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Here is a useful tip for anyone using JEV. Once you extract probability estimates using JEV, you need to perform calibration. But what should be the criteria for this calibration, and how accurate are JEV's predicted probabilities in reality? More importantly, how should we adjust our business logic based on the values generated by JEV? JEval is an open-source tool that helps you discover metrics, recommends them, and displays them across various viewers. It also provides a metric-extraction library and a CLI interface. I've even converted it into a skill so it can be fed into AI agents. JE

grep by meaning, across languages. TypeSafe Jev scores every line against a meaning; combine meanings with AND/OR/NOT. 意味で探す grep。日本語で英語を、英語で日本語を検索できる

Agent plugin that uses Jev to judge when a coding session should compact its context.

Semantic assertions for pytest: test what your LLM app's output means, judged by TypeSafe's Jev.

React components that resolve which component to render, how to order a list, and whether to show an affordance — from calibrated judgments returned by TypeSafe's Jev. No repository-level license file was found during review.

High-throughput, robust native DuckDB extension for batched and streaming TypeSafe/Jev classification, scoring, and semantic predicates from SQL.

Natural-language row filtering for MySQL, powered by TypeSafe Jev.

A lightweight, native Swift 6 bridge integrating TypeSafe AI's Jev System One decision model into Apple's Foundation Models framework.

Semantic decisions as ordinary Ruby — feels?, decide, score, Rails validations and pattern matching powered by Jev

DuckDB extension: typed Jev answers as real SQL types

SillyTavern extension where Jev scores replies against configured narration rules before adding guidance.

Conversational-avatar framework with a Jev gate that judges whether a speaker has finished a turn.

Generative UI framework with an experimental evaluator that lets Jev choose among application-supplied composition criteria.

Local Jev-like typed-decision runtime using ONNX and quantized models for offline CPU inference.

Local Jev-compatible evaluation server: POST /v1/systemone with typed noul/choice/score, open weights, no waitlist No repository-level license file was found during review.

Local Jev-like System One decision layer that returns typed choices and calibrated confidence without a hosted API.

DiffusionGemma NVFP4 structured decisions on a DGX Spark: container recipe No repository-level license file was found during review.

Type-safe Java 21 client for the TypeSafe AI Jev (System One) decision API

The bookkeeping services industry is dead. This weekend, I built a better solution using Jev from @typesafeai. I fed it 34 months of work a firm charged $20,000+ for. Jev did a better job in 20 seconds, for just $0.32 🤯 https://t.co/8lA9h32eOo

Soon as I made a project using Jev, it got outdated. The tool launched barely a week ago!!

Introducing Jevussy 💦 (Jev + Debussy) A real-time piano player using Jev. This is INSANE it actually sounds like Claire de Lune 😂 Check it out here: https://t.co/cvU0t6dCCy https://t.co/7A4USP0cay

Karpo Discover just got an upgrade ✨ We’re using Jev from @typesafeai to rank places and plans by both what you’re browsing and your taste. We also simplified the pipeline: average homepage response time is down 30%, and category pages are down 55%. Less waiting. More “oh, that’s my kind of thing.” Fun to put Jev to work here. Thanks @CompleteSkeptic and @hackgoofer!

Man this project has been addictive. New addition - fast semantic search of k8s logs using Jev. ✨ Looking for something specific? Search for things like "Database issues" or "Requests from ip https://t.co/pWCVdIuXYd" Supported in the terminal and local dashboard. #jevops https://t.co/sSRT5m4v6n

I am getting better and better results in Querypanel, in my AI Analytics tool with @typesafeai Jev. I have a test script that i use for measuring whether the changes i introduced are improved my system indeed, and with my new planning system using Jev, my app reached 20% better results quicker and cheaper.

had a lot of fun making Aesthetic Spiral using Jev by @typesafeai, try it: https://t.co/BILWgJc6iA describe the aesthetic you're looking for, Jev finds the best matches from CARI/Aesthetics wiki and shows them on the canvas. optional https://t.co/bNSvjeHygL search button https://t.co/PtKJVHVATk

I built an AI editor using Jev. And you can download it for free. Read through to learn why Jev is better at this than LLMs and how this pattern applies to other use cases. https://t.co/SjvHlwrqkK

Self-hosted Jev-compatible System One server backed by GLiNER-family models, with batching and typed question support.

3 days ago I launched made with jev. 205 people are on it right now. I still cannot believe the visitors and the LLM mentions. Live analytics and the site, below 🤯 https://t.co/XyUS3NcjvO

Claude Code plugin where Jev decides which project rules apply to each prompt and edited file.

Semantic grep CLI where Jev judges which files and exact line ranges match a natural-language code query.

Adapter that turns local language models into Jev-style Choice, Score, and Noul decision engines with typed probabilities.

Open-model Jev-style server that reads next-token logits to return typed choices, rubric scores, and truth judgments.

Pi extension with a batched Jev evaluation tool, terminal playground, and typed API for other extensions.

Text linter that uses Jev Score judgments to evaluate strings embedded in source code against configurable writing rules.

Claude Code and Codex hooks that ask Jev which parts of long shell output remain relevant before sending them into model context.

Jev-compatible API server built on open models and SGLang, with prefill-only inference and typed decision endpoints.

Trainable family of small Qwen-based Jev-like decision models with typed primitives, datasets, evaluation tools, and local inference.

Fast natural-language search for Azerbaijani names, built with Jev. 💬 "a Turkish girl's name, flower-related, not too popular" → ranked results in half a second 11,000 names, 93 semantic traits. Unlike an LLM, Jev doesn't write answers - it makes decisions. ~0.5s. $0.00025 a query. #jev #typesafe https://t.co/DaHVDrdeQ1

I built a thing using Jev. Have you ever been annoyed by not finding an old Claude Code or Codex chat? => Now you can just describe what it was about and Jev finds it in milliseconds. Open Source: https://t.co/RbAgyiZWeO https://t.co/ltuW7VNKEk

Been experimenting with using jev as an attention layer over code retrieval. On 356 held-out SWE-Explore tasks, adding Jev reranking to the same 100 zvec candidates improved core recall@1k lines from 11.96% → 22.95%. More here: https://t.co/ZNp3AnD8Bn #jev #TypeScript