Lançamentos de 16 de setembro de 2026

O agente converte controles visíveis em um espaço de ações indexado para cumprir um objetivo em linguagem natural. Jev escolhe operação e elemento compatível em uma solicitação; um LLM separado gera texto apenas para TYPE_TEXT.

Resolve pequenas decisões semânticas de roteamento, repetição e evidência lendo probabilidades, sem gerar frases de resposta. Reproduz o padrão de interface do Jev, não seu modelo ou treinamento não publicados, e não tem vínculo com a TypeSafe.

O bot substitui ordens limitadas post-only por bloco Monad e transmite eventos do livro, ordens e execuções ao painel. Com MODEL=jev e TYPESAFE_AI_API_KEY, Jev escolhe compra ou venda; o modelo mock padrão usa uma heurística de momentum.

typesafe-computer-use transforma um objetivo escrito em ações no Mac, lendo a tela com OCR e controles de acessibilidade. O TypeSafe Jev escolhe ação, alvo e site; o código fornece os fatos e um modelo separado escreve o texto livre. É preciso fornecer uma chave TypeSafe e permissões de gravação de tela e acessibilidade. A CLI permite simular antes de controlar mouse e teclado.

decider retorna probabilidades Choice, Score e Noul a partir de estado e perguntas tipadas em uma passagem. A família independente inspirada no Jev usa bases Qwen; os dados de treinamento não foram destilados do Jev. Use a interface Python Decider ou conecte SDKs TypeSafe ao endpoint HTTP local compatível.

Jev Review examina diffs Git ou bases completas em busca de problemas de correção, segurança, confiabilidade, compatibilidade e testes. Jev seleciona evidências, classifica mecanismos e avalia gravidade; quando necessário, escolhe o revisor por Choice. O código aplica os limites e a política do fluxo. Use Node.js 24+, Git e chave TypeSafe para gerar JSON ou consultar revisões salvas localmente.

O jev-router inicia as CLIs nativas por um proxy local e escolhe um nível de modelo a cada novo turno do usuário. Instale com npm, configure JEV_API_KEY e execute jev-claude ou jev-codex com a CLI original instalada e autenticada.

TypeSafe Mario é um controlador NES experimental com telemetria do emulador e observações derivadas da RAM. Com Python 3.13, chave TypeSafe e jogo local, confira state-demo antes de executar play.

Defina expectativas semânticas em perch.yaml por arquivos ou métodos e examine os problemas encontrados. O cliente System One envia perguntas tipadas sobre o estado à TypeSafe, usando jev-latest por padrão.

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

Guardrails for Pi that steer instead of interrupt: enforces your project rules on every write, holds only hard-to-undo actions (3 per 1,000 calls, measured on real sessions), catches unverified "done" claims and stuck loops, and trims tool output. Built on pi-typesafe.

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

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

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

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

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

Codebase search powered by Jev from @typesafe-ai

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

JEV-inspired parallel decisions for CUDA LLMs. One context, many decisions. vLLM API, game-agent examples, and reproducible benchmarks.

TypeSafe decisions for Pi: batched evaluation tool, terminal playground, and typed API for extension authors

Typed JSON inference with DiffusionGemma, with Every and Jev benchmark results

This is a LLM Gateway that mimics typesafe ai structured output. Like an imposter Jev.

Local bilingual probability decisions from context, questions, and candidate answers. Independent research preview inspired by TypeSafe Jev.

TypeSafe Jev controls original StarCraft shareware through keyboard and mouse with recorded action probabilities.

Agent-ergonomic CLI for TypeSafe's Jev: fast calibrated judgments (pick, rate, check, rank, triage, guard) from the shell

Unofficial study: Jev-style parallel typed decisions on stock 1.5B-8B models on an Apple Silicon laptop. Benchmarks, research notes, and a Hugging Face Space demo.

MCP server for TypeSafe Jev: typed classify, score, check, match and screen for any agent, with confidence on every answer

Typed, confidence-aware agent skill routing with TypeSafe Jev.

Community TypeSafe AI playground: 110 use cases, games, dilemmas and model challenges, with editable prompts, A/B comparisons and a mobile-friendly UI.

TypeSafe structured-output provider for RubyLLM 2

BrighTO LLM Router: free open-source, ultra-fast self-hosted Rust LLM gateway for OpenAI/Anthropic APIs, SystemOne/JEV/DJEV decisions, Ollaya/Laya, load balancing, fallback routing, team keys and token budgets.

TypeSafe (Jev) skill routing for Hermes Agent: names the one skill worth loading, before the model call. Opt-in, stdlib only, ~$0.001 per routed turn.

Async LangGraph workflow that gets a typed Jev Choice (invoice or general) and routes each inbound email to the matching handler.

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.

Can a decision model beat dedicated rerankers? TypeSafe Jev vs Cohere Rerank 4 vs ZeroEntropy zerank-2 vs a chat-model baseline: 14 datasets, every raw API response, bootstrap ranges on every gap.

Hybrid coding harness: System 2 writes, System 1 (Jev) runs reflexes.

Ruby client for typesafe.ai

macOS computer use driven by Jev (TypeSafe System One) as the decision maker

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

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

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

Discriminative Monte Carlo Tree Search using System One and Harnesses

Go client for TypeSafe's System One API and its model Jev: typed judgments and calibrated probabilities instead of generated text

Experimental multi-horizon BTC signal generator using TypeSafe Jev probabilities and Binance market data.

DeepSeek Harness (dsh) 插件:论文伴读工作台 —— PDF 转录 / 检索 / 流式问答 + 内置阅读器页面。

Eight minimal working examples of TypeSafe's Jev (a System One model) applied to mechanical and electrical engineering: CAD/CAE/CAM routing, FEM result triage, DFM screening, BOM alignment, hallucination-proof extraction. Zero dependencies.

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

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

Independent Jev 1.13.0 behavior study: report, controlled prompt experiments, raw results, and offline verification.

Drives a real browser with Jev making every decision and Vercel's agent-browser performing every action, with a benchmark.