Awesome Jev

Agent skills for building with TypeSafe's System One API

Drop-in TypeSafeClient replacement backed by LLM APIs

The official Python library for the TypeSafe API

The official TypeScript/JavaScript library for the TypeSafe API

A custom coding harness for TypeSafe AI's Jev: an LLM proposes, Jev answers narrow questions, code decides, every step leaves a receipt.

evals.typesafe.ai workflow code published

TypeSafe Jev Playground - idiomatic .NET port of typesafe-ai/typesafe-sdk-js v0.7.0

Typesafe.ai System One Model Jev navigating a Neo4j graph by using a classifier over neighbouring relationships

grep, but the pattern is a description. Filters lines by meaning with TypeSafe's Jev decision model: ~200 ms and a thousandth of a cent per line.

Stops AI coding agents from claiming work is done without evidence. Deterministic hooks decide, TypeSafe's Jev advises. Append-only ledger, zero runtime dependencies.

Swift SDK for running chat, vision and speech models on iPhone and Mac with Apple's Core AI. Model download and caching, FoundationModels integration, and runnable examples with documented OS, SDK and model requirements.

Jev / TypeSafe System One 中文精选列表：官方资料、SDK、爆款应用、Agent 工具、开源复现与独立评测，附中文上手指南，每日自动收录 GitHub 热门项目。

Ruby client for decision models such as Typesafe Jev

A database-agnostic Agentic GraphRAG framework using swappable System One models (local Laya / cloud Jev). A plug-and-play intelligence layer featuring a complete 4-phase pipeline, continuous evaluation and custom A* traversal for any graph database.

A curated list of projects, integrations, and resources for Jev, TypeSafe AI's System One model.

A curated list of tools, integrations, and experiments built on Jev, TypeSafe AI's System One model for fast, typed decisions.

Community Java SDK for Jev, TypeSafe's System One model: typed questions in, typed answers with calibrated probabilities out. Java 17+, Spring Boot starter (unofficial)

A Pi agent harness built around TypeSafe's Jev (System One model): router, context picker, gate, verifier. Tested on Neon Postgres branches.

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

Typed decisions for AI agents as an MCP tool: yes/no probability (noul), choice, and score in one fast request. Backed by the TypeSafe System One model.

.NET 10 decision SDK: Choose, Score, and Assess with Microsoft Foundry (Microsoft-Decision-1), native local Ollama System One models, and TypeSafe AI Jev. One API, probabilities instead of paragraphs.

OpenJev: an independent Jev-inspired System One decision API based on TypeSafe.ai concepts. Choice, score and noul primitives, local mock server, Python and TypeScript SDKs. Real inference planned; not affiliated with TypeSafe AI.

Optical character recognition (OCR), storefront signage verification, bounding-box grounding, and typed computer-use decision CLI powered by TypeSafe Jev 1.14.

An expressive Unix-pipeable CLI for System One models like Jev

Open-weight System One model (Jev-compatible): calibrated noul / choice / score decisions in one forward pass, no text generation. Works with TypeSafe's typesafe-sdk unchanged.

Typed, calibrated judgments from System One models: a decision written as a .jud file or as Rust types, answered by a model, verified before it is read, testable with no key. Rust crate, jud CLI (cat event.json | jud triage.jud), Claude Code plugin. Not affiliated with TypeSafe AI.

Unofficial .NET SDK for TypeSafe AI's Jev System One model. Typed Choice/Score/Noul judgments with calibrated confidence, zero dependencies.

Delphi client for System One models (Jev, tev1 and nimble on Ollama): ask typed questions about a text (choice, score, noul) and get the answer, probabilities and confidence in milliseconds. Route, triage and classify with no text to parse. One unit, RTL only, tested on Delphi 12 and 13. Apache-2.0.

Pick the model, skills and MCP servers for each coding-agent session from a library of any size, routed by a cheap System One model (Jev).

A curated list of TypeSafe Jev resources, SDKs, agents, MCP servers, integrations, benchmarks, examples, and open System One implementations.

PHP client for the typesafe.ai System One API (Jev)

Federated knowledge retrieval engine for AI agents. Routes queries across specialized backends (FTS5, ChromaDB, graph) via a registry-first orchestrator with TypeSafe/Jev AI routing and evidence re-ranking. Read-only by default, security-gated by design.

Unofficial Java SDK for TypeSafe AI's Jev System One model. Typed Choice/Score/Noul judgments with calibrated confidence, zero dependencies (JDK 21+).

Unofficial command-line interface for TypeSafe AI System One models, on TypeSafe or OpenRouter

A Graph-Compiled Specification RAG system built with Dagster, PostgreSQL (pgvector + Recursive CTEs), Diffusion LLMs (mercury-2), TypeSafe System One (jev-latest), and OpenAI (gpt-6.1-sol) to resolve multi-hop engineering specification overrides and Tier-3 safety interlocks without leaking prohibite

Calibrated policy triage and institutional risk routing: a hybrid System-1/System-2 engine on TypeSafe's Jev 1.14 with FastAPI, Pydantic, and the Vercel AI SDK

Open-source AI Trading OS, agent trading, and vibe trading, with Jev System One integration. Research, build Python strategies, backtest, and paper/live trade across crypto, stocks, and forex. Launch your own multi-tenant trading SaaS with built-in user management, billing, payments, and settlement.

Find code by asking what it does. A CLI for coding agents that uses Jev to discover relevant files and source context.

Jev-compatible API server for open decision models. Ollama for decision models: pull and serve 29 model families (Laya, pplx-decider, Clef, Winnow, coba...) locally or self-hosted behind /v1/systemone. The TypeSafe SDK works unchanged: set its base URL and pick an open model. CLI, desktop app, Docke

Ask your Postgres tables questions in plain language. A PostgreSQL extension powered by TypeSafe's Jev.

Build calibrated AI Functions from human feedback using Jev and GEPA.

Open-source, local-first social media research agent for Instagram, TikTok, and LinkedIn. Jev routes read-only steps; socai CLI captures cited browser evidence.

The pipe layer of an AI nervous system — one interface connecting provider neurons to your application, across three inference types: generate, stream, and a calibrated decide (via TypeSafe Jev). MCP-native, voice (TTS/STT/realtime), RAG, memory, file processors. Powers Tara, Yama and Clairvoyance a

Coverage, security and code quality for coding agents

Unofficial skill that teaches coding agents to build with TypeSafe AI's Jev: typed decisions, calibrated confidence, and prior art from 150+ community projects.

Browser automation where an LLM plans and Jev (Typesafe System One) decides. Library, CLI and MCP server.

High-throughput synthetic and pretraining dataset sifter for TypeSafe Jev. Rust streaming core, Parquet and JSONL I/O, typed Choice/Score/Noul judgments, speculative fan-out, 24.0 rows/sec measured.

Open-source AI email triage for Gmail. Sorts your inbox into Needs reply, Updates, Promos, Sales and Spam with Jev, TypeSafe AI's decision model, via Vercel AI Gateway. Read-only, runs locally, 1,000 emails in about a minute for 3 cents.

Free Rust SEO and GEO toolkit powered by TypeSafe Jev: 58-rule audits, site crawls, AI citation checks, rank drift, CI gates, 15-tool MCP. Zero cost.

Ask your house a question, get a number back. Home Assistant integration for TypeSafe Jev: typed answers as sensors, four actions for automations, and a conversation agent for Assist.

Typed semantic decisions for Unix pipelines and CI, powered by TypeSafe AI Jev.

Local proxy that learns your app's typed LLM decisions and answers them with a Laya head. Jev and OpenAI compatible.

MCP server that puts TypeSafe Jev on the coding loop in Cursor, Codex, and any MCP client

One-click knowledge system for documents, internal bots, and AI agents

Command-line tool for TypeSafe AI's Jev model. Ask yes/no, multiple-choice and rubric questions about any text and get calibrated probabilities back. Answers become exit codes for shells and CI, JSON for scripts, and MCP tools for AI agents.

Systematic software development framework for AI coding agents upgraded with TypeSafe Jev System One typed decisions

TypeSafe Jev for OTP: reply to Jev from a GenServer and pattern match on its answer

System One Coding Agents: code-level answers at the same score as Claude Fable 5.1 on the same 74-point xz benchmark (69 / 74, 93.2 % each), at 1/20 of the cost and 3.5× faster. No Context Loss and Memory Limits on sessions since every answer starts from the same, updatable, SQLite index.

Self-hosted decision model (selfjev-4b) with a Jev-compatible API: Python SDK, server, fine-tuning and RLCD.

Pluggable decision layer for the ego lite browser: one System One (Jev) call per step replaces the per-step LLM turn, and the backend can be swapped for a local OpenAI-compatible model. Fail-closed execution guards. The measured one — raw bench data, 16 suites, changelog with corrections.

Read-only trading journal and review harness: Jev typed judgments, agent integration, and a reproducible finance benchmark. No orders, no advice.

248 small Jev decisions for JavaScript and TypeScript: route, rerank, gate, grade, compare, and label text for agents, RAG, support, code review, and music. Each returns a typed result with explicit uncertainty. Plug into Vercel AI SDK or LangChain agents, import one recipe, or run the CLI with JSON

BrighTO LLM Router: free open-source, ultra-fast Rust LLM gateway/router and LiteLLM/Bifrost alternative with OpenAI + Anthropic APIs, Claude CLI bridge for OpenAI-compatible models, Qwen3.8 dual protocol, model load balancing, fallback routing, team API keys, token budgets, SystemOne decisions for

Open-source System One decision engine. Typed choice / score / noul with calibrated probabilities. Self-host with Ollama or any OpenAI-compatible model. Apache-2.0.

The decision layer for your Rails app. A Rails-native wrapper around TypeSafe's Jev System One API: typed, calibrated decisions in your control flow.

A ledger-backed decision network that records AI decisions and connects them to the facts and earlier answers they use.

One grounded Jev/Playwright core: typed SDK, persistent CLI, and MCP server with native browser operations and deterministic assertions.

Community .NET SDK for the TypeSafe AI System One API — typed noul, choice, and score questions with structured, confidence-scored answers. Not affiliated with TypeSafe AI.

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 AP

Unofficial Go client for the TypeSafe AI System One API (Jev) — typed questions in, calibrated answers out.

Unofficial Go and Rust clients for the TypeSafe AI System One API (Jev)

Aside agents decide with TypeSafe Jev (System One: Choice/Score/Noul). Not a Cua binding — Jev is the model, Aside is the browser runtime.

Community-maintained history of Jev / TypeSafe System One APIs, SDKs, agent guidance, and engineering best practices.

daf-jev: composable Python toolkit for TypeSafe's Jev (System One) decision API — question builders, confidence gates, evaluator, calibration, CLI, MCP server, agent skill

Unofficial .NET client for the TypeSafe AI System One API

Deterministic multi-agent task router & subscription quota scheduler for Claude Code, Codex, Gemini CLI (Antigravity), Grok, and Paseo. Routes sub-agents by model DNA, ability floor, and 5h/7d rate-limit windows with a PreToolUse spawn gate.

Android control for Dim Agent and local MCP clients, with a live screen, native UI automation, and optional Jev decisions.

Deterministic controls for LLM output in a daily news briefing. Models select and summarize; code constrains citation destinations and validates output. Jev, a decision model on OpenRouter's Decisions API, checks prose against frozen evidence and gates bounded repairs. Python stdlib core; Claude Cod

Long-term memory for AI agents driven by Jev (System One) decisions: no generative LLM in the loop. MCP server, Claude Code hooks, CLI, Python library.

Self-hosted gateway for LLMs and decision models. Claude Code, Codex, OpenCode and any OpenAI/Anthropic SDK app reach any provider (OpenRouter, Groq, Ollama…) with context pruning; Jev and open-rlcd System One decisions get audit and calibration. Live dashboard, single Go binary.

Offline $0 decision layer for coding agents: Choice/Score/Noul primitives, BELKI confidence gatekeeper, ultra-planning, red-teaming, research and RLVR self-improvement -- as an MCP server + CLI + Claude Code skill.

Idiomatic Go SDK for the TypeSafe AI API

Decision-first, OpenAI-compatible AI router powered by Jev typed decisions. Routes every request to the right LLM, agent or tool with explainable traces, fallbacks, guardrails and cost savings.

Unofficial Laravel integration for TypeSafe Jev AI with typed responses, async requests, scoped dependency injection, and testing fakes.

CLI that finds PII in text with TypeSafe Jev: presence, sensitivity, and located spans

The asking side of Jev (TypeSafe System One: noul, choice, score) in Rust: client library, mjev CLI, evaluation harness, and Jev reverse engineered from its docs. Talks to Intel-Phi-Jev.

Provider-neutral Elixir/BEAM SDK for System One semantics: typed Noul, Choice and Score decisions, prepared evaluations, provider abstraction, batching, streaming, telemetry, OTP integration, runtime controls, model catalogs and evaluation tooling. TypeSafe is the first built-in provider via typesaf

JEV-inspired typed runtime decisions over long-horizon PSG: reusable overnight encoding, sparse retrieval, and Choice/Noul/Score probabilities.

Lightweight model routing for agents, with OpenAI and Anthropic API compatibility, automatic model selection, and a management CLI.

Async Python client for TypeSafe Jev. Typed questions in, probabilities and choices out, no prose to parse.

A client for OpenRouter's decision models, TypeSafe's Jev by default: typed Choice, Score and Noul answers about text and images, fuzzy rules over them, and drawings of those rules.

Haskell client for TypeSafe's System One (Jev) decision API — typed, batched Choice/Score/Noul over http-client

Typed Scala client for Jev (TypeSafe AI): typed questions, typed answers, errors as values. Scala 3.9 and 2.13, Spark 4 ready.

三大クラウドの機能名を、TypeSafe AI の System One モデル Jev が確率つきで判定するデモ

Cognitive middleware for personal and enterprise AI agents: 14 MCP tools and a REST API for intent mirroring, persona/context profiling, decision support, memory, guardrail checks, and approval workflows. Deterministic offline engines with optional live Jev, SQLite/PostgreSQL, and Docker.

Bake-off: Playwright CLI vs CLI+Jev vs Playwright MCP (Mini Kanban + dense Shopify Sauce demo)

Typed semantic filtering for AI agents: turn retrieved records, code and logs into compact evidence with source IDs. CLI · Python · TypeScript · MCP.

TypeSafe Jev decision tools for DeepSeek Harness: dependency-free client library and stdio MCP server (jev_check, jev_route, jev_score, jev_evaluate).

Shared Typesafe judgement client used by zillow-agent and coin-launch-agent

DML is a reproducible harness for evaluating small, local decision models under one model-independent contract. It provides canonical datasets, candidate adapters, calibration and selective-automation metrics, provenance-tracked JSON artifacts, Docker-owned runtimes, and candidate-agnostic compariso

Production control plane for OpenClaw agents: intake routing with Jev, Temporal orchestration, a Process Evaluator gate before every reply, and dual-store memory (Postgres + Qdrant).

Owner-private ChatGPT integration for TypeSafe Jev structured evaluations.

A custom Jev-style decision model built from scratch on Qwen2.5-3B. Achieves 83.4% accuracy (beating GPT-4o-mini) at 24ms latency for local AI agent routing, safety gating, and RAG filtering.

Benchmark Jev's typed-question decision model on SNIPS intent detection and slot filling, testing accuracy with label names, definitions, and few examples.

TypeScript tools built on TypeSafe AI's Jev: an agent tool-call guardrail, a GitHub issue-triage Action, and a bulk classification CLI (npm workspaces monorepo).

Jev-backed guardrails for agent tool calls: library, native OpenCode/Hermes/DeepSeek Harness hooks, and a hosted eval-credit service.

Chess with Jev: every legal move's facts worked out in code, Jev's move as one typed Choice. The Jev client (MIT) and chess engine (GPL-3.0) behind chriswijnia.com/lab/chess.

Local CLI and web desk. Robinhood Agentic MCP is the tape. TypeSafe Jev votes. Simulate by default.

Shared TypeSafe Jev decision-model client for the dsh ecosystem: strict response validation, keychain-first key resolution, typed fail-open errors. Jev-optional by design.

Flink SQL ML_PREDICT provider for TypeSafe Jev semantic decisions

Semantic SQL predicates for MariaDB powered by TypeSafe Jev

Community TypeSafe Jev semantic decision integration for meilisearch

Semantic SQL predicates for Apache Pinot powered by TypeSafe Jev

Go SDK, CLI, MCP server and agent skill for TypeSafe's Jev (System One API)

Composable CLI for TypeSafe's Jev classifier: choice, score, and noul with streaming JSON output

Code for querying Jev based on dotnet sdk 11 and C# 15

jev: a per-user daemon and CLI that hold TypeSafe keys and send checked requests to Jev

Deterministic routing-policy CLI in Rust: task and candidates in, choice + confidence + fallback verdict out. std-only, checked-in benchmark, CI-enforced.

Spring Boot PDF question-answering assistant with RAG/CAG routing and PostgreSQL pgvector.

Typed Jev judgments (noul / choice / score) from TypeSafe for Python, masked before they leave the machine

A Laravel 13 playground that tells you whether a photo is a hot dog or not, using Jev's preview image input: a Noul question gives the verdict and a Box question draws a box around the hot dog. Bring your own TypeSafe key, or try it straight after cloning in fake mode.

Jev's TypeSafe judgment model, exposed via OpenRouter, as MCP tools

Universal Model Context Protocol (MCP) server and CLI runner for TypeSafe Jev decision models. Calibrated evaluation for code reviews, acceptance gating, and agent guardrails across Claude Code, OMP, and Cursor.

Governed Jev decision gateway for GPT, Claude, and MCP clients

Clean-room CLI for TypeSafe System One / Jev Decision Contracts, search, and benchmarks.

World-Class Pure System 1 AI Decision & 90/10 Hybrid Gatekeeper Skill powered by TypeSafe JEV SDK (Noul, Choice, Score, Box) & UCanLabs Benchmark

Domain-agnostic MCP server for TypeSafe Jev (System One) evaluation engine across Claude Code, Codex, Gemini CLI, and Antigravity

Unofficial typed Python SDK for TypeSafe's Jev: noul, choice and score as Pydantic classes with typed answers

CLI for System One decision models: typed questions in, calibrated probabilities out, API key kept out of the caller

Context log, search and a compact/clear advisor for Claude Code. Experimental learning project.

Prompt injection protection & tool-call guardrails for LangChain / LangGraph AI agents. Screens tool outputs, gates risky tool calls (allow / human review / block), powered by TypeSafe Jev.

TypeSafe Jev integrated into Claude Code: routing, escalation, guardrails, evals

Fast, exact inference for StartLux-Decision models on NVIDIA GPUs: the released GGUF files on int8 and 4-bit tensor cores, behind the /v1/systemone API, with up to 5.3× lower latency than vLLM in FP8 and bit-identical answers.

One CLI for all your robots. Connect them, command them, and let them work together, each with an LLM for a brain (Claude, OpenAI, Gemini, Grok, or local via Ollama or vLLM), VLAs for arms and decision LLMs (Jev, Laya, Kev). Drives Microduck, Open Duck Mini, LeRobot, XLeRobot, AlohaMini, ToddlerBot,

安卓AI聊天端:开箱即聊，无损导入酒馆卡，缓存强省用量，手机操控，专属RP模式，防空回，主动信息，前端卡支持，高定制主题，QQbot，AI群聊，插件系统，强兼容中转站。手机移动端原生支持，求 Star 收藏⭐Android AI Chatbox: lossless SillyTavern card import, aggressive cache optimization, Jev decisions, multi-dimensional memory, anti-blank-reply, proactive messages, AI group chat, SillyTavern theme

A small, type-safe client for asking AI questions about your data, powered by TypeSafe Jev.

Pre-alpha PostgreSQL extension for TypeSafe AI (Jev) categorical classification

Jev decision layer for agents: MCP server, embeddable DecisionModel library, and an escalate-only Claude Code plugin (TypeSafe AI's Jev)

Jev-shaped (TypeSafe System One) classification wrapper over OpenAI-like clients

Elixir client for System One decision models: TypeSafe Jev, Cloudflare Clef, OpenRouter, self-hosted (CLM). Full probability distributions, behaviour + stub, retries, telemetry.

Independent async and blocking Rust SDK for the TypeSafe AI System One API

让 dsh 会办事:登录态真实浏览器 × 180 站点结构化命令 + SystemOne 亚秒决策 + 执行真实性判定 + watch 监控。Logged-in browser automation for DeepSeek Harness: 180-site structured commands, sub-second decisions, action-evidence verification.

Router and proxy for OpenAI compatible LLMs machines - 1 url to rule them all ! Now compatible with JEV !

Go client for the TypeSafe System One API (Jev) — the first piece of sael, a content-safety classifier for an AI request relay

A VGI worker exposing TypeSafe System One questions (choice, noul, score) to DuckDB/SQL as LATERAL-joinable table functions

System One TypeSafe AI Rust Client (unofficial)

Classify an artist's discography by theme, mood, and lyrical complexity with Jev (TypeSafe AI), and view it as a colorful terminal dashboard

Ask Jev typed questions from the shell: noul, choice, and score answers as numbers, not prose

CLI and agent skill for TypeSafe System One (Jev): typed Choice, Score, and Noul judgments.

Unofficial Elixir client for the TypeSafe AI API — typed structs, Req-based HTTP with retries, and ergonomic noul/choice/score questions. Not affiliated with TypeSafe AI.

开源大模型部署可行性鉴别工具 · 168 个开源模型动态评估（含 12 个 JEV/System One 决策模型）· 硬件自动检测 · 一键部署脚本（CLI/MCP/HTTP 三协议）· 每周自动从 HuggingFace API 补模型

Typed, source-generated decision questions for .NET: one IDecisionClient for TypeSafe's Jev, OpenRouter, local /v1/systemone servers and OpenAI's Decisions API. Native AOT and allocation-conscious.

Ergonomic CLI for Laya — typed decisions (choice/score/noul) for Humans & AI Agents, with streaming batch mode for pexels-cli pipelines

Unofficial Rust client for TypeSafe's System One API (Jev): typed Noul, Choice, Score and Box questions, images, async and blocking

Claude Code plugin that replaces the compaction summary with Jev decisions: every tool call and result is scored in one fast request, stale ones are dropped or truncated, everything kept stays verbatim.

Route to the cheapest model in claude code for your task using jev-router

Per-turn model & reasoning routing for Codex, driven by Jev (TypeSafe System One): picks the model, thinking depth and speed mode for every turn.

An agent supervisor and software factory foreman powered by decision models

A small, fast decision model, or system one model, for agentic workflows. Pick between options or rate on a scale faster than an LLM, with a calibrated confidence on every decision.

Awesome Jev: a source-backed field guide to TypeSafe's System One model, with SDKs, live demos, agent tools, and independent evaluations.

Fast, cheap, typed judgments from TypeSafe's Jev model, as MCP tools.

Awesome list of TypeSafe AI Jev use cases: 74 demos ranked by likes, 150+ GitHub repos, limits, cost and API examples. CC0

A curated list of Jev use cases, projects, SDKs, and resources. Jev is TypeSafe AI's System One model for fast, typed decisions in software — Choice, Score, and Noul with calibrated probabilities.

Typed decisions with TypeSafe's Jev, the first System One model

A skill for writing and improving programs that call Jev, TypeSafe's System One model

TypeSafe Jev as a decision layer for the Pi coding agent: a measured tool-call gate plus jev_ask for typed, calibrated answers

jevmem saves the decisions, rules and failed approaches from your Claude Code chats to JEVMEM.md in your repo, and brings the relevant ones back next session.

Connect TypeSafe Jev to Grok Bot as a cheap decision layer - usage gates, skill template, examples

A calibrated context sieve for Claude Code: every tool result is judged by a System One model before it enters context.

Agent evals and guardrails as Jev decisions: one request per trace, a fraction of a cent, fast enough for the agent loop. Runs locally with Kev or Laya.

微信（FkWeChat 插件）：长按消息分析意图 / 情绪 / 回复姿态，只在本机弹提示，对方无感知

A list of System One models (also called decision models): Jev, the first, plus open and hosted alternatives, agents, tools, SDKs and benchmarks.

A standalone local-only Python MCP server that gives any IDE persistent, workspace-isolated memory. works with any IDE supporting MCP servers

The most complete gallery of what people build with Jev, TypeSafe's System One model: 3,400+ projects, demos, and write-ups by scenario, each with its original link, image, and description.

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.

Fast, typed Python framework for Jev-as-a-Judge/Decision-model evaluation for LLMs, RAG, and AI agents along with human-label calibration.

Claude Code/Codex skills, evals, harnesses/context tools, and learning resources that make it easy to build with Jev, TypeSafe's System One model. All materials pull directly from their canonical cookbooks and documentation.

OpenJev: An Opencode plugin that replaces text-generation decisions with Jev (TypeSafe System One).

⚡ Foq — the FREE, local, open-source alternative to Jev. Typed System 1 decisions in ~25 ms — no waitlist, no cloud, no per-token cost. foq.fr

Demos with Jev, TypeSafe's System One model, one folder per demo. Code behind the videos on The Agentic Enterprise.

NanoJev Snake Arena: 1v4 human-vs-AI battleship + 100-agent swarm simulator. Local demo of Jev System-One model (open-source mini replica).

Verdict-based context compaction for DeepSeek Harness — replaces lossy LLM summaries with fast keep/truncate/drop decisions from jev-latest; everything kept stays verbatim. Port of tamaratran/fast-jev-compaction.

A runtime where decision models propose and your code decides: typed questions, hard rules, quotes checked against the text, escalation with a guaranteed error budget, replayable traces. Works with any System One model.

An eval-first MCP server for TypeSafe's Jev, a System One model that returns typed judgments (noul, choice, score) with probabilities instead of generated text.

Can a System One model (TypeSafe Jev, open-source Kev) replace an LLM-as-judge in agent guardrail callbacks?

Semantic ArkUI linter for coding agents: catches state and lifecycle bugs that compile cleanly, judged by System One models

MCP server wrapping TypeSafe's Jev System One models — typed noul/choice/score judgments for AI agents

Agent Skill + estudo verbatim sobre a Jev (TypeSafe AI System One Model): choice/score/noul, RLCD, patterns, SDKs.

A practical demo of how can we leverage the power of System One Models like Jev to create effective and lighting fast agentic gates and guardrails

plugin for OpenCode harness - blocks tool calls outside of scope using a fine-tuned system one model

An extension for pi and Oh My Pi (omp) that classifies each incoming prompt with a System-One model and routes the session to the tools/skills that fits it.

Sovereign 10-Bot Operational Execution Platform & Jev-AI (jev-latest) System One Production Gate

TypeSafe AI is a San Francisco AI lab building System One models — a class of model trained to return typed, calibrated decisions for software instead of generated text. Its flagship and first System One model, Jev, is served by a single HTTP endpoint: POST /v1/systemone takes a state (a string, JSO

Agent Harness for System One model

TypeSafe JEV resume screening: LLM writes the questions, JEV (System One classification model) answers them for every resume in your ATS with the exact evidence line. 8× faster, 31× cheaper than an LLM.

An independent, evidence-first field guide to Jev (TypeSafe AI's System One model) — for people and for coding agents. Not affiliated with TypeSafe AI.

TypeSafe's System One launch claims, each as a runnable experiment: Jev vs Claude vs keyword rules through one typed interface. Calibration, parallel questions, Wikiracing. Offline in CI; --live for the real thing.

Use and fine-tune System One models (Jev, Laya) on your data, with an LLM as the teacher.

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

UiPath bring-your-own-guardrail connector backed by the TypeSafe Jev System One model: plain-language agent policies enforced as calibrated probabilities.

Run fast, calibrated, typed decisions on open System One models with a Jev-compatible API.

Fastest and cheapest web agent

The chat decision assistant: before you reply, Jev reads the chat, judges intent and risk, and drafts replies you fill in with one tap. You press send. Android · Windows · macOS · iOS | 聊天辅助决策工具：先看懂对方再回复，发不发由你。

✨ All your agents and workspaces in one place, on every device you own. Track tasks on a board, accessible from desktop, mobile, browser, or API. Self-hosted.

Jev-powered model routing, memory, compaction, skill selection, computer and browser use for Hermes agents (also Claude Code and Codex)

AI agents can generate code, but still struggle to understand what they build. Reticle brings Jev-style machine-native runtime perception to web & desktop applications.

5–10x faster browser operations: Jev clicks, Codex thinks and verifies. Built at EZCollegeApp.

Awesome Jev: source-backed open-source ecosystem radar, plain-language project discovery, and automatic GitHub sync

A staged code-review workflow and local dashboard built with TypeSafe Jev.

A curated list of decision models (System One / typed decision models): hosted APIs, open-weight models, runtimes, SDKs, applications, benchmarks, and papers.

An awesome collection of Jev use cases, workflows, and agent skills.

Launch what you built on Jev, TypeSafe AI's System One decision model: a weekly board where makers launch and people vote, over an index of public Jev examples by decision pattern, with source citations, dated link checks and call-site text checks; runtime and performance are not independently teste

MinusPod is a self-hosted server that removes ads before you ever hit play

A lightweight Jev-powered router for models, tools, and subagents

AgentJev-0.6B - a fast 'System One' decision model for AI Agents: feed it any unstructured state (diffs, traces, logs) and structured questions, get calibrated probability distributions back in one ~50ms forward pass. Zero output-token decoding.

An easy way to use jev with your coding agent for tool calling reasoning

Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE)

Camera-only autonomous drone in MuJoCo with a small judgment model (TypeSafe Jev) in the loop at 2.5Hz

Jev demos, projects, SDKs and skills, with source links and a curated X gallery.

🚀🚀 A multimodal System One decision model that gives calibrated answers to typed questions about screens, photos, video and text in one forward pass.

A source-backed Jev project directory with a reusable Jev-only GitHub review workflow.

Reusable project context and specialist workflows for the AI tools you already use. Agents, skills, rules and MCP tools for Claude Code, Codex, Copilot, Cursor and more, plus SDLC workflows, session memory, and Jev decision support that offloads bounded LLM decisions to save tokens.

Native DeepSeek Harness (DSH) plugin integrating TypeSafe Jev or Decision api like luna as a System One decision layer for agent selection, supervision, corrections, and approvals.

System 1 decision models (Jev, Laya, Cua-S1) as brain for agents: Browser use, computer use, games and robotics

Claude Code plugin: trim long Bash output with TypeSafe Jev before the model sees it

Agentic RL 中文零基础教程（33 章）：从概念到 GRPO 实战，含 TRL 最小可跑示例；26–33 章附一套可运行的三方判别模型实证工程（encoder vs LLM-LoRA vs 规则基线）。第 25 章讲清 Jev / TypeSafe System One 与 RL 的能力边界 | Chinese Agentic RL tutorial (33 chapters) + a reproducible discriminative-model benchmark

Claude Code skill: hand bulk judgment calls to Jev. 86% fewer Claude tokens on a 12-task benchmark, up to 20x faster. One-line npx install.

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

Typed-decision models (noul / choice / score) trained by a self-improving loop of AI agents — checkpoints, the code that produced them, and every version that failed.

PyTorch ML quant research: 213 factors, bias correction, portfolio optimization, backtesting, and System-One/Jev-style decisions.

TypeSafe Jev for DeepSeek Harness, the Model Context Protocol, and plain Node: typed judgments instead of prose, offline by default.

Intent compiler for AI agents — converges vague requests into typed IntentSpec contracts (probe, ask, or halt before routing), the input layer for routers and typed-decision models like Jev & Laya

Fast, cheap, typed decisions for Claude Code. Jev (TypeSafe's decision model on OpenRouter) sorts, checks, scores, gates and verifies at 0.3s and a fraction of a cent per item. Claude keeps the reading, writing and judgment.

Everything you need to run TypeSafe's Jev with Claude Code: a tool-call guard, tier guard, file search, browser agent, review, belay, compaction and installers.

A 4B decision model: calibrated probabilities for yes/no, choice and score questions over long inputs.

TypeSafe Jev action selection inside Codex Computer Use

Provider-neutral System One runtime for TypeScript and Pi

Semantic tool routing and typed System One decisions for the Pi coding agent using TypeSafe Jev

JevStyle: local decision models for AI agents. Classify, route and score with a probability per option. systemone-compatible local server, MCP tools, Claude Code guard. Open weights, Apache-2.0.

OpenJev: open-source, local alternative to Jev. System-One decision models: typed questions in, calibrated probabilities out; general decisions and browser-agent steps.

Best Roblox Studio Instance Reviewer 2026: Typed Jev Criteria and Exact Citations

Claude Code / Codex / pi plugin that hands agent steps needing no text output to Jev (TypeSafe's judgment model) — measured p50 ~230 ms and ~$0.02 per 1,000 judgments, with typed escalation back to the LLM

Jev, TypeSafe's System One classifier, as a tool inside Claude Code, Codex, Pi, and OpenCode: typed classify, check, score, rank, and ask, plus one-command setup.

Open reproduction of TypeSafe Jev: a 150M typed decision engine (noul/choice/score in one non-autoregressive pass, calibrated confidence). 0.697 vs Jev's 0.727, 2.5x better calibrated, 4x faster, free. Trains on a Colab T4 in 30 min.

Claude Code plugin that picks the model and effort for each turn (micro, low, medium, or high tier), advised by Jev, Clef, OpenAI, or a local Ollama classifier.

📡 The most rigorously verified tracker of the Jev (TypeSafe AI System One) ecosystem — 2802 confidence-graded entries (A1977·B825) · last scan 2026-11-05 · bilingual EN/中文 · API access guide

⚡ Millisecond System-1 judgement for every tool call in DeepSeek Harness — Jev-powered risk classification & evidence-gated auto-approval. Fail-closed by construction. dsh 生态第一个 System-1 决策插件

Jev (TypeSafe System One) backed auto mode for the Pi coding agent: semantically auto-approves bash, write, and edit tool calls and fails closed when a decision cannot be made.

Open-source, local decision models inspired by TypeSafe’s Jev and System One - built on Qwen3 for NVIDIA DGX Spark.

A typed, replayable, budget-aware programming language for LLM agents. Compiles to Python & TypeScript.

Evidence-backed index of real-world Jev (TypeSafe AI System One) use cases: repos, patterns, benchmarks, and measured results

Decisions, not essays: a choice, a yes or no, or a score with a calibrated probability. Open weights, Jev-compatible API, adaptive thinking.

The complete cookbook for Jev by TypeSafe AI — 120+ use cases, 10 runnable examples, 4 composition patterns, and first-principles theory for the world's first System One AI model.

JevGPT: Thinking Fast and Slow in Autonomous Research

Configurable semantic linting powered by Jev, with file-level NOUL judgments and a magic-strings plugin.

Local gateway that routes coding-agent tool decisions through Jev and can attach a read-only advisor (GPT or Claude).

Curated TypeSafe Jev / System One GitHub projects, open-source alternatives, and Jev news

Question patterns, anti-patterns & calibration notes for typed decision models — Jev & every System One-compatible engine. 60 entries / 10 categories + linter + engine cookbook. EN/中文.

TypeSafe Jev (System One) decision tools for Hermes Agent: jev_check / jev_route / jev_score / jev_evaluate

x402 risk-check provider: signed pre-payment verdicts — OFAC, phishing feeds, transaction simulation, drainer-kit code, and our own EIP-7702 kit watch (poisoners, sweepers). $0.001 per check with prepaid credits; per call via x402 from $0.0035.

Jev (System One decision model) plugin suite for DeepSeek Harness (dsh)

⚡ 100% offline, zero-cost drop-in alternative to TypeSafe Jev System One decision models in Rust (<18ms on CPU)

Independent Pi extension for four-tier model routing with optional privacy-gated Jev advice, deterministic baselines, budget controls, and safe fallbacks.

Jev (TypeSafe) vs Claude Haiku 4.5 on 2,000 phishing emails: accuracy, calibration, latency, cost. Reproducible benchmark by Anisse Lebadi.

Real-world Jev use cases, open-source projects, benchmarks and criticism — what people actually build with TypeSafe AI's Jev, and where it fails. Machine-readable, updated daily.

Training data and models for "system one" classifiers: one forward pass, no reasoning. 100 Hugging Face datasets turned into one format (state → question → options → label), a Qwen3.5-2B LoRA model with a pointer head, held-out benchmarks, and a vLLM plugin that serves TypeSafe's system_one API.

Conversation agent based on Laya, a multilingual, non-autoregressive System 1 decision model.

JevHub · Jev 生态热榜 把值得关注的 Jev 项目、教程和讨论，汇到一页。 自动发现 · 每日更新 · 按 Stars 排序 · 中英双语

Jev (TypeSafe AI) as a second pair of eyes for Claude Code: request triage, automatic effort, command guard, rule review and a final check. Claude Code plugin, zero dependencies.

A language for safe, universal spatiotemporal composability (Cordis paradigm) and orchestration.

Local Hybrid System-One Decision Engine (JevCompat 32/32 MUST, AnyJev L0 Prefill, StartLux Cascade, OpenJev-RLCD² Trainer, Zero-Shot AI Safety Guardrails)

AskJev — Jev autopilot for any website + guard on irreversible clicks (TypeSafe System One, not Claude)

Jev (TypeSafe System One) vs Claude Opus 5 driving a simulated robot arm in MuJoCo

MCP server that lets Claude Code, Codex & AI agents drive a real Chrome browser with JEV (TypeSafe System One): ~100 ms typed decisions, calibrated confidence, human-in-the-loop questions, safety gates and a debug trace.

Curated Jev and System One papers, models, datasets, benchmarks and SDKs, with versioned sources and transparent verification levels. English / 中文.

Fact-checking skill for DeepSeek Harness: multi-source verification against the open web, domestic and international ecosystems, optional TypeSafe Jev (System One) judgements.

Sub-100ms security gate for AI agent tool calls, powered by TypeSafe's Jev (System-1) decision model. Single-request Choice/Noul/Score evaluation, dual-factor blocking matrix, calibrated-confidence routing, fail-closed parsing, zero-config local fallback. LangChain-ready.

MCP server for Laya typed decisions (noul / choice / score): warm model sidecar, token-budget preflight, persisted calibration, honest confidence.

Computer use MCP server for Claude Code, Codex and other AI agents. Reads the UI tree instead of taking a screenshot every step, with Jev (TypeSafe System One) as a fast judge that can also look at the screen to find icons and game objects. Local macOS, local Windows, or remote Windows over SSH.

Natural-language MCP tool dispatcher powered entirely by TypeSafe's Jev — no general-purpose LLM. Discovers a simple MCP server's tool signatures at runtime and uses Jev's typed primitives (Choice/Noul) to pick the right tool and extract its arguments straight out of the sentence.

Not every coding task needs your best model. Experimental Jev-powered model routing for Claude Code — V3 prototype runs today, V4 routes at the task boundary.

DSH plugin: register TypeSafe Jev (System One decision model) as an agent tool — jev_decide returns calibrated probabilities (noul/choice/score) for routing/triage/guardrail judgments, no text generation. 把 TypeSafe Jev 决策模型注册为 DSH agent 工具

MCP server and agent skill for the TypeSafe AI System One API (Jev): decompose a judgment into Choice / Score / Noul questions, lint them, measure on labelled data, and put calibrated thresholds in code

A curated hub of the Jev / TypeSafe "System One" model ecosystem — concepts, 51 verified projects (12 categories), 10 community lists, official repo screenshots.

Open-source Jev skills for Claude Code, Codex, Cursor, and Grok. TypeSafe System One: choice, noul, score. Proceed, ask, or stop.

Run System 1 decision models locally. Pull a model by name and get calibrated probabilities for your questions in one forward pass. One binary with a TypeSafe-compatible API and a playground.

Comparing LLM-as-judge vs TypeSafe Jev for agent guardrails: same rules, same agent, measured on cost, latency, calibration and coverage.

Jev (TypeSafe System One) gives the human a second opinion, not the agent. When DSH's model asks you a multiple-choice question, dsh-jev-advisor builds a structured Jev request and floats the recommendation, probability spread and confidence beside the options - one click to adopt. API key lives in

Stop burning premium model tokens on simple prompts. Save tokens. Spend them where they matter. Local-first self-evolving LLM router with a decision model + feedback flywheel. Judges each request and picks the right model on the fly. 10MB Rust, zero cloud, fully auditable. Works with OpenAI/Anthropi

Paper trading real Polymarket and Kalshi markets with a fake $100k: can Jev + Claude research beat the market?

Cursor plugin that routes subagents with TypeSafe Jev and exposes Choice, Score, and Noul as MCP tools.

DSH bundle that registers jev_ask for TypeSafe Jev noul, choice, and score answers.

A curated list of resources for Jev — TypeSafe AI's System One decision model. Maintained by RoboKrunch.

System-architecture skill for TypeSafe AI Jev/System One — find fuzzy semantic judgment and turn it into small Choice/Score/Noul primitives.

Minimal herdr-native subagent spawning for pi — markdown agent roles, model profiles with classifier routing

MCP server for Jev (TypeSafe System One): the three official question types — choice, score, noul — plus batch classify. Calibrated probabilities, ~0.5s, <$0.001/call.

FINS demo: a UiPath coded agent for AML alert triage where every decision is made by TypeSafe's Jev model (Noul/Score/Choice) instead of an LLM

PAVE skill for non-autoregressive decision models (Jev / Laya / TypeSafe): typed Choice, Score and Noul answers with calibrated probabilities

Jev System One for Claude Code — typed Choice/Noul/Score via live TypeSafe API (mirror of opencode-openjev, live-only MCP)

TypeScript/Bun launcher routing Codex turns through JEV with model and reasoning effort selection

LLM permission gate for OpenCode agents: blocks irreversible actions (force-push, rm -rf, curl | sh, credential reads) before they run, auto-approves routine work, and only asks a human when it matters. Red-teamed live on opencode 2.2.3.

A UiPath coded agent with a custom PII detection guardrail on the LLM boundary, built on the TypeSafe Jev model as a LangChain awrap_model_call middleware.

Team piedpiper: failproofai + Jev guardrails that stop the Helix IT agent from failing (Jev Buildathon)

DeepSeek Harness WSL plugin: TypeSafe Jev / OpenRouter System One (jev_ask / check / rank)

3+1 Cognitive Hierarchy Toolkit & Model Context Protocol (MCP) Server for Agentic Coding (Codex, Claude Code, Cursor)

ComfyUI semantic judgement node: Noul/Choice/Score, typed outputs, workflows and evaluation evidence (AI-assisted project)

DeepSeek Harness (DSH) 执行前安全阀门:bash/pwsh 真正执行前先经静态规则 + TypeSafe Jev 语义判定,破坏性操作按 允许/修正/拦截/上报人工 四态处置,含额度降级与审计日志。

Gemini implementation, TypeSafe Jev evidence triage, and Codex final-review skills.

A real-time safety guardrail and interactive monitoring dashboard for AI agent tool calls powered by TypeSafe AI's Jev model."

Ultra-lean supervisor for Pi: zero-token regex guardrails, test verification tracking, and TypeSafe Jev semantic slop gate.

The Jev projects actually worth your time — picked, compared, kept current.

JEV Gateway: PII/PHI guardrails for coding agents. Local MCP server, 0.8B inspector, and agent skills for Codex and Claude Code.

A Jev-powered model routing and skill selection MOD for Claude. Compatible with VSCode Claude extension

A self-hosted Jev with a pluggable decision-backend fleet — typed, calibrated decisions from Clef, a vLLM logprob reader, Laya or any System One server, behind one wire contract

Support-triage inbox where an LLM answers typed questions and a deterministic, tested policy routes each message, with human review below a confidence threshold.

Generative painting canvas where Jev, TypeSafe's calibrated decision model, picks paint materials and colour palettes for 105 procedural sketches. FastAPI + React + TypeScript.

Decisões tipadas em milissegundos com o Jev (System One da TypeSafe) via OpenRouter — state + perguntas noul/choice/score entram, decisões calibradas saem. Validação pesada baseada nos conceitos do modelo, bandas auto/hitl/abstain, deteção de prompt injection e servidor MCP. Zero dependências (Node

Route Claude Code subagents to the right Anthropic model per task, using TypeSafe's Jev to score the task first. Includes the measurements that say where it doesn't work.

Six Jev decision workflows for agentic AI: model routing, guardrails, tool-call gating, reranking, output evaluation, and confidence-based autonomy.

O Jev (TypeSafe AI) como guardrail em tempo real e como pré-avaliador de agentes conversacionais de negócio. Next.js + LangChain via Vercel AI Gateway, com estudo de calibração em português.

Jev toolkit for building fast, testable AI decision points for agent permissions, routing, semantic change detection, and evidence checks.

Serveur MCP pour piloter Firefox avec Claude : Jev (TypeSafe) choisit les clics, Claude planifie. Benchmark inclus.

Jev-inspired calibrated decision & safety shell for typed PID autotuning (temperature + conformal abstention) with C99/MATLAB ports. Not affiliated with TypeSafe AI.

A curated list of Jev / TypeSafe System One projects, SDKs, tutorials, and evaluations. English and 简体中文.

Claude Code plugin: TypeSafe-judged hooks that make Claude finish the job before it reports

Persistent governance for AI agents — turn Jev judgments into replayable relation state with R2R.

DeepSeek Harness 审批守护者：权限请求先由审查模型裁决。内置 System One 决策 API 后端（TypeSafe Jev / 硅基流动 systemone / 自部署 Laya）、onLowConfidence 低置信度策略与 allowRules 免审白名单。fork 自 gbthui/dsh-auto-review。

Open-source projects built on TypeSafe's Jev decision model: open replicas, agents, dev tools, SDKs and MCP servers, apps. By type, each security-graded. English / 中文.

Single-surface TypeSafe Jev model router for the Pi coding agent: one virtual model (jev/auto) with judgment routing, mid-turn transient fallback, and first-class routing profiles.

Agent Development Kit for System-One AI: Sub-100ms non-autoregressive decision pipelines, guardrails, and dual-brain agent orchestrator powered by TypeSafe AI's Jev

One command (npx jev-stack) to install the Jev-powered agent toolset: context compaction, pre-push guardrail, 0.5s browser agent, skill router, CDP launcher

An agent that reads incoming student queries (email/WhatsApp/form text), classifies them (refund, batch access, payment, technical, academic doubt), drafts a reply from a knowledge base, and decides: auto-reply or escalate to a human.

This is a small side project where I built an MCP server with a collection of tools and connected it to Meta's Llama 3.2 model through Ollama. The interesting part is the routing layer: instead of relying on Llama to decide which skills or tools to use, I use TypeSafe AI's Jev decision model to make

Jev (TypeSafe AI System One) agent harness guide + video manual — from the LangChain 'Building a Harness with Jev' post. Learn Jev Noul/Choice/Score questions, Model Router, and AutoMode guardrails with LangChain.

Jev (TypeSafe's typed classifier) as a cc-cockpit plugin service: choice, yes/no and score questions for other plugins, with usage counts and a monthly cap.

Xano template: Jev agent tool-call guardrail. Jev decides, Xano enforces.

Jev-routed two-phase virtual model for pi: plan on a strong model, implement on a cheap one. 利用决策模型根据任务难度进行路由。

Multi-agent coding workbench — hashed context injection, explainable routing, local-first / 多 Agent 编码工作台：注入字节可核对、决策链路可解释

Jev-powered semantic pre-screening for local files in DeepSeek Harness: code walks the directory, Jev scores every file, the agent reads only the shortlist.

Local Anthropic-compatible gateway that routes Claude Code traffic across Claude, a cheap tier and Gemini

Jev-judged agent driving a visible tab in Tern's picture-in-picture browser: click, type, popups, canvas clicks, markdown back. MIT.

An Interactive Collection Of AI Game Worlds Where Typesafe Jev Agents Choose Legal Strategies While Deterministic TypeScript Handles Rules, Movement, Clashes, Scoring & Winners. Built With Next.js, TypeScript & PixiJS

Context compaction for agent harnesses: a decision model scores transcript spans and tombstones the ones that stop mattering. Includes an MCP restore server.

MCP tool exposing TypeSafe's Jev browser agent (via browser-use/jev-ultrafast) to Claude Code/Desktop

jev-sim: Jev-style typed decisions (Choice/Noul/Score) on the ZCode session model. In-session, zero-network, calibrated. Paste this repo URL into ZCode Plugin Marketplace to install.

Experimental Dice Chess webhook bot powered by TypeSafe Jev, with adaptive legal-turn selection and no heuristic pre-ranking.

Policy firewall for MCP tool calls with one-time human approvals and JSONL audit.

Decision contracts, adversarial testing, tracing, stability, and privacy controls for TypeSafe Jev

Replayable finite decisions grounded in exact Obsidian notes

WordPress Playground validation for the PHP plugin.

Route coding tasks between Claude Code, Codex and Hermes: pattern, model and effort per role, decided by a $0 jury with a read-only scout fallback

让 LLM 理解任务、让 Jev 做带概率的路由决策、再由显式策略控制执行的 Agent 运行时：Jev 决定下一步，策略引擎决定能不能执行。

The self-updating, trilingual directory of everything built on Jev (TypeSafe AI System One) — typed decisions, calibrated probabilities. AI-curated on free models.

Editable policy trees and a 2B classifier for agent request and action review. Website only.

Pocket explorer of the tiny decision-model wave

Search-flood adjudication plugin for DeepSeek Harness: ripgrep + TypeSafe Jev typed judgments (noul/choice) so the agent reads ~30 calibrated rows instead of hundreds. Configurable provider (OpenCode Zen free tier / TypeSafe / OpenRouter), degrades to raw grep when offline. Zero deps, cross-platform

Suggested next steps for Claude Code, scored by how much each one helps your goal. Claude proposes up to three prompts, TypeSafe's Jev scores them, and the best comes first.

Experimental Hermes Agent plugin for bounded Jev checkpoints and advisory route screening.

Obsidian plugin: route incoming notes to the right folder using JEV (TypeSafe System One) typed decisions. It never writes your content — it only decides where it belongs.

Jev (TypeSafe System One) patterns in n8n: PII screening, agent and coding-agent guardrails, workflow evals. Synthetic data, MIT.

MCP server exposing Jev-style System One decision models (Ollama /v1/systemone) as tools

Pi coding agent extension: Jev-powered model routing with a native TUI model manager (experimental MVP)

Per-task Jev routing for Claude Code and Codex: inline, subagent or sibling session, plus subagent model

A source-verified atlas of GitHub projects built with JEV / TypeSafe System One

Claude Code mod: routes every turn, subagent and ultracode workflow agent to a model and effort picked by TypeSafe's Jev. Fork of satviksinha/jev-model-router.

Jev Gate without the local Laya lane: MCP judgment tools, tool-output pruning, and hybrid compaction for Claude Code / Codex (Jev drops tool payloads verbatim; built-in compaction takes over when that saves too little)

Goal-driven Playwright E2E testing where TypeSafe Jev (System One) decides the next action. Reviewed and refactored: fixed agent callback signature and cleaned observer dead code.

Learn to gate tool calls, guardrails, and risky actions with Jev judgment plus LLM agents in 13 notebooks.

TypeSafe Jev (System One) wired into Claude Code: a decision primitive for hooks, and the Skylence herdr agent-org with Jev measuring tier and review gates.

A Claude Code mod that picks the effort of each model request, with TypeSafe's Jev as the judge

Research on whether AI decision workflows can trust and combine uncertainty signals from heterogeneous primitives (TypeSafe Jev's choice/score/noul), using pre-registered protocols and hash-locked data.

Servidor MCP que expõe o Jev (TypeSafe System One) como ferramenta de julgamento tipado para agentes: Noul, Choice e Score com validação por regra.

Independent calibration benchmark for System One decision models (TypeSafe Jev) vs Claude and a TF-IDF baseline on human-labeled data

Jev (TypeSafe decision model) as a risk guardrail inside a Google ADK agent's before_tool_callback. Working example + tests.

Source-bound evidence checks and bounded Jev judgments for coding agents via MCP, Pi extension, and shared Skill.

Optional session-pinned Jev routing plugin for DeepSeek Harness

Experiment: a judgement model (TypeSafe Jev) next to Claude. Router mod for Claude Code. Learning project, not original.

Judge interface, offline fake judge and a model/effort router for Claude Code, built on a typed judgement model. Experimental learning project.

AI agent tool-call guardrail for Xano: Jev (TypeSafe AI) judges each call; Xano allows, blocks, or holds it for human review.

Jev-powered Discord thread status titles for Hermes Agent: [Running] → [Needs feedback] → [Done], auto-retitle, delayed archive.

latest top 100 showcases for jev (keep updating) from x / github / latest sources

a mcp by jev decision and ocr detection for device testing

Continuously updated public index of real TypeSafe JEV projects, videos, articles, and open-source demos.

Astra planner and JEV controller for Minecraft, with native recording, tested routes, and run verification.

VexJoy AI Agent with Jev Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop.

System One MCP connector to evaluate anything fast and cheap. Give your AI agent direct access to models like: Jev, D1, CLM and Laya

Nerve is a supervisory nervous system for Hermes agents, adding typed System One decisions, ranking, verification, token-aware oversight, and an opt-in tool gate powered by TypeSafe Jev or Open Source Laya

Open-source guardrails for AI agents powered by decision models like Jev; checks prompts, retrieved content, tool calls and responses for prompt injection, jailbreaks and secret/PII leaks.

Probabilistic AI decisions as composable backend primitives — typed judgments (noul/score/choice), deterministic thresholds, and explainable workflows. Powered by TypeSafe's Jev, provider-agnostic.

Herds coding agents: autopilot builds from your own Claude Code session — Claude plans and verifies, Codex and opencode write the code, Jev picks the model.

Typed decisions over text in milliseconds. A Rust inference engine and server that answers choice, score and yes or no questions with calibrated probabilities, compatible with Jev and Laya. The bar is 10x faster on every benchmark at equal or better accuracy.

A coding agent and personal assistant with System One reflexes (TypeSafe Jev) on top of the Pi coding agent

vLLM-SR Decision — System One decision models (choice/noul/score/box) for OpenCode and magpie

⚡ Universal System-1 AI Runtime & Dual-Brain Gateway. Sub-15ms calibrated decisions (noul, choice, score) for agents. Drop-in OpenAI proxy that slashes 80% token costs. Make decisions, not strings.

Guardrails for decision models: stop text inside the content from steering classifiers, Jev-like models and LLM judges. LLM Guard for decision models.

NGA (Next Generation Agent) on openJiuwen: Jev-like decision models, S1 + S2 orchestration and an enterprise workbench for idempotent, replayable and auditable business decisions.

A System1 decision orchestrator for AI agents. Leverage Laya for fast local tool routing, auto fallback to LLM on low confidence. Built-in choice/noul/score pipeline, circuit breaker & evaluation suite.

The System One harness for System One models. Run Jev and other System One models locally or directly on HarnessRouter.ai.

Rust CLI powered by Jev from TypeSafe.ai that ranks agent skills for the next step using live session context. Includes Claude Code hooks, structured JSON, abstention, and local feedback. Requires a TypeSafe API key.

Curated list of papers on the security, robustness, and safety of TypeSafe Jev / System One models

Autonomous, self-hosted AI code reviews, calibrated scoring gauges, and merge gates from any open model.

Hands-on System One decision lab: Jev-compatible Noul/Choice/Score over local open-weight Laya, with playground UI, routing/eval examples, and CI smoke tests.

Mini benchmark of TypeSafe's jev-1.13 structured decision model (OpenRouter Decisions API) on labeled support-triage: noul/choice/score, consistency, cost, lessons learned

bot for matrix spam filtering using system one models.

Code for a course on System One models (Jev, Kev, Laya): LLMs that answer with a choice and a probability instead of free text. Build a support-ticket triage service with Choice, Score and Noul questions, check whether the confidence can be trusted, set thresholds, and run it in production.

Triage your Gmail inbox with a System One model (Jev or Laya): typed yes/no judgements, probabilities, and reversible actions.

Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.

Local decision engine with typed, calibrated outputs (Noul/Choice/Score): cheap→strong cascade, ECE/Brier calibration, SystemOne protocol interop. Apache-2.0.

Self-evolving cognitive decision engine & TypeSafe Jev alternative. Deliberative decision flywheel ('explore first, crystallize later' System 2→1) with 100% option-order invariance. Typed probabilistic API (Choice, Noul, Score) for Open LLMs, Laya (ModernBERT), & commercial Jev with calibrated logpr

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

Production TypeSafe Jev (System One) use-case harnesses with confidence-gated decision logic

Draw on a tldraw canvas with your voice and a pointing finger. Jev (TypeSafe System One) decides action, target and place in ~350 ms per spoken word.

Minimal grammY Telegram anti-spam bot powered by TypeSafe Jev

🌳 Groot 1 is a open typed-decision model, with a pure Bend engine. Beats Jev on zero-shot typed decisions (75.3% vs 73.7%).

Autonomous 24/7 Zero-Inbox triage for Gmail powered by TypeSafe Jev System One

Route OpenAI-style chat requests to a model and reasoning effort, using TypeSafe's Jev decision model as the classifier

Open-source, local Jev alternative: a System One decision layer with provably calibrated confidence (ECE 0.170→0.071). Typed decisions, runs offline, no API key, no waitlist.

NIRNAY 450M: open-weight calibrated decision model (Apache-2.0). Banking77 intent classification 87.9%, fitted ECE 0.045. Laya fork plus concept bottleneck, coarse-to-fine pointer and RLCD. Built by Eulogik.

Typed decisions (choice/score/noul) from one encoder pass - faster, better-calibrated successor to Laya, Jev wire compatible

Pi extension: auto-approve routine Bash commands with a System One (Jev) decision model, escalate the rest to a human

Eight decision models, measured as the candidate list grows, the option order changes, and the wrong answers stop being obvious.

Small local judges and guards for LLM apps: calibrated decisions, prompt-injection and leak checks on a CPU. English, Arabic, Persian. Runs in your browser too.

Zero-shot spam filtering with TypeSafe Jev Noul questions, compared with TF-IDF baselines

Ultra-low-latency, 6-tier prompt router in Rust to triage traffic between Jev decision heads and generative LLMs

JevDuo: a fast–slow computer-use harness with JEV as the fast policy.

Relevance re-ranking for query/candidate lists, powered by TypeSafe's Jev Noul primitive. OpenRouter and TypeSafe direct API supported.

Independent benchmark of TypeSafe's Jev against GPT-4.1-mini on 200 labelled enquiries. Finding: a calibrated confidence score tells you when to ask a human, not when to ask a bigger model.

OpenRouter benchmark harness: same decision task for rule baselines and pinned LLMs on synthetic earnings narratives vs DuckDB ground truth. Calibration metrics, dual API dispatch (/chat/completions & /api/alpha/decisions). Synthetic fixtures only—not audit or investment advice.

Windows event classification and confidence-aware review routing with TypeSafe Jev.

a cost-aware LLM gateway with three-tier model routing, optional verification and semantic caching, and reproducible evaluation of answer quality, latency, and cost.

Typed AI decision layer for FHIR R4 data using TypeSafe AI's Jev: quality scoring, Bundle routing and notifiable-disease detection. 95% routing accuracy on 204 labelled records. Synthetic data.

Fit act, review and escalate thresholds per question from labeled data with Jev; checks calibration. Stdlib Python.

Typed decisions from TypeSafe AI's Jev model inside TYPO3: a decision editor and playground in the backend, powermail_cond operators, and submission routing

Evidence-gated registry of Jev question packs — curated questions, golden cases and measured evidence for Jev-compatible decision endpoints

GitHub Action that labels and prioritizes new issues with TypeSafe's Jev model. Jev judges, code decides.

Jev (TypeSafe AI System One) 生态调研：开源复现规模、JevBench v1.4.2.2 全量解读、可获取性与许可证陷阱、router 生态、Jev+安全科研切入点

Compare règles, Jev et un serveur Shieldstral sur les mêmes cas français.

Declared semantic merge policies with exact changed-line evidence.

Typed Jev decisions in Redpanda Connect pipelines

Guarded asynchronous decision nodes for Unity Behavior, using a server-side DecisionPacks gateway.

arJev — a daily arXiv digest engine that ranks against your living Obsidian vault: deterministic lexical front-line, Jev confidence-gated rerank, and a learning loop where your own notes are the ground truth.

Pandu, is mini Jev model, inspired by Jev. Tiny policy models for fast, local, uncertainty-aware decisions in closed-loop AI environments.

X draft gate powered by TypeSafe Jev (System One): hard secret checks + Noul scores before you publish.

Benchmark: Jev decision model (TypeSafe, via OpenRouter) vs local ML on SST-2, AG News and UCI Adult — accuracy, calibration, latency and cost

Automated customer ticket triage engine in Python built with TypeSafe Jev (System One AI) for sub-100ms structured decision routing.

Google Apps Script that classifies Gmail messages with Jev, TypeSafe's decision-only AI model. Runs on a time-driven trigger, sends each email's sender, subject, and trimmed body to Jev (via TypeSafe, OpenRouter, or Vercel AI Gateway), and applies Gmail labels based on typed Choice, Score, and Noul

Calibrated DevOps tool-router distilled from Beam-1.0-501B via RLCD. Implements an honest S1 → S2 confidence cascade for low-latency, high-reliability routing.

Dynamic per-turn model router on free OpenCode Zen + Go models (fork of gargpratyush/jev-router)

A GitHub Action that fails a pipeline when code breaks a quality rule using Jev decision model.

Do you need TypeSafe's Jev, or is cosine similarity enough? A $0, reproducible benchmark on Banking77 intent routing.

Jevを用いたLLM・Web検索・RAGのルーティング実装と、GPT-4.1-miniとの判定精度・処理速度・APIコストの比較検証

The Goodvibes platform with Jev integrated into the decision model for guardrails, safety, etc.

Typed-decision routers between a small and a large LLM, fine-tuned from nano-jev

Minimal Jev judgment core for Pi and other TypeScript apps: noul/choice/score across TypeSafe, OpenRouter, Cloudflare, Vercel, and local llama-server (JevK5)

Cost-conditioned intent routing: selected Qwen3 belief model, recovery actor, reproducible benchmarks and inference.

An mini node orchestrator that uses Jev to handle routing to different LLMs based on difficulty.

Cache-aware Jev model orchestration for Pi with sticky routing and safe DeepSeek failover

A local-first employee expense reimbursement demo exploring Jev's Choice, Noul, Score, confidence-aware decisions, and human-in-the-loop clarification.

Put Jev's three headline claims on trial on your own GPU. One command, a 0.5B local model: measures constrained-scoring speedup, structural type safety, and the confidence calibration nobody has published evidence for. Runs on a 6GB laptop card.

BYOK playground for TypeSafe Jev (System One): Choice, Score, Noul examples for production gates.

JEV Router macOS downloads and release notes. Source code is private.

Merged, deduplicated, daily-updated catalog of Jev / TypeSafe System One projects with a filterable GitHub Pages site

Hotdog / not hotdog demo with TypeSafe Jev (noul + box) via OpenRouter

JEV 检索台 · 对话式网络信息筛选工作台：官方 /v1/systemone 协议接入、noul/score/choice 三原语判定、真伪+匹配+分类、脱敏前置、知识库与提示词工作台（纯前端，双击即用）

RLCDAlignBench: 44 alignment-failure detection benchmarks and code for 'Just Ask Jev' (RLCD zero-shot detector of AI alignment failures)

A family of System One-style models fine-tuned from Qwen3.5, designed for one-pass typed decisions with calibrated probabilities.

A curated list of tools built for Jev — TypeSafe AI's System One model for typed decisions.

🔥🔥 Awesome Jev: open-source models, projects, benchmarks, and independent evaluations for Jev and System One models.

JevBench by Benchmark Heaven: benchmark and leaderboard for AI decision models, measuring typed decision accuracy, calibration, latency and cost.

Unofficial TypeSafe Jev showcase — System One decisions, not chat.

Open-source alternative to TypeSafe's Jev: a System One style model layer that gives typed, calibrated decisions from any open-weights LLM in one forward pass (HF + vLLM), with honest benchmarks

Use Jev (TypeSafe's System One model) as a calibrated reranker: one call, up to 30 documents, a probability per document. Apache-2.0.

🔥🔥 Research papers on Jev, TypeSafe's System One model, and the open models built in its shape.

Open-source Jev-style System One decision model. Gemma 3 270M with a scoring head — fast, calibrated decisions in a single forward pass. No text generation. Inspired by TypeSafe.ai's Jev.

ReflexBench — open benchmark and evaluation harness for System One models and typed decision engines

A System One model in the browser: typed, calibrated text decisions on the user's device. Learns from an LLM when it isn't sure.

Code and data for evaluating Jev, a System One model, on scientific decisions and how its choices affect downstream results. arXiv:2609.24965

Typed, calibrated decisions for software in a single forward pass: an independent implementation of the System One model architecture.

Decision forensics for System One models. Jev returns a calibrated probability and cannot explain it — jev-xray measures which part of the input caused it, by ablation.

Measuring Exploration and Exploitation in Organizational Text with Open System One Models

Open System One model for Apple Silicon: typed decisions with calibrated probabilities from one forward pass.

playing with jev-latest evaluation model

A comparative study of Jev 1.13 and Large Language Models (LLMs) for phishing email detection, evaluating classification performance, probability calibration, inference latency, token consumption, and API costs.

Reproducible benchmark evaluating TypeSafe AI's Jev (System One paradigm) on Brazil's ENEM 2025 standardized exam. Evaluates typed decision-making, domain-specific accuracy, and RLCD uncertainty calibration against open LLM baselines with an interactive GitHub Pages dashboard.

Benchmarking lightweight and open-source LLMs and system one models on automatic schema mapping and AI data categorization from sample data.

Distilling a calibrated decision model into Qwen3-1.7B: SFT vs. distillation vs. GRPO, plus deployment pitfalls. Free Colab T4.

Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)

JevK5: open-weight alternative to TypeSafe Jev. Typed decisions with probabilities in one forward pass; Apache-2.0 weights and code.

Non-autoregressive decision engine on ModernBERT (151M) with calibrated uncertainty (RLCD), TypeSafe AI Jev benchmark audit, and in-browser WebGPU playground

24/7 market-making system around Jev (TypeSafe System One) decisions: deterministic state, calibrated judgments, hard risk vetoes.

Let Jev read every new arXiv paper each morning and surface the few you should read. Plain-English interests, calibrated probabilities, ~$0.06/day, fork and go.

Open, calibrated System 1 decision models: typed choice / score / yes-no answers with a probability for every option. 400M to 80B; CPU, NVIDIA, Apple Silicon, LM Studio, Ollama and vLLM.

A tiny jev-like model that answers Choice, Score and Noul questions in one forward pass and returns calibrated probabilities. MLX or PyTorch, fully offline, System One compatible.

Train JEV-style System 1 decision models on Qwen3.5 and Qwen3.8-27B: 24-slot decision head, KL distillation, calibration and serving. 在 Qwen 上训练 JEV 风格的 System 1 决策模型。

A decision model that returns calibrated answers to yes/no, choice, score and multi-label questions.

A 4B decision model built for Korean.

A driving simulator on real OpenStreetMap streets where TypeSafe's Jev model drives the car, with a decision inspector and a benchmark.

Local Jev-compatible evaluation server: POST /v1/systemone with typed noul/choice/score, open weights, no waitlist

Nushell module for the TypeSafe System One API: typed decisions with calibrated probabilities

Benchmarks and a playground for TypeSafe's Jev (System One) model: chess, and who-is-the-player-talking-to for speech-to-text game NPCs

Three 4B decision models for English, Turkish and German, with per-option probabilities, GGUF builds and the Jev API.

A word-level language model whose output layer is Jev: n-gram drafter, Noul chunk verification, bits-per-token eval

Tests, calibration audits and failure-mode studies of Jev (TypeSafe System One): jaggedness, consistency, injection, abstention.

Fine-tune Laya, the open System One decision model, for any typed-decision task: synthetic data, RLCD, calibration and long context.

Local Jev-style System One decision models on Apple Silicon Macs: AnyJev, Kev, Laya (Ollaya) and CLM benchmarked on English, Korean and BANKING77 (accuracy, latency, order-flip)

First Chinese-language calibration benchmark for Jev-class 'System One' decision models: dataset (CC BY 4.0), 5-model eval incl. Jev & NeoHorse, temperature refit, robustness tests

Evaluation tools for Jev-compatible decision models, covering accuracy, calibration, hidden instructions, padding and consistency.

An open-source System One decision model — Kahneman's term for the fast, automatic judgment faculty. Calibrated choice/score/noul probabilities in one forward pass on small fine-tuned open models. Local on Apple Silicon (MLX), Jev-compatible API, audited experiment log.

How good is Jev, a decision model, at finding personal information? Head-to-head against rules, local PII models and LLMs on three datasets: accuracy, leaks, calibration, cost and latency.

An 184M-parameter monolingual decision engine delivering calibrated distributions from one 22ms forward pass while matching models 2.3x its size.

1,000-decision behavioral benchmark of Jev across 25 deterministic reasoning families.

jev-lite: a ModernBERT-based typed decision model (Bool / Choice / Score) with calibrated probabilities — a fast replacement for LLM-as-a-judge

Measures how well TypeSafe's RLCD-Jev model spots real secret credentials in file snippets

EarthJev: calibrated, non-generative answers to natural-language questions about 12-band Sentinel-2 images. One forward pass returns a probability for every option of every question; no token is generated.

Local benchmark comparing TypeSafe Jev and Laya-MLX for structured issue classification

JEV-guided context compaction for OpenCode, built on a measured finding: noul calibration drifts with the shape of the state, so a fixed keepThreshold is unreliable

Jev System One AI & RLCD Reproduction Engine: Sub-70ms Calibrated Decisions with SmolLM-135M and 0 Output Tokens

Code for 'When Multiple Choices Are Correct: Asymmetric Probability Allocation in Jev and Other Decision Models' (arXiv:2610.29217)

Zero-shot multilingual named entity recognition (NER) powered by TypeSafe System One decision models - nested entity trees, custom JSON taxonomies, calibrated probabilities, 24-language coverage, bundled tokenizer, no local model. A GLiNER/spaCy alternative that runs jev, mammoth-1, clef and 20+ dec

A lightweight Jev-style decision head, plus the Strata patches that let one resident Strata process serve both LLM generation and Jev-style typed decisions from a single set of weights. Ships trained head weights, patches, and training/eval code.

Decision models via Ollama (Jev /v1/systemone): ask typed questions — choice, yes/no, score — get calibrated probabilities back, no prose to parse. Stdlib-only Python; CPU-first with tev1:0.8b.

Small image+text decision model: Qwen3-VL-2B + LoRA + answer head scores your options in one forward pass (no generation). Ollama-generated synthetic data, runs on an 8 GB GPU.

One-line local AI decisions. Calibrated choice/noul/score from open models. No API keys, no cloud. Auto-downloads models. Native ARM64/Linux or Docker fallback.

Does a System One classifier belong in a sequential decision loop? A regret and calibration audit of TypeSafe's Jev, using 2048 as an instrument.

Does a System One classifier belong in a sequential decision loop? A regret and calibration audit of Jev classifier, using 2048 as an instrument.

Rebuilding Jev's calibrated-decision interface on scikit-learn, and testing it against the real Jev API. Code for the TDS article.

Ru-Jev: an open Russian typed-decision model. Calibrated choices over a fixed option set, on CPU.

Does splitting a typed-decision question help? Jev vs OpenAI Decisions vs Laya, OpenJev and a local decoder, in English and Turkish

System-1 Non-Autoregressive Single-Pass Neural Decision Engine with RLCD Calibration, Choice/Noul/Score Primitives & JevEval

Reproducible zero-shot Jev benchmark on all seven LexGLUE tasks

E-commerce review classification with TypeSafe's Jev: Google Sheets → typed decisions (topic, sentiment, defect/refund flags) with calibrated confidence → BI dashboard. Includes accuracy, calibration and cost benchmarks vs. an LLM.

Free, open-source Jev alternative: fine-tune the open Laya decision model on your own labelled data with Unsloth. Typed choice/score/yes-no questions, calibrated probabilities, base vs fine-tuned eval, Jev-compatible serving, and a RunPod launcher with a spend cap. Works with runhug.

Position paper: the Hidden-Markov and fuzzy primitives missing from TypeSafe AI's Jev and System-One decision models. Two lemmas, one principle (Deferred Crispification), one architecture (BSF-S1).

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

Where one-pass decision models break: a local Jev-style model + 4 stress tests

Benchmarking Local LLM's against the TypeSafe AI watermark for logic processing

Jev-style “System 1” decision models replace autoregressive text generation with a single forward pass that returns typed, structured answers. I introduce an open, deterministic evaluation bench that audits Jev-style decision models

Reproducible MLX and Colab experiments for Jev-like typed decision models with Qwen, Nimble, AutoJev, and JevBench

Independent calibration audit of TypeSafe AI's Jev: when it says 80%, is it right 80% of the time? Tested on EIA weekly petroleum reports against an LLM classifier via LangChain, with exact ground truth from EIA data.

Atelier web pour mettre à l'épreuve Jev (TypeSafe AI) face à un LLM : missions, duel, calibration. FastAPI + HTML/JS, sans build.

TypeSafe's Jev vs gpt-5.4-mini and gpt-5.6-luna on four public classification sets: cases, per-item answers, scoring, charts

Turn any state into structured, calibrated decisions. Open-source System One playground: typed questions (choice / score / noul) → probability distributions, not free text. Four backends incl. Mapika/decider RLCD weights and an offline mock. Docker-ready, MIT.

Comparing Jev’s zero-shot predictions with few-shot SetFit and ModernBERT models across label budgets, confidence calibration, and selective prediction.

Local playground for TypeSafe System One (Jev): compose Choice/Noul/Score questions in the browser, call the live API, and pull real questions from the arXiv 2609.37647 benchmark (offline)

Get typed decision probabilities from context and candidates, instantly—no autoregressive generation or JSON parsing.

LogitTrail (formerly Mini Jev): inspectable local typed decisions, candidate probabilities, and reproducible evaluation.

Curated arXiv papers on Jev, System One and typed decision models, with bilingual notes, evidence and BibTeX.

DATASCAPE 2026: Package Quality Control using Jev System One Decision Architecture & RLCD (Reinforcement Learning for Calibrated Decisions)

Calibrated single-pass decision model: typed yes/no, choice and score questions answered with probability distributions. ModernBERT encoder, segment-masked packing, full MLOps stack.

Serve the experimental DiffusionGemma-Jev diffusion model on Cloud Run GPUs (vLLM, RTX PRO 6000), with demo apps and published benchmarks.

Tax document page classifier built on Jev decisions. 100% strict accuracy across 261 IRS forms, ~$0.001 per page.

Open Jev-style typed-decision model that also takes images: photo + app state + typed questions in, calibrated probabilities out, locally.

Small System One decision models (17M–400M) for Yes/No, Choice, Score, and reranking. Training toolkit, Python inference, and ONNX support for Node.js and browsers.

Open replica of TypeSafe's Jev: typed calibrated decisions in one forward pass, on Gemma 4 E2B / Gemma 3 270M (Modal)

openvons (open-Jev): 有限選択肢に確率で答える判断層 — テキスト / 画像 / 日本語音声コマンド

Simplified RL for Calibrated Decisions: parallel constrained JSON decoding + GRPO with proper-scoring-rule rewards + calibration eval (Jev/RLCD reconstruction)

Typed decisions (noul / choice / score) with calibrated probabilities in ComfyUI, on the text-encoder engine. Currently imajev.

Jev-shaped typed-decision model (state + Choice/Score/Noul questions -> calibrated probabilities, one pass) on ModernBERT / DeBERTa / LLaDA-MoE, with measured latency, accuracy, calibration and training cost

Brier, by Mangaba AI: typed, calibrated decisions (choice/score/noul) for Portuguese text, on your own servers. Decisões tipadas e calibradas em português. Brier v5 e Brier-0 (Qwen3-4B + LoRA); API /v1/decide. Licença comercial desde 5.0.0; v2 e v3 seguem Apache-2.0.

Zero-shot reranking and calibration of decision models (Jev, Laya) on TREC DL 2019/2020

Jev-style calibrated decision model (Choice/Score/Noul) on Qwen3.5-0.8B

A small open decision model: state + typed questions -> calibrated probabilities. A Jev / System One re-creation on Qwen3.5.

A nano replica of Jev: parallel decisions, dynamic candidates, and an end-to-end training pipeline.

mini-Jev: what a Jev-style typed-decision interface looks like on a frozen Qwen3-4B — read the option letter's logits instead of generating JSON. Preregistered experiment, results, teaching bench.

EmbodiedJev: MuJoCo robot decision workbench with MiniCPM5-2B, Jev and compatible model APIs

Blazing fast, self-hosted structured decisions for open models with a TypeSafe AI compatible API, written in Rust.

Dohnuts builds small multimodal models for direct decisions. -> System One model

A drop-in, CPU-only proxy for System One models (Jev, Rune, Clef). It learns each repeated question from the lead, then answers it itself.

clawd-JEV-trading machine: JEV-on-Solana paper trader. TypeSafe jev-latest brain, dynamic action space, CoinGecko regime + Supermemory memory, Jupiter/DFlow venues. Dry-run only.

Empirical experiments and API research for TypeSafe's Jev System One model trained using RLCD

Semantic ifs from open models, on a 3090 at home. Independent; not affiliated with Jev or TypeSafe.

Native vLLM serving for Jev decision models

The open-source System One decision model. Sub-15ms, non-autoregressive, local drop-in alternative to TypeSafe Jev.

Turn any open model into a classifier/jev endpoint

Jev-compatible API endpoint based on open models (prefill-only)

A decision cache for TypeSafe Jev-class models — memoize decisions so repeats are free, deterministic, and shareable. One 2 MB binary.

Distill a repeated Jev classification task into a local model, on the fly — same answers, your hardware.

Open-source paper-trading research app integrating TypeSafe Jev, animated 3D model pets, and a safe offline demo.

Jev-like decision models that can also see images — open weights on Qwen3.5, run on your own GPU

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.

Lev: Thinking Fast and Deep with Looped Decision Models. One checkpoint, multiple depths: System 1.5 decision models that think in loops, not tokens.

Awesome list of Jev use cases. Compared with GPT Models (LLMs) across on cost and speed.

JEV-mini is a minimal learning project to train a fast decision-making model.

Zero-memory System-1 decision engine & TypeSafe Jev runtime powered by Procedural Boundary Dynamics (The Zero-VRAM Gauntlet).

Declarative semantic linter and architectural code auditor for local open-weight kev and remote System One (/v1/systemone) models.

MiniCPM5-2B-Jev: High-Speed System 1 Decision Model (/v1/systemone, Jev-Compatible), #1 <=2B Model on JevBench

Curso completo, cuadernos Google Colab y guía bibliográfica sobre Jev (TypeSafe AI), Modelos System One y RLCD

A super lightweight structured decision engine for edge devices. 零依赖 <10MB 11378 QPS

A super lightweight structured decision engine for edge devicea

Typed Jev decision primitives for Reachy Mini applications.

Jev (TypeSafe) vs GPT-4o-mini: classifying YouTube comments with a Judgement Model — demo for CT-YT-2026-043

MiniMind Jev probability scoring page (GitHub Pages)

Images or videos in → YOLO model out / 图片视频扔进去 → YOLO 模型训出来 | A one-key integrated pipeline for the automated labelling and training of YOLO, based on the capabilities of Jev bBox.

Compare GPT-6 Astra, GPT-4.1 mini, and Jev controlling an xArm7 in MuJoCo via direct Cartesian commands.

Reproducible Jev and GPT-4o mini classification experiments, findings, and explanatory report.

Prépare la revue des cumuls d’aides de minimis à partir d’éléments sourcés.

Interactive workspace for comparing baseball roster acquisitions — positional coverage, playing-time transfers, and reproducible scenario analysis, demoed on five 2026 Red Sox storylines.

Comparative judgment of ASAP essays by Jev (a decision-model API), gpt-oss-20b and gpt-5.4-mini: code, prompts, judgment logs and results

Three-layer UAV tracking research: multi-object perception, Jev target grounding, and learned action policies coordinated by a Harness. Design-stage; no rollout planner.

Clasificador de comandos de shell de una sola pasada (solo lectura / reversible / destructivo), inspirado en Jev

Reachy Mini contrôle les commandes : modèles de décision (Jev, OpenAI) contre LLM génératif, latence et coût mesurés sur le robot.

MiniCPM5-2B-8bit as a Jev-compatible typed-decision service on Apple Silicon (llm2jev + MLX), with reproducible JevBench results

a simple model trained on inspiration of jev

Train your own tiny Jev (System One decision model) — v0.1: frozen Chinese encoder + decision head trained from scratch, CPU-friendly. Private during build. 从零训练你自己的迷你 Jev。

A lightweight and easy-to-follow implementation of Jev.

Three AI trading bees on OKX, every decision by Jev. Paper trading by default. Not financial advice.

Rust reimplementation of Laya, a sub-35ms non-autoregressive typed-decision engine (ModernBERT encoder + RLCD-trained decision head), built from scratch on candle.

Where does a decision model's generalisation come from? RLCD on Qwen3.5-4B, held-out sets grouped by training-data coverage, JevBench and three external suites.

Telegram bot for group chats: karma for useful messages, anti-spam for newcomers, a Mini App leaderboard. Judged by the Jev model.

Local MLX decision probability demo derived from harshatheg/Qwen-2.5-1B-RLCD on Hugging Face

[中文化] Multilingual, non-autoregressive System 1 decision engine. Typed decisions over 100+ languages in a single forward pass (33 ms), trained with RLCD.

A verified, community-maintained catalog of 992 open-source projects built with Jev.

Search the web with TypeSafe's Jev: source selection, query understanding and relevance ranking. Built with Search1API.

VideoJev: System One Model for Video Understanding and Interaction

Fish-style zsh history autosuggestions ranked by Jev (TypeSafe)

🧹 Fun project: a Chrome extension that asks a tiny AI decision model (TypeSafe Jev) "is this DOM element an ad?" and pops it off the page. BYOK, no backend, not a real ad blocker.

Trading bot with the all new TypeSafe AI's first system one model named as Jev

PowerShell decisions with TypeSafe AI's Jev model: https://typesafe.ai/blog/introducing-system-one-models-and-jev

Control your Mac by voice. Speech → Jev (TypeSafe AI System One model) typed decisions → macOS actions. Menu-bar Swift app.

本项目是对 jev 模型 的调用尝试，基于 search_jev使用文档.md（内部使用文档）与 jev官方文档.md（来自 https://docs.typesafe.ai 的官方文档）整理而成

Verified, screenshot-rich awesome list of projects built on Jev — TypeSafe AI's System One model for typed decisions

Control a robotic arm using systemone models

Using Jev/SystemOne Models to build use cases

Basic sample app to demonstrate primitive types for system one models like TypeSafe's latest Jev decision model

Title and abstract screening for systematic reviews with Jev, a System One model

An AI-driven macro-sentiment analysis terminal that reads thousands of breaking global financial news events in real-time, scores their market impact using a custom Contrastive-LM System-One model on a serverless GPU!

Xtags: a Chrome extension that labels each post in an X timeline with what it is trying to get from you — inducement, provocation, promotion, machine-generated, persuasion, entertainment, information — drawn as a tag plus a probability right after the timestamp. A side panel reports session counts (

One AI trade decision every Monad block. Jev on Kuru MON-USDC.

Open, lightweight multimodal decision models for text, images, and video. Train and run them yourself.

Control a real browser by voice. Jev (TypeSafe System One) decides intent + target in ~300 ms per spoken word; Playwright acts — often before you finish the sentence.

Browser use using Typesafe's Jev model

An educational Jev-like visual inference experiment on Apple Silicon: shared context, direct candidate scoring, and local visual demos.

Live Jev trader on Hyperliquid

Talk to your Mac. Local whisper.cpp + one Jev (TypeSafe) call per command + macOS automation.

Jev 模型介绍与实测：通过 Choice / Score / Noul 将自然语言转为带类型的判断与概率，用于分类、评分和路由；支持与 DeepSeek 等模型对比评论打标、速度与结果，含 CSV/Excel 导入、原速回放与离线报告。

Bud Decision studio is an easy to use cross platform desktop application & serving system for Jev like models locally.

Jev 决策模型驱动的 UI 自动化测试框架：不生成「下一步做什么」的文字，而是在候选元素里直接打分；一句话写用例，pytest 执行，Allure 出报告，每个操作决策中位 458 ms（UI test automation driven by the Jev decision model — scored decisions, not generated prose; ~458 ms per decision）

Can a model design a dashboard? A console whose whole design system is generated at runtime by Jev from a one-sentence brief.

1v1 Jev quickscope arena — Three.js + TypeSafe System One

Backtest Jev (TypeSafe) as a BUY/SELL/HOLD trader on NQ L10 order-book data

Dashboard and local proxy for SystemOne decision models - Jev (TypeSafe API) and self-hosted Laya. Send one state with up to 8 typed questions, get structured decisions (choice / score / noul) rendered as charts.

Community index of tools, models, and real-world use cases built on Jev.

Showcase of TypeSafe JEV classifying emails: spam (noul), category (choice) and urgency (score) - Next.js + MUI app and Python script

Jev (TypeSafe System One) × ASReview SYNERGY abstract screening demo — Choice/Noul vs gold labels

Daily demo applying TypeSafe's Jev model to Spain's official gazette (BOE).

Run efficient local Choice, Score, and Noul services with KaLM-Reranker-V1-R2 for structured judgments without generating text.

Jev Buddy: the developer guide to Jev, the AI decision model (choice, noul, score), with code, business examples, a playground and calculators

Six small apps and a workbench that show what TypeSafe's Jev (System One) model can do

Type anything. 200 yes/no AI judgments light up as a live heatmap in ~200 ms, from one parallel call to TypeSafe Jev. Zero deps, bring your own key.

Jev Made Simple - Interactive decision engine and probability visualizer

Non-blocking, provenance-checked Jev decisions for Bevy ECS.

Turn typed Jev review findings into exact, undoable Premiere timeline markers.

Native Unreal Engine 5.8 StateTree tasks for finite, revision-guarded Jev decisions.

Jev（TypeSafe System One）判定模型演示：Choice / Noul / Score 三原语 + 三档阈值分流，离线可跑

Semantic search for log and code fragments using Jev

Laboratorio progresivo para aprender y demostrar Jev, el modelo de decisión tipada de TypeSafe (Choice, Score y Noul), desde lo básico hasta técnicas de producción.

Run a Pokémon Champions battle arena locally with Jev, TypeSafe, and Pokémon Showdown.

Playwright + TypeSafe Jev (System One) decision-map demo for semantic test automation. Compact snapshots, batched typed questions, offline mock.

TypeSafe AI の Jev で、飲食店の売上低下の原因を判定してみる作業場所

Snake auto-played by TypeSafe's Jev model: one System One choice per tick, legal moves and facts generated in code

字面理解、计数不可靠、日期比较、多级间接、无关细节大 state、对抗内容、指令与 criteria 矛盾、结构性不变量、生成。官方建议"拆成原子问题在代码里组合"。

同一审核 rubric、8 个 Choice × 每条件重复 15 次。TypeSafe raw agree 90.8%（8 题有 2 题翻转，但 conflicts=0 —— 翻转只发生在「具体标签 ↔ uncertain」之间）。加一条 abstention 规则（用 probabilities 要求 top probability ≥ 0.60，否则 uncertain 转人工）后一致率升到 99.2%，automatic 74.2%，不增加模型调用。对照：Haiku t=0 100%、Opus 4.8 reasoning 92.5%、gpt-5.4-mini t=0 99.2%。官方声

官方明说 confidence 是从 probabilities 分布算出的统计量，分布越集中越确定；Noul 不带 confidence；官方明说"你没有被锁死在我们的定义上"；阈值应"从保守值开始、在你自己数据上测"。

*"Coding harnesses like claude, codex, cursor have shipped some kind of way to classify dangerous actions… Up until now, this classifier step has been locked away in the closed source parts of the harness. Now that a cheap and performant classifier model exists, we can take the same pattern and adop

TypeSafe 新增 Team 页 + 导航新增 Enterprise 入口（产品化/商业化信号）

Vercel 官方 changelog 宣布 Jev 上架 AI Gateway

llms.txt 全站 111 页；本地原有 30 页，diff 后发现 cookbooks/ 下 18 篇配方（含可跑代码、真实数据集、实测数字）。逐篇拉取并加 type: official-cookbook frontmatter。

npx -y skills@latest add typesafe-ai/skills -s typesafe-ai -g -a '*' --copy -y → 安装到 ~/.agents/skills/typesafe-ai/（单文件 SKILL.md，149 行 / 10 KB，覆盖 79 个 agent）。安全评估：Gen=Safe、Socket=0 alerts、Snyk=Low Risk。唯一失败项是 Eve（不支持全局安装，无影响）。

本轮 diff 未发现新增页（30 篇 + 18 cookbook 已齐）；说明采集器的 diff 逻辑正确。

端点 POST https://api.typesafe.ai/v1/systemone；jev-1.13.0，别名 jev-latest/jev-preview；64k/请求、state+最长问题 32k；限流 250k tok/s、1200 req/min 且动态调整；仅文本输入；Choice≤255、Score 2–10 档；不 fine-tune、所有账号共用同一套权重；不用客户数据训练；英语最准。

preview 端点 /preview/evaluation → 稳定版 POST /v1/systemone；prompts 数组 → questions map；responses 数组 → answers map；字段改名 probability→noul、chosen→choice、expectation→score；choice 概率由数组变 map；score 概率为 v1 新增；认证不变。

A curated list of public projects, integrations, and discussions built on Jev — TypeSafe AI's System One model for typed decisions.

A curated, source-backed list of projects built with Jev, TypeSafe AI's System One model for typed decisions.

A community directory of projects built on Jev, TypeSafe AI's System One model.

Everyone now talks about the architecture that&#x27;s not auto regressive and does lightning fast probability prediction with a json schema. I worked on this literally one year back in March 2025, published an arxiv paper, pushed the model to huggingface along with the pypi pack

I built a quiz game around TypeSafe&#x27;s new Jev model, which has vision capabilities. Jev looks at a picture and a question and picks an answer (or taps a place) in about a quarter of a second. I&#x27;ve always loved quiz games and thought this would be a fun way to see what i

Evidence-backed use cases, patterns, and guidance for building with Jev, TypeSafe AI's System One model. Every claim is labeled and sourced.

A curated projects built with Jev, TypeSafe's System One model.

Hi HN, I&#x27;m Furqan founder of Nebula. We&#x27;ve spent a lot of time building, using and breaking browser agents. The main issues we ran into were costs and speed. So we built our own and open sourced it. It&#x27;s pretty simple, give it a task and it drives Chrome faster and

! pip install jev-bisect ! export TYPESAFE_API_KEY=&quot;your-typesafe-api-key&quot; ------- from jev_bisect import bisect result = bisect( &quot;How many slices in a pizza?&quot;, min=0, max=100, ) print(result.answer)

I&#x27;ve been fine tuning my own decision models lately and I&#x27;ve been interested in how there are no real multi-turn harnesses out there for decision models. I decided to build my own. The standard claude code style interface doesn&#x27;t quite make sense with a decision mo

Just a few weeks ago, all that my feed had was people JEV-maxxing. Everywhere, on every platform, every other post, just JEV. Then we got these CDMs from ScaleDown, which not only classify like jev but also do compression and summarization, as well as extraction, with no token ou

I&#x27;ve seen a few impressive demos of Typesafe&#x27;s new paradigm with System One models, and it seemed really clear to me that this was probably the direction the industry, especially for autonomous agents, was headed. It&#x27;s been a while though, and the major providers s

All software makes decisions. Code handles the deterministic ones. Decision models like Jev handle the judgement calls. A decision model takes context and questions with a fixed set of choices, and returns answers with a calibrated probability. No prose, nothing to parse. Decisio

Open-source projects that provably call Jev, TypeSafe AI's System One model. Every entry links to the line of code that proves it. Refreshed daily.

A crawler-maintained directory of projects built on Jev, TypeSafe AI's System One model. Daily GitHub + npm sweep, human-merged.

Projects built on Jev (TypeSafe AI's System One model), curated by Jev itself.

108 条声明对照：Jev 的校准并不独特（含作者自我修正）

cellcog.ai 追踪页 自称分层记录 *"claims / concedes / measured / found, dated"* → 与本库锚页设计意图一致，值得长期对照

在两个带标签数据集（Banking77 与 Web of Science）上独立测量 Jev 校准，协议在出结果前冻结，原始 JSONL 与图表已提交。原话：*"no routing parameter transferred between the two datasets"* —— 最优阈值、两模型精度差的符号、以及路由是否划算，全都变了。诚实标注：Web of Science 的标签来自出版物元数据，部分误差是标签歧义。

无 API key、无账号的零样本分类 HTTP 服务；提供 openapi.json(OpenAPI 3.1) 与 llms.txt；附带可安装的 agent skill（*"paste it into any coding agent… npx skills add"*）

6 个问题的准确率/Brier/分桶可靠度：Brier 0.036–0.149；高置信桶（0.9–1.0）多为 100%（egress 15/15、outside_repo 14/14、prod 10/10、secrets 10/10）；但 destructive 在 0.8–0.9 桶是 0%（3 个样本全错），仓库自认有一个问题不达其 0.85 门槛。仓库默认以 observe（影子模式）发版，原话"不该凭 README 说分类器好就开强制，自己跑"。

三个分类任务上对比"每题一个直接 Jev 问题" vs "12–14 个打分维度 + 本地拟合权重"；5,477 条测试行、3,410 万输入 token、共 $1.43。日语 NLI 上分解 0.9076 vs 直接问 0.8373，但分解把约 25× 更多"困难的良性样本"误判为攻击，四次修复尝试全部失败。

在自有带标签数据上拟合每问题置信度阈值 → 留出集验证 → 估算回退流量 → 在 CI 里复查已锁定阈值。索引标注：*"thresholds fitted on fewer than about 100 labeled rows should not be trusted"*。

把 Jev 接进 DSH，注册原生工具 jev_decide（走 ctx.tools.register + DSH 强制 JSON Schema 子集，即 MCP 工具的注册路径），零依赖、不 import 任何 @deepseek-ai 包；两种传输（原生 TypeSafe API / Vercel AI Gateway）。

A complete System One model for emotion — emotion reading as typed decisions

New：InfoWorld（2026-09-17） —— 企业媒体视角，用例清单：*"which tool to invoke, which action to take next, whether a request should be approved, when a task…

OOD 校准审计："Jev 知道自己不知道吗？"

把 Jev 描述为 *"a small and customizable System One decision model"*，并明确 *"Jev can't reason or write explanations; anything heavier gets split into separate questions and recombined in code"*。

*"Your AI reports 0.95 confidence. That is not automatically a 95% chance of being right."*，并讨论"把阈值设在人仍会复核的位置"——与本库铁律 + 阈值方法论一致，且他是 AI 教育知名账号 → 概念正在被正确传播

X：@heyDhavall 的简洁概括（*"why make software wait for an LLM to generate paragraphs when it really just needs decisions?"*）

*"got Jev to review my PRs. ~200x cheaper than Claude and it answers in half a second, 6 real PRs… $0.00007 each. 1,000 PRs = 7 cents vs ~$14.50 on Opus 5"*；粘贴一个 diff → 一次调用 → 14 个类型化检查以概率返回（硬编码密钥、SQL 注入…）

1862 points；评论里正面为主（doom demo 酷、态度好），有一条被广泛引用的猜测"能替代 pipeline 里 40–70% 的 LLM 调用"。

先例清单：ibl.ai 指出 CriticGPT 与 Prometheus 2 都在 2024 年就做"判断模型" → 写进优先权争议页 §4d，与 Laya 的指称分开记；本库判断 Jev 与它们的真实差异是"把判断做成带类型与概率的一等 API"

创始人第二段"原始初衷"（比第一段更锋利）：Diogo Almeida 经 Pat McGuinness Substack 转引

又一份"本地 vs 托管"选型实录：astgl.com（James Cruce，09-22）—— 421M 开源模型在其 frozen replay 上胜过自制路由；他最终不选 Jev 是因为约束（数据不出本机、可控回退），并主动限定*"whether Laya beats Jev…

*"Teach your coding agent to try Jev before it writes another regex, and to write down what happened."* 它让 coding agent 在发现某步骤是有界判断（classify/tag/triage/size/rank/route/filter/verify/match/gate）时先试 Jev，再把这次尝试（无论是否采纳）记录到一个 lab 目录供下个项目复用。支持 Claude Code / Codex / Cursor / Gemini CLI / OpenCode / Pi / He

把我们此前零散收集的生态补成一张完整的图，结构为「官方资源 / 客户端与集成 / agent 与开发者工具 / 浏览器 agent / 游戏机器人与交互 demo / 评测与独立研究 / 展示与实战记录」。最后复核 2026-09-17，并明确声明与 TypeSafe 无隶属关系。

四条精准告诫（已写入 IO 契约 §S8） —— Wavect 独立评测：

litellm/types/proxy/guardrails/guardrail_hooks/typesafe.py 实现了 compaction guardrail：*"A completed tool exchange is dropped when Jev scores the probability that it is still needed below this value. Defaults to 0.2."*

独立第三方与我们的术语纪律收敛：HarnessRouter（2026-09-20）

*"A server implementing the TypeSafe/Jev HTTP API with Qwen3.6-35B-A3B on SGLang."* 单容器单 B200；SGLang 0.5.19 Rust 前端 + radix caching + breakable prefill CUDA graphs；可一键部署 Modal（unauthenticated=True）拿到公网端点。工程细节值得抄：API 与推理同生共死（推理挂了 API 也退出，让 Modal 换容器，而不是留一个 HTTPS 活着但后端已死的进程）；预热时故意请求一个未使用 token 的概率以绕开

第 11 份独立评测：克罗地亚语零样本 vs 自己微调的 BERT（Senko Rašić，09-22）

第 12 份独立评测：注入鲁棒性系统测试《Can Jev be jailbroken?》（tanh.xyz，14,495 次记录调用，原始记录公开）

第 9 份独立评测：@juanmacias《Testing TypeSafe's Jev - Legal Corpus》

加上此前的 Go / Rust（×3）/ Elixir / Ruby（×3）/ .NET / Rails，第三方 SDK 已覆盖主流服务端语言

① Noul 的 0.5 不等于"中等"，意味模型分不出来；② Score 不是测量，不要在档位间插值（1.0 可能是"全在第 1 档"或"一半 0 一半 2"）；③ 批量打包 13 问 = 便宜 12.2×、快 10×（测试用 53,777 字符长文档；重复测试未见批量效应）；④ criteria 里放"像真输入的例子"有效：同一 Safari bug，纯档位 score 1.30 @ conf 0.54 → 加相关例子 1.07 @ 0.90，加不相关例子几乎无变化；⑤ 生产规模实测：1,018 篇摘要分类 24 主题共 $0.08、中位 256 ms/篇；⑥ 护栏影子测试：含糊 shel

40 次调用全成功、10/10 分类正确；median 0.643–0.674 s；$0.000025–0.000027/call；confidence 在 medium 档明显更低（0.57–0.67 vs 1.0）——目前唯一独立观测到的校准行为。保留意见：官方 benchmark 里 Jev 76.0% 未明显超过 Luna 76.1% / DS v4 Flash 76.8%。

fx 默认 auto 模式的 safety reviewer 原跑 GPT Luna，换 Jev 后 p95 快 18× 且更准；"coming to Vercel AI Gateway and likely new default"；同司 @fazxes 称 ~5–18× 更快且更准。

财经媒体的独立表态：TechStock²《…Its 445× Cost Claim Is Still Self-Tested》（2026-09-17）：*"…leaves the biggest performance numbers dependent on TypeSafe's…

闭环信号：Archer 那篇文章上了 HN —— Jev's Architecture Unmasked（item 49766351，1pt）

首个上市公司级集成：Aurora Mobile（NASDAQ: JG）的 GPTBots.ai 集成 Jev（09-22）→ *"two-layer AI architecture: one layer that thinks, one layer that…

Jev 报出的概率反映「模型有多确定」，不反映「世界有多随机」。实测——公平六面骰 Choice：参考 16.7%、Jev 报 82.9%、实际准确率 19.0%（76/400）；公平硬币：参考 50%、报 92.0%、实际 52.0%；20 等概率选项 Noul：参考 5%、报 15.0%；预测文档写明 45% → 输出 6.6%；写明 55% → 输出 95.9%（50% 处有陡崖，模型不保留上游陈述的不确定性）。作者免责：*"results are specific to the prompts and conditions in this repository"*。

Curated Jev resources and runnable examples for typed AI decisions.

Source-linked Jev use cases, open-source projects, and official cookbooks

integrated latest decision making model jev to classify emoji reading the semantics of text

新的优先权主张（与 Laya 无关）：HN I built the Jev architecture one year ago and open-sourced it（2pts）→ r/LocalLLaMA 帖；本轮只登记 URL 与主张，未读原文、不下判断，下一轮核实

@typesafeai 在发布日 11:17am 发布后，5:43pm 被 CEO 报告被盗，5:54pm 确认钓鱼，11:49pm 夺回（"JEV LIVES"）。

Contrastive Language Models (CLMs) are a new class of System One model trained with a contrastive learning objective that connects states and actions.

jevos: an open-source alternative to Jev and Microsoft-Decision-1. Yes/no decisions, choices and scores with probabilities, on your laptop's CPU.

JevChat-Windows：聊天窗口旁挂的回复辅助。窗口截图 + 本地离线 OCR 读对方消息 → Jev 判断意图 → 3 条候选一键填入，发送永远手动

⚡ 适合中国宝宝的 Jev 入门教程｜手把手带你了解关于 Jev 的一切——Jupyter Notebook 轻松实验，从三种问题原语到 18 篇实战配方、语音智能家居、模型评测与本地微调，全面掌握 System One 判断模型的开发范式

Turn local language models into Jev-style structured decision models. Get results from text and images with prefill alone—no token-by-token decoding required.

A self-hosted drop-in replacement for TypeSafe's jev, powered by GliFormer.

Jeva: System One Models from First Principles

An on-demand crypto market intelligence and decision-support terminal powered by TypeSafe AI's System One model

typesafe.ai注册机，极致优化，无限jev

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