jev-model

Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)

A curated list of tools built for Jev — TypeSafe AI's System One model for typed decisions.

Awesome Jev: source-backed open-source ecosystem radar, plain-language project discovery, and automatic GitHub sync

Self-hosted Enterprise and Personal AI + agent runtime in .NET (NativeAOT-friendly)

Awesome list of TypeSafe AI Jev use cases: 74 demos ranked by likes, 150+ GitHub repos, limits, cost and API examples. CC0

AgentJev-0.6B - a fast 'System One' decision model for AI Agents: feed it any unstructured state (diffs, traces, logs) and structured questions, get calibrated probability distributions back in one ~50ms forward pass. Zero output-token decoding.

Deep research tool for local knowledge base.

Open infrastructure for training, evaluating, and deploying System 1 decision models across language and multimodal backbones.

GoEventBus — high-performance event bus with a decision layer.

Open-source platform for creating, distributing and running sovereign EU-compliant LLMs. Verticalize any model for your domain, language and brand. AI Act ready.

A Bangla-focused adaptation of Laya for typed decisions, classification, and routing.

Filter your X.com feed with Jev or any LLM

A tiny jev-like model that answers Choice, Score and Noul questions in one forward pass and returns calibrated probabilities. MLX or PyTorch, fully offline, System One compatible.

A measured tool-call firewall for AI agents: allow, ask or deny before the agent acts. Claude Code mod and hook, MCP proxy. Built on TypeSafe Jev, measured on real sessions with a blind second labeler.

这是 Jev 海龟汤 — 一款基于 Jev 模型打造的开源海龟汤情境猜谜游戏。每一道「是 / 否 / 无关」的判断，都由 Jev 现场推理；每一次「还原真相」的内容，都由 Jev 给出判断。没有预设脚本，只有会思考的 Jev，和一款"活"过来的海龟汤。UGC 内容支持，题库因你而丰富。

Teach your agent to work with evals: WHEN you actually need an eval or benchmark, HOW to build one that holds up, and how to read what it tells you. Deterministic-first, tool-agnostic.

When Jev meets LLM -- Pair a small local LLM (System 2) with Jev model (System 1) to make small model more faster and accurate — OpenAI-compatible, private, and almost free to run in Kilo code and Cline.

Run Laya locally with Bun: native ONNX inference, a TypeSafe-compatible API, and a bilingual decision playground.

Cut LLM context and Claude Code sessions 40–80% without rewriting a byte. Offline, verbatim, with an audit receipt for every block dropped. CLI, MCP server and Claude Code/Open AI Plugin. MIT.

Claude Code/Codex skills and learning resources that make it easy to build with Jev, TypeSafe's System One model. All materials pull directly from their canonical cookbooks and documentation.