Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API.
Local Laya typed decisions on Apple Core ML and Neural Engine. Validated ports, ~5 ms short decisions on M3 Max, reproducible speed and energy benchmarks.
Awesome Jev: a source-backed field guide to TypeSafe's System One model, with SDKs, live demos, agent tools, and independent evaluations.
🔥🔥 Papers, open reproductions and independent evaluations behind System One models and Jev.
Typed decisions with TypeSafe's Jev, the first System One model
Open-source alternative to TypeSafe's Jev: a System One style model layer that gives typed, calibrated decisions from any open-weights LLM in one forward pass (HF + vLLM), with honest benchmarks
Open reproduction of TypeSafe Jev: a 150M typed decision engine (noul/choice/score in one non-autoregressive pass, calibrated confidence). 0.697 vs Jev's 0.727, 2.5x better calibrated, 4x faster, free. Trains on a Colab T4 in 30 min.
离线可用的本地类型化决策：4 核 CPU 单题 15.6ms。Local & offline Jev / System One inference on CPU — ONNX + INT8, no torch at runtime. 支持 laya / kev / PlayJev
Fast Rust CLI for TypeSafe Jev: typed decisions, offline linting before you pay
Open-source projects that provably call Jev, TypeSafe AI's System One model. Every entry links to the line of code that proves it. Refreshed daily.
Curated catalog of System One / Decision Models — contributions for modelsystem.one
Jev-style typed decisions from Qwen3.5-2B logits — one forward pass, zero decoding, zero fine-tuning.
An instrumented 2048 web lab where every move is a Jev (TypeSafe AI System One) Choice, with no heuristic fallback | 用 Jev 决策模型驱动每一步的 2048 网页实验台，概率、置信度、延迟与成本全部摊开可见，且刻意不做启发式兜底
A quiet spoiler blocker for YouTube comments. One typed Jev (TypeSafe System One) Noul decision per comment; covered while checked, still covered if the check fails.
Browses hyperlinks by what they mean, not by routes — and measures whether an application declares enough for that to be possible.
Self-improving agents that know what's noise: typed decisions instead of generated text, a policy that evolves from the agent's own mistakes, and a report of how much of the gain was selection luck.
Generation-free typed decisions with Ternary Bonsai 2 27B on a consumer 8GB GPU.
Open-source implementation of the typed-decision pattern popularised by TypeSafe's Jev: read a decision out of a small language model's logits, in the browser. Library, benchmarks and paper. Not affiliated with TypeSafe.
Local real-time Snake decisions with Laya, PyTorch and CUDA — live FastAPI/WebSocket dashboard, no training or cloud API.
Native Entscheidungsmaschine für macOS: kev-0.6b und laya als Core ML in Swift, ohne Python zur Laufzeit, ohne Cloud
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