Jev ecosystem projects — page 105

An 11-level crash course on Jev, TypeSafe AI's System One decision model — runnable examples against the real API, plus a capstone project with unit tests and evals. Works with any LLM provider via LiteLLM.

Run a declared factorial audience grid through typed Jev reactions and expose disagreement.

Ticket-Routing im Kundenservice mit dem Entscheidungsmodell Jev von typesafe.ai

Resolve ambiguous joins between French public datasets with auditable candidates.

Same maze, same rules, N decision backends. A zero-key, zero-dependency arena that measures Jev / LLM / local decision models on speed, cost, calibration error (ECE) and invalid-output rate.

让 AI Agent 直接调用 TypeSafe AI「Jev」决策模型的 Skill —— 免 API key，纯标准库 CLI。Agent Skill for TypeSafe AI Jev (System One).

A general-purpose decision tool powered by TypeSafe Jev. Users bring their own API key.

Local web app for TypeSafe Jev decision probabilities

A concise TypeScript and Bun demo of TypeSafe Jev with live OpenAI comparisons.

a demo of Typesafe's Jev a System One Model for structured decsion making

基于 TypeSafe jev 模型（System One）的桌面端评估工作台

Windows voice desktop agent powered by TypeSafe Jev and Python

Open-source developer harness and runtime safety toolkit for AI coding agents powered by TypeSafe AI / Jev

Researching shared-axis comparison of independent divination readings with Jev / TypeSafe AI.

Classify financial PDFs with TypeSafe Jev via OpenRouter, with a probability per document type.

Watch Jev play Freedoom in a local dashboard. TypeSafe direct and Vercel AI Gateway, inspectable decisions, and bounded spending.

Run visible criterion-by-criterion text duels and honestly progressive elimination brackets with Jev.

A 2D grid-island ecosystem where each animal is a Jev (TypeSafe) agent; the food chain emerges from calibrated typed decisions. Pure-stdlib Python + browser UI.

A Claude Code plugin that takes skills already in use and adds one thing to each: a typed judgment from [TypeSafe](https://typesafe.ai) Jev (System One) at the points where the skill previously relied on the agent's own impression.

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.

Experiments with TypeSafe Jev — a Gmail organiser built on typed System One decisions

Trace DeFi incident exposure through sourced portfolio dependencies with optional Jev classification.