A Stop hook that keeps coding agents from quitting early: rules written in plain language are scored by Jev in about 0.7 seconds, and a violated rule sends the agent back to work instead of letting it stop.
Each stop sends the last three messages, this turn's tool calls, and the final message, and Jev answers one yes/no question per rule in parallel.
A violated rule exits 2 with a one-line reason; the second stop of the same chain is always allowed through, so the agent is pushed back at most once.
One Python file using only the standard library; a stop costs about 1,000 to 2,000 input tokens, roughly a hundredth of a cent.
Installs as a Claude Code or Codex plugin, or as a Stop hook for any agent with Claude-style hooks, with the key stored in the OS keychain.
suggest and calibrate read local transcripts to draft rules and report AUROC; failures exit 0 silently so a hook never blocks work.
Quickstart
bash
claude plugin marketplace add noplan-inc/limpet
claude plugin install limpet@limpet
Watch out
MIT-licensed. Needs Python 3.9+ on PATH and a TypeSafe or Vercel AI Gateway key; tested on macOS and Linux, and suggest and calibrate read transcripts of every Claude Code project and Codex session on the machine.
A collection of 26 production-ready agent skills for Claude Code, Cursor, Kiro, Windsurf, and OpenCode; four skills call TypeSafe Jev for calibrated Score and Noul judgments and fall back to heuristics when it is unavailable.
A security hook for coding agents that asks Jev three typed questions before every tool call and scans tool results for prompt injection, with adapters for Claude Code, Codex, Copilot CLI, Gemini CLI, Cursor, pi, OpenCode and ACP.
An Agent Skill that sends closed coding-agent judgments to TypeSafe Jev and acts on the probabilities, asking for yes/no, choice, and score answers in about 250 ms while the main coding model keeps writing code.
This made me rethink where AI actually fits into security engineering.
For purely engineering work, forget about ChatGPT or Claude.
TypeSafe AI just released Jev, and I think it’s going to change how we build AI into security workflows.
Instead of asking an LLM to “investigateShow more
TypeSafe AI
@typesafeai
we are officially out of stealth! join the frontier and get access to Jev on our website (link on profile)
Jev 发布没几天,开源社区已经开始疯狂复刻了🔥
最值得推荐的五个模型:
1、Laya 421M:原生决策模型,支持 Mac
2、Decider-2B:最像 Jev,基于 Qwen3.5
3、NanoJev 0.6B:专门的 Decision Head
4、Reflex:Qwen3.5 + Direct Logits
5、System-One 4B:专门做概率校准Show more
小墨同学
@xiaomovps
Jev 刚发布没几天,开源社区就出现了同款🔥
Decider-2B模型,是基于 Qwen3.5-2B 做了特殊调整
它和 Jev 模型是一样的 只做选择 评分和判断 不是文本类的 LLM 模型
但两者还是有几个明显区别:
1、模型
Jev:闭源 System One Model
Decider:Qwen3.5-2B,约 1.9B 参数,Apache 2.0 开源
2、价格
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
• 40-400xShow more
I built a trading bot with Jev!
Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades.
It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block.
Demo link → jev-trader.vercel.app