Jev ecosystem projects — page 30

shadcn-style reusable components and blocks for using 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

Orchestrate your own AI agents and build extensions for them — summon an agent by name, watch it think, ship its work. Runs on nostr, so no company owns your agents, your data, or your identity.

Auditable vision and cross-platform Computer Use runtime for DeepSeek Harness — strict evidence, health-checked failover, original pixels, and Token accounting.

Make the model you already use work more like a frontier model with better planning, persistent context, skills, hooks, failure handling, and verification.. Orchestrated Multi-Specialist Agentic Lifecycle Harness

LegalForecast-MTD benchmark alpha and official evaluation workflows

Battlesnake Tournaments

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

Jev-powered Playwright test selection

A Claude Code plugin that keeps your AI agents honest. Claude plans tasks as phases with checks, and when it tries to stop, Jevflow runs them and asks Jev if the work is really done. Many agents, one plan, with a live viewer to watch it all.

Prompt Claude Code: tre sistemi costruiti su Jev di TypeSafe — routing del modello, compattazione del contesto e code review

Let Jev steer Claude Code: the right reasoning effort, subagent model and skill for every prompt. A Claude Code plugin powered by TypeSafe's Jev (OpenRouter / TypeSafe).

Skills without the context tax. Claude Code and Codex plugin: TypeSafe's Jev decides on every turn which skills the model sees. Always-on context cost: 0 tokens.

When and Where Actually to use Jev in your code base.

Run Laya locally with Bun: native ONNX inference, a TypeSafe-compatible API, and a bilingual decision playground.

DeepSeek Harness (DSH) 推理等级自动选择插件：由 Jev System One 模型判断每条消息值多少思考量，按模型声明的等级自动推导档位，上下文信封让「继续」这类追问继承话题深度，低置信度向上取，任何失败都静默沿用原等级。 | Jev-driven reasoning effort per message: per-model ladders derived from what each model advertises, a fixed-size context envelope so follow-ups inherit topic depth, ties break upward, silent fallback on every failure path.

Jev-driven native GPUI testing: Rust accessibility bridge, bounded goal runners, and one-call pi integration.

A hundred hands through Git history — reproducible commit analytics powered by Jev.

JEV-powered market decision bot for stocks, crypto and memes. State in, BUY/SELL/HOLD/AVOID out, paper by default

Self-hosted Jev decision workbench and Feishu bot: automatic choices, probabilities, and experimental word/character writing.