Wprowadzenie do Jev
Diogo Almeida przedstawia Jev, model strukturalnych decyzji TypeSafe. Obejrzyj film prezentacyjny i eksploruj oryginalny wątek, aby poznać podejście modelu, przykłady i zgłoszone wyniki.
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Recent activityA FIELD GUIDE TO JEV / VOL. 01
To, co najlepsze w Jev: prawdziwe projekty, praktyczne poradniki i pomysły z sieci.
Diogo Almeida przedstawia Jev, model strukturalnych decyzji TypeSafe. Obejrzyj film prezentacyjny i eksploruj oryginalny wątek, aby poznać podejście modelu, przykłady i zgłoszone wyniki.
Oświadczenie o oficjalnym uruchomieniu TypeSafe przedstawia laboratorium i kieruje programistów do Jev. Zawiera film prezentacyjny Diogo Almeidy i oryginalne wstępne wprowadzenie.
TypeSafe ogłasza publiczny dostęp do Jev bez listy oczekiwania. Oficjalny konsolę jest punktem wyjścia do testowania typowanych decyzji w własnych aplikacjach.
TypeSafe wyróżnia podejście Jev do wyjściowych typów: aplikacje dostarczają stan i pytania, a następnie działają na prawdopodobieństwach, wyborach lub ocenach. Powiązane oświadczenie obejmuje integrację API Venice.
Start with the Launch Post. Explore four official signals.
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Ultra szybki agent w przeglądarce: Jev wybiera operację i element DOM w jednym żądaniu; mały LLM pisze tylko tekst, gdy potrzebne jest wpisywanie.
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Plugin do Claude Code, który zastępuje podsumowanie kompaktowania decyzjami Jev — każda wywołanie narzędzia i wynik jest oceniany w jednym szybkim żądaniu, przestarzałe są odrzucane lub obcinane, wszystko zachowane pozostaje bez zmian.
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A developer project providing a local MCP server for running pre-made Jev question packs.
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Testing Jev as a search reranker shows limited improvement over vector retrieval, with mixed results depending on evaluation methods.
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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 about improving a Slack agent's speed using Jev for faster skill and tool classification.
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A community post describing an offline decision system using Jev to evaluate research branches efficiently.
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A community project enabling hands-free web navigation by having Jev handle browser actions while Claude manages conversation.
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An agent plugin using Jev to determine optimal points for session compaction, improving efficiency in context management.
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Jev analyzes 724 live ads from 37 brands in 40 seconds, breaking down their elements and formats.
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Vercel's open agent framework, which ships Jev as the default evaluation model in its experimental evaluate path.
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A developer project that uses Jev as a sensor to control access to agent tools, enhancing security and functionality.
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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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Marcel Pociot's browser extension that collapses posts based on a Jev judgment.
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MCP server that puts TypeSafe Jev on the coding loop in Cursor, Codex, and any MCP client.
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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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Agent Skill: send closed coding-agent judgments to 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 resource explaining Jev Engineering methodology for AI agent development.
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Jev-Tool-Runner is a GitHub project where Jev selects developer tools and Codex generates code based on those selections.
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An agent project aimed at reducing token usage for coding agents via an MCP server.
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A developer project for agent classification and selective reading using an agent plugin and MCP tool.
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Official TypeSafe reference: Skill suggestion.
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A Jev powered LinkedIn recruiting agent. Watch it browse relevant profiles, save links, and review evidence against your hiring brief.
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A collection of 19 open-source projects using Jev technology for various applications including AI agents and game interactions.
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A developer project demonstrates Jev's rapid response to browser tasks, with decisions made in approximately 100 milliseconds.
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5–10x faster browser operations: Jev clicks, Codex thinks and verifies. Built at EZCollegeApp.
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An agent project that enforces policies by judging Claude Code tool calls.
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Jev achieved 100% task success on the WebMCP benchmark with significantly lower model costs compared to other systems, using a combination of tool selection and argument generation.
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Detects low-quality content in real-time as users scroll.
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Fast Rust CLI for TypeSafe Jev: typed decisions, offline linting before you pay.
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A community post discussing Jev's use in enforcing coding rules that linters cannot handle.
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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 post demonstrating Jev's ability to score 700 leads and predict outreach message performance in 40 seconds.
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One grounded Jev/Playwright core: typed SDK, persistent CLI, and MCP server with native browser operations and deterministic assertions.
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A YouTube guide explores using Jev within an agentic coding loop, focusing on cost efficiency for development tasks.

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A content marketing tool that analyzes 12.8M viral videos to generate data-backed social media scripts.
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A Chrome extension using Jev to filter out AI-generated content from YouTube, caching results for efficiency.
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Claude Code plugin: trim long Bash output with TypeSafe Jev before the model sees it.
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A Chrome extension enabling agents to browse and interact with websites, utilizing Jev for decision-making.
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Ultra-fast model routing using Jev for agent-based systems.
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A developer project using Jev for plain-language MCP tool calls without an LLM.
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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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Daf-jev: composable Python toolkit for TypeSafe's Jev (System One) decision API — question builders, confidence gates, evaluator, calibration, CLI, MCP server, agent skill.
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Jev decision layer for agents: MCP server, embeddable DecisionModel library, and an escalate-only Claude Code plugin (TypeSafe AI's Jev).
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A community post providing a tutorial on Jev, including demos for browser control and AI memory.
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A community post about humor management on a social platform.
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A developer project for agent-native routing that selects models based on task complexity.
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Enables instant compaction by scoring and filtering tool calls efficiently.
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A log analysis tool that groups 22.8M lines into patterns and uses Jev to review key patterns.
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A Codex plugin that uses Jev to streamline and reduce excessive tool outputs.
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A Brave extension that scores posts for logical fallacies in real time, highlighting well-reasoned content with a green flag.
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Proof of concept MCP for Typesafe's new Jev AI model.
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A developer project for automatic context clearing in Pi without losing conversation history.
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Write Cucumber tests with just the .feature file. No step definitions — Jev (TypeSafe AI) resolves each Gherkin step and Playwright runs it.
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AgentRun leverages Jev for creating self-sufficient agent workflows.
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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.
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Tutorial covers API setup and building prototypes with Jev and LLMs.

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A YouTube guide explaining Jev's functionality, including browser use, classification, and code review without an LLM.

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A guide explaining how to integrate Jev with Pydantic AI agents for type-safe operations.
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Route to the cheapest model in claude code for your task using jev-router.
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A community project exploring Jev's potential to evaluate agent progress beyond basic trace data.
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Jev handles seven SEO and GEO workflows, including competitor page analysis, citation likelihood, and content optimization.
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A GitHub project converting agent skills into Oxlint plugins that involve Jev.
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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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Browser use for coding agents, 3-5x faster than browser-use. MCP server + CLI; TypeSafe Jev decides every step in ~300 ms.
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A calibrated context sieve for Claude Code: every tool result is judged by a System One model before it enters context.
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A tested resource evaluating Jev's capabilities in browser automation and support tasks.

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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.
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An open-source model for form-filling tasks with low latency.
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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 developer project for agents and browsers, where Jev selects actions within Codex Computer Use.
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