Converts Command Code API to OpenAI chat completions
Jev ecosystem projects — page 26
5191 repositories with documented relationships and source evidence.
Pattern MCP helps coding agents make better UI decisions before they start building. It checks component libraries against product requirements, helping agents avoid missing important needs or adding unnecessary complexity. If nothing fits, Pattern finds real references from Mobbin and Figma, so agents can build from something concrete, not guess.
No project description available.
Per-turn model routing for the Pi coding agent: a small judge picks the cheap or the strong tier for each message, with multi-model failover, task-level orchestration, and an optional decision-model judge (Jev) that answers with a calibrated probability instead of prose.
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Local Context Compiler (lcc): clean, dedupe and compact prompt context before it reaches the model, then report every block dropped, the cache tokens a pass invalidates and when pruning pays off. Runs offline with a local 1K decision model. MIT, no API key, zero telemetry.
Search engine experimentation on the TREC collections. Currently focused on zero-shot reranking implementations with typesafe.ai's JEV model
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顶想云启智AI SDK
Watch X for the news that matters to you, judged by Jev, and get alerted in Slack. Set up by an AI agent.
Small, calibrated decision models on your own machine: systemone-compatible local server, 6 agent skills, Claude Code guard, MCP tools. Weights on Hugging Face.
Fast, cheap browser sub-tasks for Claude and other agents: TypeSafe Jev decisions on top of browser-harness
Windows 微信回复助手（非官方修改版）：本地离线 OCR 读屏 + 模型起草候选回复 + 一键填入微信输入框，可选自动发送。不 hook、不注入、不读微信数据库。
SAP Commerce extension using TypeSafe's Jev to moderate product reviews and suggest product categories and classification attribute values: dry runs on your own data first, an audit record per decision. Plus a Claude Code skill.
DeepSeek Harness plugin for TypeSafe Jev: typed decisions, configurable guardrails, and Web UI result cards.
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PowerShell decisions with TypeSafe AI's Jev model: https://typesafe.ai/blog/introducing-system-one-models-and-jev
DeepSeek + TypeSafe JEV 微信回复助手：Windows 正式版、Apple Silicon macOS 预览版；本地 OCR 与人工可控回复
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Benchmark TypeSafe JEV against LLMs, fine-tuned BERT, Laya and zero-shot NLI on text classification: accuracy, calibration, latency, throughput, cost
Awesome projects built with Jev from Typesafe AI
Trying to combine Jev with ontology
把对双休的支持，带进每一次购物。逛淘宝、京东时，JEV 实时猜测商品背后的工作制，疑似非双休直接盖上 PASS。原页生效，边逛边选。
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Unofficial Go client for the TypeSafe AI System One API (Jev) — typed questions in, calibrated answers out.
Use local and hosted classifiers aka decision models aka Jev-like models, all through a single TypeSafe API
Confidence-gated incident routing with TypeSafe Jev
Curated list of the open System One ecosystem: open models, independent benchmarks, calibration and constrained-decoding tooling.
Find, design, and evaluate TypeSafe Jev decision loops.