typesafe's jev is fun! live demo you can play with: typesafe-demo.val.run
jevmlx
An Apple Silicon decision layer that scores every allowed option of a schema (booleans, enums, multi-selects) from MLX model logits in one prefill, assembles schema-valid JSON itself, and exposes a System One-compatible endpoint.
- Category
- Repos & SDKs
- Published by
- Community
- Author
- bnsd55
- Added
- 2026-09-22
Tagscommunitypythonopen-modelsmlxapple-silicon
Highlights
- One prefill reads a logits vector per field with a restricted softmax over allowed options; no text is generated and the JSON is valid by construction.
- Model aliases resolve to MLX 4-bit Qwen2.5 builds: quality (7B, default), fast (3B), and test (1.5B); first use downloads roughly 2-4.5 GB.
- Ships a CLI, an HTTP server with a /v1/systemone route, a zero-dependency TypeScript client, and golden-prompt plus benchmark tooling.
- Ordered enums add ordinal telemetry (expected_index, expected_score_normalized) with no extra model call; allow_none_of_above adds an explicit escape option.
- FieldResult includes probability, alternatives, probability_margin, and legal_mass as a leakage signal.
Quickstart
bash
git clone https://github.com/bnsd55/jevmlx && cd jevmlx && ./setup.sh
jevmlx decide --preset support_triage --json
jevmlx serve --model fast --port 8000Watch out
MIT-licensed and not affiliated with TypeSafe AI. Requires an Apple Silicon Mac (M1+) and Python 3.12+, downloads weights on first use, and its probabilities are over the supplied options, so thresholds need fitting on labeled data.
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From the community
Posts from builders shipping with Jev right now.
A playable 16-judgment demo
AI multiple choice, not essay writing
WTF is Jev by @typesafeai? Here’s the tl;dr ELI5: Think AI multiple choice, not AI essay writing. It doesn’t chat. It makes decisions your software can act on: “Spam or not?” “Which tool should this agent use?” “Does this need a human?” The exciting part: roughly 200x faster Show more
Screening agent actions with Jev
Tested TypeSafe’s Jev (no-text, probability-only model) as an AI agent safety monitor. Checking each action first worked well caught most attacks with almost no false blocks, and much faster than Gemini.
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
Cua's small System One models
1/ Introducing CUA-S1: a family of System One Models, small, specialized, and built for computer use. Today we're open-sourcing CUA-S1-FORMS, the first in the family: github.com/trycua/cua
A 706K-parameter form filler
cua open sourced a 706k param model that fills a whole form in one 50ms pass the llm agent doing the same form took 23 turns and 39.6 seconds the specialists are going to eat the generalists from the bottom
1/ Introducing CUA-S1: a family of System One Models, small, specialized, and built for computer use. Today we're open-sourcing CUA-S1-FORMS, the first in the family: github.com/trycua/cua
Navigating Neo4j with Jev
Jev 这个 waitlist 还是很给力的，昨天申请，今天就能用上。 给已经拿到 API、但还不知道怎么玩的人整理了一份 Awesome Jev，目前我能确认到的 Jev 项目基本都在这里： 1. jev-ultrafast Browser Use 做的高速浏览器 Agent。Jev Show more
前 OpenAI 研究员 Diogo Almeida 创办的 TypeSafe AI 推出新模型 Jev。它有点像一个能读懂自然语言的超级分类器，不生成文本，只返回选项、分数和概率，专门给软件做判断。 普通大模型需要一个 token 一个 token 往外生成，Jev 则可以并行给出多个结果。TypeSafe 还用新的 RLCD