Jev ecosystem projects — page 109

https://www.webreactiva.com/blog/empezar-jev-typesafe

A Github Action for a Code Review Classifier built with the System One model Jev

Score declared writing dimensions and cite only exact source spans for weak results.

Benchmark harness evaluating TypeSafe's Jev model on six public safety benchmarks against Shieldstral-1.0-3B and top SOTA guard models

Title and abstract screening for systematic reviews with Jev: explicit criteria, include/exclude/maybe with reasons, PRISMA counts, RIS export, recall against human screeners.

Customer service bot that never generates text: Jev (TypeSafe AI) makes typed decisions with probabilities, plain Python does the rest. Chat UI, example data, Docker.

Test whether TypeSafe Jev answers your questions correctly before you let it decide anything.

A cached Jev prior for active learning: reusable rank fusion, matched ASReview controls, and a no-key evidence replay.

Jev plays Pokemon Showdown: 40 battles, 940 decisions, reproducible results, and an annotated replay.

Agent skill for TypeSafe AI's Jev decision model: fit assessment, integration recipes, calibration, multi-Jev, benchmarks

Empirical research, agent skills, and a production-grade swarm engine coordinating local SLMs under TypeSafe AI System One (Jev) supervision.

Fork of wuyoscar/jev-skill v0.2.0 adapted to call a local keyless Laya decision API by default (no cost); OpenRouter/TypeSafe routes unchanged

Jev-powered skill router & security auditor for any AI agent (Codex, Claude Code, OpenCode, Hermes): ONE cheap decision per request tells the model WHICH skill to load; scans skill libraries for prompt injection & dangerous commands.

Jev plays a slot machine until the money runs out. The local version of jevslots.live.

Smart paste: copy a whole resume, paste into a job application, and Jev (TypeSafe) routes only the relevant pieces into the right fields.

Snake auto-played by TypeSafe's Jev System One model: one typed decision call per tick, code owns legality and safety.

用 TypeSafe Jev（System One）驱动的自动贪吃蛇：每一步都是结构化判断，由代码组合。

Minimal Python starter for TypeSafe Jev typed decisions — UIbuckets

Jev parses and tracks states of AI models so only actions available to the AI model are chosen based on the models current state.

JEV Decision is a live demo that turns market data into structured decisions. It pulls real-time prices, valuation ratios and sector context, then runs a 20-question against TypeSafe Jev model to score buy/sell conviction, financial health and risk

Use TypeSafe Jev to select subagent models and reasoning effort in Codex and Claude Code.