2026-09-18", "category": "interpretation", "url": "https://github.com/logicrw/awesome-jev-projects", "base_model": "662+ Jev projects", "params": "", "metric": "", "license": "", "paper": "", "stars": 531, "likes": "", "detail": "arch: 'System-1 Agent Architecture Radar' · every project anchored to verifiable, commit-pinned public open source · submissions via GitHub Issues only, no pull requests; data: 662+ projects across 17 real-world domains · positions Jev at 50–100 ms against System-2 reasoning LLMs at 1.5–5 s", "arch": "A curated list of System One agent-architecture projects, over 690 of them, organized as a live radar with categories, badges, a hosted search-and-filter site, English, Chinese, Japanese and Korean READMEs, an installable agent skill and issue-based submissions", "approach": "", "training": "", "data": "", "result": "452 stars", "notes": "'System-1 Agent Architecture Radar': every project anchored to verifiable, commit-pinned open source; 452 stars", "domain": "general", "score": "", "latency": "50–100 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "devto-nandakishor-year-ago", "name": "I built non-autoregressive decision models a year ago", "author": "Nandakishor M", "date": "2026-09-18", "category": "interpretation", "url": "https://dev.to/nandakishor_m_6cc0adfde9f/i-built-non-autoregressive-decision-models-a-year-ago-then-a-frontier-lab-called-it-a-18me", "base_model": "SalesRLAgent, PPO", "params": "", "metric": "", "license": "", "paper": "arXiv:2503.23303, arXiv:2510.01237", "stars": "", "likes": "", "detail": "Nandakishor's dev.to account: in March 2025 he spent months building a non-autoregressive decision model, published arXiv 2503.23303, released the sales-conversion-model-reinf-learning weights on Hugging Face, published the saas-sales-conversations dataset, built a PyPI package, and posted the approach on r/LocalLLaMA; in September 2025 a second paper, arXiv 2510.01237, laid out the framework for schema-based decisions guided by reinforcement learning. Then in September 2026 TypeSafe AI, founded by Diogo Almeida, a co-inventor of ChatGPT at OpenAI, launched Jev and proposed the exact same non-autoregressive decision concept as if it were a brand-new scientific breakthrough, launching without technical papers, without open weights, and with zero open training datasets. His earlier model used PPO over sequence representations to output turn-by-turn conversion trajectories, probabilities from 0.0–1.0, in vertical sales conversations; Jev generalized parallel sampling via RLCD to output confidence distributions and schema choices horizontally, charging 0.042 USD per million input tokens with typical response times around 150 ms. Instead of staying bitter, he built Laya: a completely open, horizontal System 1 decision model on a bidirectional encoder, running in 33–38 ms on a GPU, roughly 4× faster than Jev's published 150 ms. The post walks through the architecture and the math of RLCD with strictly proper scoring rules.", "arch": "First-person prior-art account: in March 2025 Nandakishor M published arXiv:2503.23303, released the sales-conversion-model-reinf-learning weights on Hugging Face, published the saas-sales-conversations dataset, shipped a PyPI package and posted the approach on r/LocalLLaMA; in 2025-09 the second paper arXiv:2510.01237 laid out the framework for schema-based decisions guided by reinforcement learning. The earlier model used PPO over sequence representations to output turn-by-turn conversion trajectories in vertical sales conversations; Jev generalized the same non-autoregressive decision concept to parallel schema choices with RLCD, charging 0.042 dollars per million input tokens at around 150 ms. Argues the concept was overlooked because it was built for a vertical use case before a funded lab packaged it as a breakthrough", "approach": "", "training": "", "data": "saas-sales-conversations dataset", "result": "", "notes": "Companion to the HN story and the r/LocalLLaMA post making the same claim The companion HN thread (2026-09-19, news.ycombinator.com/item?id=49765348): commenters tie Jev to the architecture Laya's author proposed in March 2025; the prior-art claims cannot be independently verified, TypeSafe has not published Jev's architecture.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "reddit-jev-architecture-year-ago", "name": "I literally built the Jev architecture one year ago and open-sourced it", "author": "r/LocalLLaMA OP, the Laya author", "date": "2026-09-18", "category": "interpretation", "url": "https://www.reddit.com/r/LocalLLaMA/comments/1wijo3e/i_literally_built_the_jev_architecture_one_year/", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "r/LocalLLaMA post, 2026-09-18, by the author of Laya: I literally built the Jev architecture one year ago and open-sourced it, with model, dataset, and paper, cross-linked from Hacker News. The thread's reading of Jev: parallel sampling plus RLCD training. The claim is part of the launch-week prior-art debate that also includes the dev.to account and the arXiv papers; OP's identity and repo were not yet resolved at sweep time, and Reddit's bot wall blocked the thread body.", "arch": "Reddit post by the author of Laya claiming the Jev architecture was built and open-sourced a year earlier, cross-linked from Hacker News; OP claims to have 'completely open-sourced it with model, dataset and paper'; the thread's reading of Jev: parallel sampling plus RLCD training; complements the dev.to account and the prior-nandakishor papers row", "approach": "", "training": "", "data": "", "result": "HN 28 points, 1 comment; the thread went viral as 'Reddit's biggest Jev thread is about who built it first', quoted in X, LinkedIn and press roundups, including Marcos Besteiro's 'WAIT A MOMENT'", "notes": "09-18 post, prior-art claim during launch coverage; OP identity and repo not yet resolved", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "classmethod-openjev-analysis", "name": "I read OpenJev and thought of an alternative solution using non-generative AI", "author": "Classmethod dev blog", "date": "2026-09-18", "category": "interpretation", "url": "https://dev.classmethod.jp/en/articles/openjev-non-generative-ai-alternatives", "base_model": "OpenJev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Morishige from Classmethod's Manufacturing Business Technology Department: within about 3 days of Jev's announcement, several implementations calling themselves OpenJev appeared on GitHub and Hugging Face, and even though they share the same name, reading through them reveals completely different contents, some simply change how an existing model is used without adding anything, while others retrained a model specifically for judgment. He first compares two kinds of LLM-router judges, a judge that generates decisions as text and NeMo Switchyard's prefill router, which determines the routing destination from the internal state at the moment the request is read, noting he had already been observing the idea of stopping the writing in the router world. The article then covers what OpenJev reproduces from Jev and what it does not, based on the published code, numbers, and light measurements on a DGX Spark. On Jev itself: the three question types are choice, probability per candidate plus the selected candidate and a confidence; score, scoring along verbally defined ordered levels; and noul, the probability of yes from 0–1; the price is 0.042 USD per million input tokens with output free; the current jev-1.13.0 accepts text only, with no per-customer fine-tuning, and an official response time of 70–500 ms; and RLCD's goal, in addition to selecting the correct candidate, is to bring the returned probabilities close to actual frequencies, which he explains with a weather-forecast analogy.", "arch": "Analysis of the two projects both named OpenJev: the TheoLeeCJ version reads the score tables of existing models, the AlexWortega version retrains a model for the judgment task", "approach": "", "training": "", "data": "", "result": "\"OpenJev was not a copy of Jev. In response to Jev's question of whether generation is necessary if you only want judgments, the TheoLeeCJ version answered that you just need to read the score tables of existing models, while the AlexWortega version answered that you should retrain for the purpose of judgment. The name is the same, but the approaches differ.\"", "notes": "Published 2026-09-18.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "yurin-jev-confidence", "name": "Is Jev confident?", "author": "Stanislav Yurin", "date": "2026-09-18", "category": "interpretation", "url": "https://bernoulli.app/articles/is-jev-confident", "base_model": "TypeSafe Jev", "params": "", "metric": "Mean absolute residual of the reconstructed confidence formula on 1M plus live answers", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Reverse-engineers the confidence field of Jev's Choice and Score answers from a million-plus live responses: Choice confidence is the top probability rescaled from pure chance, p_max minus one over N divided by one minus one over N; Score confidence is a modal concentration measure based on mean absolute deviation, clipped at zero and normalized by the uniform reference, and the numerator is the 1-Wasserstein distance to the point mass at the modal level", "approach": "", "training": "", "data": "738,164 live Choice answers and 166,880 Score answers from 83,347 requests, collected 2026-09-17–2026-09-18", "result": "The rescaled Choice formula fits with a mean absolute residual of 0.005 and a largest single miss of 0.023, never exceeding 0.010 across 76,807 two-option answers; the Score formula fits at about 0.008 residual; padding an option list with zero-probability entries lifts the reported confidence from 0.20–0.58 as the option count grows from 2–20 at a fixed top probability of 0.60; a two-horse race and a broad spread get nearly identical confidence, 0.41 versus 0.42, while normalized entropy separates them at 0.57 versus 0.39; byte-identical payloads return different numbers run to run, with confidence moving 0.84–0.88 across ten calls on one ambiguous ticket and the winning label itself flipping 7–2 on a flat question", "notes": "Skanavi Labs, published on Bernoulli; argues TypeSafe's own docs are wrong to call the distribution shape a certainty signal since the shape is exactly what the Choice API omits; proposes normalized entropy and a meta-probability across repeated calls as a real confidence; cited by Kanta Hayashi's dice-roll calibration probe; closes with the observation that a single call near a threshold is a coin toss wearing a decimal", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "wavect-jev-review", "name": "Jev AI Review: Decision Models for Agent Workflows", "author": "Wavect", "date": "2026-09-18", "category": "interpretation", "url": "https://wavect.io/blog/jev-ai-decision-model-review", "base_model": "TypeSafe Jev", "params": "", "metric": "End-to-end latency, dollars per million input tokens", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Documentation and architecture review based on Jev 1.13, explicitly not an independent latency benchmark or production deployment; checks the speed and cost claims against precise attribution", "approach": "", "training": "", "data": "", "result": "TypeSafe reports 70–500 ms end-to-end latency and $0.042 per million input tokens with no metered output charge; the launch article's 40×–200× range holds for System One-shaped queries; the headline 193.6× faster and 444.6× cheaper come from TypeSafe's own four-workflow evaluation and are explicitly likely near the high end of real-world gains; the structured workflow form outperformed the same policy expressed as one prompt in the tested setup", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "70–500 ms", "benchmark": "", "cost": "", "speedup": "193.6× faster"}
{"id": "qbitai-jev-vs-decitron", "name": "QbitAI: Jev vs Decitron, which one is the future?", "author": "QbitAI", "date": "2026-09-18", "category": "interpretation", "url": "https://36kr.com/p/3990270814107013", "base_model": "TypeSafe Jev + Decitron", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "The first Chinese head-to-head of Jev and Decitron; frames both as System One plays with different foundations: Jev as typed probabilistic decisions on a closed API, Decitron as a decision-making large model with a MetaWorld world-model foundation and 1–2 s decisions in 1 GB of memory", "approach": "", "training": "", "data": "", "result": "Decitron: 99.4% equilibrium on TMGBench, 44.2% on the hardest task, 20–30% accuracy gain over general LLMs, unverified; Jev: 193.6× faster, 444.6× cheaper, output tokens free", "notes": "09-17/18; the Jev-vs-Decitron framing was republished across Chinese media, including 163, CSDN and AtomGit", "domain": "general", "score": "20–30%", "latency": "1 s–2 s", "benchmark": "", "cost": "", "speedup": "193.6× faster"}
{"id": "truefoundry-system-one-are", "name": "TypeSafe AI's Jev: What 'System One Models' Actually Are", "author": "Truefoundry", "date": "2026-09-18", "category": "interpretation", "url": "https://www.truefoundry.com/blog/typesafe-ai-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Hosted GPU inference platform's framing: the bottleneck was never raw intelligence - a model that replies in prose is an awkward component to build software on; a System One model is defined by what it gives up", "approach": "", "training": "", "data": "", "result": "Etymology: the name comes from W. S. Jevons, whose paradox holds that falling cost drives rising consumption; System One is a nod to Kahneman's fast, intuitive System 1 as distinct from slow, deliberate System 2", "notes": "Cites TypeSafe's manifesto: today's AI was trained on the assumption that a human sits on the other side; Jev is the first model built on that premise", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "primeline-jev-vs-claude", "name": "TypeSafe Jev vs Claude Code: 4 Models, 2 Real Jobs", "author": "primeline.cc", "date": "2026-09-18", "category": "interpretation", "url": "https://primeline.cc/blog/typesafe-jev-pre-registered-test", "base_model": "Jev, Claude Opus 5, Claude Haiku 4.5, GPT-5.6", "params": "", "metric": "Accuracy and selective-prediction accuracy on 800 commit messages and 450 knowledge notes, cost per call", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Pre-registered three-test comparison of Jev against Claude Opus 5, Claude Haiku 4.5 and GPT-5.6 on two real jobs: guessing a commit's type from 800 of the author's own commit messages, and guessing a saved-knowledge item's category from 450 real entries, with labels written before the test existed", "approach": "", "training": "", "data": "800 commit messages with pre-existing labels plus 450 knowledge-base notes, roughly 9,750 Jev API calls in total", "result": "About 0.38 dollars total for all Jev calls, zero failures: on the commit job Jev scores 65.8%, Opus 5 63.5, GPT-5.6 59.5, Haiku 4.5 54.6, beating Haiku by 11.12 points at p 4.4e-09; on the knowledge job the ranking inverts, Haiku leads by a wide margin and beats Opus 5 by 10.89 points at p 1.8e-11, Jev ranks third; keeping only each model's most-confident 30%, Jev reaches 89.6% against 80.8 for Opus 5 on the commit job; the only ordering that holds on both jobs is Jev ahead of Opus 5; Jev's input rate is roughly 24× cheaper than Haiku 4.5, 95× cheaper than GPT-5.6 and 119× cheaper than Opus 5; identical 13.4KB packets re-billed in full, 2,838 input tokens all three times, so there is no prefix caching", "notes": "Pre-registered design with both self-corrections left in; answers the launch skepticism that any LLM with JSON-constrained output could be Jev: you can get the same answer out of any LLM, you cannot get the same number out; Jev's confidence is not textbook-calibrated but reliably tells you which answers to trust; it cannot return the wrong shape of answer, but it can return the wrong answer with full confidence", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-typesafe-jev", "name": "Awesome Jev / TypeSafe, AbdelStark", "author": "AbdelStark", "date": "2026-09-17", "category": "interpretation", "url": "https://github.com/AbdelStark/awesome-typesafe-jev", "base_model": "Projects + benchmarks curation", "params": "", "metric": "", "license": "", "paper": "", "stars": 519, "likes": "", "detail": "arch: one documented Jev call, live projects, starters and independent tests · the site lives at abdelstark.github.io; data: opens with the documented jev-1.13.0 support-ticket example — choice technical at 0.85, score 1 on a 0–2 frustration rubric, noul 1.0 urgency; notes: 'Jev gives your software a typed judgment. Your code stays in charge.'", "arch": "Jev projects & benchmarks list", "approach": "", "training": "", "data": "", "result": "499 stars", "notes": "Community field guide to the Jev ecosystem: one documented Jev call, live projects to try, starters to copy, independent tests to inspect; 499 stars", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-jev-hellogumbo", "name": "Awesome Jev, hellogumbo", "author": "hellogumbo", "date": "2026-09-17", "category": "interpretation", "url": "https://github.com/hellogumbo/awesome-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 193, "likes": "", "detail": "The directory opens with its own framing of Jev: a state plus a set of typed questions, Choice, Score, Noul, returns typed answers with calibrated probabilities in one request, no text generation; model id jev-latest, endpoint POST api.typesafe.ai/v1/systemone, early access since 2026-09-15, docs at docs.typesafe.ai", "arch": "The main community directory of projects built on Jev, with the searchable site awesomejev.com and GitHub stars refreshed daily; not affiliated with TypeSafe AI, projects are added by pull request or issue", "approach": "", "training": "", "data": "", "result": "", "notes": "Other GitHub lists in the same wave: kraayenjon/awesome-jev, 132 stars, a curated list of use cases, projects, SDKs and resources; Promethe-us/awesome-jev, 129; ckaraca/awesome-jev, 9; daftAI2026/awesome-jev, 7; townie/awesome-system-one, 5; rupeshpoojary9/awesome-open-system-one, 6.", "domain": "misc", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-jev-yibie", "name": "Awesome Jev, yibie", "author": "yibie", "date": "2026-09-17", "category": "interpretation", "url": "https://github.com/yibie/awesome-jev", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": 1680, "likes": "", "detail": "arch: the README aggregates the current category files · each entry sits in exactly one category chosen by its direct Jev application domain · a Related Practices / Discussions category captures X threads, Reddit discussions and interviews without a standalone case page; data: 461 entries · inclusion rules only — public, citable, genuinely uses Jev for a typed decision, one-sentence summary · no review of code quality, security or maturity; notes: 'a listing is not an endorsement' · the list tracks who is actually building with Jev and which patterns transfer across industries", "arch": "Curated Jev ecosystem list", "approach": "", "training": "", "data": "", "result": "1,511 stars", "notes": "Curated awesome list of public projects and practices built with Jev; 1.5k stars Companion measurement repo: yibie/laya-jev-lab, 9 stars, independent measurements of typed-decision models, Jev API vs open Laya.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "awesome-jev-gallery", "name": "awesome-jev-gallery", "author": "OmniJev", "date": "2026-09-17", "category": "interpretation", "url": "https://github.com/OmniJev/awesome-jev-gallery", "base_model": "", "params": "", "metric": "", "license": "MIT", "paper": "", "stars": 392, "likes": "", "detail": "", "arch": "Tiers: Jev Models / Benchmarks / Jev API, SDK / Jev Projects / Jev Research. Top open reproductions listed: OpenJev, AlexWortega/openjev, jevlike, Qwen-2.5-1B-RLCD, CUA-S1-FORMS, Dohnuts, OpenThai-SystemOne, Open-Jev", "approach": "", "training": "", "data": "", "result": "259 stars", "notes": "Gallery; 545 projects counted.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hn-nandakishor-story", "name": "HN: open-sourced the Jev architecture a year ago with model, paper and dataset", "author": "nandakishor_ml", "date": "2026-09-17", "category": "interpretation", "url": "https://news.ycombinator.com/item?id=49736660", "base_model": "", "params": "", "metric": "", "license": "", "paper": "arXiv:2503.23303", "stars": "", "likes": "", "detail": "", "arch": "Story on the front page of Hacker News in which the author says everyone now talks about the architecture that is not autoregressive and does lightning-fast probability prediction with a JSON schema, and that he worked on it literally one year back in March 2025, published an arXiv paper, pushed the model to Hugging Face along with a PyPI package and training dataset, and then a year later a frontier lab came proposing the same idea like a literal breakthrough", "approach": "", "training": "", "data": "", "result": "96 points, 17 comments", "notes": "Same author as Laya and the prior-nandakishor papers row", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "netlify-ai-gateway", "name": "Jev on Netlify AI Gateway", "author": "Netlify", "date": "2026-09-17", "category": "interpretation", "url": "https://www.netlify.com/changelog/typesafe-jev-ai-gateway", "base_model": "", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: changelog; base: distribution channel; result: day-one, 'adopted faster than any other model in AI Gateway history' per Vercel", "arch": "TypeSafe's Jev model available through Netlify's AI Gateway with zero configuration; install @typesafe-ai/sdk and use it", "approach": "", "training": "", "data": "", "result": "Third major gateway distribution after Vercel AI Gateway and opencode/zen", "notes": "Changelog entry, Sep 18 2026.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "arch-unmasked", "name": "Jev's Architecture Unmasked", "author": "archerhume.com", "date": "2026-09-17", "category": "interpretation", "url": "https://archerhume.com/posts/jevs-architecture-unmasked", "base_model": "Reverse-engineering of Jev's probable architecture", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "training: analysis", "arch": "Detailed read of what Jev likely is inside: causal transformer, likely sparse MoE, repurposed for decisions; 'shared-state encoding → parallel question branches → probability readouts'. The Kev family is built directly on this article's architecture", "approach": "", "training": "", "data": "", "result": "Architectural interpretation; Kev's README links it as its basis", "notes": "Reverse-engineering piece from the first 48 hours; jaredpalmer's Kev cites it explicitly.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "langchain-harness-jev", "name": "What Is Jev? A Guide to TypeSafe AI's System One Model", "author": "LangChain", "date": "2026-09-17", "category": "interpretation", "url": "https://www.langchain.com/blog/building-a-harness-with-jev", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: blog; base: framework guide; arch: LLM decides, tool executes, model evaluates, repeat; notes: day 2 after launch", "arch": "'Building a harness with Jev': agents run in a loop — 'even with tool calling and structured outputs, the agent loop is still slow and costly: every decision requires another model call. Enter Jev.' System One models: evaluate a state, return typed answers + probabilities, trained with RLCD; code uses the results to guide the next agent step without a full chat-LLM call per decision", "approach": "", "training": "", "data": "", "result": "Cites TypeSafe: 'up to 200× faster inference and 400× lower cost than comparable LLMs on classification tasks'; full LangChain usage guide", "notes": "09-17 post", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "zhihu-jev-2400w", "name": "Zhihu: 24M views on \"a model that never speaks\"", "author": "Zhihu question thread", "date": "2026-09-17", "category": "interpretation", "url": "https://www.zhihu.com/question/2057166359420147033", "base_model": "TypeSafe Jev", "params": "", "metric": "Judgments per run, wall time and cost per task versus the long-form model", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Early Chinese explainer before the full release: unstructured state in, typed probabilistic decisions out; 20–200× speed and at most 1/400 cost; output tokens free; 70–500 ms; $0.042/MTok input; 193.6× / 444.6× workflow eval; Almeida four-year question: if LLMs are already good at chat, is chat the core ability software automation needs?", "approach": "", "training": "", "data": "", "result": "Cites Every Mike Taylor: 37 documents, 777 judgments in under 0.7 s; writing-quality task 0.35 s vs Fable 5.1 at 8.83 s, about 25× faster and about 580× cheaper; HF researcher Niels Rogge: \"A JSON classifier with 12M views? We are in a bubble\"; RLCD framed as the answer to RLHF; \"calibration is not a metric for the hallucination rate itself\"", "notes": "Question page at 24M views and ~1K answers by 09-17; carries the skeptic position that a developer could build similar structured parallel judgments on a 1B model in two hours, and the \"fly close to the ground\" critique", "domain": "general", "score": "", "latency": "700 ms", "benchmark": "", "cost": "", "speedup": "25× faster"}
{"id": "agentpedia", "name": "Agentpedia claim-vs-evidence", "author": "agentpedia", "date": "2026-09-16", "category": "interpretation", "url": "https://agentpedia.com/blog/jev-claims-vs-evidence", "base_model": "Claim audit", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "arch: speed, cost, accuracy, zero type errors; notes: agent-judge agreement", "arch": "Item-by-item check of TypeSafe's launch claims against what is verifiable", "approach": "", "training": "", "data": "", "result": "Separates verifiable claims from marketing", "notes": "Independent claim audit; also re-scores TypeSafe's evals workflows. URL unverifiable via automated fetch, host blocks crawlers; not confirmed dead.", "domain": "agent", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "ainews-launch", "name": "AINews: Jev launch", "author": "Latent", "date": "2026-09-16", "category": "interpretation", "url": "https://www.latent.space/p/ainews-typesafe-launches-jev", "base_model": "Launch analysis", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: latent.space", "arch": "First-day analysis of Jev: positioning, pricing, what 'System One' means, early takes", "approach": "", "training": "", "data": "", "result": "The reference launch-day write-up", "notes": "Latent's AINews launch coverage.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "flowtivity-jev-200x", "name": "Jev by TypeSafe AI: Is the 200× Faster Decision Model Too Good to Be True?", "author": "Flowtivity", "date": "2026-09-16", "category": "interpretation", "url": "https://flowtivity.ai/blog/jev-typesafe-ai-decision-model", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Skeptical early review: launched 2026-09-15 after two years in stealth; the aggressive pitch is 20–200× faster and 40–400× cheaper than frontier LLMs on decision-shaped work, $0.042 per million input tokens, free output tokens, 70–500 ms end-to-end latency versus 3–329 seconds for frontier models", "approach": "", "training": "", "data": "", "result": "Founder is Diogo Almeida, the ex-OpenAI researcher whose team page credits him with co-inventing RLHF and InstructGPT, the methods behind ChatGPT", "notes": "Last updated 2026-09-16.", "domain": "general", "score": "", "latency": "70–500 ms", "benchmark": "", "cost": "", "speedup": "200× faster"}
{"id": "seangoedecke-structured-output", "name": "Jev means structured output is interesting again", "author": "Sean Goedecke", "date": "2026-09-16", "category": "interpretation", "url": "https://www.seangoedecke.com/jev-means-structured-output-is-interesting-again/", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Analysis of why a model that only produces structured output picks up interesting properties: Jev is consistently fast because it is not autoregressive, with the fastest response around 70 ms and the slowest only 500 ms, since it can answer many questions in parallel in a single forward pass where an LLM must spend successive passes emitting the tokens of the wrapper. The most compelling example of its speed is that the model can play Doom from a text-based state", "approach": "", "training": "", "data": "", "result": "HN 15 points", "notes": "", "domain": "general", "score": "", "latency": "70 ms", "benchmark": "", "cost": "", "speedup": ""}
{"id": "theregister", "name": "The Register: TypeSafe AI debuts model for machines that plays Doom", "author": "The Register", "date": "2026-09-16", "category": "interpretation", "url": "https://www.theregister.com/ai-and-ml/2026-09-16/typesafe-ai-debuts-model-for-machines-that-plays-doom/5296711", "base_model": "News coverage", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "The piece walks through the interface with a concrete example: a customer-service query returns billing 0.08, technical 0.85, sales 0.07 with a confidence of 0.82, not useful to a person seeking an answer, but just the thing for a developer making a customer service function run reliably. Jev is a System One model on the RLCD architecture; Diogo Almeida, co-founder and CEO, a former OpenAI researcher and co-inventor of RLHF and ChatGPT, says TypeSafe was founded to pursue machine-native AI and that people cannot be the only consumers of intelligence. TypeSafe claims 70–500 ms response times, 40–200× faster than traditional LLMs, because the System One architecture returns all outputs at once instead of predicting the next token sequentially; its website demo shows 0.114 s for Jev versus 8.566 s for OpenAI GPT-5.6 Terra. Cost: 0.042 dollars per million input tokens with output free, against 2 dollars input and 12 dollars output for GPT-5.6 Terra. The article also covers the Doom demo, fed structured data describing the players game state, and closes on the implication for drone targeting decisions", "arch": "The Register, day-2 coverage: TypeSafe AI, a startup bestowed with 40 million dollars in funding, declared itself a maker of frontier models with Jev, a model intended for interaction with machines rather than people; Jev does not chat, it produces typed probabilistic decisions, which is how it avoids the parsing and validating that text responses from LLMs need", "approach": "", "training": "", "data": "", "result": "Press record of the launch", "notes": "Day-2 press: the $40M raise, TypeSafe's background. Follow-up: 'TypeSafe AI says its Jev model is the future'.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hn-launch", "name": "HN: Jev launch thread", "author": "Hacker News", "date": "2026-09-15", "category": "interpretation", "url": "https://news.ycombinator.com/item?id=49717558", "base_model": "Community discussion", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: 49717558; arch: confirmed zero-shot-classifier + encoder-with-heads reading", "arch": "1,850 points / 485 comments; CEO engaged; top skepticism: 'it's an encoder with extra steps', pricing math, calibration questions; 'Astra' ~3B-param estimate from pricing", "approach": "", "training": "", "data": "", "result": "Skeptical thread on the launch; the reproduction wave followed", "notes": "Top comment: 'This is a zero-shot classifier with a very good PR team'", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "kingy-jev-review", "name": "TypeSafe Jev Review: The AI Model That Doesn't Generate Text", "author": "kingy.ai", "date": "2026-09-15", "category": "interpretation", "url": "https://kingy.ai/blog/typesafe-jev-review-the-ai-model-that-doesnt-generate-text", "base_model": "TypeSafe Jev", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Review framing: Jev is not a smaller chatbot but closer to a general-purpose semantic classifier, reranker, and risk gate packaged as a programmable API; 'System One' is called out as a company term, not a standardized model category", "approach": "", "training": "", "data": "", "result": "The design choice is a constrained decision rather than an explanatory paragraph: a yes/no probability, a choice among fixed options, or a score over ordered levels; the application decides what to do with the output", "notes": "Observed 2026-09-24.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "novcog-jev-file", "name": "The Jev File", "author": "Novel Cognition", "date": "2026-09", "category": "interpretation", "url": "https://jev.novcog.us.com/", "base_model": "TypeSafe Jev", "params": "", "metric": "claim audit; independent tests ~5× faster, 8.6× cheaper", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Independent claim-checking site: each launch claim scored against what holds up; sections are What Shipped, The Trade, The Evals, The Guarantee, Predecessor work, Reactions, Sources and Method; every performance and price figure is TypeSafe's own, linked to where it was published", "approach": "", "training": "", "data": "", "result": "The 193.6×/444.6× multiples hold only against frontier reasoning models on TypeSafe's own workflows; independent tests measured ~5× faster and 8.6× cheaper than Mistral Small 4, and 1.6× cheaper than DeepSeek V4.1 Flash. On a real event-moderation task Jev accuracy 96% vs 84% and 86% for the two cheap LLM alternatives, 5× faster and 8× cheaper. On TypeSafe's own eval Jev ties Sonnet 5 at 67.8% and trails Sol 74.1% and Opus 5 73.1%, at $0.0004 per case vs $0.08-$0.18. Zero-hallucination is true of shape, not of truth, and TypeSafe's own footnote says the 0% figure is not empirical; RLCD calibration is unverified: no paper, no calibration curve, no ablation", "notes": "Independent, not affiliated with TypeSafe, no early access; on the zero-shot-classifier description the CEO replied exactly right", "domain": "general", "score": "accuracy 96%", "latency": "", "benchmark": "", "cost": "", "speedup": "5× faster"}
{"id": "thursdai", "name": "ThursdAI take", "author": "ThursdAI", "date": "2026-09", "category": "interpretation", "url": "https://thursd.ai/jev", "base_model": "Expert commentary", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "author: ex-OpenAI RLHF lead commentary", "arch": "RLHF-insider reading of Jev: what RLCD-style training can and cannot deliver", "approach": "", "training": "", "data": "", "result": "Expert take", "notes": "Insider commentary on the training story.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "warmersun", "name": "warmersun.com Jev deep-dive", "author": "warmersun.com", "date": "2026-09", "category": "interpretation", "url": "https://warmersun.com/blog/jev", "base_model": "Deep-dive + re-scoring of TypeSafe evals", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "", "arch": "Re-scored the four TypeSafe eval workflows: ~67.8% mean agent-judge agreement for Jev vs ~73–74% for the best frontier LLM — nuance to the marketing numbers", "approach": "", "training": "", "data": "TypeSafe eval examples", "result": "Independent re-scoring of the official evals", "notes": "Important counterweight to TypeSafe's published accuracy.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}
{"id": "hermes-ai-jev", "name": "What Are People Building with Jev", "author": "hermes-ai.net", "date": "2026-09", "category": "interpretation", "url": "https://hermes-ai.net/jev", "base_model": "Use-case catalog", "params": "", "metric": "", "license": "", "paper": "", "stars": "", "likes": "", "detail": "Data snapshot 2026-09-20 from 507 X posts via the QMuse archive and Jevable; methodology preserves public posts with attribution and original links, categories are editorial labels, independent and unofficial. Sample cases: 724 live ads from 37 brands broken down in 40 seconds, every hook, format, offer and CTA; a linter whose rules are plain-English sentences, scoring every function of zustand against 14 rules in 2.6 seconds at a fifth of a cent over 464 judgments; a judgement cache compared against a semantic cache with a 0.90 cosine threshold; a town called Jevton where Jev makes every decision for 120 people, 7 roads, 20 businesses, a council and a newspaper every hour; and a game of codenames", "arch": "What Are People Building with Jev, a case library of 507 real use cases in 7 groups: Coding 109, Automation 101, Other 177, Research 43, Creative and media 36, Commerce 31, Data and evaluation 10", "approach": "", "training": "", "data": "", "result": "507 use cases", "notes": "Use-case catalog.", "domain": "general", "score": "", "latency": "", "benchmark": "", "cost": "", "speedup": ""}