โพสต์เปิดตัว Jev
Diogo Almeida แนะนำ Jev โมเดลการตัดสินใจที่มีโครงสร้างของ TypeSafe ดูภาพยนตร์เปิดตัวและสำรวจสายการสนทนาต้นฉบับเพื่อดูแนวทาง ตัวอย่าง และประสิทธิภาพที่ผู้เขียนรายงาน
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Recent activityA FIELD GUIDE TO JEV / VOL. 01
รวมโปรเจกต์จริง คู่มือใช้งาน และไอเดีย Jev จากทั่วอินเทอร์เน็ต
Diogo Almeida แนะนำ Jev โมเดลการตัดสินใจที่มีโครงสร้างของ TypeSafe ดูภาพยนตร์เปิดตัวและสำรวจสายการสนทนาต้นฉบับเพื่อดูแนวทาง ตัวอย่าง และประสิทธิภาพที่ผู้เขียนรายงาน
ประกาศเปิดตัวอย่างเป็นทางการของ TypeSafe แนะนำห้องทดลองและชี้ให้ผู้สร้างไปยัง Jev มีภาพยนตร์เปิดตัวของ Diogo Almeida และการแนะนำตัวอย่างแรก
TypeSafe ประกาศการเข้าถึงสาธารณะของ Jev โดยไม่มีรายชื่อรอ คอนโซลทางการคือจุดเริ่มต้นในการลองใช้การตัดสินใจแบบมีประเภทในแอปพลิเคชันของคุณเอง
TypeSafe ชี้ให้เห็นถึงแนวทางการออกผลแบบมีประเภทของ Jev: แอปพลิเคชันให้สถานะและคำถาม แล้วดำเนินการตามความน่าจะเป็น ตัวเลือก หรือคะแนน ประกาศที่เชื่อมโยงยังครอบคลุมการผสานรวม API Venice
Start with the Launch Post. Explore four official signals.
1,198 แหล่งข้อมูล
AI-assisted summaries and translations. Check original sources for context and performance claims.
Jev-assisted file retrieval and request caching for faster Pi workflows.
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Open, Jev-compatible System One decision server on DiffusionGemma.
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A show-and-tell capability study for Jev, TypeSafe's System One decision model.
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Interactive explorer and Jev question workspace for Jev Board datasets.
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What your last session knew, scored against what this one is doing. MCP server: a per-project ledger written as things happen, recalled per task with TypeSafe's Jev evaluation model via Vercel AI Gateway.
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A tech publication highlights Jev as an AI model offering a cost-effective and efficient approach to software intelligence.
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Decision harness for TypeSafe Jev — confidence gates, shadow mode, recipes, and evals. Claude CLI 48.9s → Jev 1.3s on the same row-filter job.
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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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Recursive Jev choice over a taxonomy. Select from more than 255 options without breaking TypeSafe Jev's choice cap.
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A developer project using Jev for plain-language MCP tool calls without an LLM.
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Claude Code mod that routes decisions to TypeSafe's Jev model: ranks installed skills per prompt, and answers the agent's own this-or-that questions when confident.
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A logic interpreter uses plain English to enable Jev to perform reasoning tasks it cannot handle alone.
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Grep by meaning, across languages. TypeSafe Jev scores every line against a meaning; combine meanings with AND/OR/NOT. 意味で探す grep。日本語で英語を、英語で日本語を検索できる.
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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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Independent, evidence-based map of when TypeSafe's Jev actually holds up vs. breaks down — real API-call receipts, not a leaderboard. 中文為主的雙語 repo。
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Rust client for the TypeSafe System One API (Jev).
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TypeSafe ชี้ให้เห็นถึงแนวทางการออกผลแบบมีประเภทของ Jev: แอปพลิเคชันให้สถานะและคำถาม แล้วดำเนินการตามความน่าจะเป็น ตัวเลือก หรือคะแนน ประกาศที่เชื่อมโยงยังครอบคลุมการผสานรวม API Venice
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A tool for instant field mapping between templates and data sources, simplifying data alignment with one-click functionality.
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Pre-install security gate for npm lifecycle scripts using TypeSafe System One.
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Official TypeSafe reference: Score.
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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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A demonstration of Jev competing against OpenJev in a first-person shooter game.
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A free AI tool built on Jev that quickly scans text for low-quality content, processing around 10,000 words in 2 seconds.
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Explains Jev's advantages over older LLMs in decision-making and building.
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Probabilistic decisions for Python. Use Jev or bring your own provider; crawl with Playwright.
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Jev (TypeSafe) exploratory thread: claim audit, live demos, and runnable code.
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Hunch is a GitHub tool for code review that applies rules written in plain English to analyze and improve code quality.
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TypeSafe Jev as a decision layer for the Pi coding agent: a measured tool-call gate plus jev_ask for typed, calibrated answers.
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A moderation system for Mastra agents that processes inputs in a single file.
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Stops AI coding agents from claiming work is done without evidence. Deterministic hooks decide, TypeSafe's Jev advises. Append-only ledger, zero runtime dependencies.
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Official TypeSafe reference: Composite scoring.
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Official TypeSafe reference: Agent skill.
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Jev from @typesafeai plays Balatro, making quick decisions on cards and gameplay within 200-500ms.
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On-device iPhone visual decision tool using MLX and Qwen3-VL direct option logits.
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Runtime constraints for the pi coding agent: checks every side-effecting tool call against what you said, before it runs. Powered by TypeSafe Jev.
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Small Python package that uses typesafe.ai to evaluate code comments on certain heuristics.
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Enables instant compaction by scoring and filtering tool calls efficiently.
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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.
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⚡ Sub-100ms cognitive reflexes for autonomous coding agents. Powered by TypeSafe AI's Jev & get-fable.
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Chrome extension that triages Gmail with TypeSafe's Jev model: category, priority, spam % and reply % on every email.
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A local computer-use fast path for Codex and Waku, designed for developers.
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Screens 360 resumes in 24.2 seconds with low cost and automated scoring.
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DuckDB extension that classifies rows in CSV, Parquet, or DuckDB tables with Jev, about 10 seconds per 1,000 rows.
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Official TypeSafe reference: TypeSafe Python SDK.
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A system tested on 1,500 emails for classification effectiveness. Demonstrates potential for automated email management.
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Low-latency audio censorship POC using Jev typed decisions and ffmpeg.
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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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A guide explaining three types of micro decisions used by Jev AI.

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A Jev-inspired decision interface for existing LLMs. Explicit choices, scores, calibration, and review thresholds.
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Home Assistant integration for TypeSafe Jev. Ask a question about your house and get a probability, a choice or a score as an entity.
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Claude Code / Codex / pi plugin that hands agent steps needing no text output to Jev (TypeSafe's judgment model) — measured p50 ~230 ms and ~$0.02 per 1,000 judgments, with typed escalation back to the LLM.
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Local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev.
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I kept watching coding agents burn context on decisions that aren't hard - triage 400 tickets, tag 600 files, route to one of six teams. jev-mode moves those verdicts to a typed-judgment model. I A/B'd it: 78% fewer tokens, 16x less work-attributable input, accuracy 96.1% vs 93.7%. Python, no deps, MIT.
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Racing UI wired to Jev driving decisions.
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A YouTube guide discussing Jev's functionality, pricing, and performance comparisons with Astra, including benchmarking and parallel decision analysis.

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Reusable GitHub Action: agent fix loop gated by checks, an AI reviewer, and TypeSafe Jev.
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Talk nicely or nastily over time; structured state tracks the mood.
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Local tactics shrink 225 moves to about 40 candidates, then Jev picks among tiered options.
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The Neuron's explainer on AI decisions without a chatbot.
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NPCs of River Oaks Houston, Texas using Jev to power NPCs.
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A user tested the new Jev model and shared insights about its significance for System One models.

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Hand the browser work off: an MCP server where a decision model drives the page for your agent, so a flow costs one tool call instead of a turn per click. Ref-based element tables, code-checked assertions, zero-model macro replay. Speaks CDP to your Chrome.
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Cost-optimized OpenRouter model router using TypeSafe's Jev, with a live full-catalog scorer instead of a hardcoded model list.
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Official TypeSafe reference: System One.
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If you're experimenting with jev it will be easier from here.
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Almond-fastloop: Almond's browser computer-use rig (Chrome DevTools + TypeSafe Jev), and the Browser Use Olympics benchmark it is measured on.
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Marcel Pociot's browser extension that collapses posts based on a Jev judgment.
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A community post suggesting Jev could serve as a fast, low-cost control layer for AI agents.
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Grok skill: Jev as a judgment sensor in a builder-agent loop (priors × probabilities → next act).
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A guide-directed World of Warcraft agent designed to reduce expenses as it runs longer, developed on GitHub.
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Autonomous Jev pull-request review with typed decisions, calibrated approval gates, and trusted-owner escalation.
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CLI and agent skill for TypeSafe System One (Jev): typed Choice, Score, and Noul judgments.
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