Lanci del 16 settembre 2026

L'agente trasforma i controlli visibili in uno spazio di azioni indicizzato per seguire un obiettivo in linguaggio naturale. Jev sceglie operazione ed elemento compatibile in una richiesta; un LLM separato genera testo solo per TYPE_TEXT.

Gestisce piccole decisioni semantiche di instradamento, nuovi tentativi e verifica delle prove senza generare frasi di risposta. Riproduce il modello d'interfaccia Jev, non il modello o l'addestramento non pubblici, e non è affiliato a TypeSafe.

Il bot sostituisce ordini limite post-only a ogni blocco Monad e invia eventi di libro, ordini ed esecuzioni al pannello. Con MODEL=jev e TYPESAFE_AI_API_KEY, Jev sceglie acquisto o vendita; il modello mock predefinito usa un'euristica momentum.

typesafe-computer-use traduce un obiettivo scritto in azioni sul Mac, leggendo lo schermo tramite OCR e controlli di accessibilità. TypeSafe Jev sceglie azione, bersaglio e sito; il codice calcola i fatti e un modello separato scrive il testo libero. Servono una chiave TypeSafe e i permessi di registrazione schermo e accessibilità. La CLI permette una simulazione prima del controllo effettivo.

decider restituisce probabilità Choice, Score e Noul da stato e domande tipizzate in un solo passaggio. Questa famiglia indipendente ispirata a Jev usa basi Qwen; i dati non sono stati distillati da Jev. Usa l’interfaccia Python Decider oppure collega gli SDK TypeSafe all’endpoint HTTP locale compatibile.

Jev Review esamina diff Git o intere basi di codice per correttezza, sicurezza, affidabilità, compatibilità e copertura dei test. Jev seleziona prove, classifica meccanismi e valuta la gravità; quando previsto dal flusso, sceglie anche il revisore tramite Choice. Il codice applica soglie e regole del processo. Usa Node.js 24+, Git e una chiave TypeSafe per generare JSON o consultare revisioni salvate localmente.

jev-router avvia le CLI originali tramite un proxy locale e sceglie un livello di modello per ogni nuovo turno utente. Installa con npm, configura JEV_API_KEY e avvia jev-claude o jev-codex con la CLI originale installata e autenticata.

TypeSafe Mario è un controller NES sperimentale che usa telemetria dell’emulatore e osservazioni ricavate dalla RAM. Con Python 3.13, chiave TypeSafe e gioco locale, controlla state-demo prima di eseguire play.

Si definiscono aspettative semantiche in perch.yaml per file o metodi e si esaminano i problemi trovati. Il client System One invia domande tipizzate sullo stato a TypeSafe, usando jev-latest per impostazione predefinita.

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.