Bài đăng ra mắt Jev
Diogo Almeida giới thiệu Jev, mô hình ra quyết định có cấu trúc của TypeSafe. Xem bộ phim giới thiệu và khám phá chuỗi gốc để tìm hiểu cách tiếp cận, ví dụ và hiệu năng được báo cáo bởi tác giả.
A FIELD GUIDE TO JEV / VOL. 01
Tinh hoa Jev: dự án thực tế, hướng dẫn hữu ích và ý tưởng từ khắp internet.
Diogo Almeida giới thiệu Jev, mô hình ra quyết định có cấu trúc của TypeSafe. Xem bộ phim giới thiệu và khám phá chuỗi gốc để tìm hiểu cách tiếp cận, ví dụ và hiệu năng được báo cáo bởi tác giả.
Thông báo chính thức ra mắt TypeSafe giới thiệu phòng thí nghiệm và hướng người phát triển đến Jev. Bao gồm bộ phim giới thiệu của Diogo Almeida và lời giới thiệu ban đầu.
TypeSafe thông báo truy cập công khai vào Jev mà không cần danh sách chờ. Bảng điều khiển chính thức là điểm bắt đầu để thử nghiệm các quyết định có kiểu trong ứng dụng của bạn.
TypeSafe nhấn mạnh cách tiếp cận đầu ra có kiểu của Jev: các ứng dụng cung cấp trạng thái và câu hỏi, sau đó thực hiện hành động dựa trên xác suất, lựa chọn hoặc điểm số. Thông báo liên quan cũng đề cập đến tích hợp API Venice.
Start with the Launch Post. Explore four official signals.
1.198 tài nguyên
AI-assisted summaries and translations. Check original sources for context and performance claims.
Agent Skill: send closed coding-agent judgments to TypeSafe Jev.
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Voice command integration for the Autumn billing dashboard to streamline tasks.
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Jev automates browser tasks by selecting actions from a dynamic action space.
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A predictive app launcher using Jev to interpret user intent and enhance app discovery.
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A developer project for the agents shelf featuring a local MCP gateway with Jev tool discovery and a dashboard.
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Explains Jev's role in automating decision-making processes and enabling new business models through efficient task sorting and classification.
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Jev on Sol. A community post describing an experiment where Jev analyzes Solana blockchain data in real time.
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X timeline labeler. A community post describing a Chrome extension that categorizes X posts into types like engagement bait or promo.
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Agent makes rapid shopping decisions using Ego Lite and Jev. Completes 20 product evaluations in under 4 seconds.
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Jev demonstrated over 5x faster performance than other models in testing.
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An experiment using Jev to interpret voice commands and trigger tool actions before the user finishes speaking.
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A small, extensible decision-to-action harness for TypeSafe Jev.
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Minimal grammY Telegram anti-spam bot powered by TypeSafe Jev.
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Jev is demonstrated as a classification model with practical examples.

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Jev autonomously controls multiple characters in a video game with rapid decision-making.
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Multi-axis writing quality checker powered by TypeSafe AI's Jev model. Separate named checks, each with its own verdict and confidence.
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Agentcard integrates Jev and Kernel to enable secure and efficient online shopping by agents, avoiding token costs and safety issues.
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A tool enhancing zsh autosuggestions with Jev-based ranking instead of prefix matching.
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A stock evaluation tool using Jev for fast market analysis.
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A livestream analyzing Jev's architecture for JSON-predicting models.

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A community post describing Jev's use for analyzing competitors' ads and generating creative shortlists.
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Test report using Jev to check each agent action first: most attacks caught, almost no false blocks.
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A community tool allowing Jev to make decisions through command-line interactions for agent systems.
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Offline Obsidian search with optional Jev reranking of results, requiring user approval before reranking.
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A community post explores Jev's decision-making in ethical scenarios, comparing different approaches.
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Steve Krouse's playable 16-judgment demo and video.
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A developer project providing a local MCP server for running pre-made Jev question packs.
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Typed, confidence-aware agent skill routing with TypeSafe Jev.
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A system enabling rapid agent deployment on digital canvases without relying on slow LLM processes.
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A mahjong coach that uses Jev to suggest tiles to discard in Japanese.
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Jev filters social media posts to reduce negative content exposure by categorizing them.
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A community post describing an SEO audit using Jev to analyze website internal links in 45 seconds.
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A source-backed Jev project directory with a reusable Jev-only GitHub review workflow.
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A staged code-review workflow and local dashboard built with TypeSafe Jev.
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Fast structured Android control loops with TypeSafe Jev and Mobile MCP.
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A small agent using Jev compaction to manage searchable memory efficiently.
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Jev demonstrates real-time decision-making in the game Doom, making moves up to ten times per second.
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Jev model demonstrates fast decision-making in a strategy game. Achieves high-speed gameplay with minimal visual feedback.
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A Rubik's Cube that uses Jev AI to solve itself using a human-like method rather than optimal algorithms.
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A community post describing a system that matches one candidate to 400 companies with a confidence score in 12 seconds.
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A method for expressing emotions through sound-based communication.
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A self-hosted skills library with optional Jev recommendations.
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A developer project offering semantic code search for coding agents, benchmarked on SWE-bench Lite.
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An experiment using Jev for real-time model selection in AI agents, collected for the agents shelf.
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A focus group experiment using Jev to make scroll or stop decisions on ads.
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Tutorial covers API setup and building prototypes with Jev and LLMs.

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A gaming project enabling seamless switching between manual and AI-driven gameplay using Jev.
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A developer project generating paintings based on Jev's pixel probability calculations, collected for the tools shelf.
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Creates a dynamic audience of 100 simulated personalities that respond individually to user input using Jev.
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A community post exploring Jev's behavior in a game of Catan, where multiple Jevs struggle with negotiation.
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Jev is HERE. How to use it. A resource explaining how to access Jev through the Vercel AI Gateway and its potential business applications.

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A developer project using Jev to play Pokémon Red by typing each turn.
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Jev controls live 3D character expressions with real-time decision-making.
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Jev plays Tetris by making real-time decisions to move blocks.
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An agent that plays Tetris with rapid decision-making capabilities. Shows potential for real-time game automation.
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Claude Code plugin: trim long Bash output with TypeSafe Jev before the model sees it.
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Browser automation where an LLM plans and Jev (Typesafe System One) decides. Library, CLI and MCP server.
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A tool that improves end-to-end testing efficiency, completing tasks faster and cheaper than other models.
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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.
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Orus uses Jev to review and approve trade strategies before execution, enhancing decision-making with structured answers.
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A GitHub Action using Jev for automated PR triage and classification.
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A real-time computer assistant using Jev and local Whisper to process voice input, classify actions, and interact with the screen through a Swift app for accessibility.
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A prose linter that sniffs out AI writing tells. Zero dependencies, countable rules plus one judgment model.
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An agent project aimed at reducing token usage for coding agents via an MCP server.
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A community post describing a robot arm controlled by Jev, using cameras and decision-making at two decisions per second.
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A community post discussing Jev's use in enforcing coding rules that linters cannot handle.
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A developer project that selects applicable rules for Claude, ensuring only relevant information is shown.
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Jido agents use Jev to make moves in Tic Tac Toe, demonstrating Jev's application in game-playing agents.
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A community post detailing a dashboard that classifies X posts across eight dimensions using Jev for content research.
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A Go client for the TypeSafe API, aimed at developer tools and applications.
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A community post describes Jev attempting to play Resident Evil, testing its ability to handle 3D environments, objectives, and combat scenarios.
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Starchild uses Jev for real-time prompt classification, reducing costs by 20x and increasing speed by 6x with average processing time of 140ms.
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