2026年9月21日 發布的專案

Android 助手透過無障礙服務及本機 OCR 讀取可見對話，在浮動視窗顯示分析與回覆建議。 Jev 判斷意圖及回覆選項，生成模型草擬三個候選，再交由 Jev 排序。

AnyJev 從開放模型讀取機率，回答選擇、是非與評分問題，無需產生文章。 這是獨立的 Jev 風格方案；L2 預測頭不會修改基礎模型權重。 可用不需標籤的 L0 讀出，或透過 transformers、vLLM 為特定問題擬合 L2 預測頭。

Rizzo Flow 在本地執行開放決策模型，透過相容 Jev 的 HTTP 介面回傳選擇、機率、量表評分與數值。 它重現 Jev 的介面模式，並未重現專有架構或訓練方法，屬於獨立專案。 使用 llama.cpp 部署所選模型，讓相容 TypeSafe 的應用程式連接本地端點。

JevChat-Windows 透過本地 OCR 讀取微信與 KakaoTalk 對話，在聊天視窗旁提供候選回覆。 Jev 判斷意圖、緊張程度並為三則候選排序；獨立語言模型負責起草。 在 Windows 10 1903+ 或 Windows 11 上，分別設定判斷服務與起草服務的金鑰。

浮動聊天面板顯示意圖、風險判斷及候選回覆，使用者可複製或填入聊天輸入框。 使用 macOS 13 或更新系統的 Apple Silicon Mac。螢幕截圖讀取需要螢幕錄製權限，文字介面讀取或填入需要輔助使用權限；在設定中選擇判斷與生成服務。

JevHarness 讓語言模型編寫任務專用特徵、問題與控制邏輯，可用獎勵及完整軌跡改進。 可用網站腳本在本機瀏覽封存的寶可夢執行，或安裝技能並描述任務來建立框架。

候選評分頭直接為閘控、路由及評分回傳分布，不需解碼文字或 JSON 答案。 README 比較以基準資料訓練的 AgentJev 專用模型與零樣本評估的 Jev 1.13.0，兩者訓練條件不同。

Left-pad strings with TypeSafe AI's Jev. For reasons.

An open-source JEV-powered workbench for streaming data selection, quality evaluation, automatic LoRA training and held-out model evaluation.

An unofficial local visualizer for Jev (TypeSafe): a live view of every call your code makes. Not affiliated with TypeSafe AI.

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

an open-source, from-first-principles reconstruction of the ideas behind TypeSafe AI's Jev, written in pytorch

Typed-judgment admission control at the traffic edge: three-layer prompt-injection and abuse filter for nginx/OpenResty, powered by TypeSafe Jev. Fail-open, cached, hot-reloadable.

5m BTC Up/Down Polymarket trading agent using Typesafe Jev as the decision layer & TUI

A Clash Royale bot with no trained policy: Jev (TypeSafe System One) makes every decision from the live game state

亚微秒级 System-1 模型，准确率服从高斯分布

Unofficial PoC: a BERT-style encoder decision engine behind a typed-decision (noul / choice / score) HTTP API. Not affiliated with TypeSafe.

Calibrate Jev questions against your own labels: tune criteria on labelled examples, confirm on a held-out set, get a verdict per question. Unofficial.

Laya (open source, local) vs TypeSafe Jev (API): two AI models race in Snake and fight in a Mortal-Kombat-style arena. Every move is a real model decision.

Jev shadcn-block generator

Score every test against a git diff with Jev, and emit the filter arguments vitest, node:test, Playwright, cargo test and go test already understand

微信里的话不知道怎么接？macOS 悬浮聊天助手：识别消息意图与沟通风险，GPT 生成多种话术，Jev 评估候选，一键填入微信。话我帮你想，发送你来定。

Run Jev-style typed decisions locally on your Mac with low RAM usage and fast responses

Software, by reflex. Say it, and the right small program runs: chosen by a calibrated classifier, run only when it is sure enough, and it asks before anything that cannot be undone. A CLI, a package manager and a TypeScript SDK: the first implementation of the idea.

A Minesweeper benchmark for LLM agents. One identical board, up to nine models in parallel, one clock, one tool layer.

Semantic "Did you mean?" for any CLI — wraps commands and uses TypeSafe's Jev to match subcommand typos by intent, not edit distance.

Craft Type-Safe Uncertainty-aware semantic pattern matching, control flow, and smart procedures for Effect DecisionModel and Jev

System One judgments (noul/choice/score) from any LLM in one prefill — a Rust, Jev-compatible /v1/systemone engine

Local, open-source Jev alternative: browser agent decisions with Laya (System One model) on your own machine. No cloud, no API key. Playwright/CDP, MCP-friendly.

A Swift package for typed decisions from language models (probabilities, choices, and scores), with support for local MLX models and the TypeSafe Jev API.

ego lite skill — each DOM step decided in ~0.4s, no LLM turn

ProphetLab's Texas Hold'em benchmark and playground for decision models: cash and SNG leaderboards, live replays, and bring-your-own-agent tables.

Hermes Agent plans, Jev (TypeSafe) picks bounded actions, Mineflayer executes: Minecraft with no screenshots or keypresses from a model. Includes the reproduction of rmalde/minecraft-agent's Ender Dragon run.

A local, offline System One server compatible with TypeSafe's Jev API. It answers typed yes/no, category and score questions with small open models.

jev_elixir is a small, idiomatic interface for making typed decisions with Jev. It exposes application-level functions such as Jev.ask?/2 and Jev.analyze/3, while provider adapters handle the external API wire format. The first release supports TypeSafe's System One API and OpenRouter's Decisions API. Both providers support all three System One qu

Route form submissions to the right people with Jev and AI SDK.

📏 Claude Code plugin that makes Claude follow your AGENTS.md: every reply and edit checked by TypeSafe Jev ✅

Zero-dependency System One decision harness: 5 semantic gates saving frontier AI agent tokens on trivial errors & doom loops. Python + TypeScript + Rust. MCP-compatible.

Site search that understands the question. Ranked by TypeSafe's Jev model.

Community-maintained JEV runner integration for Midscene Test

使用 TypeSafe Jev 在 X/Twitter 时间线上识别低质量、垃圾和广告帖子，并在文字区域显示醒目的半透明水印。

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。

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

Claude Code plugin: asks TypeSafe Jev which installed skill fits each prompt and logs the answer (shadow-first). A working reference for the skill-suggestion cookbook on Claude Code — the README records why it is unlikely to help a strong model as a router.

Agent skill and CLI that turn natural language, an LLM prompt, or the code that runs one into a TypeSafe Jev decision: typed state, Choice/Score/Noul questions, and a runnable script

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

Give your AI JEV wings. A skill and plugin to build with TypeSafe Jev, upgrade LLM-only workflows and measure the result.