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| Resource | Stars | Description | Category | Save |
|---|---|---|---|---|
| 10 Jev commandmentswww.reddit.com | — | Ten rules of thumb for designing Jev calls: ask tiny questions, run independent ones in parallel, chain only on dependencies, use Jev as a bouncer, and treat probabilities as signals. | Articles, tutorials & talksGuides & tutorials | |
| 3 free ways to run Jevwww.youtube.com | — | Short guide to three free ways to try Jev at the time of recording, including a no-signup playground and OpenCode Zen, plus use cases like email sorting and lead scoring. | Articles, tutorials & talksGuides & tutorials | |
| 8 copyable Jev use cases (Chinese)x.com | — | Chinese study notes explaining what Jev is, four ways to get it running in 30 minutes, 8 real cases to copy, counterintuitive points, and when not to use it. | Articles, tutorials & talksGuides & tutorials | |
| A Beginner's Guide to Jevx.com | — | Short beginner guide explaining Jev as a model for focused judgments, using a support ticket as the state and a decision sheet of typed questions, and where it fits in production. | Articles, tutorials & talksGuides & tutorials | |
| A deep dive into Jevflaviocopes.com | — | Thorough walkthrough of request and response shapes, picking a primitive, error codes, version pinning, and limits. | Articles, tutorials & talksGuides & tutorials | |
| AI That Doesn't Talkziplyne.agency | — | Plain-English guide to what Jev is, where it fits and its limits, with starting points in the playground, the Python and JS SDKs, raw HTTP and the agent skill, plus advice to shadow-run it before trusting it. | Articles, tutorials & talksGuides & tutorials | |
| awesome-jev-promptsgithub.com | 0 | Question patterns, anti-patterns & calibration notes for Jev (TypeSafe AI) — the prompt engineering of typed decisions. 43 entries, 8 categories. EN/中文. | Articles, tutorials & talksGuides & tutorials | |
| Baoyu’s Chinese explanationx.com | — | X (Chinese): explains the System One category to Chinese readers as a calibrated, typed decision layer for code. | Articles, tutorials & talksGuides & tutorials | |
| Build a Jev Judgex.com | — | Tutorial on replacing an LLM judge with Jev for agent evaluation, asking bounded questions such as whether an answer was grounded in policy and whether the claimed action actually happened. | Articles, tutorials & talksGuides & tutorials | |
| Build Anything with Jev, Here's Howwww.youtube.com | — | Explains how to use Jev, the architecture behind it, and how to build a business on top of fast typed decisions. | Articles, tutorials & talksGuides & tutorials | |
| Build your own Jev (100% local)x.com | — | Tutorial on turning an open-source LLM into a local decision engine without retraining, using next-token scoring over fixed choices with SGLang, benchmarked against normal text generation. | Articles, tutorials & talksGuides & tutorials | |
| Building a Harness with Jevx.com | — | Guide to adding Jev to a LangChain agent harness, covering how Jev works, where it fits in the agent loop, model routing and pre-tool risk checks as middleware. | Articles, tutorials & talksGuides & tutorials | |
| claude-code-jev-compactiongithub.com | 0 | Reduza tokens de entrada no Claude Code com compactação de contexto por relevância (LiteLLM + TypeSafe Jev). Tutorial bilíngue PT-BR / EN. | Articles, tutorials & talksGuides & tutorials | |
| Codex x Jev textbookx.com | — | Japanese guide to splitting decisions out of Codex into Jev, reviewing public routers and integrations for model choice, evidence checks and fallback behavior. | Articles, tutorials & talksGuides & tutorials | |
| Confidence routingdocs.typesafe.ai | — | Official guidance on handling uncertain decisions. | Articles, tutorials & talksGuides & tutorials | |
| Connecting Jev to Claude Code via MCPnote.com | — | Japanese beginner walkthrough that reaches Jev through the Vercel AI Gateway, tests it with an AI SDK evaluate script, then wraps it as an MCP server so Claude Code can triage tickets, including the snags hit along the way. | Articles, tutorials & talksGuides & tutorials | |
| CopilotKit Jev generative UI recipegithub.com | — | Cookbook recipe for a Next.js workspace picker where Jev decides whether to ask a clarifying question or show options, ranks the rooms, and CopilotKit and AG-UI render the result. | Articles, tutorials & talksGuides & tutorials | |
| CopilotKit Jev generative UI recipe — docsdocs.copilotkit.ai | — | Cookbook recipe for a Next.js workspace picker where Jev decides whether to ask a clarifying question or show options, ranks the rooms, and CopilotKit and AG-UI render the result. | Articles, tutorials & talksGuides & tutorials | |
| DAIR.AI 发布 Jev 入门介绍与 Playgroundaihot.news | — | Jev 的初学者入门介绍。另外我们还搭建了一个 Jev Playground，供你测试多个用例。 | Articles, tutorials & talksGuides & tutorials | |
| DAIR.AI 发布 Jev 入门介绍与 Playgroundx.com | — | Jev 的初学者入门介绍。另外我们还搭建了一个 Jev Playground，供你测试多个用例。 | Articles, tutorials & talksGuides & tutorials | |
| deploychan Jev guidesgithub.com | — | Public MCP server for coding agents that ships guides on wiring Jev beside an existing model: the HTTP contract, Choice/Score/Noul, key handling, model pinning and failure handling. | Articles, tutorials & talksGuides & tutorials | |
| deploychan Jev guides — appmcp.deploychan.webcam | — | Public MCP server for coding agents that ships guides on wiring Jev beside an existing model: the HTTP contract, Choice/Score/Noul, key handling, model pinning and failure handling. | Articles, tutorials & talksGuides & tutorials | |
| deploychan Jev guides — repogithub.com | — | Public MCP server for coding agents that ships guides on wiring Jev beside an existing model: the HTTP contract, Choice/Score/Noul, key handling, model pinning and failure handling. | Articles, tutorials & talksGuides & tutorials | |
| deploychan Jev guides — routegithub.com | — | Public MCP server for coding agents that ships guides on wiring Jev beside an existing model: the HTTP contract, Choice/Score/Noul, key handling, model pinning and failure handling. | Articles, tutorials & talksGuides & tutorials | |
| docsgithub.com | 76 | OpenClaw docs + translation | Articles, tutorials & talksGuides & tutorials | |
| Elvis Saravia 推荐 typesafe 编码智能体笔记aihot.news | — | Elvis Saravia 推荐一份关于 typesafe 与编码智能体结合的笔记文档，认为其中给出了在 agent harness 中放置 Jev 的实用思路。他提到文档涵盖审批门、MCP/工具调用路由、模型路由、动态子智能体模式和结构化技能等此前分享过的想法。他建议把文档喂给自己的智能体来探索。 | Articles, tutorials & talksGuides & tutorials | |
| Elvis Saravia 推荐 typesafe 编码智能体笔记x.com | — | Elvis Saravia 推荐一份关于 typesafe 与编码智能体结合的笔记文档，认为其中给出了在 agent harness 中放置 Jev 的实用思路。他提到文档涵盖审批门、MCP/工具调用路由、模型路由、动态子智能体模式和结构化技能等此前分享过的想法。他建议把文档喂给自己的智能体来探索。 | Articles, tutorials & talksGuides & tutorials | |
| Elvis Saravia 用 Jev 为智能体框架 /goal 功能构建自定义验证器aihot.news | — | Elvis Saravia 用 Jev 为智能体框架的 /goal 功能构建自定义验证器，每轮对话后检查目标是否真正完成，使持续验证成本低到可规模化。此前这类验证由另一个昂贵的推理模型承担，现在可更频繁运行以让智能体保持正轨。 | Articles, tutorials & talksGuides & tutorials | |
| empezar-jev-typesafewww.webreactiva.com | — | jev-review - https://www.webreactiva.com/blog/empezar-jev-typesafe · TypeScript | Articles, tutorials & talksGuides & tutorials | |
| everything-jevgithub.com | 0 | Typed Jev decisions, 22 executable recipes, and integration guides for automation harnesses. TypeScript, zero runtime dependencies, MIT. | Articles, tutorials & talksGuides & tutorials | |
| Gate Agent Tool Calls with Jevopenrouter.ai | — | OpenRouter cookbook recipe that checks each refund tool call of an Agent SDK agent against the ticket, so safe refunds run, unsupported ones are refused and only ambiguous ones reach a human. | Articles, tutorials & talksGuides & tutorials | |
| Generating text with Jevx.com | — | Hack that makes Jev write text by asking one Choice question per character position, with a STOP option, and reading off the most likely letters. | Articles, tutorials & talksGuides & tutorials | |
| Getting started with Jevwww.youtube.com | — | Tutorial on setting up a Jev API key, making a raw HTTP request, reading the response structure, and using the JavaScript and Python SDKs with choices and scores. | Articles, tutorials & talksGuides & tutorials | |
| Getting started with the TypeSafe skillx.com | — | Two-step starter: install the official typesafe-ai agent skill, then ask your coding agent to use /typesafe-ai to find slow, costly LLM calls that Jev could replace. | Articles, tutorials & talksGuides & tutorials | |
| Giving your agents a decision brainx.com | — | X article with a 10-step guide to moving an agent's yes/no, routing and relevance calls from an expensive LLM to Jev's three question types. | Articles, tutorials & talksGuides & tutorials | |
| GPT 6 Astra and Jev in one appwww.youtube.com | — | Portuguese tutorial that builds a project with GPT 6 Astra and then plugs Jev in for the decision steps, showing how the two fit together. | Articles, tutorials & talksGuides & tutorials | |
| hands-on-jevgithub.com | 1 | very first hands on jev, foundation models by typesafe | Articles, tutorials & talksGuides & tutorials | |
| How Jev-style decoding worksx.com | — | Visual explanation, based on the open Qwen2.5-RLCD model, of reading field probabilities from a cached single decoder pass instead of generating JSON token by token. | Articles, tutorials & talksGuides & tutorials | |
| How to build things with Jev and OpenJevswww.youtube.com | — | Sam Witteveen builds a model router that runs on both the hosted Jev API and the open SemIf approach, walking through the architecture and stats for local and cloud models. | Articles, tutorials & talksGuides & tutorials | |
| How to classify, route, and score with Jev and AI SDKvercel.com | — | Mixes choice, score, and yes-or-no judgments in one AI SDK call, dispatches only above a confidence bar, and unit-tests the thresholds with a mock model. | Articles, tutorials & talksGuides & tutorials | |
| How to classify, route, and score with Jev and AI SDK — discussionnews.ycombinator.com | — | Mixes choice, score, and yes-or-no judgments in one AI SDK call, dispatches only above a confidence bar, and unit-tests the thresholds with a mock model. | Articles, tutorials & talksGuides & tutorials | |
| How to master Jev (Full Guide)x.com | — | Long guide covering what Jev is good at, using it beside existing LLMs, question patterns, confidence gates against bad decisions, and five money-making workflows. | Articles, tutorials & talksGuides & tutorials | |
| How to set up Jev with Claude Codewww.youtube.com | — | Guide to getting Jev access through OpenRouter, wiring it into Claude Code, and having Claude suggest use cases such as scoring trending news and triaging social comments. | Articles, tutorials & talksGuides & tutorials | |
| How to use Jev (daikidomon)note.com | — | Japanese step-by-step guide from getting an API key to a first judgment, branching on confidence, and common design patterns, with Python, HTTP, and JavaScript examples. | Articles, tutorials & talksGuides & tutorials | |
| How to use Jev (Ruben Hassid)ruben.substack.com | — | Non-developer guide to using Jev through the TypeSafe skill in Claude Code or Codex, with case studies for classifying LinkedIn, triaging Gmail, and screening research papers, each with a copy-paste prompt. | Articles, tutorials & talksGuides & tutorials | |
| How to use Jev for GTM Automationx.com | — | Step-by-step guide to using Jev in go-to-market automation, explaining calibrated probabilities over accepted answers and how to structure lead and account decisions at volume. | Articles, tutorials & talksGuides & tutorials | |
| How to use Jev: a practical guidedev.to | — | Five patterns with code: speculative fan-out, per-action confidence gates, composite scoring, cascades, and retrieve-then-judge. | Articles, tutorials & talksGuides & tutorials | |
| Jev + Asidewww.youtube.com | — | Korean tutorial connecting Jev to Aside with three use cases: ranking urgent Gmail replies, buy decisions for a stock bot, and routing for Korean SAT math question analysis. | Articles, tutorials & talksGuides & tutorials | |
| Jev + Herdrgithub.com | 1 | Guide to orchestrating coding agents such as Claude, Codex and Hermes with Jev and Herdr, with a small routing prototype and a review of a Jev browser worker. | Articles, tutorials & talksGuides & tutorials | |
| JEV : tuto complet en françaiswww.youtube.com | — | French tutorial covering System One models, RLCD, installing the Jev skill in Claude Code and Codex, testing in the Playground, the three primitives, and OpenRouter access. | Articles, tutorials & talksGuides & tutorials | |
| Jev AI explained in Hindiwww.youtube.com | — | Hindi tutorial on state vs questions and the Noul, Choice, and Score primitives, worked through a customer course-access complaint in the Playground and a custom dashboard. | Articles, tutorials & talksGuides & tutorials | |
| Jev AI for freewww.youtube.com | — | Hindi-language guide to trying Jev without paying, covering TypeSafe console early access and free routes through gateways such as OpenRouter and Vercel AI Gateway. | Articles, tutorials & talksGuides & tutorials | |
| Jev and System One models explainedoutcomeschool.com | — | Beginner-friendly explainer of System One versus System Two thinking, why using an LLM for small decisions is slow and costly, and how Jev's typed decisions plug into software. | Articles, tutorials & talksGuides & tutorials | |
| Jev beginner tutorialzhuanlan.zhihu.com | — | Beginner Chinese tutorial on the core concepts, getting access, the console Playground, and writing Noul, Choice and Score questions, with advice on blind-testing thresholds and Chinese inputs. | Articles, tutorials & talksGuides & tutorials | |
| Jev beginner tutorial (Chinese)x.com | — | Step-by-step Chinese tutorial for newcomers covering state, questions and answers, the Noul, Choice and Score types, and how to get started in the playground. | Articles, tutorials & talksGuides & tutorials | |
| Jev Claude Code: Quick Setupwww.youtube.com | — | Step-by-step setup for wiring Jev into Claude Code as a fast decision layer for the coding agent. | Articles, tutorials & talksGuides & tutorials | |
| Jev complete beginner's guideqiita.com | — | Long Japanese introduction to Jev for Python developers, covering state, typed questions, and how to wire typed decisions into software. | Articles, tutorials & talksGuides & tutorials | |
| Jev de TypeSafe AI: el modelo que NO escribe textowww.youtube.com | — | Spanish-language explainer of System One models, how Jev evaluates state against typed questions, and when it makes sense to use it. | Articles, tutorials & talksGuides & tutorials | |
| Jev desde cerogithub.com | — | Spanish step-by-step Jupyter tutorial accompanying a YouTube video: builds a support-ticket state, asks Choice, Noul and Score questions with the Python SDK, and combines the answers with explicit Python thresholds. | Articles, tutorials & talksGuides & tutorials | |
| Jev desde cero — repogithub.com | — | Spanish step-by-step Jupyter tutorial accompanying a YouTube video: builds a support-ticket state, asks Choice, Noul and Score questions with the Python SDK, and combines the answers with explicit Python thresholds. | Articles, tutorials & talksGuides & tutorials | |
| Jev di TypeSafe AI: capiamo insieme la novitàwww.youtube.com | — | Italian-language explainer of Jev, RLCD, and how it differs from traditional LLMs, walking through the official use-case map and smart-home demo. | Articles, tutorials & talksGuides & tutorials | |
| Jev Engineering in 10 stepsx.com | — | X article laying out a 10-step setup that moves an agent's yes/no, next-worker and relevance-scoring calls from an LLM to Jev, then adds a model router and a gate for risky tool calls. | Articles, tutorials & talksGuides & tutorials | |
| Jev explained for kidsqiita.com | — | Japanese explainer that starts at an elementary-school level and ends with engineering advice on designing questions, safe use, and common pitfalls. | Articles, tutorials & talksGuides & tutorials | |
| Jev explained in one infographicwww.reddit.com | — | One-page infographic of what TypeSafe's docs and evals actually say about Jev, covering the primitives, pricing, limits, and why the confidence field matters. | Articles, tutorials & talksGuides & tutorials | |
| Jev Explained — appjev-explained-repo.vercel.app | — | Interactive playground that teaches how Jev makes typed, probabilistic decisions by running Noul, Choice, and Score questions step by step with your own TypeSafe or Vercel AI Gateway key. | Articles, tutorials & talksGuides & tutorials | |
| Jev Explained — demox.com | — | Interactive playground that teaches how Jev makes typed, probabilistic decisions by running Noul, Choice, and Score questions step by step with your own TypeSafe or Vercel AI Gateway key. | Articles, tutorials & talksGuides & tutorials | |
| Jev explained: no chat, only decisionswww.youtube.com | — | Mandarin-language explainer on what Jev is, how to split work between it and general models, community projects, and wiring it into a customer-support agent flow. | Articles, tutorials & talksGuides & tutorials | |
| Jev explainer (Russian)sereja.tech | — | Russian-language blog explainer on TypeSafe's Jev: how a model that picks from given options helps route requests, steer assistant actions and check results, citing the launch post, docs, workflow evals and early third-party tests. | Articles, tutorials & talksGuides & tutorials | |
| Jev explainer (Russian) — repogithub.com | — | Russian-language blog explainer on TypeSafe's Jev: how a model that picks from given options helps route requests, steer assistant actions and check results, citing the launch post, docs, workflow evals and early third-party tests. | Articles, tutorials & talksGuides & tutorials | |
| Jev for Devsx.com | — | Illustrated developer guide to building with Jev: states, typed question dicts, SDK setup, LiteLLM routing and common patterns, written to be agent-friendly. | Articles, tutorials & talksGuides & tutorials | |
| Jev for Marketingx.com | — | X Article walking through five marketing workflows that use Jev to pick which content ideas, SEO topics, clips and leads deserve work, with adaptable prompts and notes on the limits of early tests. | Articles, tutorials & talksGuides & tutorials | |
| Jev for system operationsqiita.com | — | Japanese summary of what the docs say about Jev, followed by ideas for applying it to system operations work such as alert handling. | Articles, tutorials & talksGuides & tutorials | |
| Jev hands-on review and tutorial (Chinese)x.com | — | Chinese beginner guide and hands-on review explaining System One models, the Noul, Choice and Score primitives with request examples, and how to try Jev now that it is open. | Articles, tutorials & talksGuides & tutorials | |
| Jev in 5 minutesqiita.com | — | Compact Japanese quickstart for signing up in the TypeSafe console and making a first Jev call. | Articles, tutorials & talksGuides & tutorials | |
| Jev in Claude Code and Codex (Chinese)x.com | — | Chinese tutorial covering API keys, a first curl request to the System One endpoint, pricing, and installing the official TypeSafe skill in Claude Code and Codex. | Articles, tutorials & talksGuides & tutorials | |
| Jev in Java and Spring Bootwww.youtube.com | — | Dan Vega's getting-started guide for Java developers: the same support-ticket call from plain Java 25 in one file and from Spring Boot 4 with a RestClient, returning urgency, team, and severity. | Articles, tutorials & talksGuides & tutorials | |
| Jev in Java and Spring Boot — repogithub.com | — | Dan Vega's getting-started guide for Java developers: the same support-ticket call from plain Java 25 in one file and from Spring Boot 4 with a RestClient, returning urgency, team, and severity. | Articles, tutorials & talksGuides & tutorials | |
| Jev in Java and Spring Boot — repo2github.com | 5 | Dan Vega's getting-started guide for Java developers: the same support-ticket call from plain Java 25 in one file and from Spring Boot 4 with a RestClient, returning urgency, team, and severity. | Articles, tutorials & talksGuides & tutorials | |
| Jev interactive handbookgithub.com | — | Chinese interactive HTML handbook on Jev with hands-on widgets: a probability translator, request builder, ambiguous ticket routing, Score level designer, Noul balance, calibration comparison and risk-threshold routing. | Articles, tutorials & talksGuides & tutorials | |
| Jev interactive handbook — repogithub.com | — | Chinese interactive HTML handbook on Jev with hands-on widgets: a probability translator, request builder, ambiguous ticket routing, Score level designer, Noul balance, calibration comparison and risk-threshold routing. | Articles, tutorials & talksGuides & tutorials | |
| Jev is here: how to use itwww.youtube.com | — | Tutorial covering what Jev is, what you can build with it, a walkthrough of the Jev Playground, and calling the Jev API from Python. | Articles, tutorials & talksGuides & tutorials | |
| Jev is more than you thinkwww.youtube.com | — | Explainer on how Jev choices can be actions such as tool selection, model routing, or game moves, with demos from email sorting to Doom and a driving simulator. | Articles, tutorials & talksGuides & tutorials | |
| Jev lesson onejuejin.cn | — | Chinese introduction to Jev as a decision function embedded in business flows, explaining three structural differences from LLMs: output interface, sampling, and confidence. | Articles, tutorials & talksGuides & tutorials | |
| Jev nasıl kullanılır?www.youtube.com | — | Turkish step-by-step tutorial on Jev's input and output schemas, demonstrating 1,700 emails classified for 18 cents in total at about 200 milliseconds each. | Articles, tutorials & talksGuides & tutorials | |
| JEV Nedir?x.com | — | Turkish-language explainer of Jev as a decision engine rather than an LLM, with where it fits in agents, caveats about the claims, and setup steps for Claude Code and Codex. | Articles, tutorials & talksGuides & tutorials | |
| Jev nedir? 3 gerçek testwww.youtube.com | — | Turkish guide with three live tests: trading decisions in a stock simulation, tile discards at a 101 Okey table, and finding the right note in an Obsidian second brain. | Articles, tutorials & talksGuides & tutorials | |
| Jev quickstart (npaka)note.com | — | Japanese quickstart walking through Jev's state-questions-typed-decisions structure, the Choice, Score, and Noul types, and first API calls with code. | Articles, tutorials & talksGuides & tutorials | |
| Jev Skillsgithub.com | 3 | Agent skills for coding agents building with Jev, covering API setup, question design, and evaluating accuracy, review rate, latency, and cost, plus runnable routing, ranking, and evidence-check examples. | Articles, tutorials & talksGuides & tutorials | |
| Jev Starter Kitgithub.com | — | Free Early AI-dopters starter kit that pairs a YouTube walkthrough with an interactive Jev explainer, an Ask Jev playground and a coding-assistant guide including a hotel fine-print API example. | Articles, tutorials & talksGuides & tutorials | |
| Jev Starter Kit — gumroadmarkkashef.gumroad.com | — | Free Early AI-dopters starter kit that pairs a YouTube walkthrough with an interactive Jev explainer, an Ask Jev playground and a coding-assistant guide including a hotel fine-print API example. | Articles, tutorials & talksGuides & tutorials | |
| Jev Starter Kit — repogithub.com | — | Free Early AI-dopters starter kit that pairs a YouTube walkthrough with an interactive Jev explainer, an Ask Jev playground and a coding-assistant guide including a hotel fine-print API example. | Articles, tutorials & talksGuides & tutorials | |
| Jev Starter Kit — videowww.youtube.com | — | Free Early AI-dopters starter kit that pairs a YouTube walkthrough with an interactive Jev explainer, an Ask Jev playground and a coding-assistant guide including a hotel fine-print API example. | Articles, tutorials & talksGuides & tutorials | |
| Jev Supercharged All Of Your AI Agentswww.youtube.com | — | Shows where to add Jev to existing agents to classify work, check criteria, and route uncertain cases for review, across content selection, recruiting, and inbox triage. | Articles, tutorials & talksGuides & tutorials | |
| Jev System One model guidekelen.cc | — | Chinese introduction to Jev covering Choice, Score and Noul, a Python SDK ticket-triage example with confidence-based escalation, and a roundup of early community demos. | Articles, tutorials & talksGuides & tutorials | |
| Jev the savantx.com | — | Explainer on where Jev fits in pipelines and eval harnesses, walking through Choice, Score and Noul with a worked example that asks three judgments about a finished agent run in one request. | Articles, tutorials & talksGuides & tutorials | |
| Jev will 10x your Claude Codewww.youtube.com | — | Shows how to put Jev to work inside Claude Code for fast checks and decisions, with a free companion guide. | Articles, tutorials & talksGuides & tutorials | |
| Jev x Codex practical guidex.com | — | Japanese guide to installing the TypeSafe skill in Codex, separating generation from Jev judgments, published experiments, work applications, and ways to improve decision accuracy. | Articles, tutorials & talksGuides & tutorials | |
| Jev 入门指南与 Playground 发布aihot.news | — | 🔥 推出 Jev Primer 与 Playground 想了解 Jev 如何运作，不用再找了。 这是你入门所需的唯一交互式指南。 不再困惑 Jev 能做什么、不能做什么。我们还打造了专属 Jev Playground，让你现在就能试用各种用例。 | Articles, tutorials & talksGuides & tutorials | |
| Jev 入门指南与 Playground 发布x.com | — | 🔥 推出 Jev Primer 与 Playground 想了解 Jev 如何运作，不用再找了。 这是你入门所需的唯一交互式指南。 不再困惑 Jev 能做什么、不能做什么。我们还打造了专属 Jev Playground，让你现在就能试用各种用例。 | Articles, tutorials & talksGuides & tutorials | |
| Jev 新手入门指南：用 System One 模型做结构化判断aihot.news | — | DAIR.AI 发布 Jev 新手指南，Jev 是 TypeSafe 推出的通用 System One 模型，专为快速、聚焦的判断设计，而非长推理或开放式写作。 | Articles, tutorials & talksGuides & tutorials | |
| jev-agent-harnessgithub.com | 0 | Jev (TypeSafe AI System One) agent harness guide + video manual — from the LangChain 'Building a Harness with Jev' post. Learn Jev Noul/Choice/Score questions, Model Router, and AutoMode guardrails with LangChain. | Articles, tutorials & talksGuides & tutorials | |
| jev-aigithub.com | 4 | Jev AI quickstart & FAQ — TypeSafe AI's System One model. Try it free: jevtypesafeai.com | Articles, tutorials & talksGuides & tutorials | |
| jev-by-harshgithub.com | 0 | What is JEV? A simple explanation of its architecture, reasoning, and how it works. | Articles, tutorials & talksGuides & tutorials | |
| jev-classification-guidegithub.com | 0 | Classify companies and job titles with Jev (TypeSafe AI) — calibrated probabilities at $0.042 per million input tokens, output free | Articles, tutorials & talksGuides & tutorials | |
| jev-cookbookgithub.com | 47 | ⚡ 适合中国宝宝的 Jev 入门教程｜手把手带你了解关于 Jev 的一切——Jupyter Notebook 轻松实验，从三种问题原语到 18 篇实战配方、语音智能家居、模型评测与本地微调，全面掌握 System One 判断模型的开发范式 | Articles, tutorials & talksGuides & tutorials | |
| jev-crash-coursegithub.com | 1 | An 11-level crash course on Jev, TypeSafe AI's System One decision model — runnable examples against the real API, plus a capstone project with unit tests and evals. Works with any LLM provider via LiteLLM. | Articles, tutorials & talksGuides & tutorials | |
| jev-crash-coursegithub.com | 0 | An 11-level crash course on Jev, TypeSafe AI's System One decision model — runnable examples against the real API, plus a capstone project with unit tests and evals. Works with any LLM provider via LiteLLM. | Articles, tutorials & talksGuides & tutorials | |
| jev-docsgithub.com | 5 | Community-maintained history of Jev / TypeSafe System One APIs, SDKs, agent guidance, and engineering best practices. | Articles, tutorials & talksGuides & tutorials | |
| jev-docs-zhgithub.com | 5 | Unofficial Chinese translation of the official Jev documentation at docs.typesafe.ai, built into a static site. | Articles, tutorials & talksGuides & tutorials | |
| jev-field-guide-skillgithub.com | 0 | A Claude Code skill of field notes on Jev: which question shape fits which job, how to test a use before trusting it, and the traps. Companion to TypeSafe's official skill. | Articles, tutorials & talksGuides & tutorials | |
| jev-how-togithub.com | 0 | How to use Jev from Java: typed AI decisions. A runnable Jev example. | Articles, tutorials & talksGuides & tutorials | |
| Jev-like decisions from open LLMsx.com | — | Thread explaining how to get Jev-style fast decisions from a cheap open-source LLM without training, by ending the prompt at "Answer:" and comparing the logits of the allowed answer tokens. | Articles, tutorials & talksGuides & tutorials | |
| jev-model-labsgithub.com | 1 | Companion code for Engineering Decision Systems with JEV (AI Engineering Insider). A production-style toolkit (jevkit) plus eleven hands-on labs, one per chapter, for building decision systems on TypeSafe's Jev System One model. | Articles, tutorials & talksGuides & tutorials | |
| jev-newbiegithub.com | 0 | Hands-on starter kit for TypeSafe Jev: what it can do, what to install, runnable examples, a jev CLI with a viewer, and a bilingual tutorial | Articles, tutorials & talksGuides & tutorials | |
| jev-reviewgithub.com | 0 | https://www.webreactiva.com/blog/empezar-jev-typesafe | Articles, tutorials & talksGuides & tutorials | |
| jev-skillgithub.com | 0 | A Claude Code skill for TypeSafe's Jev (System One) model — Choice/Score/Noul design guidance, patterns, and SDK reference. | Articles, tutorials & talksGuides & tutorials | |
| jev-studygithub.com | 0 | Jev(TypeSafe AI System One Model) 스터디 — 타입화된 결정·RLCD·confidence-gated routing을 한국어 노트와 TypeScript 목업으로 정리 | Articles, tutorials & talksGuides & tutorials | |
| jev-system-one-referencegithub.com | 2 | Independent Jev / System One reference with API examples, implementation guidance and reusable prompts for engineers and coding assistants. | Articles, tutorials & talksGuides & tutorials | |
| Jev: decisions in millisecondswww.youtube.com | — | Explainer on how Jev returns probabilistic typed outputs, its speed, and how it differs from chat LLMs. | Articles, tutorials & talksGuides & tutorials | |
| JEV: Diese neue KI ist der Wahnsinnwww.youtube.com | — | German explainer and tutorial covering an n8n example, Jev's limitations, use cases, a comparison with open-source alternatives, and how to use the models. | Articles, tutorials & talksGuides & tutorials | |
| Jev: Full Tutorialwww.youtube.com | — | Tutorial that starts from the API key and builds three prototypes, a voice-controlled browser, memory retrieval, and a YouTube topic scorer, noting where an LLM or code is still needed. | Articles, tutorials & talksGuides & tutorials | |
| Jev: Full Tutorial with Demoswww.youtube.com | — | Tutorial that compares Jev with LLMs on an LLM router, support-ticket triage, an inbox sorter, a live slop filter, and a browser agent, with the demo code on GitHub. | Articles, tutorials & talksGuides & tutorials | |
| Jev: how it works and what you can buildwww.youtube.com | — | Portuguese-language explainer on how Jev differs from an LLM, where it fits in a workflow, its limits, and demos spanning Doom, Tesla, trading, browser control and email. | Articles, tutorials & talksGuides & tutorials | |
| JEV: How It Works and What You Can Buildwww.youtube.com | — | Starts with a model-router demo, then explains Choice, Score, and Noul, structured output speed and cost, monitoring use cases, and the context window. | Articles, tutorials & talksGuides & tutorials | |
| Jev: RLCD Explainedwww.youtube.com | — | Short introduction to Jev and RLCD, reinforcement learning for calibrated decisions. | Articles, tutorials & talksGuides & tutorials | |
| Jev: structure and usagezenn.dev | — | Japanese guide built from the official docs and SDK source, covering Jev's data model, state and question types, setup steps, and operational tips. | Articles, tutorials & talksGuides & tutorials | |
| Jev: the AI that can't write a single wordnervegna.substack.com | — | Newsletter explainer of why a model that only decides is useful, followed by a step-by-step setup walkthrough for trying Jev. | Articles, tutorials & talksGuides & tutorials | |
| Jev: The Schema-Safe AI for Automationwww.youtube.com | — | Explainer on how Jev takes unstructured state plus a predefined schema and returns decisions with calibrated probabilities in one parallel pass. | Articles, tutorials & talksGuides & tutorials | |
| Jev: TypeSafe's System One Model Explainedwww.datacamp.com | — | Explainer on System One models and Jev's Choice, Score and Noul primitives, pricing and vendor workflow evals, with a minimal Python example calling the API. | Articles, tutorials & talksGuides & tutorials | |
| Jev: what it is and how to use itzhuanlan.zhihu.com | — | Long Chinese explainer that treats Jev as a calibrated discriminative classifier, with API and Vercel AI SDK examples, how confidence calibration works, limits, and pointers to community reproductions. | Articles, tutorials & talksGuides & tutorials | |
| Jev: what it is and how to use it — originaljev.kuhung.me | — | Long Chinese explainer that treats Jev as a calibrated discriminative classifier, with API and Vercel AI SDK examples, how confidence calibration works, limits, and pointers to community reproductions. | Articles, tutorials & talksGuides & tutorials | |
| Jev: what it is, how to get access, how I use itwww.youtube.com | — | Explainer on Jev's three question types, schema rules and calibration, four access routes (Vercel, OpenRouter, Cloudflare, TypeSafe), and its use in hm, a terminal tool for checking text. | Articles, tutorials & talksGuides & tutorials | |
| langchain-jev-tutorialgithub.com | 0 | LangChain + Jev (TypeSafe) tutorial: a support-ops agent whose small decisions (triage, model routing, tool guarding, evals) are made by Jev. Real run outputs included. | Articles, tutorials & talksGuides & tutorials | |
| learn-jevgithub.com | 0 | Learn Jev — TypeSafe System One typed decisions for software. Personal GitHub Pages reference hub (deck, shorts, sources). Unofficial. | Articles, tutorials & talksGuides & tutorials | |
| Let's look at Jevblog.lepine.pro | — | Walkthrough of the Choice, Noul, and Score primitives, calibrated confidence, and batching several questions per call, ending with a complete Python project. | Articles, tutorials & talksGuides & tutorials | |
| LocalForgeLLM Jev guidegithub.com | — | Guide in a local-AI-stack framework on pairing hosted Jev with a local LLM: Jev picks among observed browser-use actions, routes and checks results while the local model writes text and code. | Articles, tutorials & talksGuides & tutorials | |
| LocalForgeLLM Jev guide — repogithub.com | — | Guide in a local-AI-stack framework on pairing hosted Jev with a local LLM: Jev picks among observed browser-use actions, routes and checks results while the local model writes text and code. | Articles, tutorials & talksGuides & tutorials | |
| Making Jev generate textx.com | — | Hack that makes Jev produce text despite being non-generative, managing up to 20 words for $0.5. | Articles, tutorials & talksGuides & tutorials | |
| OpenRouter 用 Jev 自动分类 LLM 请求aihot.news | — | 提示：使用 @typesafeai 的 Jev，通过 Classifiers 自动分类你的 LLM 请求：https://openrouter.ai/workspaces/default/classifiers 随后你可以在 Explore 中分析结果：https://openrouter.ai/activity/explore | Articles, tutorials & talksGuides & tutorials | |
| OpenRouter 用 Jev 自动分类 LLM 请求x.com | — | 提示：使用 @typesafeai 的 Jev，通过 Classifiers 自动分类你的 LLM 请求：https://openrouter.ai/workspaces/default/classifiers 随后你可以在 Explore 中分析结果：https://openrouter.ai/activity/explore | Articles, tutorials & talksGuides & tutorials | |
| Simplest guide to Jevwww.reddit.com | — | Short primer on sending state plus questions to Jev and reading back Choice, Score, and Noul answers, with an example request body. | Articles, tutorials & talksGuides & tutorials | |
| System One Decisions tutorialgithub.com | — | Chapter of a hands-on AI agents course that separates producing from deciding, with runnable scripts for a first Jev decision, Jev as judge, judge calibration, model routing, agent-loop gating and a cost/latency bench. | Articles, tutorials & talksGuides & tutorials | |
| System One Decisions tutorial — repogithub.com | — | Chapter of a hands-on AI agents course that separates producing from deciding, with runnable scripts for a first Jev decision, Jev as judge, judge calibration, model routing, agent-loop gating and a cost/latency bench. | Articles, tutorials & talksGuides & tutorials | |
| Testing Jev's probability calibrationjuejin.cn | — | Chinese article on how QA engineers should test an AI decision system like Jev, checking not just whether answers are right but whether stated confidence such as 95% matches real accuracy. | Articles, tutorials & talksGuides & tutorials | |
| The Complete Guide to TypeSafe's Jevuditgoenka.medium.com | — | Long-form guide to Choice, Score and Noul, calling the API, and patterns for replacing JSON-prompted LLM decisions, starting from the 41 such calls the author counted in his own product. | Articles, tutorials & talksGuides & tutorials | |
| Three primitives and tiered thresholdsx.com | — | Chinese guide to using Jev: the Noul, Choice and Score primitives and their return shapes, five ways to get started (playground, HTTP, SDKs), and setting tiered confidence thresholds. | Articles, tutorials & talksGuides & tutorials | |
| tri-emails-jevgithub.com | 0 | Trier sa boîte mail avec Jev (TypeSafe) : 4 templates prêts à l'emploi - script Python, workflow n8n, scénario Make, skill Claude Code. | Articles, tutorials & talksGuides & tutorials | |
| TypeSafe AI 的 System One 模型 Jev 编程指南：类型化决策、置信度与投机式批量提问aihot.news | — | 这篇教程演示 TypeSafe AI 的首个 System One 模型 Jev 的用法，该模型不生成文本，而是对程序状态返回 Choice、Score、Noul 三种类型化判断供代码直接分支。 | Articles, tutorials & talksGuides & tutorials | |
| TypeSafe AI 的 System One 模型 Jev 编程指南：类型化决策、置信度与投机式批量提问www.marktechpost.com | — | 这篇教程演示 TypeSafe AI 的首个 System One 模型 Jev 的用法，该模型不生成文本，而是对程序状态返回 Choice、Score、Noul 三种类型化判断供代码直接分支。 | Articles, tutorials & talksGuides & tutorials | |
| TypeSafe Jev Explained for Chatbotswww.youtube.com | — | Explains Jev for chatbot builders: typed yes/no, pick-one, and scale answers with confidence, and where they fit next to a conversational model. | Articles, tutorials & talksGuides & tutorials | |
| TypeSafe 决策模型 Jev 使用教程：用 TypeScript 在 OpenRouter 上实现市场商品审核aihot.news | — | OpenRouter 发布 Jev 使用教程，Jev 是 TypeSafe 推出的决策模型（模型 ID typesafe/jev-1.13），通过 OpenRouter Decisions API 接收 state 和问题，返回带概率的类型化答案而非生成文本。 | Articles, tutorials & talksGuides & tutorials | |
| TypeSafe 决策模型 Jev 使用教程：用 TypeScript 在 OpenRouter 上实现市场商品审核openrouter.ai | — | OpenRouter 发布 Jev 使用教程，Jev 是 TypeSafe 推出的决策模型（模型 ID typesafe/jev-1.13），通过 OpenRouter Decisions API 接收 state 和问题，返回带概率的类型化答案而非生成文本。 | Articles, tutorials & talksGuides & tutorials | |
| typesafe-handbookgithub.com | 0 | TypeSafe AI 官方文档中文手册（明/暗/彩三套风格，官网同步日志，GitHub Pages） | Articles, tutorials & talksGuides & tutorials | |
| Use any LLM like Jevnews.ycombinator.com | — | Recipe for running any GGUF in llama.cpp with one predicted token and top logprobs to get Jev-style class probabilities, with notes on where calibrated probabilities still differ. | Articles, tutorials & talksGuides & tutorials | |
| Use any LLM like Jev — redditwww.reddit.com | — | Recipe for running any GGUF in llama.cpp with one predicted token and top logprobs to get Jev-style class probabilities, with notes on where calibrated probabilities still differ. | Articles, tutorials & talksGuides & tutorials | |
| What is Jev and how to use it?www.youtube.com | — | Hands-on Playground and TypeScript SDK walkthrough with a companion repository. | Articles, tutorials & talksGuides & tutorials | |
| What is Jev and how to use it? — repogithub.com | 16 | Hands-on Playground and TypeScript SDK walkthrough with a companion repository. | Articles, tutorials & talksGuides & tutorials | |
| What Is Jev?mohammedshehu.com | — | Short practical introduction to how Jev works and how to call it from Python, built around a support-ticket triage example. | Articles, tutorials & talksGuides & tutorials | |
| What is Jev?vercel.com | — | Vercel explainer on what state and typed questions mean in Jev, how Choice, Score, and Noul answers come back, and where its type safety ends. | Articles, tutorials & talksGuides & tutorials | |
| What is Jev?whatisjev.com | — | Independent multilingual guide to Jev with task templates, a no-key playground, a first-request walkthrough, and pages on pricing, limits, and how it differs from LLMs. | Articles, tutorials & talksGuides & tutorials | |
| What is Jev? Quick start walkthroughwww.reddit.com | — | Notes from reading the Jev docs plus a video walkthrough of the quick start in the TypeSafe Playground, with curl, the Python SDK, and Claude Code. | Articles, tutorials & talksGuides & tutorials | |
| What is Jev? Quick start walkthrough — videowww.youtube.com | — | Notes from reading the Jev docs plus a video walkthrough of the quick start in the TypeSafe Playground, with curl, the Python SDK, and Claude Code. | Articles, tutorials & talksGuides & tutorials | |
| What is Jev? — discussionnews.ycombinator.com | — | Vercel explainer on what state and typed questions mean in Jev, how Choice, Score, and Noul answers come back, and where its type safety ends. | Articles, tutorials & talksGuides & tutorials | |
| What is Jev? — zhwhatisjev.com | — | Independent multilingual guide to Jev with task templates, a no-key playground, a first-request walkthrough, and pages on pricing, limits, and how it differs from LLMs. | Articles, tutorials & talksGuides & tutorials | |
| What is TypeSafe AI's Jev?zenn.dev | — | Japanese overview of Jev's input and output spec for Choice, Score, and Noul with sample responses, and what it changes in day-to-day engineering work. | Articles, tutorials & talksGuides & tutorials | |
| What the heck is Jev?!www.jrzs.dev | — | Short beginner explainer of Jev as a sub-second decision model, with the Choice, Noul, and Score question types and a sample request and response. | Articles, tutorials & talksGuides & tutorials | |
| What the heck is Jev?! — discussionnews.ycombinator.com | — | Short beginner explainer of Jev as a sub-second decision model, with the Choice, Noul, and Score question types and a sample request and response. | Articles, tutorials & talksGuides & tutorials | |
| Why JEV matters: practical patternsjuejin.cn | — | Chinese overview of patterns for a decision model with pseudocode: agent routing, RAG relevance scoring, code-review risk gates, and SQL-style row judgments. | Articles, tutorials & talksGuides & tutorials | |
| 如何在 Together 平台上用 17 美元训练自己的 Jev 分类器aihot.news | — | Together AI 在 Qwen3.5 4B 基础上推出 Jev 类分类器 together/Tev1-4B-experimental，已上线其 serverless 平台。该博客展示了如何以 17 美元微调出自己的版本。 | Articles, tutorials & talksGuides & tutorials | |
| 如何在 Together 平台上用 17 美元训练自己的 Jev 分类器www.together.ai | — | Together AI 在 Qwen3.5 4B 基础上推出 Jev 类分类器 together/Tev1-4B-experimental，已上线其 serverless 平台。该博客展示了如何以 17 美元微调出自己的版本。 | Articles, tutorials & talksGuides & tutorials | |
| 如何用 Pi SDK 和 Jev 构建自定义 Agent harnessaihot.news | — | 作者发布交互式教程，演示用 Pi SDK（TypeScript Agent 工具包）和 TypeSafe AI 的小模型 Jev 构建自定义 Agent harness。 | Articles, tutorials & talksGuides & tutorials | |
| 如何用 Pi SDK 和 Jev 构建自定义 Agent harnessx.com | — | 作者发布交互式教程，演示用 Pi SDK（TypeScript Agent 工具包）和 TypeSafe AI 的小模型 Jev 构建自定义 Agent harness。 | Articles, tutorials & talksGuides & tutorials | |
| 爆速で判断だけするAI「Jev」www.youtube.com | — | Japanese explainer on how to use Jev, why a judgment-only AI is useful, and everyday ways to apply it. | Articles, tutorials & talksGuides & tutorials | |
| 用 Jev 构建 Harness：TypeSafe AI 的 System One 模型如何接入 LangChainaihot.news | — | TypeSafe AI 的 System One 模型 Jev 主打快速、结构化的决策，可嵌入 AI 智能体的 agent loop 中。LangChain 发布教程，介绍 Jev 的定位以及如何将其与 LangChain 配合使用。 | Articles, tutorials & talksGuides & tutorials | |
| 用 Pi 和 Jev 构建自定义 harnessaihot.news | — | 学习用 Pi 和 Jev 构建自定义 harness。 附带一个交互式 playground 来测试该 harness。 https://x.com/omarsar0/status/2102762406204076532?s=20 | Articles, tutorials & talksGuides & tutorials | |
| 用 Pi 和 Jev 构建自定义 harnessx.com | — | 学习用 Pi 和 Jev 构建自定义 harness。 附带一个交互式 playground 来测试该 harness。 https://x.com/omarsar0/status/2102762406204076532?s=20 | Articles, tutorials & talksGuides & tutorials | |
| 用读取 token 概率实现 Jev 风格的单函数 LLM 封装器，支持视觉模型allanrbo.blogspot.com | — | 作者 allanrbo 分享一个借鉴 Jev 思路的单函数封装方法：让模型只输出一个选项字母，通过 logprobs 读取各选项的 token 概率作为结构化答案，并扩展 attachments 字段支持图片输入。 | Articles, tutorials & talksGuides & tutorials | |
| 用读取 token 概率实现 Jev 风格的单函数 LLM 封装器，支持视觉模型aihot.news | — | 作者 allanrbo 分享一个借鉴 Jev 思路的单函数封装方法：让模型只输出一个选项字母，通过 logprobs 读取各选项的 token 概率作为结构化答案，并扩展 attachments 字段支持图片输入。 | Articles, tutorials & talksGuides & tutorials | |
| 話題のAI『Jev』を解説www.youtube.com | — | Japanese explainer on how a judgment-only model works and why to pair it with generative AI, with an AI-writing detection demo, a roundup of use cases, and setup steps. | Articles, tutorials & talksGuides & tutorials | |
| 화제의 초고속 TypeSafe Jevwww.youtube.com | — | Korean-language explainer covering System 1 versus System 2, the parallel sampler, RLCD and Brier-score calibration, pricing, and use cases such as fraud detection. | Articles, tutorials & talksGuides & tutorials |
Talks & videos43 of 43 matches
| Resource | Stars | Description | Category | Save |
|---|---|---|---|---|
| 22-minute Jev walkthroughx.com | — | Video of about 22 minutes explaining what Jev can do, with demos of three apps built on it. | Articles, tutorials & talksTalks & videos | |
| 45-second Jev TL;DRx.com | — | Short explainer video that walks through Jev's core idea more simply than the launch video. | Articles, tutorials & talksTalks & videos | |
| AI: too good to be true, too bad to be useful — videowww.youtube.com | — | Official post with Diogo Almeida's AI Council talk arguing that preference-optimized chat models are the wrong fit for automation and making the case for decision models. | Articles, tutorials & talksTalks & videos | |
| All About Jev (ai that works)github.com | — | Episode 75 of the ai that works live-coding podcast, with BAML's Vaibhav Gupta and HumanLayer's Dex Horthy, on how token generation holds back agent architecture and where Jev fits. | Articles, tutorials & talksTalks & videos | |
| All About Jev (ai that works) — appluma.com | — | Episode 75 of the ai that works live-coding podcast, with BAML's Vaibhav Gupta and HumanLayer's Dex Horthy, on how token generation holds back agent architecture and where Jev fits. | Articles, tutorials & talksTalks & videos | |
| All About Jev (ai that works) — demowww.youtube.com | — | Episode 75 of the ai that works live-coding podcast, with BAML's Vaibhav Gupta and HumanLayer's Dex Horthy, on how token generation holds back agent architecture and where Jev fits. | Articles, tutorials & talksTalks & videos | |
| Diogo Almeida - TypeSafe AI | Founders You Should Knowwww.youtube.com | — | Pre-launch startup-showcase pitch in which TypeSafe AI's founder describes building models for fully automated, no-human-in-the-loop decisions rather than chat. | Articles, tutorials & talksTalks & videos | |
| InstructGPT 前成员 Diogo Almeida 谈离开 OpenAI 与不聊天模型 Jev 的由来aihot.news | — | TypeSafe 联合创始人兼 CEO Diogo Almeida 在 Latent Space 访谈中讲述其离开 OpenAI 的经历，他参与过 InstructGPT 和 RLHF 早期工作，但认为优化方向过度面向人而公司难以转向为代码服务的智能，遂创业开发非聊天的大 型可编程模型 Jev（System One）。 | Articles, tutorials & talksTalks & videos | |
| InstructGPT 前成员 Diogo Almeida 谈离开 OpenAI 与不聊天模型 Jev 的由来x.com | — | TypeSafe 联合创始人兼 CEO Diogo Almeida 在 Latent Space 访谈中讲述其离开 OpenAI 的经历，他参与过 InstructGPT 和 RLHF 早期工作，但认为优化方向过度面向人而公司难以转向为代码服务的智能，遂创业开发非聊天的大 型可编程模型 Jev（System One）。 | Articles, tutorials & talksTalks & videos | |
| Jev arrives (M觀點 EP338)www.youtube.com | — | Mandarin-language talk-show episode whose opening segment, about 39 minutes long, discusses Jev's launch, what it is and why it matters. | Articles, tutorials & talksTalks & videos | |
| Jev explained in two minutesx.com | — | Short explainer video on splitting work between an LLM that plans and Jev that picks the next move, using Minecraft and browser-automation demos plus OpenRouter speed and cost benchmarks. | Articles, tutorials & talksTalks & videos | |
| Jev explained plus mini reproductionx.com | — | Video explaining what Jev is and how people use it, then building a small local reproduction of the idea to play a Sonic-inspired side-scroller. | Articles, tutorials & talksTalks & videos | |
| Jev for GTM engineeringwww.youtube.com | — | Talk on applying Jev to go-to-market work such as lead qualification, reply classification and sequence selection, with confidence gates that escalate fuzzy calls to a human. | Articles, tutorials & talksTalks & videos | |
| Jev in 34 secondsx.com | — | Short video explainer of how Jev's typed-decision loop works. | Articles, tutorials & talksTalks & videos | |
| Jev model routerx.com | — | Model router that asks Jev which language model best fits each incoming request and forwards the request to that model, shown in a demo video. | Articles, tutorials & talksTalks & videos | |
| Jev vs traditional LLMs: hands-on deep divewww.youtube.com | — | Hour-long live session on Jev's primitives, state and questions, and parallel evaluation, building a simple agent harness with LangChain and LangGraph using gates, routers and guards. | Articles, tutorials & talksTalks & videos | |
| JEV vs. ChatGPT & Claudewww.youtube.com | — | Portuguese-language video comparing Jev with ChatGPT and Claude and explaining what a decision model does differently. | Articles, tutorials & talksTalks & videos | |
| Jev 专题：AI That Works 第75期aihot.news | — | 关于 Jev 的一切：🦄 AI That Works #75 https://x.com/i/broadcasts/1qJVmyramPAGB | Articles, tutorials & talksTalks & videos | |
| Jev 专题：AI That Works 第75期x.com | — | 关于 Jev 的一切：🦄 AI That Works #75 https://x.com/i/broadcasts/1qJVmyramPAGB | Articles, tutorials & talksTalks & videos | |
| Jev: System One models for Prod, not Godwww.latent.space | — | Latent Space podcast episode, about 2 hours 20 minutes, with TypeSafe CEO Diogo Almeida on System One models, RLCD, and what Jev is for, with show notes linking notable community demos. | Articles, tutorials & talksTalks & videos | |
| Jev: System One models for Prod, not God — discussionnews.ycombinator.com | — | Latent Space podcast episode, about 2 hours 20 minutes, with TypeSafe CEO Diogo Almeida on System One models, RLCD, and what Jev is for, with show notes linking notable community demos. | Articles, tutorials & talksTalks & videos | |
| Latent Space 在 OpenAI DevDay 专访 Ari Weinstein 与 Nikunj Handa，谈 Computer Use 进展与 Decisions API 由来aihot.news | — | Latent Space 在 OpenAI DevDay 发布两段专访：Computer Use 负责人 Ari Weinstein 称 Computer Use 已比几个月前"180 度不同"。 | Articles, tutorials & talksTalks & videos | |
| Latent Space 在 OpenAI DevDay 专访 Ari Weinstein 与 Nikunj Handa，谈 Computer Use 进展与 Decisions API 由来www.latent.space | — | Latent Space 在 OpenAI DevDay 发布两段专访：Computer Use 负责人 Ari Weinstein 称 Computer Use 已比几个月前"180 度不同"。 | Articles, tutorials & talksTalks & videos | |
| Latent Space 访谈 TypeSafe CEO Diogo Almeida：Jev 是面向生产环境的 System One 模型而非万能 God 模型aihot.news | — | Latent Space 发布对 TypeSafe AI CEO Diogo Almeida 的约两小时访谈，围绕其新模型 Jev 展开。 | Articles, tutorials & talksTalks & videos | |
| Livestream Coding with the TypeSafe Jev Modelwww.youtube.com | — | Livestream that takes Jev apart and sketches network architectures for models that predict JSON answers with parallel constrained decoding; content starts at 6:57. | Articles, tutorials & talksTalks & videos | |
| OpenClaw Enterprise 推出 ClawCast 第12期aihot.news | — | The ClawCast — Jev & OpenClaw Enterprise（第12期）https://x.com/i/broadcasts/1mxPaZewLAmKN | Articles, tutorials & talksTalks & videos | |
| OpenClaw Enterprise 推出 ClawCast 第12期x.com | — | The ClawCast — Jev & OpenClaw Enterprise（第12期）https://x.com/i/broadcasts/1mxPaZewLAmKN | Articles, tutorials & talksTalks & videos | |
| OpenClaw 播客第12期谈决策模型与智能体aihot.news | — | Jev & OpenClaw Enterprise @hrudolph 和 @Pat_Erichsen 与来自 @typesafeai 的 @allietheicon 以及 @jlehman_ 讨论决策模型。来自 @OpenAI 的 @kevins8 稍后加入，讨论工作中的智能体。 观看或收听 The ClawCast 第 12 期： https://openclaw.ai/podcast/episode-12 | Articles, tutorials & talksTalks & videos | |
| OpenClaw 播客第12期谈决策模型与智能体x.com | — | Jev & OpenClaw Enterprise @hrudolph 和 @Pat_Erichsen 与来自 @typesafeai 的 @allietheicon 以及 @jlehman_ 讨论决策模型。来自 @OpenAI 的 @kevins8 稍后加入，讨论工作中的智能体。 观看或收听 The ClawCast 第 12 期： https://openclaw.ai/podcast/episode-12 | Articles, tutorials & talksTalks & videos | |
| OpenClaw 播客聊 Jev 与企业版aihot.news | — | 今天的 Clawcast 我们和 @allietheicon 聊 Jev 的一切，和 @kevins8 & @jlehman_ 聊 OpenClaw Enterprise 我们将在多个平台同步直播，点击这里在你选择的平台加入我们：https://openclaw.ai/podcast/episode-12 | Articles, tutorials & talksTalks & videos | |
| OpenClaw 播客聊 Jev 与企业版x.com | — | 今天的 Clawcast 我们和 @allietheicon 聊 Jev 的一切，和 @kevins8 & @jlehman_ 聊 OpenClaw Enterprise 我们将在多个平台同步直播，点击这里在你选择的平台加入我们：https://openclaw.ai/podcast/episode-12 | Articles, tutorials & talksTalks & videos | |
| Qué es Jev en 4 minutosx.com | — | Four-minute Spanish video explaining what Jev is, what it does not do, and why it changes where you place a model in a system. | Articles, tutorials & talksTalks & videos | |
| The brand new AI: Jevwww.youtube.com | — | Under-a-minute introduction to Jev from the Supabase channel. | Articles, tutorials & talksTalks & videos | |
| Trying Jev: the new style of AIwww.youtube.com | — | Long livestream of ThePrimeagen trying Jev hands-on and working out what a decision-only model is and is not good for. | Articles, tutorials & talksTalks & videos | |
| TypeSafe AI 谈 Jev 模型与自动化aihot.news | — | 又录了一期播客，更深入地聊了我的哲学和对软件的热爱！这次超级好玩 | Articles, tutorials & talksTalks & videos | |
| TypeSafe AI 谈 Jev 模型与自动化x.com | — | 又录了一期播客，更深入地聊了我的哲学和对软件的热爱！这次超级好玩 | Articles, tutorials & talksTalks & videos | |
| TypeSafe CEO 谈 System One Model 与可靠 AIaihot.news | — | TypeSafe CEO Jev 在播客中提出 System One Model，主张 AI 应做软件内的可靠决策而非聊天优先。他称 TypeSafe 拒绝公开 benchmark 和 API 层拒答，认为数据与任务选择比堆算力更重要，System One Model 或重塑编程智能体。即便有 10 亿美元，他也不会从头预训练模型。 | Articles, tutorials & talksTalks & videos | |
| TypeSafe CEO 谈 System One Model 与可靠 AIx.com | — | TypeSafe CEO Jev 在播客中提出 System One Model，主张 AI 应做软件内的可靠决策而非聊天优先。他称 TypeSafe 拒绝公开 benchmark 和 API 层拒答，认为数据与任务选择比堆算力更重要，System One Model 或重塑编程智能体。即便有 10 亿美元，他也不会从头预训练模型。 | Articles, tutorials & talksTalks & videos | |
| TypeSafe founder in five minutesx.com | — | Five-minute clip of TypeSafe's founder explaining why earlier LLMs were good at talking but poor at deciding, and what Jev changes. | Articles, tutorials & talksTalks & videos | |
| TypeSafe founder tech talkx.com | — | Recorded 36-minute tech talk in which TypeSafe's founder explains why agents without a human in the loop come next and how models like Jev are trained. | Articles, tutorials & talksTalks & videos | |
| What's behind the Jev hype? (Techwav EP155)www.youtube.com | — | Mandarin-language podcast episode on why Jev took off, its technical details, how it differs from LLMs, practical applications and the hosts' own tests. | Articles, tutorials & talksTalks & videos | |
| What's next after RLHF?www.youtube.com | — | TypeSafe's CEO at AI Engineer World's Fair 2026 on training models for calibrated decisions instead of human approval. | Articles, tutorials & talksTalks & videos | |
| Why we made Jevwww.youtube.com | — | Latent Space interview with TypeSafe CEO Diogo Almeida on System One models, RLCD vs RLHF and RLVR, refusing public benchmarks, model versioning, and how to build with small decisions. | Articles, tutorials & talksTalks & videos |
News & announcements109 of 109 matches
| Resource | Stars | Description | Category | Save |
|---|---|---|---|---|
| AINews: Jev, a System One Model that only decideswww.latent.space | — | Latent Space's launch-day roundup: over 100x faster and 200x cheaper than small frontier LLMs. | Articles, tutorials & talksNews & announcements | |
| AI뉴스 - Jev 열풍www.youtube.com | — | Korean AI news roundup that opens with the Jev launch and the open-source Laya alternative before covering other industry news. | Articles, tutorials & talksNews & announcements | |
| CLM-8B 发布：比 Jev 快 9 倍的 System One 模型aihot.news | — | Contrastive Language Model（CLM）发布，这是一个用对比学习目标训练的 System One 模型，CLM-8B 推理速度最高比 Jev 快 9 倍，在 computer-use、游戏和工具调用任务上性能相当。 | Articles, tutorials & talksNews & announcements | |
| CLM-8B 发布：比 Jev 快 9 倍的 System One 模型x.com | — | Contrastive Language Model（CLM）发布，这是一个用对比学习目标训练的 System One 模型，CLM-8B 推理速度最高比 Jev 快 9 倍，在 computer-use、游戏和工具调用任务上性能相当。 | Articles, tutorials & talksNews & announcements | |
| Contrastive-LM 发布开源模型 CLM-8B，零-shot 下评分 Agent 动作比 Jev 快最多 9 倍aihot.news | — | Contrastive-LM 发布开放模型 CLM-8B，首个对比语言模型（CLM），不为生成文本而训练，而是对候选动作按当前状态打分并返回概率，主打与 TypeSafe AI 的 System One 模型 Jev 相同的接口。 | Articles, tutorials & talksNews & announcements | |
| Contrastive-LM 发布开源模型 CLM-8B，零-shot 下评分 Agent 动作比 Jev 快最多 9 倍www.marktechpost.com | — | Contrastive-LM 发布开放模型 CLM-8B，首个对比语言模型（CLM），不为生成文本而训练，而是对候选动作按当前状态打分并返回概率，主打与 TypeSafe AI 的 System One 模型 Jev 相同的接口。 | Articles, tutorials & talksNews & announcements | |
| Convai 发布开源决策模型 Laya，对标 TypeSafe AI 的 Jevaihot.news | — | 作者 nandakishor_ml 发布开源非自回归决策模型家族 Laya，基于双向编码器，单 GPU 推理 32.8 ms（批量 7.2 ms/问题），自称比 TypeSafe AI 的 Jev 快 7.8 倍，权重以 Apache 2.0 开源。 | Articles, tutorials & talksNews & announcements | |
| Convai 发布开源决策模型 Laya，对标 TypeSafe AI 的 Jevlaya.convaiinnovations.com | — | 作者 nandakishor_ml 发布开源非自回归决策模型家族 Laya，基于双向编码器，单 GPU 推理 32.8 ms（批量 7.2 ms/问题），自称比 TypeSafe AI 的 Jev 快 7.8 倍，权重以 Apache 2.0 开源。 | Articles, tutorials & talksNews & announcements | |
| Databricks 发布 AI Function ai_decide，在治理数据上快速做出结构化决策aihot.news | — | Databricks 推出新的 AI Function ai_decide（Beta），由 TypeSafe AI 的 Jev 决策模型驱动，对文本评估一个或多个问题，在几分之一秒内返回概率、命名标准中的选项或有序量表评分，延迟和成本低于 LLM。 | Articles, tutorials & talksNews & announcements | |
| Databricks 发布 AI Function ai_decide，在治理数据上快速做出结构化决策www.databricks.com | — | Databricks 推出新的 AI Function ai_decide（Beta），由 TypeSafe AI 的 Jev 决策模型驱动，对文本评估一个或多个问题，在几分之一秒内返回概率、命名标准中的选项或有序量表评分，延迟和成本低于 LLM。 | Articles, tutorials & talksNews & announcements | |
| DSPy 3.4.0 原生支持 Jev 与 System One 模型aihot.news | — | DSPy 3.4.0 发布，原生支持 Jev 和 System One 模型，并新增专为置信度校准输出设计的优化器 ReAnchor。Elvis Saravia 称用 Jev 构建自定义 harness 在 guardrails、路由和验证器上效果良好，并看好其在技能结构化、工具调用、动态工作流与上下文工程中的应用。 | Articles, tutorials & talksNews & announcements | |
| DSPy 3.4.0 原生支持 Jev 与 System One 模型x.com | — | DSPy 3.4.0 发布，原生支持 Jev 和 System One 模型，并新增专为置信度校准输出设计的优化器 ReAnchor。Elvis Saravia 称用 Jev 构建自定义 harness 在 guardrails、路由和验证器上效果良好，并看好其在技能结构化、工具调用、动态工作流与上下文工程中的应用。 | Articles, tutorials & talksNews & announcements | |
| DSPy 3.4.0 发布：原生支持 Jev 与 System Oneaihot.news | — | DSPy 方法论 🤝 System One 编程，而非提示！ DSPy 3.4.0 刚刚发布！ 此版本在 DSPy 中原生支持 Jev 和 System one 模型！可与兼容的 signatures 一起使用。此版本还包含一个全新的优化器 ReAnchor，专门用于以置信度校准输出。 | Articles, tutorials & talksNews & announcements | |
| DSPy 3.4.0 发布：原生支持 Jev 与 System Onex.com | — | DSPy 方法论 🤝 System One 编程，而非提示！ DSPy 3.4.0 刚刚发布！ 此版本在 DSPy 中原生支持 Jev 和 System one 模型！可与兼容的 signatures 一起使用。此版本还包含一个全新的优化器 ReAnchor，专门用于以置信度校准输出。 | Articles, tutorials & talksNews & announcements | |
| DSPy 3.4.0 发布：新增 ReAnchor 优化器aihot.news | — | 太荣幸了！🥹 DSPy 3.4.0 刚刚发布！ 此版本在 DSPy 中原生支持 Jev 和 System one 模型！可与兼容的 signatures 一起使用。此版本还包含一个全新的优化器 ReAnchor，专门用于校准输出的置信度。 | Articles, tutorials & talksNews & announcements | |
| DSPy 3.4.0 发布：新增 ReAnchor 优化器x.com | — | 太荣幸了！🥹 DSPy 3.4.0 刚刚发布！ 此版本在 DSPy 中原生支持 Jev 和 System one 模型！可与兼容的 signatures 一起使用。此版本还包含一个全新的优化器 ReAnchor，专门用于校准输出的置信度。 | Articles, tutorials & talksNews & announcements | |
| Fastino 发布 GLiNER2.5-Decide：可在 CPU 上运行的 340M 开源权重决策模型aihot.news | — | Fastino Labs 发布 340M 参数开源权重决策模型 GLiNER2.5-Decide，接受文本和类型化问题 schema，返回带概率分布、置信度和约束可行性元数据的结构化答案，权重采用 Apache 2.0，可运行于 CPU、GPU 或气隙环境。 | Articles, tutorials & talksNews & announcements | |
| Fastino 发布 GLiNER2.5-Decide：可在 CPU 上运行的 340M 开源权重决策模型www.marktechpost.com | — | Fastino Labs 发布 340M 参数开源权重决策模型 GLiNER2.5-Decide，接受文本和类型化问题 schema，返回带概率分布、置信度和约束可行性元数据的结构化答案，权重采用 Apache 2.0，可运行于 CPU、GPU 或气隙环境。 | Articles, tutorials & talksNews & announcements | |
| Inception 的 Mercury Decide 决策模型上线 OpenRouter，免费早期访问aihot.news | — | Inception 的 Mercury Decide 模型已在 OpenRouter 上线，提供免费早期访问。它是决策模型，接收应用状态和类型化问题，返回附带概率的类型化答案；Inception 称其在 JevBench v1.4 上是 OpenRouter 上最智能的决策模型。地址：https://openrouter.ai/inception/mercury-decide:free | Articles, tutorials & talksNews & announcements | |
| Inception 的 Mercury Decide 决策模型上线 OpenRouter，免费早期访问x.com | — | Inception 的 Mercury Decide 模型已在 OpenRouter 上线，提供免费早期访问。它是决策模型，接收应用状态和类型化问题，返回附带概率的类型化答案；Inception 称其在 JevBench v1.4 上是 OpenRouter 上最智能的决策模型。地址：https://openrouter.ai/inception/mercury-decide:free | Articles, tutorials & talksNews & announcements | |
| Introducing CUA-S1x.com | — | X: Cua open-sources a family of small, specialised System One models for computer use, starting with form filling and asking what the next specialist should learn. | Articles, tutorials & talksNews & announcements | |
| Jeff 发布 0.8B 与 2B 决策模型，兼容 Jev 请求格式，单次决策约 22 毫秒aihot.news | — | Jeff 发布基于 Qwen3.5 与 Gemma 4 微调的零样本分类模型 Jeff-Qwen3.5-0.8B、Jeff-Qwen3.5-2B 和 Jeff-Gemma4-E2B，与 Jev 使用相同请求格式，单次前向输出各选项校准概率。 | Articles, tutorials & talksNews & announcements | |
| Jeff 发布 0.8B 与 2B 决策模型，兼容 Jev 请求格式，单次决策约 22 毫秒github.com | — | Jeff 发布基于 Qwen3.5 与 Gemma 4 微调的零样本分类模型 Jeff-Qwen3.5-0.8B、Jeff-Qwen3.5-2B 和 Jeff-Gemma4-E2B，与 Jev 使用相同请求格式，单次前向输出各选项校准概率。 | Articles, tutorials & talksNews & announcements | |
| Jev launch threadx.com | — | Founder Diogo Almeida's launch thread introducing Jev and the RLCD training method, claiming 20-200x faster and 40-400x cheaper decisions than frontier chat models. | Articles, tutorials & talksNews & announcements | |
| Jev 上线 Venice API 测试版aihot.news | — | 是 Jev，不是 Jev Jev（@typesafeai 出品）现已上线 Venice API，处于测试阶段。 它只回答，不写作。把你的应用状态和带类型的问题发给它，就能拿回一个代码可以分支处理的带类型答案：一个概率、一个选定选项，或按你的评分标准给出的分数。无需从聊天模型里费劲套出 JSON。 | Articles, tutorials & talksNews & announcements | |
| Jev 上线 Venice API 测试版x.com | — | 是 Jev，不是 Jev Jev（@typesafeai 出品）现已上线 Venice API，处于测试阶段。 它只回答，不写作。把你的应用状态和带类型的问题发给它，就能拿回一个代码可以分支处理的带类型答案：一个概率、一个选定选项，或按你的评分标准给出的分数。无需从聊天模型里费劲套出 JSON。 | Articles, tutorials & talksNews & announcements | |
| Jev 回归并开放注册aihot.news | — | 女士们先生们，agents 和 assistants 们， 我们非常激动地宣布 Jev 回来了。 容量已提升，注册现已开放！ | Articles, tutorials & talksNews & announcements | |
| Jev 回归并开放注册x.com | — | 女士们先生们，agents 和 assistants 们， 我们非常激动地宣布 Jev 回来了。 容量已提升，注册现已开放！ | Articles, tutorials & talksNews & announcements | |
| Jev 回归开放注册，免费额度暂停aihot.news | — | 好消息：任何人都可以注册了 💪 坏消息：我们不得不暂时关闭免费额度（仅针对新用户）——我们真的很想让大家都能体验 jev，但少数不良用户让所有人都很难受 😢 [引用 @typesafeai]：女士们先生们，智能体和助手们， 我们非常激动地宣布 Jev 回来了。 容量已提升，注册已开放！ | Articles, tutorials & talksNews & announcements | |
| Jev 回归开放注册，免费额度暂停x.com | — | 好消息：任何人都可以注册了 💪 坏消息：我们不得不暂时关闭免费额度（仅针对新用户）——我们真的很想让大家都能体验 jev，但少数不良用户让所有人都很难受 😢 [引用 @typesafeai]：女士们先生们，智能体和助手们， 我们非常激动地宣布 Jev 回来了。 容量已提升，注册已开放！ | Articles, tutorials & talksNews & announcements | |
| Jev 因需求激增暂停新用户注册aihot.news | — | AI 产品 Jev 因需求激增已暂停新用户注册，现有用户服务不受影响。官方称此举是为保障现有用户的服务质量，同时让团队得以休息，并将尽快恢复开放注册。 | Articles, tutorials & talksNews & announcements | |
| Jev 因需求激增暂停新用户注册x.com | — | AI 产品 Jev 因需求激增已暂停新用户注册，现有用户服务不受影响。官方称此举是为保障现有用户的服务质量，同时让团队得以休息，并将尽快恢复开放注册。 | Articles, tutorials & talksNews & announcements | |
| Jev 因需求激增暂停注册aihot.news | — | 我们看到了极其巨大的需求涌入，不得不暂时暂停 Jev 的注册。我们需要确保现有注册用户的服务质量，他们的服务将继续正常运行。我们正在努力尽快让所有人都能开放使用 Jev。谢谢。 | Articles, tutorials & talksNews & announcements | |
| Jev 因需求激增暂停注册x.com | — | 我们看到了极其巨大的需求涌入，不得不暂时暂停 Jev 的注册。我们需要确保现有注册用户的服务质量，他们的服务将继续正常运行。我们正在努力尽快让所有人都能开放使用 Jev。谢谢。 | Articles, tutorials & talksNews & announcements | |
| Jev 在 Vercel AI Gateway 限时免费aihot.news | — | 免费 【引用 @vercel_dev】：Jev by @typesafeai 在 Vercel AI Gateway 上免费至 9 月 25 日。 免费使用 Gateway 上采用速度最快的模型。 https://vercel.com/ai-gateway/models/jev https://x.com/vercel/status/2101077346203971900?s=20 | Articles, tutorials & talksNews & announcements | |
| Jev 在 Vercel AI Gateway 限时免费x.com | — | 免费 【引用 @vercel_dev】：Jev by @typesafeai 在 Vercel AI Gateway 上免费至 9 月 25 日。 免费使用 Gateway 上采用速度最快的模型。 https://vercel.com/ai-gateway/models/jev https://x.com/vercel/status/2101077346203971900?s=20 | Articles, tutorials & talksNews & announcements | |
| Jev 新模型明日发布aihot.news | — | Jev 明天发布 【引用 @CompleteSkeptic】：在联合发明 ChatGPT 之后，我一直在问自己：为什么超人类水平的聊天模型没有带来 AGI？ 过去两年我一直在 stealth 模式下构建一种新的模型训练方式（RLCD），以及一种新型前沿 AI 模型，今天正式发布：Jev • 快 20-200 倍 • 便宜 40-400 倍（输出 token 免费）• 为决策优化的前沿可组合智能 据我所知，这是通往 AI 驱动的经济革命的最短路径 | Articles, tutorials & talksNews & announcements | |
| Jev 新模型明日发布x.com | — | Jev 明天发布 【引用 @CompleteSkeptic】：在联合发明 ChatGPT 之后，我一直在问自己：为什么超人类水平的聊天模型没有带来 AGI？ 过去两年我一直在 stealth 模式下构建一种新的模型训练方式（RLCD），以及一种新型前沿 AI 模型，今天正式发布：Jev • 快 20-200 倍 • 便宜 40-400 倍（输出 token 免费）• 为决策优化的前沿可组合智能 据我所知，这是通往 AI 驱动的经济革命的最短路径 | Articles, tutorials & talksNews & announcements | |
| Jev 模型上线 OpenRouter 测试版aihot.news | — | 由 @typesafeai 开发的 Jev 现已上线 OpenRouter，处于 beta 阶段。 Jev 是一个 System One 模型。它不生成文本，而是接收你的应用状态加上一个带类型的问题，返回一个带类型、附带概率的决策。无需 JSON 提示词、解析层，也没有需要校验的东西。 | Articles, tutorials & talksNews & announcements | |
| Jev 现已向所有人开放aihot.news | — | 。无需等待名单。在此开始使用：https://console.typesafe.ai | Articles, tutorials & talksNews & announcements | |
| Jev 面向所有人开放，无需等待名单aihot.news | — | 哎呀。以前"快"可是件坏事 💅 Jev 现已面向所有人开放，无需等待名单。 在这里开始使用：https://console.typesafe.ai | Articles, tutorials & talksNews & announcements | |
| Jev 面向所有人开放，无需等待名单x.com | — | 哎呀。以前"快"可是件坏事 💅 Jev 现已面向所有人开放，无需等待名单。 在这里开始使用：https://console.typesafe.ai | Articles, tutorials & talksNews & announcements | |
| jev-ai-decision-model-typesafe-diogo-almeida-system-onewww.winzheng.com | — | 赢政天下 / winzheng | https://www.winzheng.com/article/jev-ai-decision-model-typesafe-diogo-almeida-system-one | search-hit | News-style: ChatGPT co-inventor / cost claims | Articles, tutorials & talksNews & announcements | |
| JevBench v1.4.1 发布：面向类型化决策模型的可重复基准测试aihot.news | — | JevBench v1.4.1 发布，这是 Benchmark Heaven 面向 Jev 类决策模型的可重复基准，输入状态与有界评分标准、输出类型化答案，基于 534 个公开加 308 个密封决策、协议 jevbench::v1.4 评分。榜单共 77 个系统，Jev 1.13.0 以 77 分居首，JevK5 v0.2.0 与 Hopper 分列二、三。 | Articles, tutorials & talksNews & announcements | |
| jevmem v0.5 发布：为 Claude Code 自动保存项目记忆aihot.news | — | 作者发布 jevmem v0.5，一个为 Claude Code 自动保存项目记忆的工具，将对话中的决策、约束、bug 和 todo 写入 JEVMEM.md，旧结论标记为 superseded 而非删除，下次会话注入相关记忆行；也支持 Cursor 和 Codex。 | Articles, tutorials & talksNews & announcements | |
| jg CLI 开启 alpha 测试aihot.news | — | 谁想 alpha 测试我的新 jg CLI（没错，就是 jevgrep） | Articles, tutorials & talksNews & announcements | |
| jg CLI 开启 alpha 测试x.com | — | 谁想 alpha 测试我的新 jg CLI（没错，就是 jevgrep） | Articles, tutorials & talksNews & announcements | |
| Julia-1 分类模型发布，训练仅花 104 美元aihot.news | — | supersonicai 发布首个分类模型 Julia-1，号称几乎能在任何设备上运行，训练与实验的云 GPU 花费约 R$540（US$104.08）。 | Articles, tutorials & talksNews & announcements | |
| Julia-1 分类模型发布，训练仅花 104 美元x.com | — | supersonicai 发布首个分类模型 Julia-1，号称几乎能在任何设备上运行，训练与实验的云 GPU 花费约 R$540（US$104.08）。 | Articles, tutorials & talksNews & announcements | |
| Kev-4B 上线硅基流动aihot.news | — | 不是每次模型调用都需要一个答案。有时，它只需要一个决策。👏 欢迎 Kev-4B 加入硅基流动。Kev-4B 是 Jev 的开源社区版，基于 Qwen3.5-4B 构建，用于结构化决策。路由。排序。审批。升级——无需再生成一段回复。无需部署或适配。一个硅基流动 API key，Kev 即可接入你的工作流。特别感谢 @jaredpalmer 开源 Kev。❤️ 在硅基流动上试用 Kev-4B。⚡️ | Articles, tutorials & talksNews & announcements | |
| Kev-4B 上线硅基流动x.com | — | 不是每次模型调用都需要一个答案。有时，它只需要一个决策。👏 欢迎 Kev-4B 加入硅基流动。Kev-4B 是 Jev 的开源社区版，基于 Qwen3.5-4B 构建，用于结构化决策。路由。排序。审批。升级——无需再生成一段回复。无需部署或适配。一个硅基流动 API key，Kev 即可接入你的工作流。特别感谢 @jaredpalmer 开源 Kev。❤️ 在硅基流动上试用 Kev-4B。⚡️ | Articles, tutorials & talksNews & announcements | |
| Kev：基于 Qwen3.5 的开源小型决策模型家族发布aihot.news | — | Kev 发布了一组基于 Qwen3.5 的小型决策模型，包含 0.8B、4B 和 9B 三个规格，采用 rank-16 LoRA 加 pointer head 架构，支持在同一请求中处理 yes/no、多选和打分问题并返回概率。 | Articles, tutorials & talksNews & announcements | |
| Kev：基于 Qwen3.5 的开源小型决策模型家族发布github.com | 8,135 | Kev 发布了一组基于 Qwen3.5 的小型决策模型，包含 0.8B、4B 和 9B 三个规格，采用 rank-16 LoRA 加 pointer head 架构，支持在同一请求中处理 yes/no、多选和打分问题并返回概率。 | Articles, tutorials & talksNews & announcements | |
| LangSmith 上线 Jev-as-a-Judge 评估功能aihot.news | — | LangSmith 现已支持将 Jev 用作评估裁判（Jev-as-a-Judge），可对智能体 trace 进行结构化反馈评估，覆盖生产运行、数据集与回归测试场景。官方称该方式更快、更便宜。 | Articles, tutorials & talksNews & announcements | |
| LangSmith 上线 Jev-as-a-Judge 评估功能www.langchain.com | — | LangSmith 现已支持将 Jev 用作评估裁判（Jev-as-a-Judge），可对智能体 trace 进行结构化反馈评估，覆盖生产运行、数据集与回归测试场景。官方称该方式更快、更便宜。 | Articles, tutorials & talksNews & announcements | |
| Meet Jev: the schema-safe AIwww.youtube.com | — | Explainer on the Jev launch: typed questions over program state, decisions with calibrated probabilities in 70 to 500 milliseconds, and an early open rebuild on a used RTX 3090. | Articles, tutorials & talksNews & announcements | |
| Modal 与 CMU 推出 Quail：联合优化查询规划器与推理引擎，加速 AI-SQLaihot.news | — | Modal 与 CMU Full Stack Data Lab 联合推出查询感知推理层 Quail，通过联合优化查询规划器与推理引擎加速 AI-SQL 查询。在某个多表连接查询上，Quail 单张 H100 GPU 每分钟处理超 10 亿 token，比同硬件 vLLM 基线快 10 倍以上，Modal 上成本低于每 10 亿 token 6 美分。 | Articles, tutorials & talksNews & announcements | |
| Modal 与 CMU 推出 Quail：联合优化查询规划器与推理引擎，加速 AI-SQLmodal.com | — | Modal 与 CMU Full Stack Data Lab 联合推出查询感知推理层 Quail，通过联合优化查询规划器与推理引擎加速 AI-SQL 查询。在某个多表连接查询上，Quail 单张 H100 GPU 每分钟处理超 10 亿 token，比同硬件 vLLM 基线快 10 倍以上，Modal 上成本低于每 10 亿 token 6 美分。 | Articles, tutorials & talksNews & announcements | |
| NaceAI 发布 Drex 决策模型，输出选项概率aihot.news | — | NaceAI 推出 Drex，一个 sub-6B 决策模型，不生成文本而是直接输出各选项概率，一次前向即可完成，面向智能体路由、工具选择、重排序与策略检查等场景。Drex 采用小型扩散模型加 RLAF 架构，定价 $0.04/1M input tokens，延迟低于 1 秒，在 Decision Index 上排名 #1，40 项 benchmark 中赢下 23 项，开放权重与技术报告即将发布。 | Articles, tutorials & talksNews & announcements | |
| NaceAI 发布 Drex 决策模型，输出选项概率x.com | — | NaceAI 推出 Drex，一个 sub-6B 决策模型，不生成文本而是直接输出各选项概率，一次前向即可完成，面向智能体路由、工具选择、重排序与策略检查等场景。Drex 采用小型扩散模型加 RLAF 架构，定价 $0.04/1M input tokens，延迟低于 1 秒，在 Decision Index 上排名 #1，40 项 benchmark 中赢下 23 项，开放权重与技术报告即将发布。 | Articles, tutorials & talksNews & announcements | |
| Ollaya 发布，本地运行开源决策模型并兼容 TypeSafe APIaihot.news | — | Ollaya 发布，一个开源的本地决策模型运行工具，对文本或 JSON 的类型化问题返回毫秒级校准答案。支持 laya、decider、nli、gliclass 等开放权重模型，兼容 TypeSafe 的 /v1/systemone 和 /v1/models 接口，官方 TypeSafe Python SDK 0.7.1 可直接使用。 | Articles, tutorials & talksNews & announcements | |
| OpenAI DevDay 2026、Manus 2.0 与 Claude Sonnet 5.5 发布aihot.news | — | OpenAI DevDay 2026 发布常驻智能体 Dots、协作空间 ChatGPT Space、基于 GPT-6 Luna 的 Decisions API 及 Codex 云端环境。 | Articles, tutorials & talksNews & announcements | |
| OpenAI DevDay 2026、Manus 2.0 与 Claude Sonnet 5.5 发布x.com | — | OpenAI DevDay 2026 发布常驻智能体 Dots、协作空间 ChatGPT Space、基于 GPT-6 Luna 的 Decisions API 及 Codex 云端环境。 | Articles, tutorials & talksNews & announcements | |
| OpenAI 前 RLHF 研究者 Diogo Almeida 创办 TypeSafe AI，发布不生成文本的结构化决策模型 Jevaihot.news | — | TypeSafe AI 于 9 月 16 日发布 System One 模型 Jev，不生成文本，仅输出 Choice、Score、Noul 三类结构化决策，输入词元定价每百万 0.042 美元，输出词元免费。 | Articles, tutorials & talksNews & announcements | |
| OpenAI 前 RLHF 研究者 Diogo Almeida 创办 TypeSafe AI，发布不生成文本的结构化决策模型 Jevwww.ithome.com | — | TypeSafe AI 于 9 月 16 日发布 System One 模型 Jev，不生成文本，仅输出 Choice、Score、Noul 三类结构化决策，输入词元定价每百万 0.042 美元，输出词元免费。 | Articles, tutorials & talksNews & announcements | |
| OpenAI 在 DevDay 2026 扩展 Codex 与 API，推出 Decisions API 和 Ultrafast 高速档aihot.news | — | OpenAI 在旧金山 DevDay 2026 上宣布多项开发者产品更新。Codex 新增可复用云环境、语音控制和代码审查视图，Codex Security Cloud 可按需或定时扫描 GitHub 仓库漏洞。 | Articles, tutorials & talksNews & announcements | |
| OpenAI 在 DevDay 2026 扩展 Codex 与 API，推出 Decisions API 和 Ultrafast 高速档the-decoder.com | — | OpenAI 在旧金山 DevDay 2026 上宣布多项开发者产品更新。Codex 新增可复用云环境、语音控制和代码审查视图，Codex Security Cloud 可按需或定时扫描 GitHub 仓库漏洞。 | Articles, tutorials & talksNews & announcements | |
| OpenAI 推出 Decisions API，150 毫秒内返回低延迟分类与路由决策aihot.news | — | OpenAI 在 2026 开发者日活动中推出面向实时、低延迟分类与路由场景的 Decisions API，基于小型模型 Luna，约 150 毫秒内返回结果，比通过常规 API 使用 Luna 快约 10 倍。 | Articles, tutorials & talksNews & announcements | |
| OpenAI 推出 Decisions API，150 毫秒内返回低延迟分类与路由决策www.ithome.com | — | OpenAI 在 2026 开发者日活动中推出面向实时、低延迟分类与路由场景的 Decisions API，基于小型模型 Luna，约 150 毫秒内返回结果，比通过常规 API 使用 Luna 快约 10 倍。 | Articles, tutorials & talksNews & announcements | |
| OpenAI 推出 Decisions API，对标 TypeSafe AI 的 Jev 决策模型aihot.news | — | OpenAI 在 Dev Day 上发布 Decisions API，可为 Luna 模型预设选项并输出概率，功能类似 TypeSafe AI 本月初推出的软件自动化模型 Jev。该 API 目前为限量预览，Altman 称聚焦单一选择可让模型在保持图像理解、多语言与安全能力的同时极快运行。开发者已用 Jev 监控 AI 智能体行为，单次监控成本 2.94 美元，而前沿 LLM 需 372 美元。 | Articles, tutorials & talksNews & announcements | |
| OpenAI 推出 Decisions API，对标 TypeSafe AI 的 Jev 决策模型techcrunch.com | — | OpenAI 在 Dev Day 上发布 Decisions API，可为 Luna 模型预设选项并输出概率，功能类似 TypeSafe AI 本月初推出的软件自动化模型 Jev。该 API 目前为限量预览，Altman 称聚焦单一选择可让模型在保持图像理解、多语言与安全能力的同时极快运行。开发者已用 Jev 监控 AI 智能体行为，单次监控成本 2.94 美元，而前沿 LLM 需 372 美元。 | Articles, tutorials & talksNews & announcements | |
| OpenAI前研究员创办的TypeSafe发布非LLM新模型Jev，输出校准概率而非文本aihot.news | — | OpenAI前研究员、RLHF共同发明人Almeida创办的TypeSafe AI发布基于transformer的新模型Jev，它不输出文本而是产生概率形式的校准决策。 | Articles, tutorials & talksNews & announcements | |
| OpenClaw 2026.9.6 发布aihot.news | — | OpenClaw 2026.9.6 🦞 🤖 Opus 5.5、GPT-6 Sol/Luna、Grok 4.7 🔧 托管更新 🧵 重启恢复 📊 30 天用量 🐙 GitHub 阅读器 💻 远程文件、记忆与技能 📝 实时会议记录 🧠 Jev + 决策模型 2,614 PRs · 351 位贡献者 · 更多内容 ↓ https://docs.openclaw.ai/releases/2026.9.6 | Articles, tutorials & talksNews & announcements | |
| OpenClaw 2026.9.6 发布x.com | — | OpenClaw 2026.9.6 🦞 🤖 Opus 5.5、GPT-6 Sol/Luna、Grok 4.7 🔧 托管更新 🧵 重启恢复 📊 30 天用量 🐙 GitHub 阅读器 💻 远程文件、记忆与技能 📝 实时会议记录 🧠 Jev + 决策模型 2,614 PRs · 351 位贡献者 · 更多内容 ↓ https://docs.openclaw.ai/releases/2026.9.6 | Articles, tutorials & talksNews & announcements | |
| OpenRouter 推出 Jev 缓存感知模型路由aihot.news | — | 你从未体验过这样的路由方式。 @OpenRouter 将 Jev 带入你所有的 LLM 调用，让你的智能体工作流不再浪费任何一个 token。 一如既往，更快、更便宜、更智能。去构建未来吧。 @OpenRouter：推出 typesafe/jev-router：一个由 Jev 和 @typesafeai 驱动的缓存感知模型路由器。 Jev Router 为每个请求挑选最佳模型和推理强度，在质量、速度和成本之间取得平衡。 以下是它的工作原理 👇🏻 | Articles, tutorials & talksNews & announcements | |
| OpenRouter 推出 Jev 缓存感知模型路由x.com | — | 你从未体验过这样的路由方式。 @OpenRouter 将 Jev 带入你所有的 LLM 调用，让你的智能体工作流不再浪费任何一个 token。 一如既往，更快、更便宜、更智能。去构建未来吧。 @OpenRouter：推出 typesafe/jev-router：一个由 Jev 和 @typesafeai 驱动的缓存感知模型路由器。 Jev Router 为每个请求挑选最佳模型和推理强度，在质量、速度和成本之间取得平衡。 以下是它的工作原理 👇🏻 | Articles, tutorials & talksNews & announcements | |
| Opus 5.5 制作讲解视频的开源 agent 方案 shipvideo 发布aihot.news | — | 作者发布 launchvideo.io 与 diggerhq/shipvideo 仓库，用 anthropic/claude-opus-5.5 通过 OpenComputer serverless agent 把一个 URL 或提示词直接渲染成 MP4 讲解视频，单次运行无人工编辑。 | Articles, tutorials & talksNews & announcements | |
| Opus 5.5 制作讲解视频的开源 agent 方案 shipvideo 发布launchvideo.io | — | 作者发布 launchvideo.io 与 diggerhq/shipvideo 仓库，用 anthropic/claude-opus-5.5 通过 OpenComputer serverless agent 把一个 URL 或提示词直接渲染成 MP4 讲解视频，单次运行无人工编辑。 | Articles, tutorials & talksNews & announcements | |
| Paradigm Frontiers 活动预告：CompleteSkeptic 任嘉宾aihot.news | — | Paradigm Frontiers 活动宣布 @CompleteSkeptic 将作为特别嘉宾出席，活动时间为 10 月 12-14 日，地点在旧金山 Fort Mason，申请本周截止。主推文称近期进展超出预期，并预告几周后 Paradigm Frontiers 将有新内容发布。 | Articles, tutorials & talksNews & announcements | |
| Paradigm Frontiers 活动预告：CompleteSkeptic 任嘉宾x.com | — | Paradigm Frontiers 活动宣布 @CompleteSkeptic 将作为特别嘉宾出席，活动时间为 10 月 12-14 日，地点在旧金山 Fort Mason，申请本周截止。主推文称近期进展超出预期，并预告几周后 Paradigm Frontiers 将有新内容发布。 | Articles, tutorials & talksNews & announcements | |
| Product Hunt 9月30日举办 HYPERSHIP DAYaihot.news | — | Product Hunt 将于 9 月 30 日举办 HYPERSHIP DAY，由 Jev 开发商 @typesafeai 与 @supabase 联合赞助。想参与的开发者需在 9 月 30 日午夜前提交发布，当天需实时构建并上线多个功能，奖品包括 Jev 和 Supabase credits、swag 礼盒及后续公布的秘密奖品。不发布的用户也可当天体验新产品、提交功能请求并观察产品实时迭代。 | Articles, tutorials & talksNews & announcements | |
| Product Hunt 9月30日举办 HYPERSHIP DAYx.com | — | Product Hunt 将于 9 月 30 日举办 HYPERSHIP DAY，由 Jev 开发商 @typesafeai 与 @supabase 联合赞助。想参与的开发者需在 9 月 30 日午夜前提交发布，当天需实时构建并上线多个功能，奖品包括 Jev 和 Supabase credits、swag 礼盒及后续公布的秘密奖品。不发布的用户也可当天体验新产品、提交功能请求并观察产品实时迭代。 | Articles, tutorials & talksNews & announcements | |
| Pydantic AI 智能体现已运行于 Jevaihot.news | — | @pydantic 从一开始就是类型安全的！现在由 Jev 驱动你的 Pydantic AI 智能体调用，返回速度比以往更快 ⚡️⚡️ Pydantic AI 智能体现已运行于 Jev，这是来自 @typesafeai 的分类器。 Jev 不生成文本，它回答带类型的问题。所以你已写好的 output_type 就是问题，答案会以你的模型形式返回，每个字段一个置信度。 https://pydantic.io/8iAZ9 | Articles, tutorials & talksNews & announcements | |
| Pydantic AI 智能体现已运行于 Jevx.com | — | @pydantic 从一开始就是类型安全的！现在由 Jev 驱动你的 Pydantic AI 智能体调用，返回速度比以往更快 ⚡️⚡️ Pydantic AI 智能体现已运行于 Jev，这是来自 @typesafeai 的分类器。 Jev 不生成文本，它回答带类型的问题。所以你已写好的 output_type 就是问题，答案会以你的模型形式返回，每个字段一个置信度。 https://pydantic.io/8iAZ9 | Articles, tutorials & talksNews & announcements | |
| RespanAI Span-01 上线 OpenRouteraihot.news | — | 来自 @RespanAI 的 Span-01 和 Span-01 Lite 已在 OpenRouter 上线。 它们是用于智能体轨迹的决策模型。发送一个 span 和你关心的行为，就能得到每种行为存在的概率，比如“用户是否感到沮丧？”或“这个工具调用是否安全可运行？” | Articles, tutorials & talksNews & announcements | |
| RespanAI Span-01 上线 OpenRouterx.com | — | 来自 @RespanAI 的 Span-01 和 Span-01 Lite 已在 OpenRouter 上线。 它们是用于智能体轨迹的决策模型。发送一个 span 和你关心的行为，就能得到每种行为存在的概率，比如“用户是否感到沮丧？”或“这个工具调用是否安全可运行？” | Articles, tutorials & talksNews & announcements | |
| Simon Willison 发布 llm-typesafe 0.1a0，为 LLM 插件接入 TypeSafe AI 的 Jev 模型aihot.news | — | Simon Willison 发布 LLM 插件 llm-typesafe 0.1a0，为 TypeSafe AI 的新模型 Jev 提供支持，可通过 llm install llm-typesafe 安装并设置 API key。该插件支持 yes/no、choice 和 score 三类提问，例如 noul 问题返回 {"type": "noul", "noul": 0.99}。 | Articles, tutorials & talksNews & announcements | |
| Simon Willison 发布 llm-typesafe 0.1a0，为 LLM 插件接入 TypeSafe AI 的 Jev 模型simonwillison.net | — | Simon Willison 发布 LLM 插件 llm-typesafe 0.1a0，为 TypeSafe AI 的新模型 Jev 提供支持，可通过 llm install llm-typesafe 安装并设置 API key。该插件支持 yes/no、choice 和 score 三类提问，例如 noul 问题返回 {"type": "noul", "noul": 0.99}。 | Articles, tutorials & talksNews & announcements | |
| Stanford 与 NVIDIA Research 发布对比语言模型 CLM-8Baihot.news | — | Stanford 与 NVIDIA Research 团队发布 Contrastive Language Models（CLM）及 CLM-8B，用对比学习连接状态与动作，作为快速决策的 System One 模型。 | Articles, tutorials & talksNews & announcements | |
| Stanford 与 NVIDIA Research 发布对比语言模型 CLM-8Bcontrastive-lm.notion.site | — | 斯坦福大学与 NVIDIA Research 团队发布 Contrastive Language Models（CLM）及 CLM-8B 模型，用 InfoNCE 对比目标连接状态与动作，作为 System One 决策模型。 | Articles, tutorials & talksNews & announcements | |
| TechCrunchtechcrunch.com | — | Two days in: demand briefly took the API down, and Almeida on not wanting to be a frontier lab. | Articles, tutorials & talksNews & announcements | |
| TechCrunch 报道新型 AI 模型 Jevaihot.news | — | 感谢 @TechCrunch！！！ 【引用 @TechCrunch】：Jev，一种新型 AI 模型，正在向开发者展示一条更便宜、更快速的软件智能路径。https://spr.ly/6018BGXyam | Articles, tutorials & talksNews & announcements | |
| TechCrunch 报道新型 AI 模型 Jevx.com | — | 感谢 @TechCrunch！！！ 【引用 @TechCrunch】：Jev，一种新型 AI 模型，正在向开发者展示一条更便宜、更快速的软件智能路径。https://spr.ly/6018BGXyam | Articles, tutorials & talksNews & announcements | |
| The Rundownwww.therundown.ai | — | Short launch summary. | Articles, tutorials & talksNews & announcements | |
| TypeSafe AI debuts model for machines that plays Doomwww.theregister.com | — | The Register on the launch, the Doom demo, and the $40M seed round. | Articles, tutorials & talksNews & announcements | |
| TypeSafe AI emerges from stealth with $40Mfinance.yahoo.com | — | The funding announcement. | Articles, tutorials & talksNews & announcements | |
| TypeSafe AI releases Jev (r/singularity)reddit.com | — | Reddit: launch thread framing Jev as a low-hallucination, low-cost decision model for software rather than chat. | Articles, tutorials & talksNews & announcements | |
| TypeSafe 决策模型 Jev 通过 OpenRouter Decisions API 开放调用aihot.news | — | TypeSafe 的决策模型 Jev 已可通过 OpenRouter Decisions API 调用，模型 ID 为 typesafe/jev-1.13，于 2026 年 9 月 15 日发布早期访问。 | Articles, tutorials & talksNews & announcements | |
| TypeSafe 决策模型 Jev 通过 OpenRouter Decisions API 开放调用openrouter.ai | — | TypeSafe 的决策模型 Jev 已可通过 OpenRouter Decisions API 调用，模型 ID 为 typesafe/jev-1.13，于 2026 年 9 月 15 日发布早期访问。 | Articles, tutorials & talksNews & announcements | |
| TypeSafe 开源浏览器智能体 Jev Ultrafast，7.1 秒完成 Google Flights 搜索aihot.news | — | TypeSafe 发布开源浏览器智能体 Jev Ultrafast，采用动态、带索引的动作空间，每次决策只发一次网络请求，小 LLM 仅在 TYPE_TEXT 操作时生成文本。 | Articles, tutorials & talksNews & announcements | |
| Unsloth 支持 4GB 内存本地运行 Laya Decision 模型aihot.news | — | Unsloth 宣布可在仅 4GB 内存的设备上本地运行 Laya Decision 模型，支持 Mac、Windows、Linux 的 CPU、统一内存和 GPU 环境。默认多语言模型 678MB，另有 English 与 Typed decisions 档需 5GB RAM、846MB；可通过 Unsloth Desktop 以 Jev 兼容 API 提供服务，数据留在本地，指南见 https://unsloth.ai/docs/models/decision-laya。 | Articles, tutorials & talksNews & announcements | |
| Unsloth 支持 4GB 内存本地运行 Laya Decision 模型x.com | — | Unsloth 宣布可在仅 4GB 内存的设备上本地运行 Laya Decision 模型，支持 Mac、Windows、Linux 的 CPU、统一内存和 GPU 环境。默认多语言模型 678MB，另有 English 与 Typed decisions 档需 5GB RAM、846MB；可通过 Unsloth Desktop 以 Jev 兼容 API 提供服务，数据留在本地，指南见 https://unsloth.ai/docs/models/decision-laya。 | Articles, tutorials & talksNews & announcements | |
| 前 OpenAI 研究员打造 Jev：不做文本生成、只做选项判断的 AI 模型aihot.news | — | 初创公司 TypeSafe AI 推出模型 Jev，不生成文本，而是在软件内部输出窄域判断与概率，由开发者预设问题与候选答案、模型为选项打分。TypeSafe 称其响应时间为 70 至 500 毫秒，定价为每百万输入 token 0.042 美元、输出不收费，开发者需通过 waitlist 申请接入。公司宣称 Jev 不会产生幻觉，但该保证仅限输出结构，选项内的事实性错误仍可能出现。 | Articles, tutorials & talksNews & announcements | |
| 前 OpenAI 研究员打造 Jev：不做文本生成、只做选项判断的 AI 模型the-decoder.com | — | 初创公司 TypeSafe AI 推出模型 Jev，不生成文本，而是在软件内部输出窄域判断与概率，由开发者预设问题与候选答案、模型为选项打分。TypeSafe 称其响应时间为 70 至 500 毫秒，定价为每百万输入 token 0.042 美元、输出不收费，开发者需通过 waitlist 申请接入。公司宣称 Jev 不会产生幻觉，但该保证仅限输出结构，选项内的事实性错误仍可能出现。 | Articles, tutorials & talksNews & announcements | |
| 号称史上最快量子Jev：单次推理0.12ms，将开源aihot.news | — | 作者宣布将发布号称史上最快的"量子Jev"，单次推理仅需0.12ms，性能达Jev的400x，且不花钱、能解决Jev解决不了的问题。细节稍后公布并以MIT协议开源。 | Articles, tutorials & talksNews & announcements | |
| 号称史上最快量子Jev：单次推理0.12ms，将开源x.com | — | 作者宣布将发布号称史上最快的"量子Jev"，单次推理仅需0.12ms，性能达Jev的400x，且不花钱、能解决Jev解决不了的问题。细节稍后公布并以MIT协议开源。 | Articles, tutorials & talksNews & announcements | |
| 巴西 Supersonic Labs 发布 144.3M 参数开源决策模型 Julia 1，可在 CPU 上运行aihot.news | — | 巴西 AI 实验室 Supersonic Labs 发布 144.3M 参数的开源决策模型 Julia 1，权重以 Apache 2.0 协议托管在 Hugging Face，可在普通 CPU 上运行，不生成文本，只对 2 到 20 个候选答案输出选择和概率。 | Articles, tutorials & talksNews & announcements | |
| 巴西 Supersonic Labs 发布 144.3M 参数开源决策模型 Julia 1，可在 CPU 上运行www.marktechpost.com | — | 巴西 AI 实验室 Supersonic Labs 发布 144.3M 参数的开源决策模型 Julia 1，权重以 Apache 2.0 协议托管在 Hugging Face，可在普通 CPU 上运行，不生成文本，只对 2 到 20 个候选答案输出选择和概率。 | Articles, tutorials & talksNews & announcements | |
| 斯坦福与 NVIDIA 发布对比式语言模型 CLM-8Baihot.news | — | 斯坦福大学与 NVIDIA Research 团队发布 Contrastive Language Models（CLM）及 CLM-8B 模型，用 InfoNCE 对比目标连接状态与动作，作为 System One 决策模型。 | Articles, tutorials & talksNews & announcements |
Analysis & discussions159 of 159 matches
| Resource | Stars | Description | Category | Save |
|---|---|---|---|---|
| Anyone using Jev?www.reddit.com | — | Thread in r/opencode where users weigh Jev against open decision models such as Laya, NanoJev, and Decider, and suggest moving harness routing and verification steps to Jev. | Articles, tutorials & talksAnalysis & discussions | |
| Arbitrary classification as a type-safe primitivex.com | — | X: argues the real novelty is not classification but that Jev makes arbitrary classification a runtime-defined, type-safe programmable primitive. | Articles, tutorials & talksAnalysis & discussions | |
| Ask HN: What do you think of Noul, a new decision primitivenews.ycombinator.com | — | Hacker News: a proposal to treat Noul - the probability-of-true answer type - as a general software primitive rather than a Jev-specific one. | Articles, tutorials & talksAnalysis & discussions | |
| Can Jev take over an LLM's if-statements?qiita.com | — | Japanese analysis that checks, using only the public SDK and documented examples without calling the API, which code branches Jev can replace, and reports two contradictions between official docs. | Articles, tutorials & talksAnalysis & discussions | |
| Critique of Jev compactionx.com | — | Critique arguing per-tool-call filtering with Jev is a poor compaction strategy, since compaction should reconstruct history and the model lacks context on what came before. | Articles, tutorials & talksAnalysis & discussions | |
| Custom Jev-style models for existing workflowsx.com | — | Thread arguing that companies can put a cheap parallel-constrained-decoding classifier, trained on past decisions, in front of the LLM review steps they already run, letting confident items pass and sending the rest on. | Articles, tutorials & talksAnalysis & discussions | |
| Decoding Jevnavinpai.github.io | — | Visual technical deep dive into how Jev likely differs from an LLM: transformer inference that reads typed probabilities directly, proper scoring rules, policy gradients, and what RLCD changes. | Articles, tutorials & talksAnalysis & discussions | |
| Decoding Jev — discussionnews.ycombinator.com | — | Visual technical deep dive into how Jev likely differs from an LLM: transformer inference that reads typed probabilities directly, proper scoring rules, policy gradients, and what RLCD changes. | Articles, tutorials & talksAnalysis & discussions | |
| Dex Horthy：jev 是重读 12 factor agents 的最佳契机aihot.news | — | HumanLayer 的 Dex Horthy 认为 jev 是重读 12 factor agents 的绝佳契机，因为工具调用本身可拆解为分类+动作。若把 AI 程序设计成在分类、数据结构化、确定性代码与小型智能体式追加对话循环之间切换的流水线，jev 就是极佳的构建模块。引用推文称 jev 让 AI 被组合进系统与产品，而非让 AI 本身成为产品，像是一个此前缺失的原语。 | Articles, tutorials & talksAnalysis & discussions | |
| Dex Horthy：jev 是重读 12 factor agents 的最佳契机x.com | — | HumanLayer 的 Dex Horthy 认为 jev 是重读 12 factor agents 的绝佳契机，因为工具调用本身可拆解为分类+动作。若把 AI 程序设计成在分类、数据结构化、确定性代码与小型智能体式追加对话循环之间切换的流水线，jev 就是极佳的构建模块。引用推文称 jev 让 AI 被组合进系统与产品，而非让 AI 本身成为产品，像是一个此前缺失的原语。 | Articles, tutorials & talksAnalysis & discussions | |
| Encoder 卷土重来：Jev 要做面向 Agent 的泛化决策模型aihot.news | — | Jev 试图补上 BERT 未走完的路线，保留 GPT 的任务泛化能力，同时重获 encoder 与 classifier 式的决策效率，成为面向 service 和 Agent 的泛化 decision model。 | Articles, tutorials & talksAnalysis & discussions | |
| Encoder 卷土重来：Jev 要做面向 Agent 的泛化决策模型x.com | — | Jev 试图补上 BERT 未走完的路线，保留 GPT 的任务泛化能力，同时重获 encoder 与 classifier 式的决策效率，成为面向 service 和 Agent 的泛化 decision model。 | Articles, tutorials & talksAnalysis & discussions | |
| Has anyone tried Jev as a relevance filter for RAG?reddit.com | — | Reddit: builders ask whether Jev works as a retrieval relevance filter and reranker, probing the boundary the reported negative reranking result already hinted at. | Articles, tutorials & talksAnalysis & discussions | |
| How good is Jev 1.13?www.reddit.com | — | Thread in r/opencode debating Jev 1.13 for coding workflows, including a report that it beat cheap LLMs as an auto-mode shell-command classifier. | Articles, tutorials & talksAnalysis & discussions | |
| How is RLCD reinforcement learning?www.reddit.com | — | Thread in r/MachineLearning questioning whether RLCD training for Jev is really RL, given Choice, Score, and Noul outputs are differentiable, with speculation about its architecture. | Articles, tutorials & talksAnalysis & discussions | |
| https://x.com/andr_ec_/status/2100462185256554908 — reply labels the idea “welcome back BERT,” a comx.com | — | pact architecture-category critique. | Articles, tutorials & talksAnalysis & discussions | |
| https://x.com/awlevin/status/2100427922205209012 — Aaron’s critique: it is absurd that flight-searchx.com | — | APIs remain unavailable while agents can escape sandboxes; asks what the demo says about missing APIs; 22K views · 22 replies · 329 likes. | Articles, tutorials & talksAnalysis & discussions | |
| https://x.com/codestirring/status/2100429278026313845 — explains flight APIs are brittle because eacx.com | — | h airline has a bespoke connector; a practical reply to the critique. | Articles, tutorials & talksAnalysis & discussions | |
| Hugging Face CEO：LLM API 并非 90% 场景最佳方案aihot.news | — | Hugging Face CEO Clément Delangue 认为，对现实世界 90% 的用例而言，LLM API 远非最佳 AI 方案——它们过于重型、慢、贵且难以控制。他引用 @willdepue 关于零样本分类器 Jev 的讨论，称随着 AI 成熟和广泛采用，未来几年将逐渐认识到这一点。 | Articles, tutorials & talksAnalysis & discussions | |
| Hugging Face CEO：LLM API 并非 90% 场景最佳方案x.com | — | Hugging Face CEO Clément Delangue 认为，对现实世界 90% 的用例而言，LLM API 远非最佳 AI 方案——它们过于重型、慢、贵且难以控制。他引用 @willdepue 关于零样本分类器 Jev 的讨论，称随着 AI 成熟和广泛采用，未来几年将逐渐认识到这一点。 | Articles, tutorials & talksAnalysis & discussions | |
| Independent questions can disagreewww.reddit.com | — | Thread on how Jev answers each question in a request independently, so one call can return mutually inconsistent answers, and how to guard against that in pipelines. | Articles, tutorials & talksAnalysis & discussions | |
| Is Jev a generalized BERT?www.reddit.com | — | Thread in r/LocalLLaMA debating whether Jev is essentially a generalized BERT-style classifier that reads custom criteria at inference time. | Articles, tutorials & talksAnalysis & discussions | |
| Is Jev confident?bernoulli.app | — | Reverse-engineers how Choice confidence is computed from hundreds of thousands of live answers and shows how padding the option list inflates it. | Articles, tutorials & talksAnalysis & discussions | |
| Is Jev confident? — discussionnews.ycombinator.com | — | Reverse-engineers how Choice confidence is computed from hundreds of thousands of live answers and shows how padding the option list inflates it. | Articles, tutorials & talksAnalysis & discussions | |
| Is Jev hype or reality?www.youtube.com | — | Look at what the headline speed numbers actually compare, where Jev fits in workflows, a real-world DSPy benchmark, and what zero hallucinations does and does not mean. | Articles, tutorials & talksAnalysis & discussions | |
| Is Jev worth using?www.youtube.com | — | Critical look at the 193.6x faster and 444.6x cheaper headline, what TypeSafe's own methodology discloses, and how Jev compares with routing cheap decisions to a cheap LLM. | Articles, tutorials & talksAnalysis & discussions | |
| Is the 200x Faster Decision Model Too Good to Be True?flowtivity.ai | — | Claim-by-claim audit of TypeSafe's 200x faster, 400x cheaper launch figures, with pricing math and the skeptical pushback from Hacker News. | Articles, tutorials & talksAnalysis & discussions | |
| Is TypeSafe derived from Laya?www.reddit.com | — | Thread in r/LocalLLaMA on the similarities between Jev and the earlier open Laya model, and whether TypeSafe should have credited that work. | Articles, tutorials & talksAnalysis & discussions | |
| It's easy to dismiss Jev as just a classifiersebastianraschka.com | — | Short note on why Jev's generalization matters, with speculation on an encoder-style architecture and training setup, plus Choice and Noul API examples. | Articles, tutorials & talksAnalysis & discussions | |
| It's easy to dismiss Jev as just a classifier — discussionnews.ycombinator.com | — | Short note on why Jev's generalization matters, with speculation on an encoder-style architecture and training setup, plus Choice and Noul API examples. | Articles, tutorials & talksAnalysis & discussions | |
| Jev (TypeSafe) : 200x plus rapide... mais comparé à quoi ?www.youtube.com | — | French-language look at how TypeSafe measured its speed and cost figures, starting from a Hacker News summary the CEO called exactly right. | Articles, tutorials & talksAnalysis & discussions | |
| Jev + Claude = AGI?www.youtube.com | — | Short analysis of pairing a slow planner like Claude with Jev for split-second calls, drawing on Figure's Helix robots and TypeSafe's published Claude-with-Jev tests. | Articles, tutorials & talksAnalysis & discussions | |
| Jev - La nuova era dell'AI è arrivatawww.youtube.com | — | Italian-language deep dive on Jev as a System One model, with pointers to open Jev-like projects such as SemIf and jevlike. | Articles, tutorials & talksAnalysis & discussions | |
| Jev AI, System One: 193x Faster Than LLMs?www.youtube.com | — | Explains how Jev returns a typed decision with a confidence score instead of chatting, and treats TypeSafe's 193x speed figure as a vendor number to verify. | Articles, tutorials & talksAnalysis & discussions | |
| Jev and the future of computer usex.com | — | Cua's deep dive on which decisions inside a computer-use agent need a general LLM, turning screens into scored candidate actions for text-only decision models like Jev and their CUA-S1-FORMS. | Articles, tutorials & talksAnalysis & discussions | |
| Jev architecture speculationwww.reddit.com | — | Thread in r/LocalLLaMA (57 comments) speculating on how Jev works and whether it is just an LLM read out before text generation. | Articles, tutorials & talksAnalysis & discussions | |
| Jev as a local decision backend (RFC)github.com | — | RFC and operator-run eval arm in the Contemplative Agent project on moving its judgment-only LLM calls to Jev or local Jev-like models, keeping Jev numbers out of the public tree under TypeSafe's customer agreement. | Articles, tutorials & talksAnalysis & discussions | |
| Jev as a local decision backend (RFC) — repogithub.com | — | RFC and operator-run eval arm in the Contemplative Agent project on moving its judgment-only LLM calls to Jev or local Jev-like models, keeping Jev numbers out of the public tree under TypeSafe's customer agreement. | Articles, tutorials & talksAnalysis & discussions | |
| Jev as a smart switch statementx.com | — | Hype-free explainer arguing Jev is a very smart switch statement: it classifies, routes, scores and verifies over predefined options but cannot write code or text. | Articles, tutorials & talksAnalysis & discussions | |
| Jev can't write a line, but 13% of paid teams use itinlevel9.com | — | Newsletter essay on what pricing 'judgment' instead of text reveals about Jev, and why the author is building a model that does only five things. | Articles, tutorials & talksAnalysis & discussions | |
| Jev can't write a line, but 13% of paid teams use it — discussionnews.ycombinator.com | — | Newsletter essay on what pricing 'judgment' instead of text reveals about Jev, and why the author is building a model that does only five things. | Articles, tutorials & talksAnalysis & discussions | |
| Jev changes who owns the decisiondev.to | — | Review of Jev's API contract and SDK behavior with mocked responses, pinning down what 'zero hallucinations' means and splitting work between Jev, the LLM, and code that keeps policy and exact calculations. | Articles, tutorials & talksAnalysis & discussions | |
| Jev Destroys Claude and GPT on Simple Taskswww.youtube.com | — | Plain-language explanation of why a yes/no answer from an LLM takes seconds while Jev scores every allowed option in a single pass. | Articles, tutorials & talksAnalysis & discussions | |
| Jev Doesn't Chat. That Might Be the Point.medium.com | — | Claim-by-claim critique of the System One launch: naming, architecture, economics, the Doom demo's structured-state caveat, and why prompt-injection resistance matters for a decision layer. | Articles, tutorials & talksAnalysis & discussions | |
| Jev explained: faster, cheaper decisionswww.youtube.com | — | Explainer on whether a decision model can speed up computer-use agents, covering the Browser Use flight-search and Doom demos, Choice/Score/Noul, pricing claims and hybrid agent designs. | Articles, tutorials & talksAnalysis & discussions | |
| Jev From TypeSafe is FAST and CHEAP, But There is a Caveatwww.youtube.com | — | Explains the catch behind Jev's speed and price: it understands natural language but only replies with structured values and a confidence level. | Articles, tutorials & talksAnalysis & discussions | |
| Jev full analysis and insightswww.youtube.com | — | Korean-language analysis of whether Jev is really a new kind of model, covering calibration as its real differentiator, small classifiers as competition, and caveats such as self-reported benchmarks. | Articles, tutorials & talksAnalysis & discussions | |
| Jev impressions on r/ArtificialInteligencewww.reddit.com | — | Busy thread (155 comments) where early users share first impressions of Jev, including use as a policy prefilter, and argue about how it compares with frontier LLMs. | Articles, tutorials & talksAnalysis & discussions | |
| Jev in the Wild: A Data-Driven Analysis of the Jev Model's Functionality, Applications and Ecosystemarxiv.org | — | Research paper: the first data-driven survey and analysis of Jev's application ecosystem examines 2,170 public GitHub projects, early growth, application domains, and decision-use patterns. | Articles, tutorials & talksAnalysis & discussions | |
| Jev introduces a new shape of LLMsimonwillison.net | — | Explains Jev as a 'decision model' that returns numbers rather than text, covers its pricing and question types, notes experiments with BM25-then-Jev search reranking, and raises black-box and bias concerns. | Articles, tutorials & talksAnalysis & discussions | |
| Jev is 200x faster, but is it smart?www.youtube.com | — | Thirty-minute breakdown of how Jev works, from RLCD and parallel sampling to pricing, published benchmarks, Hacker News critiques, and when to pair it with an LLM. | Articles, tutorials & talksAnalysis & discussions | |
| Jev Is About to Change the AI Economythefinancialengineer.substack.com | — | Essay on how free output tokens and many questions per call break per-token credit metering, and where metering and entitlement enforcement should live once Jev sits beside LLMs. | Articles, tutorials & talksAnalysis & discussions | |
| Jev Is About to Change the AI Economy — discussionnews.ycombinator.com | — | Essay on how free output tokens and many questions per call break per-token credit metering, and where metering and entitlement enforcement should live once Jev sits beside LLMs. | Articles, tutorials & talksAnalysis & discussions | |
| Jev is incrediblewww.youtube.com | — | Theo explains why Jev is a fast classifier with strong safety perks that complements rather than replaces reasoning models like Astra and Fable. | Articles, tutorials & talksAnalysis & discussions | |
| JEV Is NOT an LLMwww.youtube.com | — | Breaks down what Jev's decision layer actually does, why calling it an LLM misleads, and why that makes it much faster and cheaper. | Articles, tutorials & talksAnalysis & discussions | |
| Jev is to tool use what RAG is to contextrajveerbachkaniwala.com | — | Short essay framing Jev as the mirror image of RAG: the developer fixes up front which options, including tools, the model may pick, instead of which context it reads. | Articles, tutorials & talksAnalysis & discussions | |
| Jev is to tool use what RAG is to context — discussionnews.ycombinator.com | — | Short essay framing Jev as the mirror image of RAG: the developer fixes up front which options, including tools, the model may pick, instead of which context it reads. | Articles, tutorials & talksAnalysis & discussions | |
| Jev isn't a language modelwww.youtube.com | — | Short analysis tracing where the 20-200x faster and 40-400x cheaper claims come from in TypeSafe's published test tables, how tests are graded, and where the model falls short. | Articles, tutorials & talksAnalysis & discussions | |
| Jev makes fast and cheap decisionspatmcguinness.substack.com | — | Newsletter analysis framing Jev as a classifier-style production model rather than a general model, covering its design and early community builds such as a self-driving simulator and open rebuilds. | Articles, tutorials & talksAnalysis & discussions | |
| Jev means structured output is interesting againwww.seangoedecke.com | — | Why a decision model has steady latency, and how far prefill plus single-token constrained decoding gets you with open models. | Articles, tutorials & talksAnalysis & discussions | |
| Jev mérite-t-il la hype ?www.youtube.com | — | French explainer on System One models, RLCD training, and how Jev differs from classic LLMs, ending with a verdict on the hype. | Articles, tutorials & talksAnalysis & discussions | |
| Jev pruning is not memoryx.com | — | Critique of Jev-based context compaction for Claude Code: pruning a 1M-token session to 86K in a second is scoring and deleting, and one replay dropped 16 fragments that were needed later. | Articles, tutorials & talksAnalysis & discussions | |
| Jev pruning is not memory — articlex.com | — | Critique of Jev-based context compaction for Claude Code: pruning a 1M-token session to 86K in a second is scoring and deleting, and one replay dropped 16 fragments that were needed later. | Articles, tutorials & talksAnalysis & discussions | |
| JEV sıçrama mı, hype mı?www.youtube.com | — | Turkish analysis of whether Jev is a real decision layer or a well-packaged classifier, with examples in customer messages, agent tool selection, coding harnesses, and RAG evidence filtering. | Articles, tutorials & talksAnalysis & discussions | |
| Jev vs auto-regressive LLMs and MDLMlilting.ch | — | Technical comparison of Jev's single-pass parallel sampler with token-by-token autoregressive decoding and masked diffusion language models, alongside pricing and RLCD calibration. | Articles, tutorials & talksAnalysis & discussions | |
| Jev vs LLMswww.youtube.com | — | Breakdown of parallel sampling vs autoregressive generation, RLCD calibration, the decision primitives, an expense-claim example, and Jev's limits as an LLM QA layer. | Articles, tutorials & talksAnalysis & discussions | |
| Jev won't train on your datawonderwhy-er.medium.com | — | Examines Jev's cheaper-faster-less-general trade-off and TypeSafe's customer agreement, asking what 'derived telemetry' allows when the vendor says it will not train on your data. | Articles, tutorials & talksAnalysis & discussions | |
| Jev zero-hallucination deep-divezhuanlan.zhihu.com | — | Chinese deep-dive that examines TypeSafe Jev's 'zero hallucination' System One claims and how far they hold up. | Articles, tutorials & talksAnalysis & discussions | |
| Jev 与 LLM-as-a-Judge 对比：TypeSafe 决策模型在封闭评分标准下准确率持平、成本降至 1/5aihot.news | — | TypeSafe 决策模型 Jev 在封闭评分标准且证据随请求提供时，准确率与 LLM-as-a-Judge 持平，概率校准更好，成本仅为后者的 1/5、延迟为 1/10。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 与 LLM-as-a-Judge 对比：TypeSafe 决策模型在封闭评分标准下准确率持平、成本降至 1/5openrouter.ai | — | TypeSafe 决策模型 Jev 在封闭评分标准且证据随请求提供时，准确率与 LLM-as-a-Judge 持平，概率校准更好，成本仅为后者的 1/5、延迟为 1/10。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 与无法解释的失败的常态化aihot.news | — | 作者评论 TypeSafe AI 推出的 AI 模型 Jev，认为其用户不做 evals、把置信度分数当形式或甩锅理由，丢弃了对失败原因的追查。他担忧 LLM 驱动的开发会让"有时就是烂"成为排查的可接受终点，而本可用几条提示词搭建的 eval 反而能解决部分问题。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 与无法解释的失败的常态化www.ihatethefuture.com | — | 作者评论 TypeSafe AI 推出的 AI 模型 Jev，认为其用户不做 evals、把置信度分数当形式或甩锅理由，丢弃了对失败原因的追查。他担忧 LLM 驱动的开发会让"有时就是烂"成为排查的可接受终点，而本可用几条提示词搭建的 eval 反而能解决部分问题。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 做高频交易？欠拟合与泛化误区解析aihot.news | — | Jev 对 K 线本身是欠拟合的，只有当它做到 100% 负相关时才能反着买，而 100% 负相关属于超强泛化、不可能实现，即使每次交易都亏钱也不能算 100% 负相关。此外 70ms 的延迟别说高频交易，连抢火车票都抢不到。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 做高频交易？欠拟合与泛化误区解析x.com | — | Jev 对 K 线本身是欠拟合的，只有当它做到 100% 负相关时才能反着买，而 100% 负相关属于超强泛化、不可能实现，即使每次交易都亏钱也不能算 100% 负相关。此外 70ms 的延迟别说高频交易，连抢火车票都抢不到。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 决策导向模型推荐重排序实证研究：与 Qwen 重排序器的质量-延迟权衡对比aihot.news | — | 一项受控实证研究对比了 TypeSafe AI 称为"System One Model"的 Jev 与推荐专用模型及 pointwise、listwise Qwen 重排序器在多个 Amazon Reviews 领域和候选集规模下的推荐重排序表现。结果显示 Jev 在保持较强推荐效果的同时，延迟增长比 pointwise Qwen 重排序器更为平缓，但服务延迟仍显著高于推荐专用模型。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 决策导向模型推荐重排序实证研究：与 Qwen 重排序器的质量-延迟权衡对比arxiv.org | — | 一项受控实证研究对比了 TypeSafe AI 称为"System One Model"的 Jev 与推荐专用模型及 pointwise、listwise Qwen 重排序器在多个 Amazon Reviews 领域和候选集规模下的推荐重排序表现。结果显示 Jev 在保持较强推荐效果的同时，延迟增长比 pointwise Qwen 重排序器更为平缓，但服务延迟仍显著高于推荐专用模型。 | Articles, tutorials & talksAnalysis & discussions | |
| jev 将复活架构最佳实践并与 ML 结合aihot.news | — | 太赞同了 我认为 jev 对自动化最大的好处之一，将来自复活架构最佳实践：状态管理、封装、抽象！！！！ 并将它们与 ML 的最佳实践结合：度量/评估，使用校准/不确定性 （加截图是因为我不知道怎么引用两条帖子 🤦） | Articles, tutorials & talksAnalysis & discussions | |
| jev 将复活架构最佳实践并与 ML 结合x.com | — | 太赞同了 我认为 jev 对自动化最大的好处之一，将来自复活架构最佳实践：状态管理、封装、抽象！！！！ 并将它们与 ML 的最佳实践结合：度量/评估，使用校准/不确定性 （加截图是因为我不知道怎么引用两条帖子 🤦） | Articles, tutorials & talksAnalysis & discussions | |
| Jev 或成构建可靠 AI 系统的重要原语aihot.news | — | 好观点！测试之后，Jev 感觉像是构建可靠 AI 系统的一个重要原语。我认为还有更多原语等待被发现，它们能让基于 LLM 的智能体变得更好、更快。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 或成构建可靠 AI 系统的重要原语x.com | — | 好观点！测试之后，Jev 感觉像是构建可靠 AI 系统的一个重要原语。我认为还有更多原语等待被发现，它们能让基于 LLM 的智能体变得更好、更快。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 模型：放弃自回归，靠 RLCD 做决策aihot.news | — | Jev 模型放弃传统自回归架构，无法直接输出普通文本，只能按预定义 Schema 做结构化决策输出，例如垃圾短信二分类会返回带概率的 JSON（isSpam true 0.982 / false 0.018）。其真正护城河是 RLCD（校准强化学习），使输出很难出现概率失真，但代价是模型表现刻板、只适合干活。目前仅支持文本输入，复杂场景需把内容转成文本并定义可选动作，模型再自主决策。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev 模型：放弃自回归，靠 RLCD 做决策x.com | — | Jev 模型放弃传统自回归架构，无法直接输出普通文本，只能按预定义 Schema 做结构化决策输出，例如垃圾短信二分类会返回带概率的 JSON（isSpam true 0.982 / false 0.018）。其真正护城河是 RLCD（校准强化学习），使输出很难出现概率失真，但代价是模型表现刻板、只适合干活。目前仅支持文本输入，复杂场景需把内容转成文本并定义可选动作，模型再自主决策。 | Articles, tutorials & talksAnalysis & discussions | |
| JEV 等直接决策模型存在序数尺度利用偏差，BA-LoRA 后训练可将利用率从约 47% 提升至 86%aihot.news | — | 研究分析 JEV 1.13 与三个开源 KEV 模型，发现直接决策模型存在"序数尺度利用偏差"：在 ANLI 上 JEV 准确率 74.95%，却把 38.8% 的预测和 51.3% 的错误都归为 Neutral。 | Articles, tutorials & talksAnalysis & discussions | |
| JEV 等直接决策模型存在序数尺度利用偏差，BA-LoRA 后训练可将利用率从约 47% 提升至 86%arxiv.org | — | 研究分析 JEV 1.13 与三个开源 KEV 模型，发现直接决策模型存在"序数尺度利用偏差"：在 ANLI 上 JEV 准确率 74.95%，却把 38.8% 的预测和 51.3% 的错误都归为 Neutral。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev's Architecture Unmasked — demox.com | — | Architecture teardown that probes Jev with 10,000 API calls to infer how it is built, from shared state with isolated question branches to option interaction and confidence readout. | Articles, tutorials & talksAnalysis & discussions | |
| Jev's Architecture Unmasked — discussionnews.ycombinator.com | — | Architecture teardown that probes Jev with 10,000 API calls to infer how it is built, from shared state with isolated question branches to option interaction and confidence readout. | Articles, tutorials & talksAnalysis & discussions | |
| Jev, Sortedpearpages.com | — | Reads the primary sources behind the launch claims and concludes Jev is a narrower, more interesting frontier-trained classifier for software, with every benchmark still vendor-reported. | Articles, tutorials & talksAnalysis & discussions | |
| Jev, three days inaiwithmike.substack.com | — | Takes stock three days after launch, separating what has been independently measured about Jev from what is guessed, and where it fits. | Articles, tutorials & talksAnalysis & discussions | |
| Jev-as-a-Judge 论文：廉价裁判置信时接受、不确定时上升至 GPT-6，保留 99% 准确率并省约 43% 费用aihot.news | — | 论文介绍 JEV-as-a-Judge，发现多数评测可用廉价裁判，仅把不确定判定交给前沿模型。在 510 个保留偏好对上，级联接受 JEV 置信判定、其余上升至 GPT-6 Astra，保留 GPT-6 约 99% 的准确率，费用约为其 57%。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev-as-a-Judge 论文：廉价裁判置信时接受、不确定时上升至 GPT-6，保留 99% 准确率并省约 43% 费用x.com | — | 论文介绍 JEV-as-a-Judge，发现多数评测可用廉价裁判，仅把不确定判定交给前沿模型。在 510 个保留偏好对上，级联接受 JEV 置信判定、其余上升至 GPT-6 Astra，保留 GPT-6 约 99% 的准确率，费用约为其 57%。 | Articles, tutorials & talksAnalysis & discussions | |
| JEV-as-a-Judge：置信时接受，不确定时升级aihot.news | — | JEV-as-a-Judge 用仅做决策的评审模型做低成本初筛，在普通偏好与证据支撑的事实性任务上，与最强对比的 SOTA LLM 评审差距在 3 个百分点以内，费用仅为其 0.36%。当判断需要检查推导过程或抵御精心编写的错误答案时，差距会扩大，且 JEV 与对比模型的差距集中在低置信度决策上。一个冻结的级联流程接受高置信度判定、升级不确定判定，以更低成本保留了对比模型 99% 的准确率。 | Articles, tutorials & talksAnalysis & discussions | |
| JEV-as-a-Judge：置信时接受，不确定时升级arxiv.org | — | JEV-as-a-Judge 用仅做决策的评审模型做低成本初筛，在普通偏好与证据支撑的事实性任务上，与最强对比的 SOTA LLM 评审差距在 3 个百分点以内，费用仅为其 0.36%。当判断需要检查推导过程或抵御精心编写的错误答案时，差距会扩大，且 JEV 与对比模型的差距集中在低置信度决策上。一个冻结的级联流程接受高置信度判定、升级不确定判定，以更低成本保留了对比模型 99% 的准确率。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev: promesse et réalitéwww.youtube.com | — | French review separating viral demos from reality, with email triage, intent search, Flappy Bird and Tetris tests, and real uses inside the creator's Lumail and Kliq products. | Articles, tutorials & talksAnalysis & discussions | |
| Jev: The Bar for AI hype is LOWwww.youtube.com | — | Skeptical take on the launch hype that weighs Jev against earlier non-autoregressive decision models and the optimistic System One argument. | Articles, tutorials & talksAnalysis & discussions | |
| Jev: the decision model that does not writegithub.com | — | Chinese long-form explainer on a personal knowledge blog covering where the Jev and System One names come from, how its typed outputs differ from LLM generation, and which problems it actually fits. | Articles, tutorials & talksAnalysis & discussions | |
| Jev: the decision model that does not write — repogithub.com | — | Chinese long-form explainer on a personal knowledge blog covering where the Jev and System One names come from, how its typed outputs differ from LLM generation, and which problems it actually fits. | Articles, tutorials & talksAnalysis & discussions | |
| Jev: the decision model that does not write — sitewww.zata.cc | — | Chinese long-form explainer on a personal knowledge blog covering where the Jev and System One names come from, how its typed outputs differ from LLM generation, and which problems it actually fits. | Articles, tutorials & talksAnalysis & discussions | |
| Jev: The Language Model That Won't Talkanthonymaio.substack.com | — | Essay arguing that Jev's real claim is not price but that generated language may be the wrong interface between a model and the software that must act on its output. | Articles, tutorials & talksAnalysis & discussions | |
| JevRAG 变体涌现引热议aihot.news | — | 大量 JevRAG 变体正在涌现。 至少可以说很有意思。 这些范围有限的测试其实得不出什么结论，但它引发了讨论，也指向了在当前 RAG 和智能体系统中寻找优化方向的激动人心的路径。 我一直在测试自己做的 JevRAG，用于论文探索。 到目前为止，我在用 Jev 做重排序上取得了更多成功，也找到了一些非常有意思的论文搜索方式，把语义搜索和 Jev 结合起来。 更多内容很快分享。 | Articles, tutorials & talksAnalysis & discussions | |
| JevRAG 变体涌现引热议x.com | — | 大量 JevRAG 变体正在涌现。 至少可以说很有意思。 这些范围有限的测试其实得不出什么结论，但它引发了讨论，也指向了在当前 RAG 和智能体系统中寻找优化方向的激动人心的路径。 我一直在测试自己做的 JevRAG，用于论文探索。 到目前为止，我在用 Jev 做重排序上取得了更多成功，也找到了一些非常有意思的论文搜索方式，把语义搜索和 Jev 结合起来。 更多内容很快分享。 | Articles, tutorials & talksAnalysis & discussions | |
| Jev模型生态系统数据分析研究aihot.news | — | 该研究对GitHub上2170个公开Jev项目进行了大规模数据分析，探讨Jev这一低成本决策模型在自然语言问答、二元判断和评分等场景中的应用。研究发现Jev作为可复用决策组件，其功能随工作流变化，且公众关注度集中在路由和接口代理，而非项目数量本身。 | Articles, tutorials & talksAnalysis & discussions | |
| jqv — articlearcherhume.com | — | Reconstruction of Jev's inference structure on stock Qwen3, with one shared state prefill, isolated question branches behind a block attention mask, and option-letter logits calibrated by temperature, behind /v1/systemone. | Articles, tutorials & talksAnalysis & discussions | |
| Kingy AI: "Jev review: the AI model that doesn't generate text"kingy.ai | — | The skeptical counterweight. | Articles, tutorials & talksAnalysis & discussions | |
| Laya vs Jevwww.youtube.com | — | Breakdown of the Jev vs Laya controversy: how Laya's ModernBERT-based design works, what its benchmark comparison really shows, zero-shot vs fine-tuned accuracy, and calibration issues. | Articles, tutorials & talksAnalysis & discussions | |
| LLM 已知答案概率为何还要先说话aihot.news | — | 既然 LLM 本来就知道答案概率，那为什么逼它先说话？有意思 JEV | Articles, tutorials & talksAnalysis & discussions | |
| LLM 已知答案概率为何还要先说话x.com | — | 既然 LLM 本来就知道答案概率，那为什么逼它先说话？有意思 JEV | Articles, tutorials & talksAnalysis & discussions | |
| Most people miss what Jev is aboutmedium.com | — | Essay arguing that cheap classification is the boring part of Jev, and walking through three harness designs where a typed, confidence-scored System One model sits next to an LLM. | Articles, tutorials & talksAnalysis & discussions | |
| Most people miss what Jev is about — discussionnews.ycombinator.com | — | Essay arguing that cheap classification is the boring part of Jev, and walking through three harness designs where a typed, confidence-scored System One model sits next to an LLM. | Articles, tutorials & talksAnalysis & discussions | |
| Nathan Lambert 撰文解释为何仍未接受真正的递归自我改进（RSI）aihot.news | — | Nathan Lambert 撰文阐述他仍不支持真正的递归自我改进（RSI），坚持自己的"有损自我改进"基线判断。 | Articles, tutorials & talksAnalysis & discussions | |
| Nathan Lambert 撰文解释为何仍未接受真正的递归自我改进（RSI）www.interconnects.ai | — | Nathan Lambert 撰文阐述他仍不支持真正的递归自我改进（RSI），坚持自己的"有损自我改进"基线判断。 | Articles, tutorials & talksAnalysis & discussions | |
| Open models like Jev?www.reddit.com | — | Thread in r/LocalLLM pointing to open Jev-style models such as Laya and von, with debate over whether they need per-task fine-tuning to match Jev. | Articles, tutorials & talksAnalysis & discussions | |
| Open-sourced Jev architecture last yearnews.ycombinator.com | — | A prior-art claim for non-autoregressive typed decisions, and the counter that zero-shot generality is the actual difference. | Articles, tutorials & talksAnalysis & discussions | |
| OpenJev on Hacker Newsnews.ycombinator.com | — | Whether reading logits directly is new at all, argued at length. | Articles, tutorials & talksAnalysis & discussions | |
| OpenRouter：决策模型市场潜力巨大aihot.news | — | 像 Jev 这样的决策模型，其可触达市场可能非常庞大 图表来自 https://openrouter.ai/rankings#task-spend | Articles, tutorials & talksAnalysis & discussions | |
| OpenRouter：决策模型市场潜力巨大x.com | — | 像 Jev 这样的决策模型，其可触达市场可能非常庞大 图表来自 https://openrouter.ai/rankings#task-spend | Articles, tutorials & talksAnalysis & discussions | |
| OrcaRouter: "Jev / TypeSafe System One: what we know"www.orcarouter.ai | — | The claim-vs-evidence audit (the 75× vs 193× discrepancy). | Articles, tutorials & talksAnalysis & discussions | |
| Pi.dev 官方复盘：为何把曾公开拒绝的 MCP 纳入核心aihot.news | — | Earendil 团队发文解释 Pi 此前曾公开声明不支持 MCP，如今却将其纳入核心功能。原因一是 MCP 本身一年间已有改进，二是为支持延迟工具加载等新模型能力所需的工具元数据改动本身通用，还能让 Jev 更容易在 Pi 中使用。 | Articles, tutorials & talksAnalysis & discussions | |
| Pi.dev 官方复盘：为何把曾公开拒绝的 MCP 纳入核心earendil.com | — | Earendil 团队发文解释 Pi 此前曾公开声明不支持 MCP，如今却将其纳入核心功能。原因一是 MCP 本身一年间已有改进，二是为支持延迟工具加载等新模型能力所需的工具元数据改动本身通用，还能让 Jev 更容易在 Pi 中使用。 | Articles, tutorials & talksAnalysis & discussions | |
| Por qué no voy a implementar Jev en mi harnesswww.youtube.com | — | Spanish opinion piece on why the creator will not add Jev to his gentle-ai harness: who pays for the API, risks of using it for compaction, and where a subagent makes more sense. | Articles, tutorials & talksAnalysis & discussions | |
| Rethinking security engineering with Jevx.com | — | X: argues that purely engineering decisions in security work belong to Jev rather than a chat model. | Articles, tutorials & talksAnalysis & discussions | |
| Review of public Jev trading reposx.com | — | Thread reviewing three public Jev trading repos that trade short BTC windows on Kalshi or Polymarket, finding useful code but no independently verifiable trading edge. | Articles, tutorials & talksAnalysis & discussions | |
| Sebastian Raschka 撰文解析 Jev：从词袋模型到 Jev 的文本分类技术演进aihot.news | — | Sebastian Raschka 发表长文，梳理从词袋模型、RNN/CNN 到 Transformer 的文本分类技术史，并解读 TypeSafe AI 新发布的 Jev 模型。 | Articles, tutorials & talksAnalysis & discussions | |
| Sebastian Raschka 撰文解析 Jev：从词袋模型到 Jev 的文本分类技术演进magazine.sebastianraschka.com | — | Sebastian Raschka 发表长文，梳理从词袋模型、RNN/CNN 到 Transformer 的文本分类技术史，并解读 TypeSafe AI 新发布的 Jev 模型。 | Articles, tutorials & talksAnalysis & discussions | |
| Stop hyping Jevjuejin.cn | — | Chinese contrarian take: Jev is useful for routing, guardrails, and batch document work, but it matches DeepSeek Flash on capability, 'no hallucinations' is a redefinition, and open rebuilds appeared within 48 hours. | Articles, tutorials & talksAnalysis & discussions | |
| System One Jev (Fully Explained)www.youtube.com | — | Examines Jev's speed and cost claims, what its benchmarks actually show, and the limitations behind the headline numbers. | Articles, tutorials & talksAnalysis & discussions | |
| tech-20260916-001-07ourcoders.com | — | OurCoders | https://ourcoders.com/tech/show/tech-20260916-001-07/ | 2026-09-16 · 林岚 | Short skeptical editor take: “类型正确离判断正确还有多远” | Articles, tutorials & talksAnalysis & discussions | |
| Testing Jev for Pi extensionswww.reddit.com | — | Thread where Pi coding-agent users compare early Jev experiments, starting from a tool-call safety rater and a planned prompt-complexity model router. | Articles, tutorials & talksAnalysis & discussions | |
| The Bitterest Lessonwww.completeskeptic.com | — | Essay by TypeSafe's CEO, also published on the TypeSafe blog, arguing that compute-driven progress is wasted when models are trained for the wrong task, the thesis behind building decision models for software. | Articles, tutorials & talksAnalysis & discussions | |
| The Bitterest Lesson — discussionnews.ycombinator.com | — | Official essay extending Sutton's bitter lesson: choosing the right task to optimize matters more than data, which matters more than compute and algorithms. | Articles, tutorials & talksAnalysis & discussions | |
| The car wash question on Jevwww.reddit.com | — | Tries the well-known car wash reasoning question on Jev and sparks a long debate (104 comments) on how to judge a decision model's intelligence beyond speed and price. | Articles, tutorials & talksAnalysis & discussions | |
| They gave Jev $10k to tradewww.youtube.com | — | Fact-check of a viral post about giving Jev $10,000 to trade, showing it was a simulation with one trade, and comparing a high-frequency Jev bot and real-money chatbot trading contests. | Articles, tutorials & talksAnalysis & discussions | |
| Thoughts on Jev? Any use cases?www.reddit.com | — | Thread weighing whether Jev is worth the hype, comparing it with small encoder classifiers such as GLiNER and pointing to tasks people used Haiku for. | Articles, tutorials & talksAnalysis & discussions | |
| ts2.tech: "TypeSafe AI Raises $40 Million for Jev, but Its 445× Cost Claim Is Still Self-Tested"ts2.tech | — | The skeptical audit of self-tested benchmarks, plus funding verification. | Articles, tutorials & talksAnalysis & discussions | |
| Typed Decisions, Not Chatwarmersun.com | — | Independent technical walkthrough of Jev that separates TypeSafe's launch claims from what the public evidence actually establishes, with an inspectable audit trail. | Articles, tutorials & talksAnalysis & discussions | |
| TypeSafe AI Jev reviewactionbox.cloud | — | Early-access review that checks the Playground, raw API, and SDK examples, then compares Jev with rules, classifiers, rerankers, and LLM judges and examines the limits of the launch benchmarks. | Articles, tutorials & talksAnalysis & discussions | |
| TypeSafe Jev Fact-Checkwww.youtube.com | — | Checks the viral launch claims against TypeSafe's own footnotes: the 70 ms typed-decision model holds up, while the 200x math and the no-hallucination claim do not. | Articles, tutorials & talksAnalysis & discussions | |
| typesafe-jev-model-rejects-chatbots-for-programmatic-decisions-zhwww.remio.ai | — | remio | https://www.remio.ai/zh/post/typesafe-jev-model-rejects-chatbots-for-programmatic-decisions-zh | ~2026-09-17 · Aisha Washington | Product thesis: programmatic decisions vs chatbots; schema≠truth; what to examine after launch | Articles, tutorials & talksAnalysis & discussions | |
| Understanding Jev — appaskjev.kuhung.me | — | Bilingual Chinese-English long-form essay on Jev's one-step decisions, with local tests, failure modes and production limits, plus a micro-decision demo. | Articles, tutorials & talksAnalysis & discussions | |
| Understanding Jev — repogithub.com | — | Bilingual Chinese-English long-form essay on Jev's one-step decisions, with local tests, failure modes and production limits, plus a micro-decision demo. | Articles, tutorials & talksAnalysis & discussions | |
| We need to talk about Jevwww.youtube.com | — | Matthew Berman reviews the Jev launch and early community demos and reactions from X, and what a decision-only model changes. | Articles, tutorials & talksAnalysis & discussions | |
| What is Jev? (r/LocalLLaMA)www.reddit.com | — | Large r/LocalLLaMA thread (306 comments) where people explain what Jev is, how it differs from an LLM, and what it is actually useful for. | Articles, tutorials & talksAnalysis & discussions | |
| What Jev can really dox.com | — | Chinese-language reality check on Jev that explains what it is and is not, sorts demos that actually work by use case, and lays out the caveats behind the speed and accuracy claims. | Articles, tutorials & talksAnalysis & discussions | |
| What TypeSafe is building with Jevnote.com | — | Long-form Japanese analysis of what Jev is and is not, the founders' background, and TypeSafe's bet on embedding classification, routing, scoring, approval, and verification decisions into software. | Articles, tutorials & talksAnalysis & discussions | |
| When will Jev save you money?www.youtube.com | — | Close reading of TypeSafe's eval page and rate cards to work out which model the 444.6x cheaper claim compares against and what the lower agreement costs at scale. | Articles, tutorials & talksAnalysis & discussions | |
| Where a System One model goes in your stackstackness.dev | — | Argues a System One model is a new slot beside the LLM rather than a replacement, separating what TypeSafe has demonstrated from what it has only asserted, and what must be true before adopting it. | Articles, tutorials & talksAnalysis & discussions | |
| Where a System One model goes in your stack — discussionnews.ycombinator.com | — | Argues a System One model is a new slot beside the LLM rather than a replacement, separating what TypeSafe has demonstrated from what it has only asserted, and what must be true before adopting it. | Articles, tutorials & talksAnalysis & discussions | |
| Where Jev belongs in an agent harnessranjankumar.in | — | Long analysis arguing that Jev's ordering can be trusted but its confidence numbers need a local fit, and showing where a decision model belongs in an agent harness and how to set thresholds on your own data. | Articles, tutorials & talksAnalysis & discussions | |
| Where Jev belongs in an agent harnessx.com | — | Argues that Jev's ordering can be trusted but its confidence numbers cannot, so routing and ranking can use it directly while threshold gates such as approving a transfer need calibration first. | Articles, tutorials & talksAnalysis & discussions | |
| Why Jev is orders of magnitude fasterzenn.dev | — | Japanese analysis of why dropping string generation makes Jev so much faster, and what the speed comparisons in the launch actually compare. | Articles, tutorials & talksAnalysis & discussions | |
| Why Jev might kill the text promptwww.reddit.com | — | Essay arguing that chat boxes exist because autoregressive models are too slow for UI event loops, and that 50-100ms decisions allow direct-manipulation AI interfaces. | Articles, tutorials & talksAnalysis & discussions | |
| Will TypeSafe's Jev change how we build AI applications?x.com | — | Arize AI analysis of what a decide-only model buys and costs you, with an eye on LLM-as-a-judge evaluations and the architecture choices it implies. | Articles, tutorials & talksAnalysis & discussions | |
| X is all over it, Reddit is notx.com | — | X: observes a sharp platform divide, finding only three Jev posts on Reddit while X filled with working prototypes — a useful reminder that channel coverage changes the picture. | Articles, tutorials & talksAnalysis & discussions | |
| 分析认为 OpenAI 具备快速跟进 TypeSafe 的 Jev 并将分类能力内嵌进模型的优势aihot.news | — | 作者分析 TypeSafe 的 Jev 本质上是基于常规 LLM 的 logprobs 做通用分类，OpenAI 多年来已在工具调用中用单个 token 充当微型分类器，具备快速复制 Jev 并把分类能力折叠进自家模型和智能体的条件。 | Articles, tutorials & talksAnalysis & discussions | |
| 分析认为 OpenAI 具备快速跟进 TypeSafe 的 Jev 并将分类能力内嵌进模型的优势arcturus-labs.com | — | 作者分析 TypeSafe 的 Jev 本质上是基于常规 LLM 的 logprobs 做通用分类，OpenAI 多年来已在工具调用中用单个 token 充当微型分类器，具备快速复制 Jev 并把分类能力折叠进自家模型和智能体的条件。 | Articles, tutorials & talksAnalysis & discussions | |
| 反基准测试者的Jevons悖论警告aihot.news | — | 再次重申，我极度反对基准测试（： | Articles, tutorials & talksAnalysis & discussions | |
| 反基准测试者的Jevons悖论警告x.com | — | 再次重申，我极度反对基准测试（： | Articles, tutorials & talksAnalysis & discussions | |
| 最新AIモデル「Jev」は何が凄いのかwww.youtube.com | — | Japanese discussion of TypeSafe's philosophy, Jev as a fast-thinking model, Diogo Almeida's view on the limits of RLHF, and the opportunities its cost and speed open up. | Articles, tutorials & talksAnalysis & discussions | |
| 玉伯谈 Jev 模型：系统一模型才刚开始aihot.news | — | 玉伯称这是他见过关于 Jev 最浅入深出的一篇文章，印象最深的点包括：模型该给代码用还是给人用、为什么在 OpenAI 做不出来、什么是杰文斯悖论、不看公开榜单而看内部工作流、用户数据没用、老看 PMF 容易扼杀创新。他还提到借助 Jev 这类模型 SaaS 有大机会，并把 Jev 定义为"系统一模型"，认为系统一模型才刚刚开始，同时表示不看好 neo lab。 | Articles, tutorials & talksAnalysis & discussions | |
| 玉伯谈 Jev 模型：系统一模型才刚开始x.com | — | 玉伯称这是他见过关于 Jev 最浅入深出的一篇文章，印象最深的点包括：模型该给代码用还是给人用、为什么在 OpenAI 做不出来、什么是杰文斯悖论、不看公开榜单而看内部工作流、用户数据没用、老看 PMF 容易扼杀创新。他还提到借助 Jev 这类模型 SaaS 有大机会，并把 Jev 定义为"系统一模型"，认为系统一模型才刚刚开始，同时表示不看好 neo lab。 | Articles, tutorials & talksAnalysis & discussions |
Examples & case studies20 of 20 matches
| Resource | Stars | Description | Category | Save |
|---|---|---|---|---|
| 10 wild things you can build with Jevwww.youtube.com | — | Roundup of ten community builds from Jev's first week, from real-time flight search to Super Mario and air traffic control. | Articles, tutorials & talksExamples & case studies | |
| Intent routingdocs.typesafe.ai | — | Official routing examples. | Articles, tutorials & talksExamples & case studies | |
| Jev intro + 50 open-source use cases — projectgithub.com | 14 | Intro to Jev plus a tour of 50 MIT-licensed demos, from ticket routing to fraud scoring and moderation, each comparing Jev with an OpenAI model on cost and speed. | Articles, tutorials & talksExamples & case studies | |
| Jev Labgithub.com | 1 | Minimal single-page classifier demo in Chinese that shows the full request JSON, per-label probabilities, confidence, latency, and token usage of a Jev call, using only the Python standard library. | Articles, tutorials & talksExamples & case studies | |
| Jev use cases and toolswww.youtube.com | — | Roundup of how developers use Jev for browser automation, coding agents, context compaction, model routing, skill selection, semantic SQL, agent supervision, and games. | Articles, tutorials & talksExamples & case studies | |
| Jev Web Analyzergithub.com | 5 | Small, inspectable example that fetches a SaaS website as Markdown with ReplyNodes and has Jev, through Vercel AI Gateway, judge its audience, clarity and positioning with typed questions and probabilities. | Articles, tutorials & talksExamples & case studies | |
| Jev 用例合集自动更新aihot.news | — | 🔥 Awesome Jev Collection 🔥 在我的新 Jev 合集里找灵感吧。 （记得收藏） 它会自动更新从 X 上抓取的最新热门 Jev 用例和演示。 由 Jev 自己完成策展。 意外的是，我发现 Jev 在策展方面也很擅长。 链接在此：https://academy.dair.ai/resources/jev-field-notes | Articles, tutorials & talksExamples & case studies | |
| Jev 用例合集自动更新x.com | — | 🔥 Awesome Jev Collection 🔥 在我的新 Jev 合集里找灵感吧。 （记得收藏） 它会自动更新从 X 上抓取的最新热门 Jev 用例和演示。 由 Jev 自己完成策展。 意外的是，我发现 Jev 在策展方面也很擅长。 链接在此：https://academy.dair.ai/resources/jev-field-notes | Articles, tutorials & talksExamples & case studies | |
| Jev 路由多模型协同编程演示aihot.news | — | 这就是编程的未来。 多个 AI 模型在同一个 Codex 会话中工作，由 Jev 路由它们处理任务的不同部分。 在这里，我让它构建了一个全栈演示应用： > Opus 5.5 负责规划。 > GPT-6 Astra 构建后端。 > Kimi K3 构建前端。 > DeepSeek 负责测试。 > GLM 5.3 Flash 撰写文档。 Jev 负责切换。我从未碰过模型选择器。同样的构建，成本降低 40%，速度快 15%。 | Articles, tutorials & talksExamples & case studies | |
| Jev 路由多模型协同编程演示x.com | — | 这就是编程的未来。 多个 AI 模型在同一个 Codex 会话中工作，由 Jev 路由它们处理任务的不同部分。 在这里，我让它构建了一个全栈演示应用： > Opus 5.5 负责规划。 > GPT-6 Astra 构建后端。 > Kimi K3 构建前端。 > DeepSeek 负责测试。 > GLM 5.3 Flash 撰写文档。 Jev 负责切换。我从未碰过模型选择器。同样的构建，成本降低 40%，速度快 15%。 | Articles, tutorials & talksExamples & case studies | |
| JEV 驱动分类器意外发现家庭 WiFi 后门aihot.news | — | 开发者用 JEV 驱动的分类器分析 Wireshark 抓取的家庭 WiFi 网络数据包，意外发现严重威胁，经前沿 AI 模型验证后确认，最终重置设备并加固网络。该网络数据包分析工具即将开源。 | Articles, tutorials & talksExamples & case studies | |
| JEV 驱动分类器意外发现家庭 WiFi 后门x.com | — | 开发者用 JEV 驱动的分类器分析 Wireshark 抓取的家庭 WiFi 网络数据包，意外发现严重威胁，经前沿 AI 模型验证后确认，最终重置设备并加固网络。该网络数据包分析工具即将开源。 | Articles, tutorials & talksExamples & case studies | |
| jev-ai-gateway-samplegithub.com | 0 | Sample: TypeSafe Jev via Vercel AI Gateway | Articles, tutorials & talksExamples & case studies | |
| JEV-examplesgithub.com | 0 | examples | Articles, tutorials & talksExamples & case studies | |
| jev-samplesgithub.com | 0 | Runnable samples for Jev, TypeSafe AI's System One model. Send state and questions carrying their own answer options, then branch on the typed value that comes back. | Articles, tutorials & talksExamples & case studies | |
| survey-qc-with-jevgithub.com | 0 | Survey respondent quality control with deterministic rules plus semantic judgments from TypeSafe's Jev model — a fully reproducible worked example | Articles, tutorials & talksExamples & case studies | |
| TypeSafe AI 的 Jev 的 20 个智能体用例解读aihot.news | — | MarkTechPost 整理了 TypeSafe AI 首个 System One 决策模型 Jev 的 20 个智能体用例，涵盖模型路由、工具调用风险把关、重排、引用核验与提示词注入筛查等。 | Articles, tutorials & talksExamples & case studies | |
| TypeSafe AI 的 Jev 的 20 个智能体用例解读www.marktechpost.com | — | MarkTechPost 整理了 TypeSafe AI 首个 System One 决策模型 Jev 的 20 个智能体用例，涵盖模型路由、工具调用风险把关、重排、引用核验与提示词注入筛查等。 | Articles, tutorials & talksExamples & case studies | |
| TypeSafe Typewriter — demox.com | — | Val Town demo that re-scores a sentence on every keystroke, showing 16 calibrated judgments such as tone, urgency, passive-aggressiveness and AI-written move as you type. | Articles, tutorials & talksExamples & case studies | |
| typesafe-triage-demogithub.com | 0 | Demo pública: mensaje → juicios TypeSafe (Choice/Noul/Score) vs decisión en código. Spec: Notion TypeSafe triage. | Articles, tutorials & talksExamples & case studies |
More projects & source code169 of 169 matches
| Resource | Stars | Description | Category | Save |
|---|---|---|---|---|
| All guidesgithub.com | — | Model limitations | Articles, tutorials & talksMore projects & source code | |
| awesome-jev-agent-skillsgithub.com | 0 | Jev-powered AI agent skills for Codex and Claude Code: code review, testing, debugging, extraction and QA evidence. Open source, MIT. | Articles, tutorials & talksMore projects & source code | |
| awesome-jev-agent-skills — READMEgithub.com | 0 | This collection provides coding-agent skills for reviews, tests, debugging, extraction, and QA checks powered by TypeSafe Jev. | Articles, tutorials & talksMore projects & source code | |
| awesome-jev-by-typesafe — READMEgithub.com | 891 | An independent, evidence-backed community guide to Jev use cases, patterns, prompts and starter code. | Articles, tutorials & talksMore projects & source code | |
| awesome-jev-prompts — READMEgithub.com | 0 | Curated Jev resource collects typed-question patterns, anti-patterns and calibration notes to help design decision rubrics and thresholds. | Articles, tutorials & talksMore projects & source code | |
| awesome-jev-use-cases — READMEgithub.com | 14 | A gallery offers 50 interactive Jev primitive demos alongside an OpenAI baseline; previews need no keys, and the README labels sample outputs as illustrations. | Articles, tutorials & talksMore projects & source code | |
| awesome-jev-usecases — READMEgithub.com | 21 | A use-case guide collects practical patterns for TypeSafe Jev and typed decisions; its README labels the document a dated research snapshot. | Articles, tutorials & talksMore projects & source code | |
| bf-jev-deep-researchgithub.com | 0 | Agent Skill + estudo verbatim sobre a Jev (TypeSafe AI System One Model): choice/score/noul, RLCD, patterns, SDKs. | Articles, tutorials & talksMore projects & source code | |
| bf-jev-deep-research — READMEgithub.com | 0 | A Portuguese-language package combines a Jev agent skill for Claude Code, Cursor, and Codex with a source-linked study bundle; it is educational material, not verified performance. | Articles, tutorials & talksMore projects & source code | |
| building-with-jev-skill — READMEgithub.com | 147 | building-with-jev-skill packages agent guidance for designing questions and writing programs that call TypeSafe Jev. | Articles, tutorials & talksMore projects & source code | |
| building-with-typesafe-jev — READMEgithub.com | 126 | An unofficial community skill for coding agents that teaches TypeSafe Jev design through typed questions, calibrated confidence and community examples. It is an implementation-learning resource, explicitly not made, reviewed or endorsed by TypeSafe AI. | Articles, tutorials & talksMore projects & source code | |
| claude-code-jev-compaction — READMEgithub.com | 0 | A practical guide uses Jev in a LiteLLM guardrail to decide whether each completed tool result is still needed, then removes whole blocks below a threshold. | Articles, tutorials & talksMore projects & source code | |
| Context Is a Build Artifactgithub.com | — | Preregistered study design, with an offline pilot harness and TypeSafe adapter, testing whether byte-stable context compilation improves Jev decision consistency, calibration and cost. | Articles, tutorials & talksMore projects & source code | |
| Context Is a Build Artifact — repogithub.com | — | Preregistered study design, with an offline pilot harness and TypeSafe adapter, testing whether byte-stable context compilation improves Jev decision consistency, calibration and cost. | Articles, tutorials & talksMore projects & source code | |
| Digit-logits classifier with llama.cpp — repogithub.com | — | Technique from the mt_llm library for using a small local LLM as a classifier by reading the logits of digit tokens instead of relying on llama.cpp grammars. | Articles, tutorials & talksMore projects & source code | |
| docs — typesafegithub.com | 76 | This docs mirror contains setup guidance for an optional external TypeSafe plugin and OpenClaw decision-model role; the docs say the plugin is disabled by default, not that it is live in every release. | Articles, tutorials & talksMore projects & source code | |
| everything-about-jev — READMEgithub.com | 3 | A learning resource collects Jev explanations, usage examples, community projects, and discussion, including guidance on typed decisions and API integration. | Articles, tutorials & talksMore projects & source code | |
| everything-jev — READMEgithub.com | 0 | Independent TypeScript Jev toolkit combines an HTTP client, validation and conservative policies with offline-labelled decision recipes, not connected production service integrations. | Articles, tutorials & talksMore projects & source code | |
| fake-real-jev — jev-client.jsgithub.com | 1 | Published TypeSafe Jev client and claim-check components from an evidence-review website. The client pins Jev/System One; the full site is closed source, and factual verdict accuracy is not independently verified. | Articles, tutorials & talksMore projects & source code | |
| fake-real-jev — READMEgithub.com | 1 | Published TypeSafe Jev client and claim-check components from an evidence-review website. The client pins Jev/System One; the full site is closed source, and factual verdict accuracy is not independently verified. | Articles, tutorials & talksMore projects & source code | |
| GrowthCompany_JevOutputsgithub.com | 0 | Jev (TypeSafe System One) x Hermes Agent - the calibrated decision-layer playbook: 5 end-to-end use cases, reference implementation, and a Hermes skill for 100x-cheaper structured AI judgments | Articles, tutorials & talksMore projects & source code | |
| GrowthCompany_JevOutputs — READMEgithub.com | 0 | The Hermes-oriented reference includes a jev_ask() helper with confidence gates, offline tests, and an opt-in live smoke test. | Articles, tutorials & talksMore projects & source code | |
| hands-on-jev — READMEgithub.com | 1 | A hands-on playground explains Jev state and typed questions through editable examples and a generic TypeSafe SDK route; its README says users can run all three question types against the live API. This review did not make API calls. | Articles, tutorials & talksMore projects & source code | |
| hello-jev-java — READMEgithub.com | 5 | A minimal Java 25 starter example sends a support message and three typed questions to Jev, then prints the answers. | Articles, tutorials & talksMore projects & source code | |
| instruct-jev — READMEgithub.com | 1 | An instruction/reference corpus compiled from public TypeSafe Jev/System One documentation. Included as documentation material, not an executable model or official instruction authority. | Articles, tutorials & talksMore projects & source code | |
| Jeeves：基于 Qwen3.5-9B 的推理型 Jev 式决策模型，开源权重与训练代码github.com | — | PostHog 发布 Jeeves，一个基于 Qwen3.5-9B（LoRA + pointer head）的推理型 Jev 式分类器，采用 SFT 与 CISPO 训练，并配备 block-4 扩散草稿器。 | Articles, tutorials & talksMore projects & source code | |
| jevgithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| jevgithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| Jev AI Just Dropped — repogithub.com | — | Runs Jev against GPT-6 Astra on five tasks, from a 20-email spam filter to AI-slop detection; the email test took 3.41 seconds for Jev versus 9.93 seconds for Astra. | Articles, tutorials & talksMore projects & source code | |
| Jev Cookbookgithub.com | 34 | Fifteen runnable OpenRouter recipes for Jev, from support triage, dedupe and PII scanning to invoice extraction, search reranking, a browser agent and a Gmail labeler, each with labelled samples and measured results. | Articles, tutorials & talksMore projects & source code | |
| jev — READMEgithub.com | 0 | A Spanish-language reference guide explains Jev’s typed decisions and includes SDK and endpoint examples; it distinguishes offline sample responses from live API use. | Articles, tutorials & talksMore projects & source code | |
| jev — READMEgithub.com | 0 | Single-page educational demo contrasts Jev typed decisions with chatbot responses through ticket sorting, safety-gate and side-by-side examples, without certifying agent safety. | Articles, tutorials & talksMore projects & source code | |
| jev-agent-harness — READMEgithub.com | 0 | An educational guide with a manual, video script, and examples for integrating Jev typed judgments into LangChain agent harnesses, including model routing and tool guardrails. | Articles, tutorials & talksMore projects & source code | |
| jev-ai — READMEgithub.com | 4 | A community Jev quickstart and FAQ explaining TypeSafe’s typed decision model. It is unofficial educational material; linked demo availability, free access and safety were not verified. | Articles, tutorials & talksMore projects & source code | |
| jev-ai-gateway-sample — READMEgithub.com | 0 | This Next.js sample routes TypeSafe Jev support-ticket triage through Vercel AI Gateway. | Articles, tutorials & talksMore projects & source code | |
| jev-anything — READMEgithub.com | 17 | Independent coding-agent skill guides the design, implementation, testing and tuning of bounded Jev decision layers. | Articles, tutorials & talksMore projects & source code | |
| jev-atlasgithub.com | 0 | Map where Jev and System One models actually belong in your project, test the strongest ideas, then implement them. A skill for Claude Code and Codex. | Articles, tutorials & talksMore projects & source code | |
| jev-awesome-skillsgithub.com | 2 | Open-source Jev skills for Claude Code, Codex, Cursor, and Grok. TypeSafe System One: choice, noul, score. Proceed, ask, or stop. | Articles, tutorials & talksMore projects & source code | |
| jev-awesome-skills — READMEgithub.com | 2 | An agent-skill collection uses Jev for proceed/ask/stop and then routes bounded tasks such as verification, triage, review, or next-action selection. | Articles, tutorials & talksMore projects & source code | |
| jev-bun1 — READMEgithub.com | 0 | This Bun example collection follows TypeSafe’s JavaScript SDK examples for Jev, including a Japanese Noul example and parallel requests. | Articles, tutorials & talksMore projects & source code | |
| jev-by-example — READMEgithub.com | 2 | Runnable Jev agent examples pair small semantic judgments with application logic; the README says live model behavior has not yet been verified. | Articles, tutorials & talksMore projects & source code | |
| jev-by-harsh — READMEgithub.com | 0 | Interactive Jev presentation and research workbench displays ecosystem material and illustrative comparisons, not an independently verified Jev benchmark. | Articles, tutorials & talksMore projects & source code | |
| jev-classification-guide — READMEgithub.com | 0 | This methodology uses Jev for company classification and job-title relevance scoring in go-to-market data enrichment. | Articles, tutorials & talksMore projects & source code | |
| jev-confidence-kitgithub.com | 0 | Question-design and confidence-calibration toolkit for TypeSafe's Jev (System One) API — a Claude Code plugin skill. | Articles, tutorials & talksMore projects & source code | |
| jev-confidence-kit — READMEgithub.com | 0 | A Claude Code skill for TypeSafe Jev guides question design and confidence-based routing; the README offers workflow guidance, not measured reliability. | Articles, tutorials & talksMore projects & source code | |
| jev-cookbook — READMEgithub.com | 47 | Chinese Jev tutorial uses Jupyter Notebook experiments to introduce its three typed-question primitives and application patterns. | Articles, tutorials & talksMore projects & source code | |
| jev-cookbook — READMEgithub.com | 34 | A cookbook of TypeSafe Jev decision workflows through OpenRouter, with scripts and small labeled datasets for tasks such as triage, tagging and reranking. It documents the decisions endpoint and Jev model identifier; published measurements remain the author’s results, not independently reproduced certification. | Articles, tutorials & talksMore projects & source code | |
| jev-cookbook — READMEgithub.com | 7 | A cookbook explaining Jev’s state and typed-question API through examples and decision patterns. It is an educational resource, not certification of production readiness or advertised latency. | Articles, tutorials & talksMore projects & source code | |
| jev-crash-course — READMEgithub.com | 1 | An 11-level hands-on course teaches Jev with runnable examples; its text lessons stand alone without the API key needed for live examples. | Articles, tutorials & talksMore projects & source code | |
| jev-crash-course — READMEgithub.com | 0 | An 11-level learning course introduces Jev/System One concepts and includes hands-on examples; it is educational material rather than a performance evaluation. | Articles, tutorials & talksMore projects & source code | |
| jev-deep-divegithub.com | 2 | An evidence-graded deep dive into Jev (TypeSafe AI's System One model): interface, verification, reproduction comparison, and field guide. Bilingual (EN/中文), with runnable probe tools. | Articles, tutorials & talksMore projects & source code | |
| jev-deep-dive — READMEgithub.com | 2 | An evidence-graded reference examines Jev’s interface and claimed performance while distinguishing official numbers from community measurements. | Articles, tutorials & talksMore projects & source code | |
| jev-demogithub.com | 0 | A concise TypeScript and Bun demo of TypeSafe Jev with live OpenAI comparisons. | Articles, tutorials & talksMore projects & source code | |
| Jev-demogithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| jev-demo — READMEgithub.com | 0 | A TypeScript/Bun demo workbench exposes Jev requests and typed results, with an optional same-question comparison through OpenAI/Portkey. | Articles, tutorials & talksMore projects & source code | |
| jev-demo — READMEgithub.com | 16 | A TypeScript demo sends Noul, Choice, and Score questions together and applies an illustrative threshold to show a support-review decision. | Articles, tutorials & talksMore projects & source code | |
| Jev-demo — READMEgithub.com | 0 | TypeScript SDK examples demonstrate a single Choice and combined Noul/Choice/Score request, printing the answers and call timings for support-ticket scenarios. | Articles, tutorials & talksMore projects & source code | |
| jev-demo-raggithub.com | 1 | RAG quality gate adapted for TypeSafe Jev - relevance filtering + document injection detection, fan-out in one call | Articles, tutorials & talksMore projects & source code | |
| jev-demo-rag — READMEgithub.com | 1 | A RAG quality-gate demo asks Jev to assess chunk relevance and prompt injection in one parallel call; the bundled retrieval step is simulated. | Articles, tutorials & talksMore projects & source code | |
| jev-docs — READMEgithub.com | 5 | jev-docs is an unofficial community-maintained history and reference for Jev/System One documentation, APIs, SDKs, and guidance. | Articles, tutorials & talksMore projects & source code | |
| jev-docs-zh — READMEgithub.com | 5 | The README presents this repository as a Chinese translation of Jev's official documentation and points readers to docs.typesafe.ai as the source. | Articles, tutorials & talksMore projects & source code | |
| jev-examplegithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| jev-example — READMEgithub.com | 0 | This example connects Jev to LangChain and defines typed questions, state sources, and several transports; the README describes the integration rather than a compatibility test. | Articles, tutorials & talksMore projects & source code | |
| JEV-examples — READMEgithub.com | 0 | The README presents TypeScript Jev examples using the official TypeSafe SDK through a TypeSafe-compatible Vercel AI Gateway API, with an optional local backend. This review did not test the live connection. | Articles, tutorials & talksMore projects & source code | |
| jev-explainedgithub.com | 0 | Hands-on explainer for TypeSafe AI's Jev decision model: examples, playground, and independent benchmark results | Articles, tutorials & talksMore projects & source code | |
| jev-field-guide-skill — READMEgithub.com | 0 | A Claude Code field-guide skill helps users decide whether Jev fits a task and how to test the use before relying on it; its measured results were not independently reviewed here. | Articles, tutorials & talksMore projects & source code | |
| jev-for-engineers — READMEgithub.com | 5 | This tutorial presents engineering examples in which Jev supplies typed judgments and ordinary Python code applies the policy. | Articles, tutorials & talksMore projects & source code | |
| jev-gtm-cookbookgithub.com | 6 | 15 open-source outbound recipes on TypeSafe Jev. Score your LinkedIn network or any lead list against your ICP, catch job changes, triage replies. Local, zero dependencies. 16,711 connections scored for $0.73. | Articles, tutorials & talksMore projects & source code | |
| jev-gtm-cookbook — READMEgithub.com | 6 | A local go-to-market cookbook uses Jev to score leads and interpret job changes, while code applies routing rules and people or LLMs write messages. | Articles, tutorials & talksMore projects & source code | |
| jev-handbook — READMEgithub.com | 0 | A Chinese field guide organizes practical Jev uses by scenario. | Articles, tutorials & talksMore projects & source code | |
| jev-how-to — READMEgithub.com | 0 | Java 21 tutorial calls Jev's HTTP API for typed support-ticket triage and includes a documented offline stub; the stub does not run a model. | Articles, tutorials & talksMore projects & source code | |
| jev-in-practicegithub.com | 1 | A practical Jev playground for typed, probabilistic decisions across fraud, sales, and patent screening. | Articles, tutorials & talksMore projects & source code | |
| jev-in-practice — READMEgithub.com | 1 | This developer playground lets users edit state and Noul questions for Jev examples in fraud screening, sales qualification, and fictional patent screening. | Articles, tutorials & talksMore projects & source code | |
| jev-kitgithub.com | 0 | Get coding agents to write Jev questions that work the first time: rules measured on 57 labelled suites. | Articles, tutorials & talksMore projects & source code | |
| jev-kit — READMEgithub.com | 0 | Agent skills and evaluation tools help formulate Jev decision questions and assess their behavior against labeled cases. | Articles, tutorials & talksMore projects & source code | |
| jev-labgithub.com | 0 | A local Node lab for TypeSafe's Jev (System One): typed questions in, probabilities out. The API key never leaves your machine. | Articles, tutorials & talksMore projects & source code | |
| jev-lab — READMEgithub.com | 0 | A local web app for learning and trying TypeSafe Jev’s typed state/questions workflow; README says API access is optional. | Articles, tutorials & talksMore projects & source code | |
| jev-lab — READMEgithub.com | 1 | Jev Lab is a single-page classifier demo that shows Jev's request and response, category probabilities, confidence, latency, and token usage through a local proxy. | Articles, tutorials & talksMore projects & source code | |
| jev-langgraph-ticket-appgithub.com | 2 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| jev-langgraph-ticket-app — READMEgithub.com | 2 | A LangGraph support-ticket teaching app separating Jev triage, Python routing, LLM reply writing and human approval. Keyword mock mode allows offline study and is not a real Jev measurement. | Articles, tutorials & talksMore projects & source code | |
| jev-modelgithub.com | 0 | Jev decision model: TypeScript examples, HR Suite web app and webinar lessons | Articles, tutorials & talksMore projects & source code | |
| jev-model — READMEgithub.com | 0 | Jev learning repository combines primitive and ticket-triage demos, an HR application and webinar lessons, without establishing suitability for employment decisions. | Articles, tutorials & talksMore projects & source code | |
| jev-model-labs — READMEgithub.com | 1 | Companion labs and a toolkit teaching Jev/System One decision systems. Labs run offline by default; explicit HTTP backend and TypeSafe key are required to measure real Jev. | Articles, tutorials & talksMore projects & source code | |
| jev-model-testgithub.com | 0 | Testing Jev, the System One model from TypeSafe AI, to check whether a resume and a job description align before applying. | Articles, tutorials & talksMore projects & source code | |
| jev-model-test — READMEgithub.com | 0 | This resume/job-fit example is run manually in the TypeSafe Playground: the user prepares state and typed questions, then pastes both into the UI. | Articles, tutorials & talksMore projects & source code | |
| jev-newbie — READMEgithub.com | 0 | A beginner guide explains using Jev to ask questions, classify text, and rate urgency. | Articles, tutorials & talksMore projects & source code | |
| jev-playgroundgithub.com | 0 | Demos que enseñan en qué se diferencia Jev (el modelo System One de TypeSafe AI) de un LLM normal | Articles, tutorials & talksMore projects & source code | |
| jev-playground — READMEgithub.com | 0 | This playground provides five runnable demos showing how Jev differs from a conventional language model. | Articles, tutorials & talksMore projects & source code | |
| jev-playground — READMEgithub.com | 0 | A public BYOK web playground demonstrates TypeSafe Jev decisions alongside GPT/Claude text generation, with thresholds applied in code. | Articles, tutorials & talksMore projects & source code | |
| jev-pocgithub.com | 0 | A hands-on tour of Jev, TypeSafe's decisions model: twenty demos across seven shapes, with a cost-and-agreement assessment against a chat-model baseline | Articles, tutorials & talksMore projects & source code | |
| jev-poc — READMEgithub.com | 0 | This Jev POC is a collection of demos for typed, calibrated decisions rather than generated prose. | Articles, tutorials & talksMore projects & source code | |
| jev-research — READMEgithub.com | 1 | This public guide and prototype pair Jev recommendations with a deterministic coordinator and Herdr execution; it is an architecture example, not a measured benchmark. | Articles, tutorials & talksMore projects & source code | |
| jev-review — READMEgithub.com | 0 | Four TypeScript web demos illustrate Jev judging typed questions against a state; the README presents examples, not verified live compatibility or performance. | Articles, tutorials & talksMore projects & source code | |
| jev-samples — READMEgithub.com | 0 | This repository provides runnable Jev samples, including multi-question README checks and AGENTS.md/CLAUDE.md readiness scoring. | Articles, tutorials & talksMore projects & source code | |
| jev-skillgithub.com | 0 | Agent skill for TypeSafe AI's Jev decision model: fit assessment, integration recipes, calibration, multi-Jev, benchmarks | Articles, tutorials & talksMore projects & source code | |
| jev-skill — READMEgithub.com | 0 | An independent community Claude Code skill teaches agents to design Jev questions; its README distinguishes it from TypeSafe’s official skill. | Articles, tutorials & talksMore projects & source code | |
| jev-skill — READMEgithub.com | 0 | This agent skill is practical guidance for designing, integrating, and calibrating Jev workflows, with scripts and rollout references; it is documentation and tooling around Jev, not the Jev model itself. | Articles, tutorials & talksMore projects & source code | |
| jev-skill — SKILLgithub.com | 554 | The agent-skill docs distinguish a TypeSafe-backed Jev CLI path from a no-key, user-approved host-agent simulation; they do not establish live-call results. | Articles, tutorials & talksMore projects & source code | |
| jev-skillsgithub.com | 1 | Agent-neutral Jev skills for Claude Code and Codex, from 207 Studio. | Articles, tutorials & talksMore projects & source code | |
| jev-skills — READMEgithub.com | 1 | Community agent skills guide Jev-based voice-command interfaces that turn utterances into typed actions for code to execute. | Articles, tutorials & talksMore projects & source code | |
| jev-skills — READMEgithub.com | 3 | A coding-agent skill collection covers Jev API setup, decision patterns, question design, and evidence checks. | Articles, tutorials & talksMore projects & source code | |
| jev-spring-boot-bookstoregithub.com | 1 | Spring Boot 4 bookstore API using TypeSafe Jev typed judgments via jev-spring-boot-starter | Articles, tutorials & talksMore projects & source code | |
| jev-spring-boot-bookstore — READMEgithub.com | 1 | A Spring Boot bookstore API uses TypeSafe Jev for bounded tasks such as classifying books by genre and audience. | Articles, tutorials & talksMore projects & source code | |
| jev-startergithub.com | 0 | Minimal Python starter for TypeSafe Jev typed decisions — UIbuckets | Articles, tutorials & talksMore projects & source code | |
| jev-starter — READMEgithub.com | 0 | This unofficial starter provides a small Python standard-library client and typed-decision example for TypeSafe Jev; it does not claim answers are guaranteed. | Articles, tutorials & talksMore projects & source code | |
| jev-storyboard-lab — READMEgithub.com | 3 | Companion demos comparing Google ADK and Microsoft Agent Framework storyboard generation with the same TypeSafe Jev scene-level quality-control gate. | Articles, tutorials & talksMore projects & source code | |
| jev-study — READMEgithub.com | 0 | A Korean-language personal study note about Jev and TypeSafe AI’s System One model. | Articles, tutorials & talksMore projects & source code | |
| jev-swapgithub.com | 0 | Claude Code skill: swap System 2 LLM pipeline components for System 1 TypeSafe Jev decisions via investigation, live three-arm eval, fallback, and an independent judge | Articles, tutorials & talksMore projects & source code | |
| jev-swap — READMEgithub.com | 0 | Claude Code migration skill traces downstream responsibilities before replacing a generative pipeline decision with Jev, retaining the original fallback and requiring paired live evaluation before promotion. | Articles, tutorials & talksMore projects & source code | |
| jev-system-architect — READMEgithub.com | 2 | This architecture skill recommends keeping deterministic control flow in code and using Jev for narrow structured judgments. | Articles, tutorials & talksMore projects & source code | |
| jev-system-one-reference — READMEgithub.com | 2 | An independent documentation reference guides engineers and coding assistants building with Jev; it is not an application, SDK, or deployed service. | Articles, tutorials & talksMore projects & source code | |
| jev-typesafegithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| jev-typesafe — READMEgithub.com | 0 | This Lyzr page describes a no-login Jev playground and course; it says requests run through Lyzr’s key and disclaims TypeSafe affiliation. | Articles, tutorials & talksMore projects & source code | |
| jev-typesafe-aigithub.com | 2 | Unofficial developer notes & examples for Jev, TypeSafe AI's System One model. Try it free: jevtypesafeai.com | Articles, tutorials & talksMore projects & source code | |
| jev-typesafe-ai — READMEgithub.com | 2 | An independent community reference provides developer notes and examples for Jev rather than an official TypeSafe resource. | Articles, tutorials & talksMore projects & source code | |
| jev-upwork-job-classification — READMEgithub.com | 2 | The Upwork example batches a job's classification questions into one Jev request. | Articles, tutorials & talksMore projects & source code | |
| JEV-use_casesgithub.com | 1 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| JEV-use_cases — READMEgithub.com | 1 | An enterprise playbook catalogs 30 proposed Jev use cases and explicitly labels its vendor performance figures as self-reported and unreproduced. | Articles, tutorials & talksMore projects & source code | |
| jev-web-analyzer — READMEgithub.com | 5 | Jev Web Analyzer is an example that asks Jev for typed probabilistic judgments over fetched website content. | Articles, tutorials & talksMore projects & source code | |
| jev_bloggithub.com | 0 | TypeSafe AI Jev 実践ユースケース・サンプルコード集 | Articles, tutorials & talksMore projects & source code | |
| jev_blog — READMEgithub.com | 0 | jev_blog is a Jev-focused technical blog and practice-code collection, including a guide organized around five use cases. | Articles, tutorials & talksMore projects & source code | |
| jevcngithub.com | 0 | Jev 中文社区 | Articles, tutorials & talksMore projects & source code | |
| jevcn — READMEgithub.com | 0 | jevcn is a Chinese-language community guide to Jev and software-engineering use cases, rather than an official TypeSafe API source. | Articles, tutorials & talksMore projects & source code | |
| jevcode — READMEgithub.com | 1 | JevCode is a Jev-focused solutions and best-practices site, described in its README as a practical guide and documentation collection. | Articles, tutorials & talksMore projects & source code | |
| JevDemogithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| JevDemo — READMEgithub.com | 0 | Thirteen TypeScript Jev examples focus on structured/minimal state, typed and batched questions, confidence/action thresholds and code-owned routing/composite scoring. | Articles, tutorials & talksMore projects & source code | |
| JudgeJev — READMEgithub.com | 1 | A DeepEval/Jev learning lab for inspecting typed evaluation evidence and release rules; the hosted demo replays records rather than making fresh calls. | Articles, tutorials & talksMore projects & source code | |
| just-jev-itgithub.com | 0 | Skills used to analyze the current workflows/automations/agents and integrate jev if it helps! | Articles, tutorials & talksMore projects & source code | |
| just-jev-it — READMEgithub.com | 0 | just-jev-it is an audit skill that finds existing LLM call sites where TypeSafe Jev could replace or augment them. | Articles, tutorials & talksMore projects & source code | |
| kit-jevgithub.com | 0 | Kit gratuito para probar Jev (TypeSafe) con tus datos: clasificador, test de indexación, las pruebas del vídeo y habilidad para Claude Code. Por Centry. | Articles, tutorials & talksMore projects & source code | |
| kit-jev — READMEgithub.com | 0 | Spanish learning kit includes a Jev CSV classifier, benchmark exercises and a Claude Code skill for closed decision questions. | Articles, tutorials & talksMore projects & source code | |
| langchain-jev-tutorial — READMEgithub.com | 0 | This LangChain support-ops tutorial demonstrates Jev typed classification, model routing, risky-tool middleware, and evaluation; README marks the middleware experimental and reported runs were not re-executed. | Articles, tutorials & talksMore projects & source code | |
| learn-jev — READMEgithub.com | 0 | A personal GitHub Pages hub collects learning materials about Jev. | Articles, tutorials & talksMore projects & source code | |
| project-scale-jev-demonstrationgithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| project-scale-jev-demonstration — READMEgithub.com | 0 | SCALE AI Decision Demo illustrates a layered enterprise workflow: structured processing, Jev decisions, deterministic rules, LLM-generated explanations, and human approval. The README does not present Jev as the action executor. | Articles, tutorials & talksMore projects & source code | |
| Pydantic AI + Jev examplesgithub.com | 3 | One-file runnable Pydantic AI agents where Jev makes the quick calls, such as whether a prompt is harmful, whether a shell command should run, and whether a Flappy Bird should flap. | Articles, tutorials & talksMore projects & source code | |
| pydantic-jev-examples — READMEgithub.com | 3 | Small Pydantic AI examples use Jev for bounded checks such as prompt approval or command permission; this is an example set, not a general agent product. | Articles, tutorials & talksMore projects & source code | |
| red-ciberseguridad-jevgithub.com | 0 | Ejemplo didáctico: captura los paquetes de tu red con Wireshark/tshark y deja que Jev (TypeSafe) los juzgue desde la ciberseguridad | Articles, tutorials & talksMore projects & source code | |
| red-ciberseguridad-jev — READMEgithub.com | 0 | Spanish network-learning example captures traffic, groups packets into conversations and uses Jev typed questions to classify conversations and assess risk. | Articles, tutorials & talksMore projects & source code | |
| skill-siujevgithub.com | 0 | Should I use JEV? | Articles, tutorials & talksMore projects & source code | |
| skill-siujev — READMEgithub.com | 0 | siujev is an agent skill for assessing where Jev fits and planning a pilot; its dated evidence files should not be treated as current pricing or live evaluation. | Articles, tutorials & talksMore projects & source code | |
| skillbox — READMEgithub.com | 255 | Skillbox is a self-hosted agent-skills library with optional task-aware Jev recommendations; provider credentials are user-supplied and recommendations are not enabled unconditionally. | Articles, tutorials & talksMore projects & source code | |
| skills — READMEgithub.com | 2,510 | This repository distributes an installable agent skill for building workflows with TypeSafe typed decisions, so it serves as practical Jev-development documentation. | Articles, tutorials & talksMore projects & source code | |
| survey-qc-with-jev — READMEgithub.com | 0 | This survey-quality-control example combines deterministic code rules with TypeSafe Jev semantic judgments; its dataset-level results are not validated here. | Articles, tutorials & talksMore projects & source code | |
| tri-emails-jev — READMEgithub.com | 0 | A French guide offers four Jev-based email-triage templates: a script, n8n workflow, Make scenario, and Claude Code skill; the model returns a category and confidence. | Articles, tutorials & talksMore projects & source code | |
| try-typesafe-appgithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| try-typesafe-app — READMEgithub.com | 0 | A scratchpad collects product ideas and demo projects built around the stated design rule: Jev decides, code acts. | Articles, tutorials & talksMore projects & source code | |
| tryjevgithub.com | 6 | Web playground for Jev with preset scenarios, an editable state and typed questions, and answers with probabilities, working with OpenRouter, Vercel AI Gateway or the TypeSafe API using your own key. | Articles, tutorials & talksMore projects & source code | |
| tryjev — READMEgithub.com | 6 | TryJev is a playground with preset scenarios and editable state/questions; users bring a key and choose a provider. | Articles, tutorials & talksMore projects & source code | |
| typesafe-ai-implementation-skillgithub.com | 1 | a work in progress, built from the typesafe-ai-skill for building and analyzing jev opportunities | Articles, tutorials & talksMore projects & source code | |
| typesafe-ai-implementation-skill — READMEgithub.com | 1 | An engineering skill/reference playbook for placing Jev/System One judgments at bounded decision points while retaining application authority in code. | Articles, tutorials & talksMore projects & source code | |
| typesafe-ai-jev-example — READMEgithub.com | 2 | A hands-on Jev demo includes runnable examples; without a key it uses mock mode, and the README says those samples are not Jev output. | Articles, tutorials & talksMore projects & source code | |
| typesafe-chicken-egggithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| typesafe-chicken-egg — READMEgithub.com | 0 | A Chinese-language TypeSafe example uses Choice, Noul, and Score to turn the open “chicken or egg” question into structured judgments. | Articles, tutorials & talksMore projects & source code | |
| typesafe-handbook — READMEgithub.com | 0 | A Chinese reading companion covers TypeSafe’s official System One/Jev documentation, including primitives, patterns, SDK, API, and recipes. | Articles, tutorials & talksMore projects & source code | |
| typesafe-jev-appgithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| typesafe-jev-app — READMEgithub.com | 0 | This small Python example sends support tickets to TypeSafe System One and prints typed department, frustration, and urgency judgments. | Articles, tutorials & talksMore projects & source code | |
| typesafe-triage-demo — READMEgithub.com | 0 | Next.js triage demo contrasts TypeSafe System One intent, urgency and anger judgments with deterministic policy decisions displayed side by side. | Articles, tutorials & talksMore projects & source code | |
| understanding-jevgithub.com | 0 | TypeSafe Jev ecosystem repository. | Articles, tutorials & talksMore projects & source code | |
| understanding-jev — READMEgithub.com | 0 | Learning repository organizes Jev lessons, example projects and agent skills explaining typed decision-model usage. | Articles, tutorials & talksMore projects & source code | |
| When not to use RL after Jevgithub.com | — | Chapter of a Chinese agentic RL tutorial arguing that discriminative tasks can be outsourced to Jev while policy tasks still need RL, with gradient sweeps measuring its resolution. | Articles, tutorials & talksMore projects & source code | |
| When not to use RL after Jev — repogithub.com | — | Chapter of a Chinese agentic RL tutorial arguing that discriminative tasks can be outsourced to Jev while policy tasks still need RL, with gradient sweeps measuring its resolution. | Articles, tutorials & talksMore projects & source code | |
| windows-save-token-jev-setup — READMEgithub.com | 4 | This Windows Codex setup skill does not itself install or configure save-token-jev; its documented flow associates Jev with that hook's PreCompact step. | Articles, tutorials & talksMore projects & source code | |
| ← Back to Awesome Jevgithub.com | 24 | Listed by the source without a separate description. | Articles, tutorials & talksMore projects & source code | |
| ← Back to Awesome Jevgithub.com | 24 | Listed by the source without a separate description. | Articles, tutorials & talksMore projects & source code | |
| ← Back to Awesome Jevgithub.com | 24 | Listed by the source without a separate description. | Articles, tutorials & talksMore projects & source code | |
| ← Back to Awesome Jevgithub.com | 24 | Listed by the source without a separate description. | Articles, tutorials & talksMore projects & source code | |
| ← Back to Awesome Jevgithub.com | 24 | Listed by the source without a separate description. | Articles, tutorials & talksMore projects & source code | |
| ← Back to Awesome Jevgithub.com | 24 | Listed by the source without a separate description. | Articles, tutorials & talksMore projects & source code |
More posts & discussions169 of 169 matches
| Resource | Stars | Description | Category | Save |
|---|---|---|---|---|
| 19 open-source Jev projectsx.com | — | X (Chinese): tallies 19 open-source Jev projects totalling more than 6,800 stars. | Articles, tutorials & talksMore posts & discussions | |
| 20 ways to use Jev at workx.com | — | Japanese guide to what Jev does, 20 published demos and API uses from browser control to support triage with work applications, and how to try it free through Vercel AI Gateway. | Articles, tutorials & talksMore posts & discussions | |
| 30 things you can do with Jevx.com | — | Japanese-language introduction to what Jev does and does not do, walking through 30 real prototypes and demos (flight search, browser agents, games, trading bots) with reported speed and cost. | Articles, tutorials & talksMore posts & discussions | |
| agentjournal ledger testsx.com | — | 25,174 Jev calls for $1.43 across template-trap, multi-dimension, and real-ledger account-coding tasks. | Articles, tutorials & talksMore posts & discussions | |
| AI 实验室调侃机器之神与婴儿x.com | — | 大型 AI 实验室：机器之神随时降临，别把孩子带到那个世界去！ TypeSafe： [引用 @craigweiss]：给我儿子取名 Jev | Articles, tutorials & talksMore posts & discussions | |
| All the coolest Jev projects on Xx.com | — | X: a curated thread of the strongest Jev projects posted within 72 hours of launch, by a builder who also produced the most-watched Jev tutorial. | Articles, tutorials & talksMore posts & discussions | |
| An LLM from a classifierx.com | — | Builds an autoregressive text generator out of Jev: 29 yes/no questions per character pick the next key (a-z, space, comma, period), and the text is fed back in to repeat. | Articles, tutorials & talksMore posts & discussions | |
| Anyone here using Jev?www.reddit.com | — | Thread in r/PiCodingAgent (77 comments) collecting what people build with Jev, starting from routing a natural-language request to one of 250-300 app API calls. | Articles, tutorials & talksMore posts & discussions | |
| BestBlogs 整理 Jev 模型专题x.com | — | 最近几天 Jev 模型很火，BestBlogs 整理了一期专题，可以组队一起学习一下 😆 https://www.bestblogs.dev/explore/topics/typesafe-jev-release | Articles, tutorials & talksMore posts & discussions | |
| BestBlogs 早报：Jev 决策模型与 Warp 软件工厂x.com | — | TypeSafe AI CEO 在 Latent.Space 访谈中介绍 Jev——面向软件控制流的 System One 模型，采用 RLCD（强化学习校准决策）训练，追求概率与实际信息相称，相关方法尚未公开发表。 | Articles, tutorials & talksMore posts & discussions | |
| BestBlogs 早报：Jev 模型与 AI 软件工厂实践x.com | — | BestBlogs 09-22 早报收录 10 篇内容，涵盖 Jev System One 决策模型、Warp 的 AI 软件工厂流程、UiPath 关于工作流程才是核心资产的判断，以及生产级智能体控制平面与 Loop engineering 方法论。 | Articles, tutorials & talksMore posts & discussions | |
| BestBlogs 精选周刊第 114 期：智能过剩之后，瓶颈向任务链后方移动x.com | — | BestBlogs 精选周刊第 114 期发布，梳理 Claude Opus 5.5、GPT-6 Sol 与 Luna、Grok 4.7、MiMo-V2.6 等集中在同一窗口的发布，并提出核心判断：模型能力每前进一步，系统瓶颈就向任务链后方移动一步。 | Articles, tutorials & talksMore posts & discussions | |
| Businesses Jev unlocksx.com | — | Plain-language explanation of Jev as a sorter (1,700 emails for 18 cents) plus startup ideas built on putting it at the front of expensive queues, such as instant quotes and lead scoring. | Articles, tutorials & talksMore posts & discussions | |
| Can we have Jev in Devin?reddit.com | — | Reddit: users of another coding agent ask for a Jev decision layer inside their tool, a signal that typed decisions are becoming an expected feature. | Articles, tutorials & talksMore posts & discussions | |
| ClawCast 迎来 Allie 做客 Discordx.com | — | 太酷了，@allietheicon（来自 Jev/TypeSafe）加入了我们在 Discord 上的 ClawCast！https://discord.com/invite/clawd | Articles, tutorials & talksMore posts & discussions | |
| CodeRabbit 举办 Jev 首届黑客松x.com | — | CodeRabbit 在总部举办 Jev 首届黑客松，160+ 名开发者参与约 4 小时编程，并邀请 @allietheicon 分享如何与 Jev 这类新型 AI 模型协作。她谈到 Jev 对编程与软件开发带来的范式转变，并给出实用技巧与示例。 | Articles, tutorials & talksMore posts & discussions | |
| Codex 宕机后用 Claude Code 监控x.com | — | Codex 挂了。 于是我配置了 Claude Code 来检查它什么时候恢复。 然后我用 Jev 来决定 Claude Code 该什么时候检查。 我可能有点问题了。 | Articles, tutorials & talksMore posts & discussions | |
| ConsoleChaosRacing driven by Jevx.com | — | Racing UI wired to Jev driving decisions. | Articles, tutorials & talksMore posts & discussions | |
| Cómo usar Jev (video)x.com | — | Spanish-language video tutorial explaining what Jev is and showing how to use it for structured probabilistic decisions instead of text generation. | Articles, tutorials & talksMore posts & discussions | |
| DAIR.AI 本周顶级 AI 论文盘点x.com | — | 本周顶级 AI 论文（9 月 21 日 - 27 日）： - HySparse2 - EvoOntology - Harness-Zero - JEV-as-a-Judge - Wiki Foundation Model - Self-Organizing Agent Teams - Self-Improvement via Fast Tree-search 继续阅读： [引用 @dair_ai]：https://x.com/i/article/2104259869402599424 | Articles, tutorials & talksMore posts & discussions | |
| Decision models in practicex.com | — | OpenRouter thread explaining what a decision model is through practical software-development examples where Jev answers yes/no and multiple-choice questions with confidence. | Articles, tutorials & talksMore posts & discussions | |
| dejevnerates 项目仍在建设中x.com | — | 我们正在努力为你们这些 dejevnerates 带来你们想要的东西，注册仍然关闭，敬请期待！🏗️ | Articles, tutorials & talksMore posts & discussions | |
| Dex Horthy 回应基准测试调侃x.com | — | 兄弟以为我就是一堆 benchmark 堆出来的 哈哈 | Articles, tutorials & talksMore posts & discussions | |
| Digit-logits classifier with llama.cppwww.reddit.com | — | Technique from the mt_llm library for using a small local LLM as a classifier by reading the logits of digit tokens instead of relying on llama.cpp grammars. | Articles, tutorials & talksMore posts & discussions | |
| DuckDB Jev extensionx.com | — | DuckDB extension that classifies rows of any CSV, Parquet or DuckDB table with Jev from SQL, taking about 10 seconds for 1k rows. | Articles, tutorials & talksMore posts & discussions | |
| ElevenLabs 实时情绪分析x.com | — | 这可不只是转录分析，快交出你的秘密！马上就去玩玩 @ElevenLabsDevs 上线的实时版本 [引用 @ElevenLabsDevs]：Jev 与 ElevenLabs 带来的实时情绪分析。 通话者还在说话时，每个短语就已染上它所承载情绪的颜色。右侧六个仪表实时追踪通话的情绪。 | Articles, tutorials & talksMore posts & discussions | |
| Five open Jev replicas worth tryingx.com | — | X (Chinese): rounds up Laya 421M, Decider-2B, NanoJev 0.6B, Reflex, and System-One 4B as the most promising open decision models, two of which are Mac-friendly. | Articles, tutorials & talksMore posts & discussions | |
| Five practical Jev agent scenariosx.com | — | Chinese guide to five places Jev fits in agent pipelines: content cleaning, fixed-parameter selection, intent routing, RAG reranking and tool-call safety gating, introducing the open-source JevShield. | Articles, tutorials & talksMore posts & discussions | |
| Full Jev tutorialx.com | — | Video tutorial covering what Jev is, API setup, and three demos: a voice-controlled browser, AI memory and a YouTube predictor. | Articles, tutorials & talksMore posts & discussions | |
| Fun ways to use Jev with agentswww.reddit.com | — | Thread in r/AI_Agents swapping ideas for small judgment calls inside agent pipelines, including a Jev plus vertical-CLI setup for social media research. | Articles, tutorials & talksMore posts & discussions | |
| fx auto-mode safety reviewerx.com | — | Safety reviewer that checks every command in the fx coding agent's auto mode; Jev benchmarked up to 18x faster at p95 and more accurate than the GPT Luna model it replaces. | Articles, tutorials & talksMore posts & discussions | |
| GPT Researcher 用 Jev 替代嵌入向量x.com | — | 太酷了！ | Articles, tutorials & talksMore posts & discussions | |
| Grok 回复区需要 System 1 反射x.com | — | @elonmusk X 的回复区需要的是 System 1 反射，而不是 System 2 哲学： 评论："你想看看我的福吗？" Grok（3500ms）：这是中国网络俚语，不是字面意义上问"fortune"或"blessing"………. Jev（70ms）：{"ban"： true}（成本：$0.00004） | Articles, tutorials & talksMore posts & discussions | |
| Ground Truth news-framing extensionx.com | — | Browser extension that classifies an article's framing, type, topic, and loaded language with Jev. | Articles, tutorials & talksMore posts & discussions | |
| HF 热门榜首开源多语言决策模型x.com | — | HF 上排名第一的热门模型是一个开源多语言 system 1 决策模型，就在 Jev 开始走红几天之后。开源 AI 社区太棒了！ | Articles, tutorials & talksMore posts & discussions | |
| Hook panel A/B testerx.com | — | Near-real-time scoring of TikTok and Instagram hooks against about 100 personas. | Articles, tutorials & talksMore posts & discussions | |
| How Jev makes agents cheaperx.com | — | Thread listing where Jev speeds agents up, from model routing, computer use and action-safety review to deciding whether each event should wake an expensive orchestrator, go to a subagent, or be queued. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/CompleteSkeptic/status/2099925684256899543 — important qualification: “The gains aren’x.com | — | t free: Jev can’t generate text”; compares the trade-off with LLMs; 1.9M views. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/CompleteSkeptic/status/2099925685720760404 — workflow-evals/cost post: $42 per billionx.com | — | input tokens, output free, and Jev named after Jevons; 729K views. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/CompleteSkeptic/status/2099925690682630371 — points readers to the technical release bx.com | — | log, waitlist, and Discord; 581K views. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/danshipper/status/2100251499443998766 — Dan’s skeptical framing: “LLMs are just autocox.com | — | mplete / jev is just a JSON classifier”; 17.5K views · 6 · 12 · 333 · 32. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/deanmckee757/status/2100259596262682866 — counters Dan with “Crazy lack of imaginationx.com | — | on the timeline today”; 2 · 10 · 749. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/dotey/status/2100125076091760742 — 宝玉 asks for Chinese subtitles on the launch video; x.com | — | 1 · 14 · 6.1K views. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/easeev/status/2100434381701779937 — points to a Google Flights MCP/CLI/Python library x.com | — | as an alternative to computer use. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/hamiltonulmer/status/2100297398996382047 — Hamilton: Jev addresses a pain point LLMs hx.com | — | andle badly; calls the novelty both encouraging and an indictment of the industry’s lack of imagination; 2 · 3 · 56 · 2.7K views. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/ItsCuthulhu/status/2100451965020864753 — sarcastic reply: “They live in a world Da Vinx.com | — | ci would seppuku over.” | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/sebuzdugan/status/2100556671491723639 — asks to see success rate across flight sites, x.com | — | noting that 7 seconds matters less if small DOM changes break completion. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/ShittyTwittter/status/2100450857728786754 — asks whether Jev has OCR, exposing the boux.com | — | ndary between structured DOM state and visual computer use. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/Suyanzhenq/status/2100121219521544312 — Chinese reply jokes that the fast, non-chatty x.com | — | model is a “quant-trading holy body” for bots; 4 · 574 likes. | Articles, tutorials & talksMore posts & discussions | |
| https://x.com/thirk/status/2100445159720644898 — skeptical one-liner: lack of broad flight APIs may x.com | — | be intentional to preserve a moat. | Articles, tutorials & talksMore posts & discussions | |
| HuggingFace 上 300 万个专用模型x.com | — | 我喜欢 Jev 的一点是：多年来，大型通用模型几乎吸走了 AI 领域所有的氧气。 但在更加专业化和定制化的模型上存在巨大机会，这些模型为特定任务和语言而构建，因此成本低几个数量级、速度更快、优化更好。@huggingface 上公开可用的这类模型有 300 万个。 让我们构建一个更加多元的 AI 生态！ | Articles, tutorials & talksMore posts & discussions | |
| HumanLayer 的 Jev 代码搜索 harness 调优x.com | — | 别管我，我就在这儿对 jev harness 做爬山调优，用于代码搜索，你们继续。 | Articles, tutorials & talksMore posts & discussions | |
| I reviewed 287 open-source Jev projectsreddit.com | — | Reddit: a reviewer works through 287 Jev repositories and narrows them to 20 that actually explain the model, a useful counterweight to star-count browsing. | Articles, tutorials & talksMore posts & discussions | |
| itemnews.ycombinator.com | — | Small related tweet thread | https://news.ycombinator.com/item?id=49716682 | “Jev: The Model That Gives AI the Properties of Code” — ~18 points / 4 comments — not the main launch | Articles, tutorials & talksMore posts & discussions | |
| itemnews.ycombinator.com | — | Related comments | e.g. https://news.ycombinator.com/item?id=49719001 , 49720601, 49719080 | Nested under main launch | Articles, tutorials & talksMore posts & discussions | |
| Jev 1.13 红队测试曝安全漏洞x.com | — | Jev 不好用？它是为可组合性而生的！ 需要更多 Jev！ | Articles, tutorials & talksMore posts & discussions | |
| Jev agent safety monitorx.com | — | Test of Jev as a monitor that checks each AI agent action before it runs, reported to catch most attacks with almost no false blocks and much faster than Gemini. | Articles, tutorials & talksMore posts & discussions | |
| Jev AI 模型引关注x.com | — | 人人都想知道 Jev 是什么，却没人问 Jev 过得怎么样 https://en.wikipedia.org/wiki/Jev_(AI_model) | Articles, tutorials & talksMore posts & discussions | |
| Jev as a decision primitivex.com | — | Notes from ~5,000 requests (about $2) on classification, routing and intent: p50 ~150ms and p95 ~350ms make per-turn checks viable, and Jev rewards splitting queries into independent questions. | Articles, tutorials & talksMore posts & discussions | |
| Jev at the branches — discussionnews.ycombinator.com | — | A state machine owns the plan and the legal transitions while Jev only chooses among open branches, with stricter margins on risky moves. | Articles, tutorials & talksMore posts & discussions | |
| Jev Clearly Explainedx.com | — | Explainer on the many small decisions inside an agent run (model choice, risky tool calls, loops, completion) and how Jev answers them with typed probabilities instead of generated text. | Articles, tutorials & talksMore posts & discussions | |
| Jev Clearly Explainedx.com | — | X article explaining Jev as a millisecond decision layer: how typed questions replace generate-parse-retry LLM calls, and where it sits next to an LLM in an application. | Articles, tutorials & talksMore posts & discussions | |
| Jev Engineering roadmap, summarizedx.com | — | Thread condensing a 10-step Jev setup guide: turn agent forks into Choice, Score and probability, batch decisions (13 questions ran 10x faster and 12.2x cheaper in one test), and benchmark the whole loop. | Articles, tutorials & talksMore posts & discussions | |
| Jev Engineering roadmap, summarized — sourcex.com | — | Thread condensing a 10-step Jev setup guide: turn agent forks into Choice, Score and probability, batch decisions (13 questions ran 10x faster and 12.2x cheaper in one test), and benchmark the whole loop. | Articles, tutorials & talksMore posts & discussions | |
| Jev for GTM use caseswww.reddit.com | — | Go-to-market engineers trade ideas and experience on using Jev for lead scoring, website personalization, Clay workflows, ad analysis, and company-update triage. | Articles, tutorials & talksMore posts & discussions | |
| Jev for non-engineers and designersx.com | — | Japanese introduction for non-engineers built around a demo that sorts live-stream comments into question, impression, request and other in 70-150 milliseconds. | Articles, tutorials & talksMore posts & discussions | |
| Jev full tutorialx.com | — | Tutorial video covering how Jev works, access and pricing, a resume-scoring playground demo, and five builds: an LLM router, ticket triage, inbox classifier, slop filter and a fast browser agent. | Articles, tutorials & talksMore posts & discussions | |
| Jev gomoku harnessx.com | — | Local tactics shrink 225 moves to about 40 candidates, then Jev picks among tiered options. | Articles, tutorials & talksMore posts & discussions | |
| Jev Harness blueprint summaryx.com | — | Thread summarizing a 12-page TypeSafe PDF on a Jev harness for coding agents, e.g. Opus to Sonnet to Opus hand-offs costing 6.19 vs 4.15 for pure Opus, and reading and search taking 56.2% of tool turns. | Articles, tutorials & talksMore posts & discussions | |
| Jev in a Grammarly-style Mac appx.com | — | Desktop writing app using Jev for fast structured writing judgments. | Articles, tutorials & talksMore posts & discussions | |
| Jev in the Wildx.com | — | Survey of early architecture patterns in Jev projects, such as model, skill and tool routing, supervisors and security layers, all inserting Jev at one decision point in otherwise conventional software. | Articles, tutorials & talksMore posts & discussions | |
| Jev is here and how to use itx.com | — | Startup Ideas Podcast guide with OpenCode's Ryan Vogel on how Jev works, where it breaks and startup ideas, including a demo that ran 1,700 real emails through Jev for 18 cents. | Articles, tutorials & talksMore posts & discussions | |
| Jev on Cloudflare AI Gatewayx.com | — | X: Jev goes live on Cloudflare's AI Gateway, callable from Workers. | Articles, tutorials & talksMore posts & discussions | |
| Jev repository roundup (Japanese)x.com | — | X (Japanese): rounds up the Jev repositories with the most practical promise, observing that computer use and automated trading dominate the early use cases. | Articles, tutorials & talksMore posts & discussions | |
| Jev scoring 用作 RAG 重排序x.com | — | 把 jev scoring 用作 RAG 重排序器非常合理。Rippling 内部 GTM 团队的应用做得很不错。 | Articles, tutorials & talksMore posts & discussions | |
| Jev × Tripo × Astra 联动演示：Tripo P2.0 生成 3D 资产与 VRM 模型x.com | — | Jev × Tripo × Astra 联动 Demo 展示了用 Tripo P2.0 生成 3D 资产与 VRM 模型、由 Jev 驱动语音激活战斗与逻辑的流畅工作流。Jev 负责地图无限扩展、商人文本谈判判定、战斗自由行动判定及语音动作特效分类，Tripo SmartMesh P2.0 生成随机出现的 NPC/敌人/资产与部分魔法特效资产。10 分钟试玩版链接在推文回复中。 | Articles, tutorials & talksMore posts & discussions | |
| Jev 不是聊天机器人：接收文本状态输出结构化打分x.com | — | Jev 不是 chatbot，不能对话，它接收 state（主要是 text data）后打分并输出结构化结果。其定位是固定 workflow 中的"螺丝钉"，适合做日志分析和输出检测。 | Articles, tutorials & talksMore posts & discussions | |
| Jev 与 Instructor 能否搭配使用x.com | — | 有人想用 jev 搭配 instructor 吗？ | Articles, tutorials & talksMore posts & discussions | |
| Jev 作 Judge 做智能体评估，低置信度升级到前沿模型x.com | — | Elvis Saravia 提出用 Jev-as-a-Judge 做智能体评估，认为这是目前最惊艳的 Jev 用例之一。他的早期测试指向一套兼顾准确率与成本的优化流程：高置信度场景用 Jev，低置信度判定则升级到前沿模型（GPT-6 或 Opus 5.5）。他强调 Jev 并非处处适用，前沿模型也不该包揽所有评估，完整指南即将发布。 | Articles, tutorials & talksMore posts & discussions | |
| Jev 值得关注内容持续追踪x.com | — | 1/n | Articles, tutorials & talksMore posts & discussions | |
| JEV 分类器意外发现家庭 WiFi 后门x.com | — | 用户用 @typesafeai 的 JEV 驱动分类器分析 Wireshark 抓取的网络数据包，意外发现家庭 WiFi 网络中的后门威胁，经前沿 AI 模型验证后重置设备并加固网络。该网络数据包分析工具即将开源。主推文以"做得智能又便宜、铺得到处都是"概括 JEV 的路线，并称之为"jevon's paradox"。 | Articles, tutorials & talksMore posts & discussions | |
| Jev 可组合性设计引热议x.com | — | 我们的推特小哥太会整活了 🧑🍳 | Articles, tutorials & talksMore posts & discussions | |
| Jev 如何进化智能体 harness 体验x.com | — | Elvis Saravia 将 Jev 集成进自建 harness，认为它不只是更快更便宜，而是能解锁此前受成本、延迟或缺少合适原语限制的智能体体验。Jev 可用于分类、控制流、确定性工作流和大规模标注，并通过智能决策、结构化智能与按需上下文管理提升可靠性。他还看好 Jev 在动态 UI、LLM 评审、验证器与合成高质量数据上的潜力，完整指南即将发布。 | Articles, tutorials & talksMore posts & discussions | |
| Jev 安全红队测试与防护实践x.com | — | 围绕一个简单原语做真正工程实践的好例子！！！ 不要把 Jev 直接接入高层决策，而是编程定义你想要的行为！ （跟人说"去编程"听起来很奇怪，但这真的很酷） | Articles, tutorials & talksMore posts & discussions | |
| Jev 实现动态 UI 文本框x.com | — | 应用的动态 UI 是著名的坟场，至少对 PM 来说是这样，甚至对整个产品和公司也是如此。如果真有这么简单呢？Jev 正在做这件事。 | Articles, tutorials & talksMore posts & discussions | |
| Jev 成 OpenRouter 分类请求首选x.com | — | Jev 正迅速成为 OpenRouter 上分类请求的首选。 它占据了该类别每周请求量的 27%，几乎是此前位居榜首的 DeepSeek V4 Flash 份额的两倍 | Articles, tutorials & talksMore posts & discussions | |
| Jev 招募数据人才提升可靠性x.com | — | 加入我们！让 jev 更可靠！🤘 | Articles, tutorials & talksMore posts & discussions | |
| Jev 搭配 Exa 联网搜索效果惊人x.com | — | 这是真的吗？还是夸张了？（抱歉） [引用 @TheIshanGoswami]：Jev 搭配 Exa 简直离谱。 > Jev 不用联网搜索时会自信地给出错误输出 > Jev 用上联网搜索后准确率真的高很多 免费试用 Jev（搭配 Exa 联网搜索）👇 | Articles, tutorials & talksMore posts & discussions | |
| Jev 模型介绍：放弃自回归、只做结构化决策输出x.com | — | Jev 模型放弃传统自回归架构，无法直接输出普通文本，只能做决策并输出结构化 JSON，输入时需定义 Schema 并编译为决策槽位，因此输出不会出错。它目前仅支持文本输入，二分类场景可输出如 isSpam 为 true、概率 0.982 的 JSON；复杂场景下定义好可用动作后模型即可自主决策，如玩杀戮尖塔或看盘。 | Articles, tutorials & talksMore posts & discussions | |
| Jev 模型征集社区问题反馈x.com | — | 告诉我们 Jev 哪里不好！ Jev 并不完美。我们认为它还是太慢、太贵、太笨。我们还有更多招数让它变得更好。但我们需要社区的帮助--请在 Discord 的 model-jaggedness 频道里发布你看到的问题。 也请看看我们已知的 jaggedness 问题：https://docs.typesafe.ai/model-jaggedness/jev-1.13 | Articles, tutorials & talksMore posts & discussions | |
| Jev 模型每百万输入 token 仅 $0.042x.com | — | 一旦用了 Jev，就再也回不去了 【引用 @notkevinzhang】：四个词，十八个字母，每百万输入 token 仅 $0.042 | Articles, tutorials & talksMore posts & discussions | |
| Jev 正在疯狂工作（视频未加速x.com | — | Listed by the source without a separate description. | Articles, tutorials & talksMore posts & discussions | |
| Jev 玩 Minecraft 反应速度碾压人类x.com | — | 🚀 我们让 Jev 玩 Minecraft。我们就是打不过它！😭 ⚡ Jev：24 毫秒决策 🧠 你：约 200 毫秒反应 它在你看到它动之前就已经动了。太强了！ 🎮 https://mc.alexzms.com（加入服务器来赢） | Articles, tutorials & talksMore posts & discussions | |
| Jev 现已上线x.com | — | ：https://console.typesafe.ai | Articles, tutorials & talksMore posts & discussions | |
| Jev 用 AI 自动化现实世界任务x.com | — | 每一天，Jev 都在自动化新形式的现实世界任务，把🌎级⚡️快速智能带入排序、过滤、分类和路由等基础模块。 如果 AI 能解决新的数学问题，那么 AI 就能正确地路由一通客户支持电话！ | Articles, tutorials & talksMore posts & discussions | |
| Jev 用于 RAG 的语义匹配与重排x.com | — | 继续搞，Jev 的大多数最佳实践还有待发掘！ [引用 @Vtrivedy10]：Jev 用于 RAG 几乎所有情况下，相比点积相似度，你更应信任 Jev 的语义匹配能力 在小数据场景下作为直接相似度指标非常有用 在大数据场景下则是出色的重排器 | Articles, tutorials & talksMore posts & discussions | |
| jev 登顶 OpenRouter 短上下文模型榜x.com | — | jev 是 OpenRouter 上 1k-10k 上下文的最强模型！ | Articles, tutorials & talksMore posts & discussions | |
| JEV 相关推文x.com | — | JEV --- 说明：主推文内容仅为 "JEV" 三个字母，没有更多上下文信息。根据防幻觉规则，无法确定 JEV 具体指代什么（可能是模型名、项目代号或其他），因此标题和正文均保留原文，不做扩写。如需更准确的翻译，请提供更完整的推文内容。 | Articles, tutorials & talksMore posts & discussions | |
| Jev 等分类器模型涌现，开发者好时机x.com | — | 很高兴看到像 Jev 这样的分类器模型越来越多地被构建出来。 做开发者的好时机 🥹🫶 | Articles, tutorials & talksMore posts & discussions | |
| Jev 能否玩 Overcooked？x.com | — | jev 能玩 Overcooked 吗？ | Articles, tutorials & talksMore posts & discussions | |
| JEV 让 LLM 推理实现即时反馈x.com | — | JEV 将通用语义推理引入判别式推理范式，实现任意上下文输入、校准结构化决策输出。在 JEV + Perfectly 的示例中，用复杂查询从 500 位 ECCV 2026 研究者的论文中理解 AI 研究者，性能达到 Claude 某模型同等水平。 | Articles, tutorials & talksMore posts & discussions | |
| Jev 重排序销售数据性能提升x.com | — | 给后排的朋友们： • 用 Jev 对销售数据做生产级重排序 • 快 20 倍 • 便宜 10 倍 • 准确率提升 12% 你们懂了吗？ | Articles, tutorials & talksMore posts & discussions | |
| Jev 零样本检测对齐失效，AUROC 0.886x.com | — | TypeSafe AI 的校准决策模型 Jev 只需一个通用 yes/no 问题，用其概率作为评分，无需额外训练即可区分模型失效与正常回复，中位 AUROC 达 0.886。 | Articles, tutorials & talksMore posts & discussions | |
| Jev+Treg 打造智能体版 Clay 人物搜索x.com | — | Treg 联合 Jev 推出面向 AI 智能体的开源人物搜索工具，可跨 60+ 数据源检索线索，每条线索仅 $0.0089，比 Clay 便宜 85%，并在人物搜索基准上排名第一。Jev 作为决策模型对候选线索按角色、公司等标准打分，返回结构化概率而非生成文本，可插件式接入任意智能体。 | Articles, tutorials & talksMore posts & discussions | |
| jev-codex-routerx.com | — | Per-turn model & reasoning routing for Codex, driven by Jev (TypeSafe System One): picks the model, thinking depth and speed mode for every turn. | Articles, tutorials & talksMore posts & discussions | |
| Jev-LDE 让 LLM 少样本示例一次编辑到位x.com | — | You Only Edit Once： 通过局部示例精修激发 LLM 的上下文能力 挑选最佳少样本示例是一种缓慢的 System-2 搜索：组合爆炸，且往往需要反复调用 LLM。 我们把它变成了 System 1。⚡ Jev-LDE，一个 1.7B 的编辑器，扫一眼检索到的示例，只做一次编辑。LLM 只回答一次。 平均 1-shot 准确率 81.2 → 88.1 You Only Edit Once 🧵 动画演示（示意示例）。查询：“How far is it from Denver to Aspen?” 语义 TopK 检索出三个相似示例：“Where is Aspen, Colorado?”（Location）、“What state is Denver in?”（Location）、“Who founded Denver?”（Person）。Jev-LDE，一个 1.7B 的 System-1 编辑器，标记出第一个虽然主题相同但答案类型错误，并输出一个动作：将 S1 替换为候选 C1，“How far is Boston from NYC?”（Number）。冻结的目标 LLM 随后回答“Number”，这是正确的；若不编辑，它会回答“Location”。结尾卡片：在 3 个基准和 4 个目标 LLM 上，平均 1-shot 准确率从 81.2 提升至 88.1，在 48 个设置中有 44 个达到最佳或并列最佳，墙钟时间增加 11%。 | Articles, tutorials & talksMore posts & discussions | |
| Jev-like API on open weightsx.com | — | Explains how Jev-style speed can come from inference: prefill shared state once, fork the context per question, and read logits constrained to choice labels on any open-weight LLM. | Articles, tutorials & talksMore posts & discussions | |
| Jev-Mem：受 System-One/System-Two 启发的智能体记忆架构x.com | — | Jev-Mem 是一种受 System-One/System-Two 认知启发的新型智能体记忆架构，将记忆构建提速 6.6 倍、查询延迟降低 36.7%。在 LoCoMo 上，它以 LLM 评审 0.777 的总分较最强基线相对提升 11.0%，记忆构建耗时 158 秒，平均查询延迟降至 0.93 秒。 | Articles, tutorials & talksMore posts & discussions | |
| jev-rabbit PR review botx.com | — | Work-in-progress PR reviewer with plain-English Jev rules. | Articles, tutorials & talksMore posts & discussions | |
| JevBench 发布：面向类型化决策的新基准x.com | — | 新基准 JevBench 发布，专为输出受限软件决策而非开放式文本的模型设计，紧随 TypeSafe 9 月 15 日发布 Jev--输入应用状态与固定选项，返回带概率的类型化答案。该基准综合智能、校准、速度与成本，用几何平均防止单一维度优势掩盖短板。GPT-5.6 Luna 在难题准确率上明显高于 Jev 1.13.0，但 Jev 因延迟、校准和成本更优而在综合分上领先。 | Articles, tutorials & talksMore posts & discussions | |
| Jevbot in Minecraftwww.reddit.com | — | Work-in-progress Minecraft bot driven by Jev over the API, shown fleeing a zombie horde as night falls. | Articles, tutorials & talksMore posts & discussions | |
| JevSearch 用 Jev 验证搜索结果x.com | — | 相关性很重要，但相关于什么？Jev 给你任何套路 SEO 都钻不进去的搜索智能 🪱 我构建了 JevSearch，用 Jev 搜索网络并验证你的结果。 给出一个查询和筛选标准，用 @browserbase search 获取 t25 结果，然后 Jev 打分并返回 t5 结果。 Jev 常常会选择初始前 5 之外的 url，认为它们更相关。 | Articles, tutorials & talksMore posts & discussions | |
| Kalshi prediction-market botx.com | — | Jev trades 15-minute and 1-hour BTC, ETH, and SOL markets on Kalshi. | Articles, tutorials & talksMore posts & discussions | |
| LangChain 用 Jev 增强 harness 做 agent 路由x.com | — | LangChain 已利用 Jev 增强其 harness，速度非常快。Jev 适合在既定 harness 中承担分析分类工作，例如 agent 路由、模型路由等"螺丝钉"任务。 | Articles, tutorials & talksMore posts & discussions | |
| Launch thread on Hacker Newsnews.ycombinator.com | — | Nearly 2,000 points and about 500 comments, with the TypeSafe team answering questions about semantics and limits. | Articles, tutorials & talksMore posts & discussions | |
| laya-mlx 移植版：比 Jev 快 50 倍，本地跑贪吃蛇x.com | — | 开发者将开源文本概率分类系统 Laya 移植到 MLX 并做性能优化，推出 laya-mlx，号称比 Jev 快 50 倍，设备内存占用最高 1G。该模型在本地 M3 Max 上以每秒 60 次决策的速度玩贪吃蛇，代码已开源至 GitHub。 | Articles, tutorials & talksMore posts & discussions | |
| LayerX internal Jev study session — demox.com | — | Japanese write-up of a 30-minute internal study session on Jev at LayerX that drew more than 50 engineers and produced more than 50 ideas for building it into their products. | Articles, tutorials & talksMore posts & discussions | |
| LayerX internal Jev study session — xx.com | — | Japanese write-up of a 30-minute internal study session on Jev at LayerX that drew more than 50 engineers and produced more than 50 ideas for building it into their products. | Articles, tutorials & talksMore posts & discussions | |
| LLM vs Jev at prompt difficultyx.com | — | Simplified side-by-side showing how an LLM and Jev classify a prompt's difficulty: token-by-token text versus probabilities for every option computed in parallel. | Articles, tutorials & talksMore posts & discussions | |
| LLM 蒸馏 Jev 之类x.com | — | Listed by the source without a separate description. | Articles, tutorials & talksMore posts & discussions | |
| LLMs generate, Jev decidesx.com | — | Explainer using a risky-account example: instead of prompting an LLM for a verdict, you declare risk levels and a manual-review flag up front and get back probabilities such as risk = high (96%). | Articles, tutorials & talksMore posts & discussions | |
| LLMs vs. Jev, clearly explainedx.com | — | Explains that Jev does not generate faster, it does not generate at all: independent Choice, Score and Noul questions, like urgency, owning team and command risk for a failed deploy, are evaluated in parallel. | Articles, tutorials & talksMore posts & discussions | |
| Making Jev speakx.com | — | Chat experiment that coaxes Jev into replying in words, producing short, garbled but amusing conversations. | Articles, tutorials & talksMore posts & discussions | |
| markjaquith 最短"什么是 Jev"解释x.com | — | 好吧这个挺不错的 xD 【引用 @markjaquith】：这是我最新最短的"什么是 Jev"解释 | Articles, tutorials & talksMore posts & discussions | |
| Metaview 全线接入 typesafe 的 jev，搜索提速约 10 倍x.com | — | MetaviewAI 上周末将 typesafeai 的 jev 接入其所有 agent，候选人搜索从数分钟缩短到数秒，准确率不变、约快 10 倍，且每次搜索成本明显更低。jev 让团队把智能当作软件来构建：将每个 agent 拆成最小语义单元、逐个查询、自设阈值，并通过新增问题而非修改系统提示词来修 bug。 | Articles, tutorials & talksMore posts & discussions | |
| Model router CLIx.com | — | Task plus subscription list in, Jev picks which model or agent should handle it. | Articles, tutorials & talksMore posts & discussions | |
| OpenClaw 核心支持决策模型x.com | — | 一切都在往 Jev 的方向发展！ 上周 @jlehman_ 为 OpenClaw 核心和插件推送了决策模型支持 了解我们如何思考使用它们，以及你如何用这个强大的新工具让 OpenClaw 变得更好！ https://openclaw.ai/blog/decision-models-in-openclaw | Articles, tutorials & talksMore posts & discussions | |
| OpenRouter 推出 Jev Router，为每次 LLM 调用自动选择模型和推理力度x.com | — | Jev 现以 typesafe/jev-router 形式打包上线，OpenRouter 会为每个请求自动选择模型和推理力度。作者用 Pi SDK 构建的支持智能体测试，同一 8 个案例对比固定 GPT-6 Sol 基线共 32 次真实调用，两者全部答对，路由成本低一半以上（$0.008 vs $0.018），中位响应时间也更短（1.5s vs 1.9s）。 | Articles, tutorials & talksMore posts & discussions | |
| OpenRouter 推出 Jev 缓存感知模型路由器x.com | — | 介绍 typesafe/jev-router：一个由 Jev 和 @typesafeai 驱动的缓存感知模型路由器 Jev Router 为每个请求挑选最佳模型和推理力度，在质量、速度和成本之间取得平衡。 工作原理如下 👇🏻 | Articles, tutorials & talksMore posts & discussions | |
| OpenRouter 社区 System One 用例评选x.com | — | 1/ 上周，Jev - @typesafeai 的 System One 模型在 OpenRouter 上线。关注度极高。 我们邀请社区寻找最具创新性的方式，将快速且低成本的决策应用到他们的项目中。 当然，我们得让 Jev 来选出 5 位获奖者。 | Articles, tutorials & talksMore posts & discussions | |
| OpenRouter：Jev 上线带动新用户x.com | — | 问：哪些模型受 Jev 到来的影响最大？ 答：许多实验室的 flash 版本模型。 另外：OpenRouter 上近一半的 Jev 用户在前一周还没用过任何模型。这次发布激起了足够的兴趣，把他们从场边拉了进来。 | Articles, tutorials & talksMore posts & discussions | |
| PostHog 玩梗 jev 引共鸣x.com | — | posthog 拿 jev 玩梗，感觉就像当年《南方公园》里出现 ChatGPT 那会儿 🥹 | Articles, tutorials & talksMore posts & discussions | |
| Project Jev 一周省下 50 万x.com | — | 省下五十万，token 用量翻 100 倍，小意思 💅 | Articles, tutorials & talksMore posts & discussions | |
| Rick and Morty 讲透 Jev AIx.com | — | 天哪——这该不会本该是我们的发布视频吧？🥹 [引用 @princedoesai]：天哪。 Rick and Morty 给我讲 Jev AI，比任何技术演示都讲得清楚。 | Articles, tutorials & talksMore posts & discussions | |
| Skeptical notes after a day with Jevx.com | — | Chinese notes from a day of testing: Jev reads as a faster general classifier that suits bounded, low-latency choices like DOM actions or compaction, but cannot replace parameterized agent tool calls. | Articles, tutorials & talksMore posts & discussions | |
| skillbox + Jev skill routingx.com | — | MCP skill router where Jev picks the relevant skills instead of a long agent search. | Articles, tutorials & talksMore posts & discussions | |
| Spanish AEPD corpus testx.com | — | Jev versus a hand-built regex on 544 public data-protection resolutions: 98.2% agreement for about five cents. | Articles, tutorials & talksMore posts & discussions | |
| Stagehand + Jev browser agentx.com | — | Browser agent on a remote browser where Jev picks each next action from the page's accessibility tree and Stagehand executes it; the demo task cost $0.001. | Articles, tutorials & talksMore posts & discussions | |
| StarCraft Brood War WASM MCP demox.com | — | Brood War in WASM exposed as an MCP server, with Jev playing and still losing to a Zerg rush. | Articles, tutorials & talksMore posts & discussions | |
| State-tone sensitivity testx.com | — | Shows Jev's answers depend heavily on the tone of the state: the same question about Python type annotations dropped from 0.97 to 0.07 confidence with a team-dislikes-them state. | Articles, tutorials & talksMore posts & discussions | |
| Support ticket classifierx.com | — | Jev labels category, urgency, and human-versus-auto handling for support tickets. | Articles, tutorials & talksMore posts & discussions | |
| System One 模型推动自定义 Agent Harness 新浪潮x.com | — | DAIR.AI 的 Elvis Saravia 指出，System One 模型正将自定义 agent harness 推向新高度，Jev 之后又出现 Contrastive Language Model（CLM），CLM 比 Jev 快 9 倍，在长周期任务上验证表现更优。 | Articles, tutorials & talksMore posts & discussions | |
| Tabletop MMORPG action mapperx.com | — | Eval of Jev turning free-text player intent into typed server actions: 96% agreement, 317 ms median. | Articles, tutorials & talksMore posts & discussions | |
| Things to try with Jevx.com | — | Practitioner list of promising Jev uses: LLM-as-a-judge evals, routing in agent harnesses, subagent creation, and dynamic harness generation, with the author using it as a router for a meta harness. | Articles, tutorials & talksMore posts & discussions | |
| Two techniques for working with System One models — discussionnews.ycombinator.com | — | Layered goals, where a slow loop picks the goal and a fast loop picks actions, plus tournament sampling over batches of options, shown on a Doom agent built with an open-model imitation. | Articles, tutorials & talksMore posts & discussions | |
| TypeSafe AI 谈 Jev 模型理念x.com | — | 我们的座右铭背后有很多含义：Building Prod, Not God。 这项技术将改变世界，但这要靠勤勉的努力和创造力来实现，而不是靠故弄玄虚的诉求。 @a16z 与 @CompleteSkeptic 深入探讨了这一理念以及更多内容。 | Articles, tutorials & talksMore posts & discussions | |
| TypeSafe Jev API tutorialx.com | — | YouTube getting-started tutorial on integrating Jev and System One models into applications through the TypeSafe API. | Articles, tutorials & talksMore posts & discussions | |
| TypeSafe Jev 接入 MotherDuck，文本分类快 50 倍x.com | — | TypeSafe 新模型 Jev 以 SQL 函数 prompt_jev() 接入 MotherDuck，文本分类速度提升约 50 倍、成本降至约 1%。10 万行数据仅需 40 秒、花费 $0.50，达到前沿 LLM 准确率，而 LLM 方案耗时 32 分钟、花费 $37。 | Articles, tutorials & talksMore posts & discussions | |
| TypeSafe Jevelopers Discord 首周x.com | — | Discord 里正在发生严肃的 Jevelopments，在 @allietheicon 的注视下 👀 [引用 @allietheicon]：TypeSafe Jevelopers Discord 的第一周真是疯狂 | Articles, tutorials & talksMore posts & discussions | |
| Use cases for Jev?www.reddit.com | — | Thread in r/opencode collecting practical uses, including routing in LiteLLM, cheap Home Assistant device control, and tag categorization, plus skepticism about simple intent cases. | Articles, tutorials & talksMore posts & discussions | |
| Where Jev pays off in productionx.com | — | Chinese field notes: swapping Gemini Flash or GPT Luna judgments for Jev cut cost 20 to 60x and latency by an order of magnitude, with a pattern of escalating answers below 80% confidence to a small generative model. | Articles, tutorials & talksMore posts & discussions | |
| Wiki-link clicker demox.com | — | Page-level demo where Jev picks which candidate link to click toward a goal. | Articles, tutorials & talksMore posts & discussions | |
| WTF is Jevx.com | — | Explainer article framing Jev as multiple-choice rather than essay-writing AI, and cataloguing nine patterns developers built with it in the first 72 hours. | Articles, tutorials & talksMore posts & discussions | |
| WTF is Jev — articlex.com | — | Explainer article framing Jev as multiple-choice rather than essay-writing AI, and cataloguing nine patterns developers built with it in the first 72 hours. | Articles, tutorials & talksMore posts & discussions | |
| WTF is Jev, ELI5x.com | — | X: frames Jev as "AI multiple choice, not AI essay writing", one of the clearer plain-language explanations of the System One shape. | Articles, tutorials & talksMore posts & discussions | |
| WTF Is Jev?x.com | — | Plain-language explainer of Jev as multiple choice rather than essay writing, followed by nine things people are already building with it, checked against the live posts. | Articles, tutorials & talksMore posts & discussions | |
| You could have built Jev — discussionnews.ycombinator.com | — | Short illustrated explainer arguing Jev is most likely an LLM that returns a single token, with pseudocode and comparisons to other projects that do the same, so you can reason about it from first principles. | Articles, tutorials & talksMore posts & discussions | |
| You could have built Jev — xx.com | — | Short illustrated explainer arguing Jev is most likely an LLM that returns a single token, with pseudocode and comparisons to other projects that do the same, so you can reason about it from first principles. | Articles, tutorials & talksMore posts & discussions | |
| 在人类璀璨的艺术中遨游，如此美妙，谢谢 Jevx.com | — | [引用 @ZHO_ZHO_ZHO]：Jev 正在疯狂工作（视频未加速 | Articles, tutorials & talksMore posts & discussions | |
| 对 jev 命名感到厌倦x.com | — | 还有人厌倦了 jev 这件事吗？我们的品牌是桀骜不驯、疯狂的，不是"jev"（可能是因为我正处于风暴中心） 它有趣是因为给模型起名 jev 本身就很疯狂 （另外说这个可能也很疯狂，但我们还没有营销人员） | Articles, tutorials & talksMore posts & discussions | |
| 小米 MiMo-V3 的 HySparse2 等本周 AI 论文x.com | — | 小米 MiMo 团队为即将推出的 MiMo-V3 打造了注意力架构 HySparse2，在 80B-A3B MoE 模型上把 prefill FLOPs 较 MiMo-V2 系列的 Hybrid SWA 降低 5.02x、较 HySparse 降低 2.92x，KV cache 从 12.09 GB 降至 2.69 GB。 | Articles, tutorials & talksMore posts & discussions | |
| 快速通道申请通过了，可以玩起来了 😁x.com | — | 【引用 @hongming731】：最近几天 Jev 模型很火，BestBlogs 整理了一期专题，可以组队一起学习一下 😆 https://www.bestblogs.dev/explore/topics/typesafe-jev-release | Articles, tutorials & talksMore posts & discussions | |
| 把 Jev 用作 Agent 编辑后的模糊 linter：规则筛选与置信度分层实验x.com | — | Michael Thiessen 实验将 Jev 作为 Agent harness 中编辑后运行的模糊 linter：把编码指南拆成无需额外上下文和推理的微小规则，并构建合成 eval 加 held out 集防止过拟合。 | Articles, tutorials & talksMore posts & discussions | |
| 新模型能力再进化x.com | — | 它正在彻底改变新模型能做到的事 | Articles, tutorials & talksMore posts & discussions | |
| 用 Jev 和 Pi 构建自定义 harness 的思路x.com | — | 刚刚发布了一些关于使用 Jev 和 Pi 构建自定义 harness 的思路。 这是系列的第一篇。 其中一些思路包括 gates、routing 和 verifiers。 但在后续文章中，我计划更深入地探讨更新的思路，并对成本和效率进行基准测试。 | Articles, tutorials & talksMore posts & discussions | |
| 用 Jev 整理 2.3K 篇 AI 论文，成本仅 0.14 美元x.com | — | 用 Jev 重新整理约 2.3K 篇 AI 研究论文，总成本 $0.14、耗时约 83 秒。Jev 与旧标签（由 DeepSeek V4 Flash 生成）一致率 75%，并找出约 579 处高置信度主题变更；人工抽检 30 处分歧后全部采纳，变更已在生产环境验证。作者认为通过组合 System One 与 System Two 模型可显著改进流水线。 | Articles, tutorials & talksMore posts & discussions | |
| 用豆包工作研究 Jev：从模型判断到软件决策x.com | — | 作者用豆包工作的计划模式、目标模式和任务队列完成对 Jev 的研究，产出五主题知识库、24 项声明核查记录和可点回证据的研究观察站。Jev 由 TypeSafe 发布，厂商报告 193.6 倍速度和 444.6 倍成本优势；独立早期测试中 24 份挪威语文档中位延迟 0.32 秒，加入限定词后 ECE 从 0.040 升至 0.116。 | Articles, tutorials & talksMore posts & discussions | |
| 网友玩梗：人人都在喊jev jev jevx.com | — | 所有人：jev jev jev 我奶奶： | Articles, tutorials & talksMore posts & discussions | |
| 豆包工作 /plan 与 /goal 功能获好评x.com | — | 用户用豆包工作的 /plan、/goal 及任务队列功能完成 40 份关于 Jev 的研究文件，并整理成五主题知识库和研究观察站。该用户称这些功能非常好用，并为字节团队点赞。 | Articles, tutorials & talksMore posts & discussions |
More videos & channels87 of 87 matches
| Resource | Stars | Description | Category | Save |
|---|---|---|---|---|
| Biggest breakthrough since ChatGPT?www.youtube.com | — | TheAIGRID overview of the Jev launch, what a System One model is, and why it could matter. | Articles, tutorials & talksMore videos & channels | |
| Building a harness with Jevwww.youtube.com | — | LangChain explainer on where Jev fits in the agent loop, a head-to-head with an LLM, using Jev inside LangChain, and use cases like model routing, risky tool-call gating, and evals. | Articles, tutorials & talksMore videos & channels | |
| Businesses Jev unlocks — videowww.youtube.com | — | Plain-language explanation of Jev as a sorter (1,700 emails for 18 cents) plus startup ideas built on putting it at the front of expensive queues, such as instant quotes and lead scoring. | Articles, tutorials & talksMore videos & channels | |
| Cómo usar Jev (video) — videowww.youtube.com | — | Spanish-language video tutorial explaining what Jev is and showing how to use it for structured probabilistic decisions instead of text generation. | Articles, tutorials & talksMore videos & channels | |
| Does Jev actually beat LLMs? 4 arenaswww.youtube.com | — | Head-to-head tests of Jev against Claude, GPT and DeepSeek models on inbox classification, 857 support tickets, a year-long Bitcoin backtest and Flappy Bird, for a total Jev bill of $0.49. | Articles, tutorials & talksMore videos & channels | |
| Fake Jev demos are taking over the internetwww.youtube.com | — | Builder.io's Steve calls out misleading viral Jev demos and shows what the model is actually good for. | Articles, tutorials & talksMore videos & channels | |
| How to actually use Jevwww.youtube.com | — | Cole Medin tests Jev as a gate in AI coding setups, including playing his own game, PR reviews, and model routing, and asks whether it is more than a glorified classifier. | Articles, tutorials & talksMore videos & channels | |
| I Paired Jev With Astrawww.youtube.com | — | Explains how Jev works and what people are building, then runs a benchmark where Astra plus Jev passed 24/24 strict checks versus 22/24 for Astra alone. | Articles, tutorials & talksMore videos & channels | |
| I tested Jev AI: is it faster than Gemini?www.youtube.com | — | Explainer tracing one Jev request end to end, plus a 40-case synthetic routing test where Jev and Gemini 3.5 Flash-Lite both matched 38/40 and Jev ran about 3x faster. | Articles, tutorials & talksMore videos & channels | |
| I Tested Jev on 12 Real Use Caseswww.youtube.com | — | Tests Jev on 12 use cases, compares its speed and cost with regular models, and builds an X feed classifier and a real-time paper-trading prototype live. | Articles, tutorials & talksMore videos & channels | |
| I Tested Jev: Here's What You Can Buildwww.youtube.com | — | Explains how Jev works and remixes community demos: tweet scoring, ad research, Chrome extensions, Diffusion Studio, and a voice-controlled Subway Surfers. | Articles, tutorials & talksMore videos & channels | |
| I tested the new JEV Modelwww.youtube.com | — | Tests Jev and explains why System One models matter for people building AI products. | Articles, tutorials & talksMore videos & channels | |
| I think Jev changes how we use AIwww.youtube.com | — | Web Dev Cody on why a fast, cheap decision model changes how developers build with AI. | Articles, tutorials & talksMore videos & channels | |
| I tried Jev and it beat my game hands-freewww.youtube.com | — | Korean-language walkthrough of Noul, Score and Choice questions in the Playground, an rm -rf safety check, the agent skill in Claude Code, and Jev playing the author's game from screenshots. | Articles, tutorials & talksMore videos & channels | |
| I tried TypeSafe's System One Model: Jevwww.youtube.com | — | Hands-on trial of Jev for routing, filtering, and pre-processing before an LLM call, with a companion transcript-analyzer demo. | Articles, tutorials & talksMore videos & channels | |
| Is Jev What AI Has Been Missing? I Tested It.www.youtube.com | — | Early-access look at the Playground and a test alongside Codex, plus background on RLCD, calibration research, and Vercel and OpenCode examples. | Articles, tutorials & talksMore videos & channels | |
| Is the Jev hype overblown?www.youtube.com | — | Syntax podcast segment asking whether Jev is a real leap for classification or hype and fake demos. | Articles, tutorials & talksMore videos & channels | |
| It really is. No joke.www.youtube.com | — | Maximilian Schwarzmüller on why Jev beats LLMs in the areas it was built for and what that means for developers. | Articles, tutorials & talksMore videos & channels | |
| It's impossible for Jev to be goodwww.youtube.com | — | Hands-on review testing whether Jev can recommend the right coding model and reasoning effort from a task description, while looking at community builds and questioning classification quality. | Articles, tutorials & talksMore videos & channels | |
| Jev (Fully Tested) + Browser Usewww.youtube.com | — | Tests Jev on support routing, refund detection, prompt-injection resistance, exact-value selection, and agent auditing, then tries the Jev Ultrafast browser demo. | Articles, tutorials & talksMore videos & channels | |
| Jev + Claude Code = The Cheapest Agentic Coding Loop Yetwww.youtube.com | — | Puts Jev inside a Claude Code agentic loop using the official skill, the skill-suggestion cookbook, and the jev-review workflow, and looks at what it costs to run. | Articles, tutorials & talksMore videos & channels | |
| Jev - General Classification Model First Testwww.youtube.com | — | First hands-on test of Jev as a general classifier whose labels are chosen at inference time, framed against fine-tuned BERT-style models. | Articles, tutorials & talksMore videos & channels | |
| Jev - The Ultimate Classification Model?www.youtube.com | — | Covers the System 1 idea, then demos Choice, Score, and Noul, a practical classification example, and chained actions. | Articles, tutorials & talksMore videos & channels | |
| Jev 200배 빠른지 테스트www.youtube.com | — | Korean hands-on test of the speed and cost claims: 100 customer inquiries through three pipelines, a comment feed, ad-inquiry emails, and an A/B test of Jev classifying while an LLM writes. | Articles, tutorials & talksMore videos & channels | |
| JEV : la hype est-elle justifiée ?www.youtube.com | — | French-language test of TypeSafe's speed and cost claims for Jev on closed questions a program needs answered, walking through the docs and routing patterns. | Articles, tutorials & talksMore videos & channels | |
| Jev acts in real time, Minecraft broke itwww.youtube.com | — | Review comparing how Jev does in simple action spaces, computer use and robotics demos, then testing it in an open-ended Minecraft setup built with Astra, where it falls short. | Articles, tutorials & talksMore videos & channels | |
| Jev AI Full Course (1 Hour)www.youtube.com | — | Hour-long course on the three question types, batching many decisions into one request, and ten use cases from email sorting and lead scoring to a flight-searching browser agent. | Articles, tutorials & talksMore videos & channels | |
| Jev AI getestetwww.youtube.com | — | German review that wires Jev into the creator's voice assistant, testing model routing between Gemini and Astra, real-time chat moderation, and pre-filtering before an agent. | Articles, tutorials & talksMore videos & channels | |
| JEV AI is the NEW BEAST Model with Speed and Costwww.youtube.com | — | Explainer plus a live head-to-head of Jev against GPT, DeepSeek, and other models on customer-support tasks. | Articles, tutorials & talksMore videos & channels | |
| Jev AI Just Droppedwww.youtube.com | — | Runs Jev against GPT-6 Astra on five tasks, from a 20-email spam filter to AI-slop detection; the email test took 3.41 seconds for Jev versus 9.93 seconds for Astra. | Articles, tutorials & talksMore videos & channels | |
| Jev AI model (Tamil)www.youtube.com | — | Tamil walkthrough that tests Jev through OpenRouter and compares it live against frontier models on cost and speed. | Articles, tutorials & talksMore videos & channels | |
| JEV Breakdown: The First AI Model Built For Codewww.youtube.com | — | Breakdown of Jev with a live Playground walkthrough of Choice, Score, Noul, and confidence, and where a decision model fits in real apps. | Articles, tutorials & talksMore videos & channels | |
| Jev by TypeSafe AI: Explained With Live Demoswww.youtube.com | — | Runs Jev live through OpenRouter and walks through what separates a System One model from a token-by-token LLM. | Articles, tutorials & talksMore videos & channels | |
| Jev explainedwww.youtube.com | — | Explainer on structured decisions, the Doom and Super Mario demos, parallel questions, calibration, and the gap between valid outputs and correct decisions. | Articles, tutorials & talksMore videos & channels | |
| Jev Explained for Python Developerswww.youtube.com | — | Python walkthrough from a first support-ticket classification to Choice, Score, and Noul, several questions per call, and latency and price next to Claude models. | Articles, tutorials & talksMore videos & channels | |
| Jev explained in 7 minuteswww.youtube.com | — | Seven-minute explainer on RLCD and whether a model that answers with probability distributions instead of text can unlock new use cases. | Articles, tutorials & talksMore videos & channels | |
| Jev explained in 7 minuteswww.youtube.com | — | Chinese-language explainer on Jev as a fast-thinking decision model, covering its speed and cost claims and RLCD, the calibrated-decision training it uses instead of RLHF-style post-training. | Articles, tutorials & talksMore videos & channels | |
| JEV explained simply (+5 demos)www.youtube.com | — | French-language tests of Jev on 500 emails, a game of checkers, spotting data leaks in a 27-minute video, qualifying 100 inbound leads and sorting scam SMS, plus a comparison with GPT models. | Articles, tutorials & talksMore videos & channels | |
| Jev explained: demos and use caseswww.youtube.com | — | Demos including a model router and a chatbot with no LLM behind it, with companion code. | Articles, tutorials & talksMore videos & channels | |
| JEV expliqué et testé en 9 minuteswww.youtube.com | — | French-language no-hype explainer and test of Jev sorting text into named labels with a probability for each. | Articles, tutorials & talksMore videos & channels | |
| Jev for marketing workwww.youtube.com | — | Eric Siu shows five marketing uses he is testing: shortlisting content formats, sorting SEO and AEO ideas, qualifying leads, finding clips, and reading business data. | Articles, tutorials & talksMore videos & channels | |
| Jev in under 10 minuteswww.youtube.com | — | Explainer on parallel constrained decoding, where each schema field becomes a classification over fixed choices scored in parallel, plus when not to use Jev and accessibility-tree computer use. | Articles, tutorials & talksMore videos & channels | |
| Jev is 193x faster than LLMs?www.youtube.com | — | Breakdown of TypeSafe's launch numbers, including 193.6x faster and 444.6x cheaper workflow evals and a 0% type-error chart, plus the Doom and Wikiracing demos and real limits. | Articles, tutorials & talksMore videos & channels | |
| Jev is god mode for AIwww.youtube.com | — | Walkthrough from a marketing agency of adding Jev as a decision layer to existing agents for content checks, recruiting, software renewals, creative briefs and video workflows. | Articles, tutorials & talksMore videos & channels | |
| Jev is not an LLMwww.youtube.com | — | Firecrawl explainer on how Jev works, with a demo where Jev and Firecrawl find a book on a site that has no search bar. | Articles, tutorials & talksMore videos & channels | |
| Jev is the decision layerwww.youtube.com | — | Educational overview of why chat LLMs are a poor fit for classify, route, and score jobs, how Jev differs from GPT and Claude, and what others are already building with it. | Articles, tutorials & talksMore videos & channels | |
| Jev just changed software foreverwww.youtube.com | — | Breakdown of what Jev is good at, a set of standout use cases, and a process for finding real Jev use cases inside your own product. | Articles, tutorials & talksMore videos & channels | |
| Jev plus a mini reproductionwww.youtube.com | — | Explainer that also builds a small local Mini Jev, trains it on a custom game, compares it with the real model, and runs Wikipedia racing with Jev. | Articles, tutorials & talksMore videos & channels | |
| Jev use caseswww.youtube.com | — | Mehul Mohan tours early Jev builds such as instant context compaction, Super Mario, if-else replacement, self-driving, and code review. | Articles, tutorials & talksMore videos & channels | |
| Jev vs Layawww.youtube.com | — | Short explainer on what the Jev and Laya decision engines are and the priority controversy after Laya's author said he built the approach a year earlier. | Articles, tutorials & talksMore videos & channels | |
| Jev vs LLMs: why it doesn't need to talkwww.youtube.com | — | Four-minute explainer on how Jev differs from autoregressive LLMs, why token-by-token generation is slow, and what RLCD means. | Articles, tutorials & talksMore videos & channels | |
| Jev от TypeSafewww.youtube.com | — | Russian hands-on test of tool selection, source-based claim checking, and refund-intent detection in the Playground, including a model error, plus a Python example and the Claude Code skill. | Articles, tutorials & talksMore videos & channels | |
| Jev 实测：极速决策模型的基本玩法www.youtube.com | — | Chinese-language hands-on test of Jev's basic usage, from the Playground to typed questions, with background on the launch and Vercel AI Gateway access. | Articles, tutorials & talksMore videos & channels | |
| Jev 모델 공개www.youtube.com | — | Korean introduction to Jev and System One models, walking through the Doom real-time demo and the Wiki Race comparison. | Articles, tutorials & talksMore videos & channels | |
| Jev, el lanzamiento más importante desde ChatGPTwww.youtube.com | — | Spanish explainer on why Jev is not a generative LLM, how it can be up to 200x faster and 400x cheaper, and which tasks it handles better than LLMs. | Articles, tutorials & talksMore videos & channels | |
| Jev, the AI that doesn't talkwww.youtube.com | — | Chinese-language explainer on why Jev skips text generation for classification-style decisions and how its speed and cost profile fits into enterprise agent architectures. | Articles, tutorials & talksMore videos & channels | |
| JEV, the LLM that can't write textwww.youtube.com | — | Japanese-language explainer voiced by VOICEVOX characters, covering Jev's architecture, parallel sampling, type-safe outputs, RLCD, performance and cost, and where it fits. | Articles, tutorials & talksMore videos & channels | |
| Jev, Yang & Recursive Self Improvementwww.youtube.com | — | AI news episode from sentdex that discusses the Jev launch alongside other stories of the week. | Articles, tutorials & talksMore videos & channels | |
| Jev: 30 000 testswww.youtube.com | — | French review that runs 30,000 tests on Jev, covering real pricing, where to plug it into agents, three limits, and a crash test against GPT 5.6. | Articles, tutorials & talksMore videos & channels | |
| Jev: non-autoregressive System-1 modelwww.youtube.com | — | Explainer on what Jev solves, what makes it unique, how it handles hallucinations, and a short demo with first impressions. | Articles, tutorials & talksMore videos & channels | |
| Jev総合解説www.youtube.com | — | Ten-minute Japanese overview of why Jev does not write text, how to read its benchmarks, three practical uses, pricing, and getting started with the Playground, Python SDK, and API. | Articles, tutorials & talksMore videos & channels | |
| JEV는 대체 뭐가 다른가www.youtube.com | — | Short Korean explainer on how Jev works differently from familiar LLMs and why developers are paying attention. | Articles, tutorials & talksMore videos & channels | |
| LLM devri bitiyor mu?www.youtube.com | — | Turkish explainer on how Jev differs from a normal LLM and TypeSafe's System One approach, covering the Doom and Wikipedia demos and a Google Flights search in about 7 seconds for $0.0039. | Articles, tutorials & talksMore videos & channels | |
| Ne ulan bu Jev?www.youtube.com | — | Turkish explainer comparing Jev with GPT, Claude, and classic LLMs, and where it fits in routing, tool selection, risk analysis, and approval workflows. | Articles, tutorials & talksMore videos & channels | |
| Open Jev Models Are Here!!www.youtube.com | — | Reviews seven open Jev-style models and servers, including SemIf, Laya, NanoJev, Decider, and a DiffusionGemma-based OpenJev, to see how well they work. | Articles, tutorials & talksMore videos & channels | |
| Por que o Jev é uma notícia melhor do que parecewww.youtube.com | — | Portuguese deep dive into the TypeSafe manifesto and docs, the Choice, Score, and Noul primitives, hands-on examples, and cost and latency comparisons with LLMs. | Articles, tutorials & talksMore videos & channels | |
| Qué es Jev AIwww.youtube.com | — | Spanish walkthrough that tests Jev on urgency detection and threat classification with Avengers-themed examples, compares speed and cost, and has it play Doom. | Articles, tutorials & talksMore videos & channels | |
| Qué es Jev AIwww.youtube.com | — | Spanish explainer on why a model that cannot write is useful, covering the launch claims of the same decisions as ChatGPT or Claude at 200x the speed and 400x lower cost. | Articles, tutorials & talksMore videos & channels | |
| Steerable Reranking: How JEV Solves RAGwww.youtube.com | — | Uses Jev as a steerable reranker in a RAG pipeline, explaining where cosine similarity fails and comparing it with LLM rerankers and cross-encoders. | Articles, tutorials & talksMore videos & channels | |
| Stop using ChatGPT for everything: Jev is herewww.youtube.com | — | Overview of how Jev scores options, what it costs and cannot do, notable community projects, browser use, and access through Vercel AI Gateway and the TypeSafe agent skill. | Articles, tutorials & talksMore videos & channels | |
| System 1 models: Jev explainedwww.youtube.com | — | Explainer framing Jev as a System 1 model: why single-pass inference suits yes/no production calls better than token-by-token generation, with speed, cost and calibration caveats. | Articles, tutorials & talksMore videos & channels | |
| System One模型Jevwww.youtube.com | — | Chinese explainer on Jev's RLCD training and parallel sampling, the 193.6x speed and 444.6x cost figures, and community doubts about zero hallucination and calibration evidence. | Articles, tutorials & talksMore videos & channels | |
| Testing Jev for agents and browser controlwww.youtube.com | — | Cantonese-language hands-on comparing Claude and Jev driving a browser shopping flow, moving an n8n spam-sorting workflow to Jev, and probing limits such as no image input. | Articles, tutorials & talksMore videos & channels | |
| The first typesafe model? Let's talk about Jevwww.youtube.com | — | Long livestream where Theo reads through the Jev launch material, contrasts its guaranteed output shapes with tools like BAML, and tries live experiments such as classifying his chat history. | Articles, tutorials & talksMore videos & channels | |
| Trying Jev with a lighting-control demo — videowww.youtube.com | — | Japanese hands-on intro that reviews the benchmarks critically and builds a floor-plan demo where natural-language commands dim room lights, showing how the confidence threshold decides between acting and asking. | Articles, tutorials & talksMore videos & channels | |
| TypeSafe Jev API tutorial — videowww.youtube.com | — | YouTube getting-started tutorial on integrating Jev and System One models into applications through the TypeSafe API. | Articles, tutorials & talksMore videos & channels | |
| TypeSafe Jev community cases and demoswww.youtube.com | — | Chinese walkthrough of Jev pricing, access, and community use cases, plus two hands-on demos: a rabbit adventure game and an alien border-entry judge. | Articles, tutorials & talksMore videos & channels | |
| Was Jev stolen from open source work?www.youtube.com | — | Podcast segment on the priority dispute between Jev and the open Laya model, Laya's speed claims, and where fast decision models fit in business. | Articles, tutorials & talksMore videos & channels | |
| What is Jev? (Tamil)www.youtube.com | — | Tamil explainer comparing Jev with ChatGPT and Gemini, covering calibrated confidence, structured outputs, and the Choice, Score, and Noul primitives. | Articles, tutorials & talksMore videos & channels | |
| What Is Jev? (Tech Brew Ride Home)www.youtube.com | — | Three-minute podcast news segment on TypeSafe's launch, its $40 million raise, and the nod to Jevons paradox in the name. | Articles, tutorials & talksMore videos & channels | |
| What's Jev? Here's how I'm using itwww.youtube.com | — | Walkthrough of three weekend builds with Jev: a YouTube comment classifier that mines video ideas, a fantasy football research sorter, and a Gmail inbox auto-labeler. | Articles, tutorials & talksMore videos & channels | |
| Why Jev changes the ruleswww.youtube.com | — | Japanese-language explainer on what makes Jev different, drawing on community examples such as ad blocking, voice control and game-playing agents. | Articles, tutorials & talksMore videos & channels | |
| Why Jev is going viralwww.youtube.com | — | Hindi-language breakdown of what Jev is, how System One models differ from LLMs, why decisions beat strings, and where it fits in agent workflows. | Articles, tutorials & talksMore videos & channels | |
| Will Jev replace LLMs?www.youtube.com | — | Krish Naik explains what TypeSafe's Jev is, how its typed decisions differ from LLM generation, and where it fits alongside LLMs. | Articles, tutorials & talksMore videos & channels | |
| إيه حكاية Jev؟www.youtube.com | — | Arabic explainer on how Jev differs from ChatGPT, RLCD training, speed, and pricing, with the creator's own Wikipedia-navigating agent and a customer-review classifier. | Articles, tutorials & talksMore videos & channels | |
| 判定専用AI『Jev』徹底解説www.youtube.com | — | Japanese deep dive after two days of use: provider support, the four factors that decide judgment quality, a YouTube-growth prediction test, and how to split work between code, Jev, and LLMs. | Articles, tutorials & talksMore videos & channels | |
| 챗GPT보다 200배 빠른 AI의 등장, JEV 완전 분석www.youtube.com | — | Korean-language roundup of early Jev demos, from self-driving and chess to sorting 1,000 emails and game NPCs, ending with a comparison against LLMs. | Articles, tutorials & talksMore videos & channels |
More guides & websites175 of 175 matches
| Resource | Stars | Description | Category | Save |
|---|---|---|---|---|
| 1.395元复现Jev原型：百度千帆Token Plan最佳实践aihot.news | — | 百度千帆Token Plan个人版以313.9积分（折合1.395元）支持Opencode（基座模型DeepSeek-V4-Flash）在llama.cpp中复现TypeSafe AI的Jev机制，用约400行C++把自回归LLM改造为单次前向传播的结构化决策引擎。 | Articles, tutorials & talksMore guides & websites | |
| 1.395元复现Jev原型：百度千帆Token Plan最佳实践mp.weixin.qq.com | — | 百度千帆Token Plan个人版以313.9积分（折合1.395元）支持Opencode（基座模型DeepSeek-V4-Flash）在llama.cpp中复现TypeSafe AI的Jev机制，用约400行C++把自回归LLM改造为单次前向传播的结构化决策引擎。 | Articles, tutorials & talksMore guides & websites | |
| 30 Ways to Use Jevuditgoenka.medium.com | — | Catalog of 30 concrete use cases for developers and non-coders, all framed as one input, question, answer, action shape: triage, routing, moderation, scoring and agent control. | Articles, tutorials & talksMore guides & websites | |
| 6465624.htmlwww.woshipm.com | — | 人人都是产品经理 / SenseAI | https://www.woshipm.com/share/6465624.html | 2026-09-17 · 深思 SenseAI | Popular explainer: RLHF critique, Jevons naming, Every 777/0.7s, zero-hallucination misreading, official self-disclosed limits | Articles, tutorials & talksMore guides & websites | |
| AI 实验室调侃机器之神与婴儿aihot.news | — | 大型 AI 实验室：机器之神随时降临，别把孩子带到那个世界去！ TypeSafe： [引用 @craigweiss]：给我儿子取名 Jev | Articles, tutorials & talksMore guides & websites | |
| BestBlogs 整理 Jev 模型专题aihot.news | — | 最近几天 Jev 模型很火，BestBlogs 整理了一期专题，可以组队一起学习一下 😆 https://www.bestblogs.dev/explore/topics/typesafe-jev-release | Articles, tutorials & talksMore guides & websites | |
| BestBlogs 早报：Jev 决策模型与 Warp 软件工厂aihot.news | — | TypeSafe AI CEO 在 Latent.Space 访谈中介绍 Jev——面向软件控制流的 System One 模型，采用 RLCD（强化学习校准决策）训练，追求概率与实际信息相称，相关方法尚未公开发表。 | Articles, tutorials & talksMore guides & websites | |
| BestBlogs 早报：Jev 模型与 AI 软件工厂实践aihot.news | — | BestBlogs 09-22 早报收录 10 篇内容，涵盖 Jev System One 决策模型、Warp 的 AI 软件工厂流程、UiPath 关于工作流程才是核心资产的判断，以及生产级智能体控制平面与 Loop engineering 方法论。 | Articles, tutorials & talksMore guides & websites | |
| BestBlogs 精选周刊第 114 期：智能过剩之后，瓶颈向任务链后方移动aihot.news | — | BestBlogs 精选周刊第 114 期发布，梳理 Claude Opus 5.5、GPT-6 Sol 与 Luna、Grok 4.7、MiMo-V2.6 等集中在同一窗口的发布，并提出核心判断：模型能力每前进一步，系统瓶颈就向任务链后方移动一步。 | Articles, tutorials & talksMore guides & websites | |
| Building a harness with Jevwww.langchain.com | — | LangChain's Jev classifier, a per-request model router middleware, and a middleware that stops risky tool calls before they run. | Articles, tutorials & talksMore guides & websites | |
| Can't an LLM do what Jev does?zenn.dev | — | Japanese article that reproduces Jev's parallel-decision trick with first-token logits on Gemma3 270M (77x faster than JSON output) and compares Jev with LLMs on a public Mario-playing harness. | Articles, tutorials & talksMore guides & websites | |
| Chinese-Jev：将 System One 模型引入中文任务aihot.news | — | 研究者推出 Chinese-Jev，将 Jev 这类 System One 模型扩展到中文决策任务，并发布评测基准 CJ-Bench。 | Articles, tutorials & talksMore guides & websites | |
| Chinese-Jev：将 System One 模型引入中文任务arxiv.org | — | 研究者推出 Chinese-Jev，将 Jev 这类 System One 模型扩展到中文决策任务，并发布评测基准 CJ-Bench。 | Articles, tutorials & talksMore guides & websites | |
| ClawCast 迎来 Allie 做客 Discordaihot.news | — | 太酷了，@allietheicon（来自 Jev/TypeSafe）加入了我们在 Discord 上的 ClawCast！https://discord.com/invite/clawd | Articles, tutorials & talksMore guides & websites | |
| CodeRabbit 举办 Jev 首届黑客松aihot.news | — | CodeRabbit 在总部举办 Jev 首届黑客松，160+ 名开发者参与约 4 小时编程，并邀请 @allietheicon 分享如何与 Jev 这类新型 AI 模型协作。她谈到 Jev 对编程与软件开发带来的范式转变，并给出实用技巧与示例。 | Articles, tutorials & talksMore guides & websites | |
| Codex 宕机后用 Claude Code 监控aihot.news | — | Codex 挂了。 于是我配置了 Claude Code 来检查它什么时候恢复。 然后我用 Jev 来决定 Claude Code 该什么时候检查。 我可能有点问题了。 | Articles, tutorials & talksMore guides & websites | |
| Confidencedocs.typesafe.ai | — | Official confidence documentation. | Articles, tutorials & talksMore guides & websites | |
| DAIR.AI 本周顶级 AI 论文盘点aihot.news | — | 本周顶级 AI 论文（9 月 21 日 - 27 日）： - HySparse2 - EvoOntology - Harness-Zero - JEV-as-a-Judge - Wiki Foundation Model - Self-Organizing Agent Teams - Self-Improvement via Fast Tree-search 继续阅读： [引用 @dair_ai]：https://x.com/i/article/2104259869402599424 | Articles, tutorials & talksMore guides & websites | |
| dejevnerates 项目仍在建设中aihot.news | — | 我们正在努力为你们这些 dejevnerates 带来你们想要的东西，注册仍然关闭，敬请期待！🏗️ | Articles, tutorials & talksMore guides & websites | |
| Dex Horthy 回应基准测试调侃aihot.news | — | 兄弟以为我就是一堆 benchmark 堆出来的 哈哈 | Articles, tutorials & talksMore guides & websites | |
| ElevenLabs 实时情绪分析aihot.news | — | 这可不只是转录分析，快交出你的秘密！马上就去玩玩 @ElevenLabsDevs 上线的实时版本 [引用 @ElevenLabsDevs]：Jev 与 ElevenLabs 带来的实时情绪分析。 通话者还在说话时，每个短语就已染上它所承载情绪的颜色。右侧六个仪表实时追踪通话的情绪。 | Articles, tutorials & talksMore guides & websites | |
| Every 实测 OpenAI DevDay 2026：20 多项发布与上手体验aihot.news | — | Every 评测 OpenAI DevDay 2026 发布的 20 多项产品和功能。作者实测后认为 Dots 持久智能体已改变其使用习惯但 bug 较多，Space 办公套件体验较好；Decisions API 在部分测试中以 76/78 对 73/78 的准确率和 230 毫秒对 500 毫秒的响应速度优于 Jev，但另一些测试落后，定价未公布。 | Articles, tutorials & talksMore guides & websites | |
| Every 实测 OpenAI DevDay 2026：20 多项发布与上手体验every.to | — | Every 评测 OpenAI DevDay 2026 发布的 20 多项产品和功能。作者实测后认为 Dots 持久智能体已改变其使用习惯但 bug 较多，Space 办公套件体验较好；Decisions API 在部分测试中以 76/78 对 73/78 的准确率和 230 毫秒对 500 毫秒的响应速度优于 Jev，但另一些测试落后，定价未公布。 | Articles, tutorials & talksMore guides & websites | |
| Forkastforkast.news | — | The business angle, with the reported valuation and Every's speed and cost numbers. | Articles, tutorials & talksMore guides & websites | |
| From model launch to Jevable's early projectsdigidai.github.io | — | Long read that uses the 194 early projects listed on Jevable to show the common split of work, where Jev chooses among options other software supplies, and what the low token price leaves out. | Articles, tutorials & talksMore guides & websites | |
| GPT Researcher 用 Jev 替代嵌入向量aihot.news | — | 太酷了！ | Articles, tutorials & talksMore guides & websites | |
| Grok 回复区需要 System 1 反射aihot.news | — | @elonmusk X 的回复区需要的是 System 1 反射，而不是 System 2 哲学： 评论："你想看看我的福吗？" Grok（3500ms）：这是中国网络俚语，不是字面意义上问"fortune"或"blessing"………. Jev（70ms）：{"ban"： true}（成本：$0.00004） | Articles, tutorials & talksMore guides & websites | |
| Harness engineering with Jevzenn.dev | — | Japanese post on using Jev as a deterministic-leaning piece of an agent harness, with notes on getting access via the waitlist, Vercel AI Gateway, or Cloudflare. | Articles, tutorials & talksMore guides & websites | |
| HF 热门榜首开源多语言决策模型aihot.news | — | HF 上排名第一的热门模型是一个开源多语言 system 1 决策模型，就在 Jev 开始走红几天之后。开源 AI 社区太棒了！ | Articles, tutorials & talksMore guides & websites | |
| His new AI, Jev, won't write a worddev.to | — | Walkthrough of calling Jev from TypeScript, including the Vercel AI SDK evaluate integration, and of the catch hidden inside the "zero hallucinations" claim. | Articles, tutorials & talksMore guides & websites | |
| How does Jev work? RLCD and parallel inferencewww.explainx.ai | — | What is public about the training method. | Articles, tutorials & talksMore guides & websites | |
| How is everyone using Jev?qiita.com | — | Japanese survey that counts public Jev projects on GitHub and X through the sixth day after launch and charts what people are using it for. | Articles, tutorials & talksMore guides & websites | |
| HuggingFace 上 300 万个专用模型aihot.news | — | 我喜欢 Jev 的一点是：多年来，大型通用模型几乎吸走了 AI 领域所有的氧气。 但在更加专业化和定制化的模型上存在巨大机会，这些模型为特定任务和语言而构建，因此成本低几个数量级、速度更快、优化更好。@huggingface 上公开可用的这类模型有 300 万个。 让我们构建一个更加多元的 AI 生态！ | Articles, tutorials & talksMore guides & websites | |
| HumanLayer 的 Jev 代码搜索 harness 调优aihot.news | — | 别管我，我就在这儿对 jev harness 做爬山调优，用于代码搜索，你们继续。 | Articles, tutorials & talksMore guides & websites | |
| Is Jev a 'smart if'?docs.bswen.com | — | Hands-on Jev Playground tests showing that Jev does not execute user-written rules the way deterministic if/else code does, and that its best role is a low-latency decision layer inside agents. | Articles, tutorials & talksMore guides & websites | |
| Jeeves：基于 Qwen3.5-9B 的推理型 Jev 式决策模型，开源权重与训练代码aihot.news | — | PostHog 发布 Jeeves，一个基于 Qwen3.5-9B（LoRA + pointer head）的推理型 Jev 式分类器，采用 SFT 与 CISPO 训练，并配备 block-4 扩散草稿器。 | Articles, tutorials & talksMore guides & websites | |
| Jev 1.13 红队测试曝安全漏洞aihot.news | — | Jev 不好用？它是为可组合性而生的！ 需要更多 Jev！ | Articles, tutorials & talksMore guides & websites | |
| Jev AI 模型引关注aihot.news | — | 人人都想知道 Jev 是什么，却没人问 Jev 过得怎么样 https://en.wikipedia.org/wiki/Jev_(AI_model) | Articles, tutorials & talksMore guides & websites | |
| Jev at the branchesstacktoheap.com | — | A state machine owns the plan and the legal transitions while Jev only chooses among open branches, with stricter margins on risky moves. | Articles, tutorials & talksMore guides & websites | |
| Jev by TypeSafe AIcobusgreyling.medium.com | — | Essay on Jev as machine-native intelligence and how to adopt it: shadow an existing decision, gate on confidence, target high-frequency forks, and pair it with LLMs rather than replace them. | Articles, tutorials & talksMore guides & websites | |
| Jev By TypeSafe: A model you were waiting formedium.com | — | Explainer on how Jev differs from an LLM, what 'zero hallucinations' does and does not mean, and how the three question types map to healthcare, insurance, travel and logistics scenarios. | Articles, tutorials & talksMore guides & websites | |
| Jev engineering guidewww.aibuilderclub.com | — | Agent pattern where an LLM writes, Jev decides, and code acts, shown through a Claude Code guard hook, a model router, and a log-triage cron; a replay of 600 log entries cost $0.00029 at a 302 ms median. | Articles, tutorials & talksMore guides & websites | |
| Jev explained simplyzenn.dev | — | Japanese beginner explainer that compares chat models to students writing essay answers and Jev to one filling in a multiple-choice sheet, then walks through what that means for code. | Articles, tutorials & talksMore guides & websites | |
| Jev impressionszenn.dev | — | Japanese notes on where Jev fits: pairing with an LLM that reads the state while Jev picks the next action in tools like Browser Use, and choosing the next UI from user context with an LLM fallback. | Articles, tutorials & talksMore guides & websites | |
| Jev is the fish at the poker tablebacknotprop.com | — | Poker probe showing Jev swinging 15 to 30 points when the same hand is relabelled and betting against a known made flush in 16 of 16 runs, as a warning against deploying it unevaluated. | Articles, tutorials & talksMore guides & websites | |
| Jev makes AI decisions fast enough to play Doommedium.com | — | Explains how millisecond typed decisions enable real-time control loops such as TypeSafe's Doom demo and a community Super Mario agent, where game state goes in as structured text rather than pixels. | Articles, tutorials & talksMore guides & websites | |
| Jev scoring 用作 RAG 重排序aihot.news | — | 把 jev scoring 用作 RAG 重排序器非常合理。Rippling 内部 GTM 团队的应用做得很不错。 | Articles, tutorials & talksMore guides & websites | |
| Jev × Tripo × Astra 联动演示：Tripo P2.0 生成 3D 资产与 VRM 模型aihot.news | — | Jev × Tripo × Astra 联动 Demo 展示了用 Tripo P2.0 生成 3D 资产与 VRM 模型、由 Jev 驱动语音激活战斗与逻辑的流畅工作流。Jev 负责地图无限扩展、商人文本谈判判定、战斗自由行动判定及语音动作特效分类，Tripo SmartMesh P2.0 生成随机出现的 NPC/敌人/资产与部分魔法特效资产。10 分钟试玩版链接在推文回复中。 | Articles, tutorials & talksMore guides & websites | |
| Jev 不是聊天机器人：接收文本状态输出结构化打分aihot.news | — | Jev 不是 chatbot，不能对话，它接收 state（主要是 text data）后打分并输出结构化结果。其定位是固定 workflow 中的"螺丝钉"，适合做日志分析和输出检测。 | Articles, tutorials & talksMore guides & websites | |
| Jev 与 Instructor 能否搭配使用aihot.news | — | 有人想用 jev 搭配 instructor 吗？ | Articles, tutorials & talksMore guides & websites | |
| Jev 作 Judge 做智能体评估，低置信度升级到前沿模型aihot.news | — | Elvis Saravia 提出用 Jev-as-a-Judge 做智能体评估，认为这是目前最惊艳的 Jev 用例之一。他的早期测试指向一套兼顾准确率与成本的优化流程：高置信度场景用 Jev，低置信度判定则升级到前沿模型（GPT-6 或 Opus 5.5）。他强调 Jev 并非处处适用，前沿模型也不该包揽所有评估，完整指南即将发布。 | Articles, tutorials & talksMore guides & websites | |
| Jev 值得关注内容持续追踪aihot.news | — | 1/n | Articles, tutorials & talksMore guides & websites | |
| JEV 分类器意外发现家庭 WiFi 后门aihot.news | — | 用户用 @typesafeai 的 JEV 驱动分类器分析 Wireshark 抓取的网络数据包，意外发现家庭 WiFi 网络中的后门威胁，经前沿 AI 模型验证后重置设备并加固网络。该网络数据包分析工具即将开源。主推文以"做得智能又便宜、铺得到处都是"概括 JEV 的路线，并称之为"jevon's paradox"。 | Articles, tutorials & talksMore guides & websites | |
| Jev 可组合性设计引热议aihot.news | — | 我们的推特小哥太会整活了 🧑🍳 | Articles, tutorials & talksMore guides & websites | |
| Jev 如何进化智能体 harness 体验aihot.news | — | Elvis Saravia 将 Jev 集成进自建 harness，认为它不只是更快更便宜，而是能解锁此前受成本、延迟或缺少合适原语限制的智能体体验。Jev 可用于分类、控制流、确定性工作流和大规模标注，并通过智能决策、结构化智能与按需上下文管理提升可靠性。他还看好 Jev 在动态 UI、LLM 评审、验证器与合成高质量数据上的潜力，完整指南即将发布。 | Articles, tutorials & talksMore guides & websites | |
| Jev 安全红队测试与防护实践aihot.news | — | 围绕一个简单原语做真正工程实践的好例子！！！ 不要把 Jev 直接接入高层决策，而是编程定义你想要的行为！ （跟人说"去编程"听起来很奇怪，但这真的很酷） | Articles, tutorials & talksMore guides & websites | |
| Jev 实现动态 UI 文本框aihot.news | — | 应用的动态 UI 是著名的坟场，至少对 PM 来说是这样，甚至对整个产品和公司也是如此。如果真有这么简单呢？Jev 正在做这件事。 | Articles, tutorials & talksMore guides & websites | |
| Jev 心里想"我不知道"却不说出口：Sys1Cal-v1 概率校准数据集发布aihot.news | — | 针对 System One 模型 Jev 的概率校准承诺缺乏公开测试的问题，研究者发布 Sys1Cal-v1 数据集，用已知精确概率 P(A) 的真/假命题，通过 Noul、Choice、Score 三个原语查询并以全变差距离评估。 | Articles, tutorials & talksMore guides & websites | |
| Jev 心里想"我不知道"却不说出口：Sys1Cal-v1 概率校准数据集发布arxiv.org | — | 针对 System One 模型 Jev 的概率校准承诺缺乏公开测试的问题，研究者发布 Sys1Cal-v1 数据集，用已知精确概率 P(A) 的真/假命题，通过 Noul、Choice、Score 三个原语查询并以全变差距离评估。 | Articles, tutorials & talksMore guides & websites | |
| Jev 成 OpenRouter 分类请求首选aihot.news | — | Jev 正迅速成为 OpenRouter 上分类请求的首选。 它占据了该类别每周请求量的 27%，几乎是此前位居榜首的 DeepSeek V4 Flash 份额的两倍 | Articles, tutorials & talksMore guides & websites | |
| Jev 招募数据人才提升可靠性aihot.news | — | 加入我们！让 jev 更可靠！🤘 | Articles, tutorials & talksMore guides & websites | |
| Jev 搭配 Exa 联网搜索效果惊人aihot.news | — | 这是真的吗？还是夸张了？（抱歉） [引用 @TheIshanGoswami]：Jev 搭配 Exa 简直离谱。 > Jev 不用联网搜索时会自信地给出错误输出 > Jev 用上联网搜索后准确率真的高很多 免费试用 Jev（搭配 Exa 联网搜索）👇 | Articles, tutorials & talksMore guides & websites | |
| Jev 模型介绍：放弃自回归、只做结构化决策输出aihot.news | — | Jev 模型放弃传统自回归架构，无法直接输出普通文本，只能做决策并输出结构化 JSON，输入时需定义 Schema 并编译为决策槽位，因此输出不会出错。它目前仅支持文本输入，二分类场景可输出如 isSpam 为 true、概率 0.982 的 JSON；复杂场景下定义好可用动作后模型即可自主决策，如玩杀戮尖塔或看盘。 | Articles, tutorials & talksMore guides & websites | |
| Jev 模型四大智能体应用场景盘点aihot.news | — | Elvis Saravia 列出 Jev 的四个应用方向：LLM-as-a-Judge 评估、agent harness 路由、通过 SOTA 分类能力实现更智能的子智能体创建以扩展编排、以及增强动态 harness 生成。前三个在成本和效率上 ROI 极高，他正将 Jev 用作元 harness 的智能路由器，第四个尚在测试但潜力巨大。 | Articles, tutorials & talksMore guides & websites | |
| Jev 模型征集社区问题反馈aihot.news | — | 告诉我们 Jev 哪里不好！ Jev 并不完美。我们认为它还是太慢、太贵、太笨。我们还有更多招数让它变得更好。但我们需要社区的帮助--请在 Discord 的 model-jaggedness 频道里发布你看到的问题。 也请看看我们已知的 jaggedness 问题：https://docs.typesafe.ai/model-jaggedness/jev-1.13 | Articles, tutorials & talksMore guides & websites | |
| Jev 模型每百万输入 token 仅 $0.042aihot.news | — | 一旦用了 Jev，就再也回不去了 【引用 @notkevinzhang】：四个词，十八个字母，每百万输入 token 仅 $0.042 | Articles, tutorials & talksMore guides & websites | |
| Jev 正在疯狂工作（视频未加速aihot.news | — | Listed by the source without a separate description. | Articles, tutorials & talksMore guides & websites | |
| Jev 玩 Minecraft 反应速度碾压人类aihot.news | — | 🚀 我们让 Jev 玩 Minecraft。我们就是打不过它！😭 ⚡ Jev：24 毫秒决策 🧠 你：约 200 毫秒反应 它在你看到它动之前就已经动了。太强了！ 🎮 https://mc.alexzms.com（加入服务器来赢） | Articles, tutorials & talksMore guides & websites | |
| Jev 现已上线aihot.news | — | ：https://console.typesafe.ai | Articles, tutorials & talksMore guides & websites | |
| Jev 用 AI 自动化现实世界任务aihot.news | — | 每一天，Jev 都在自动化新形式的现实世界任务，把🌎级⚡️快速智能带入排序、过滤、分类和路由等基础模块。 如果 AI 能解决新的数学问题，那么 AI 就能正确地路由一通客户支持电话！ | Articles, tutorials & talksMore guides & websites | |
| Jev 用于 RAG 的语义匹配与重排aihot.news | — | 继续搞，Jev 的大多数最佳实践还有待发掘！ [引用 @Vtrivedy10]：Jev 用于 RAG 几乎所有情况下，相比点积相似度，你更应信任 Jev 的语义匹配能力 在小数据场景下作为直接相似度指标非常有用 在大数据场景下则是出色的重排器 | Articles, tutorials & talksMore guides & websites | |
| jev 登顶 OpenRouter 短上下文模型榜aihot.news | — | jev 是 OpenRouter 上 1k-10k 上下文的最强模型！ | Articles, tutorials & talksMore guides & websites | |
| JEV 相关推文aihot.news | — | JEV --- 说明：主推文内容仅为 "JEV" 三个字母，没有更多上下文信息。根据防幻觉规则，无法确定 JEV 具体指代什么（可能是模型名、项目代号或其他），因此标题和正文均保留原文，不做扩写。如需更准确的翻译，请提供更完整的推文内容。 | Articles, tutorials & talksMore guides & websites | |
| Jev 等分类器模型涌现，开发者好时机aihot.news | — | 很高兴看到像 Jev 这样的分类器模型越来越多地被构建出来。 做开发者的好时机 🥹🫶 | Articles, tutorials & talksMore guides & websites | |
| Jev 能否成为更好的智能体评测器？aihot.news | — | LangChain 测试了 Jev 与 LLM 评委在准确率、可重复性、延迟和成本四个维度的表现，以验证 System One 模型能否为智能体评测提供新思路。 | Articles, tutorials & talksMore guides & websites | |
| Jev 能否玩 Overcooked？aihot.news | — | jev 能玩 Overcooked 吗？ | Articles, tutorials & talksMore guides & websites | |
| JEV 让 LLM 推理实现即时反馈aihot.news | — | JEV 将通用语义推理引入判别式推理范式，实现任意上下文输入、校准结构化决策输出。在 JEV + Perfectly 的示例中，用复杂查询从 500 位 ECCV 2026 研究者的论文中理解 AI 研究者，性能达到 Claude 某模型同等水平。 | Articles, tutorials & talksMore guides & websites | |
| Jev 迷宫寻路实测：纯随机数发生器反超aihot.news | — | 作者用 Rust + xoshiro256++ 搭了个与 Jev API 格式相同的纯随机数 server，与 Jev 做迷宫寻路对抗测试，结果纯随机无并发顺序请求 892 步通关、耗时不到 300ms，而 Jev 跑了 2306 步，其中 2295 步困在 (7,4) 拐角的 3 个格子里原地打转直至超时。 | Articles, tutorials & talksMore guides & websites | |
| Jev 重排序销售数据性能提升aihot.news | — | 给后排的朋友们： • 用 Jev 对销售数据做生产级重排序 • 快 20 倍 • 便宜 10 倍 • 准确率提升 12% 你们懂了吗？ | Articles, tutorials & talksMore guides & websites | |
| Jev 零样本检测对齐失效，AUROC 0.886aihot.news | — | TypeSafe AI 的校准决策模型 Jev 只需一个通用 yes/no 问题，用其概率作为评分，无需额外训练即可区分模型失效与正常回复，中位 AUROC 达 0.886。 | Articles, tutorials & talksMore guides & websites | |
| Jev+Treg 打造智能体版 Clay 人物搜索aihot.news | — | Treg 联合 Jev 推出面向 AI 智能体的开源人物搜索工具，可跨 60+ 数据源检索线索，每条线索仅 $0.0089，比 Clay 便宜 85%，并在人物搜索基准上排名第一。Jev 作为决策模型对候选线索按角色、公司等标准打分，返回结构化概率而非生成文本，可插件式接入任意智能体。 | Articles, tutorials & talksMore guides & websites | |
| Jev, RLCD, and the reinvention of the AI classifierwww.turingpost.com | — | Guide that dissects what is known about Jev and RLCD, traces the older research ideas it combines, and lists open-source alternatives and 12 related papers. | Articles, tutorials & talksMore guides & websites | |
| jev-gomoku — articlezenn.dev | — | MoonBit playground with a System One API client, a one-shot question CLI, a CLI where two Jev players play gomoku by choosing among candidate cells each move, and a tool that turns game logs into real-time GIFs. | Articles, tutorials & talksMore guides & websites | |
| Jev-LDE 让 LLM 少样本示例一次编辑到位aihot.news | — | You Only Edit Once： 通过局部示例精修激发 LLM 的上下文能力 挑选最佳少样本示例是一种缓慢的 System-2 搜索：组合爆炸，且往往需要反复调用 LLM。 我们把它变成了 System 1。⚡ Jev-LDE，一个 1.7B 的编辑器，扫一眼检索到的示例，只做一次编辑。LLM 只回答一次。 平均 1-shot 准确率 81.2 → 88.1 You Only Edit Once 🧵 动画演示（示意示例）。查询：“How far is it from Denver to Aspen?” 语义 TopK 检索出三个相似示例：“Where is Aspen, Colorado?”（Location）、“What state is Denver in?”（Location）、“Who founded Denver?”（Person）。Jev-LDE，一个 1.7B 的 System-1 编辑器，标记出第一个虽然主题相同但答案类型错误，并输出一个动作：将 S1 替换为候选 C1，“How far is Boston from NYC?”（Number）。冻结的目标 LLM 随后回答“Number”，这是正确的；若不编辑，它会回答“Location”。结尾卡片：在 3 个基准和 4 个目标 LLM 上，平均 1-shot 准确率从 81.2 提升至 88.1，在 48 个设置中有 44 个达到最佳或并列最佳，墙钟时间增加 11%。 | Articles, tutorials & talksMore guides & websites | |
| jev-leftpad：用 LLM 调用替代 padStart() 的 npm 包aihot.news | — | npm 包 jev-leftpad 把 JavaScript 的字符串补位交给大模型完成：调用 leftPad(value, targetLength) 时通过 TypeSafe 的 @typesafe-ai/sdk 请求 jev-latest，由模型在 space_0 到 space_10 中选一个选项，因此最多只能补 10 个空格。 | Articles, tutorials & talksMore guides & websites | |
| Jev-Mem：受 System-One/System-Two 启发的智能体记忆架构aihot.news | — | Jev-Mem 是一种受 System-One/System-Two 认知启发的新型智能体记忆架构，将记忆构建提速 6.6 倍、查询延迟降低 36.7%。在 LoCoMo 上，它以 LLM 评审 0.777 的总分较最强基线相对提升 11.0%，记忆构建耗时 158 秒，平均查询延迟降至 0.93 秒。 | Articles, tutorials & talksMore guides & websites | |
| Jev-Mem：用 System-One 控制平面打造高效 AI 智能体记忆架构aihot.news | — | Jev-Mem 是一种受 System-One/System-Two 认知启发的新型智能体记忆架构，通过专用 System-One 控制平面在构建阶段管理记忆类型与关系组织，并在检索时动态完成查询路由、检索预算分配、图遍历、候选打分与自适应停止，System-Two 仅用于复杂推理与答案合成。 | Articles, tutorials & talksMore guides & websites | |
| Jev-Mem：用 System-One 控制平面打造高效 AI 智能体记忆架构arxiv.org | — | Jev-Mem 是一种受 System-One/System-Two 认知启发的新型智能体记忆架构，通过专用 System-One 控制平面在构建阶段管理记忆类型与关系组织，并在检索时动态完成查询路由、检索预算分配、图遍历、候选打分与自适应停止，System-Two 仅用于复杂推理与答案合成。 | Articles, tutorials & talksMore guides & websites | |
| JevBench 发布：面向类型化决策的新基准aihot.news | — | 新基准 JevBench 发布，专为输出受限软件决策而非开放式文本的模型设计，紧随 TypeSafe 9 月 15 日发布 Jev--输入应用状态与固定选项，返回带概率的类型化答案。该基准综合智能、校准、速度与成本，用几何平均防止单一维度优势掩盖短板。GPT-5.6 Luna 在难题准确率上明显高于 Jev 1.13.0，但 Jev 因延迟、校准和成本更优而在综合分上领先。 | Articles, tutorials & talksMore guides & websites | |
| jevs-0-percent-hallucination-sits-beside-a-678-percent-accuracy-scoreagenccy.ai | — | agenccy | https://agenccy.ai/zh/news/jevs-0-percent-hallucination-sits-beside-a-678-percent-accuracy-score/ | search-hit | Frames “0% hallucination next to 67.8% accuracy” (vendor workflow score context) | Articles, tutorials & talksMore guides & websites | |
| JevSearch 用 Jev 验证搜索结果aihot.news | — | 相关性很重要，但相关于什么？Jev 给你任何套路 SEO 都钻不进去的搜索智能 🪱 我构建了 JevSearch，用 Jev 搜索网络并验证你的结果。 给出一个查询和筛选标准，用 @browserbase search 获取 t25 结果，然后 Jev 打分并返回 t5 结果。 Jev 常常会选择初始前 5 之外的 url，认为它们更相关。 | Articles, tutorials & talksMore guides & websites | |
| Jev：用 RLCD 训练的单次调用模型零样本检测 AI 对齐失效aihot.news | — | 用强化学习校准决策（RLCD）训练的模型 Jev 可在单次调用中对同一输入回答多个带类型的问题并给出校准概率，零样本检测对齐失效的中位 AUROC 达 0.886，在多数基准上超过有监督基线。 | Articles, tutorials & talksMore guides & websites | |
| Jev：用 RLCD 训练的单次调用模型零样本检测 AI 对齐失效arxiv.org | — | 用强化学习校准决策（RLCD）训练的模型 Jev 可在单次调用中对同一输入回答多个带类型的问题并给出校准概率，零样本检测对齐失效的中位 AUROC 达 0.886，在多数基准上超过有监督基线。 | Articles, tutorials & talksMore guides & websites | |
| LangChain 用 Jev 增强 harness 做 agent 路由aihot.news | — | LangChain 已利用 Jev 增强其 harness，速度非常快。Jev 适合在既定 harness 中承担分析分类工作，例如 agent 路由、模型路由等"螺丝钉"任务。 | Articles, tutorials & talksMore guides & websites | |
| LangGraph 如何编排 TypeSafe AI 决策模型 Jev 构建生产级智能体aihot.news | — | LangGraph 可编排 TypeSafe AI 的决策模型 Jev，用于构建更快、更便宜的生产级智能体。该方案展示了 LangGraph 在智能体编排中的实际用法。 | Articles, tutorials & talksMore guides & websites | |
| LangGraph 如何编排 TypeSafe AI 决策模型 Jev 构建生产级智能体www.langchain.com | — | LangGraph 可编排 TypeSafe AI 的决策模型 Jev，用于构建更快、更便宜的生产级智能体。该方案展示了 LangGraph 在智能体编排中的实际用法。 | Articles, tutorials & talksMore guides & websites | |
| laya-mlx 移植版：比 Jev 快 50 倍，本地跑贪吃蛇aihot.news | — | 开发者将开源文本概率分类系统 Laya 移植到 MLX 并做性能优化，推出 laya-mlx，号称比 Jev 快 50 倍，设备内存占用最高 1G。该模型在本地 M3 Max 上以每秒 60 次决策的速度玩贪吃蛇，代码已开源至 GitHub。 | Articles, tutorials & talksMore guides & websites | |
| Laya（OS Jev）在 Mac M4 CoreML 离线环境下每秒 45 次决策aihot.news | — | Laya（OS Jev）可在 Mac M4 上通过 CoreML 离线运行，达到每秒 45 次决策。用户可用 uv 安装 laya-coreml【demo】，并下载 aac6fef/laya-multilingual-coreml-ane 模型到本地，再运行 laya-coreml-snake 演示。 | Articles, tutorials & talksMore guides & websites | |
| LayerX internal Jev study sessiontech.layerx.co.jp | — | Japanese write-up of a 30-minute internal study session on Jev at LayerX that drew more than 50 engineers and produced more than 50 ideas for building it into their products. | Articles, tutorials & talksMore guides & websites | |
| Liquid AI 发布决策模型 d1：零输出 token 返回校准概率aihot.news | — | Liquid AI 发布决策模型 d1，专为结构化选择设计，通过 Noul、Choice、Score 三种原语一次调用返回类型化答案和校准概率，output_tokens 为 0。 | Articles, tutorials & talksMore guides & websites | |
| Liquid AI 发布决策模型 d1：零输出 token 返回校准概率www.marktechpost.com | — | Liquid AI 发布决策模型 d1，专为结构化选择设计，通过 Noul、Choice、Score 三种原语一次调用返回类型化答案和校准概率，output_tokens 为 0。 | Articles, tutorials & talksMore guides & websites | |
| LLM 蒸馏 Jev 之类aihot.news | — | Listed by the source without a separate description. | Articles, tutorials & talksMore guides & websites | |
| markjaquith 最短"什么是 Jev"解释aihot.news | — | 好吧这个挺不错的 xD 【引用 @markjaquith】：这是我最新最短的"什么是 Jev"解释 | Articles, tutorials & talksMore guides & websites | |
| Metaview 全线接入 typesafe 的 jev，搜索提速约 10 倍aihot.news | — | MetaviewAI 上周末将 typesafeai 的 jev 接入其所有 agent，候选人搜索从数分钟缩短到数秒，准确率不变、约快 10 倍，且每次搜索成本明显更低。jev 让团队把智能当作软件来构建：将每个 agent 拆成最小语义单元、逐个查询、自设阈值，并通过新增问题而非修改系统提示词来修 bug。 | Articles, tutorials & talksMore guides & websites | |
| Mini-Vibe Checkevery.to | — | Hands-on review where Jev made 777 judgments over 37 of the author's articles in 0.7 seconds for a quarter of a cent, catching six of seven planted defects to Fable's seven. | Articles, tutorials & talksMore guides & websites | |
| Mirror | https://whnex.com/items/49717558 | Full comment scrape used for themeswhnex.com | — | Listed by the source without a separate description. | Articles, tutorials & talksMore guides & websites | |
| Nokia 开源 AnyJev：无需训练将开放 LLM 变成校准决策模型aihot.news | — | Nokia 应用研究团队开源 AnyJev，一个 Python 库，无需训练即可把开放 LLM 变成可输出概率的决策模型，接口借鉴 TypeSafe AI 于 2026 年 9 月发布的 Jev。 | Articles, tutorials & talksMore guides & websites | |
| Nokia 开源 AnyJev：无需训练将开放 LLM 变成校准决策模型www.marktechpost.com | — | Nokia 应用研究团队开源 AnyJev，一个 Python 库，无需训练即可把开放 LLM 变成可输出概率的决策模型，接口借鉴 TypeSafe AI 于 2026 年 9 月发布的 Jev。 | Articles, tutorials & talksMore guides & websites | |
| ollaya-dev/ollaya websiteollaya.dev | — | Run open decision models locally: pull and serve Laya, decider, NLI and GLiClass behind a TypeSafe-compatible API. Ollama for decision models. | Articles, tutorials & talksMore guides & websites | |
| OpenClaw 核心支持决策模型aihot.news | — | 一切都在往 Jev 的方向发展！ 上周 @jlehman_ 为 OpenClaw 核心和插件推送了决策模型支持 了解我们如何思考使用它们，以及你如何用这个强大的新工具让 OpenClaw 变得更好！ https://openclaw.ai/blog/decision-models-in-openclaw | Articles, tutorials & talksMore guides & websites | |
| OpenJev 推出纯浏览器本地决策实验，对比直读概率与逐 token 生成 JSONaihot.news | — | OpenJev 是一个纯浏览器、无后端的本地实验站点，用户可在自己的 GPU 上加载模型，对比两种决策方式：直接读取选项 logits 并归一化，或让模型逐 token 写出 JSON 概率分布，并用 performance.now（） 实测各阶段耗时。 | Articles, tutorials & talksMore guides & websites | |
| OpenRouter 介绍决策模型 Jevaihot.news | — | 什么是决策模型？ Jev 由 @typesafeai 打造，回答是/否和多项选择题，并给出置信度分数。软件开发的大部分工作是一连串决策，而 Jev 比 LLM 便宜 10 倍、快 10 倍。 让我们通过实际例子来理解： | Articles, tutorials & talksMore guides & websites | |
| OpenRouter 实测 Jev 1.13 与 Claude Opus 5 在 Banking77 分类任务上的准确率、延迟与成本aihot.news | — | OpenRouter 用 Banking77 测试集的 3,080 条客服语料对比 Jev 1.13 与 Claude Opus 5 的意图分类表现。Jev 准确率 81.0% 比 Opus 的 84.4% 低 3.3 个百分点，但中位延迟 175 ms 约为 Opus（2,266 ms）的 1/13，每千次请求成本 $0.11 对 $2.42（启用提示词缓存）。 | Articles, tutorials & talksMore guides & websites | |
| OpenRouter 实测 Jev 1.13 与 Claude Opus 5 在 Banking77 分类任务上的准确率、延迟与成本openrouter.ai | — | OpenRouter 用 Banking77 测试集的 3,080 条客服语料对比 Jev 1.13 与 Claude Opus 5 的意图分类表现。Jev 准确率 81.0% 比 Opus 的 84.4% 低 3.3 个百分点，但中位延迟 175 ms 约为 Opus（2,266 ms）的 1/13，每千次请求成本 $0.11 对 $2.42（启用提示词缓存）。 | Articles, tutorials & talksMore guides & websites | |
| OpenRouter 实测 Jev 决策模型对比 LLM，何时该用决策模型替代生成文本aihot.news | — | OpenRouter 发布教程，实测 TypeSafe 的决策模型 Jev 1.13 与 GPT Luna、Claude Opus 在工单分诊和提示词注入筛查上的表现：Jev 每 1000 张工单成本 $0.0248、中位延迟 194ms，准确率与 LLM 相当。 | Articles, tutorials & talksMore guides & websites | |
| OpenRouter 实测 Jev 决策模型对比 LLM，何时该用决策模型替代生成文本openrouter.ai | — | OpenRouter 发布教程，实测 TypeSafe 的决策模型 Jev 1.13 与 GPT Luna、Claude Opus 在工单分诊和提示词注入筛查上的表现：Jev 每 1000 张工单成本 $0.0248、中位延迟 194ms，准确率与 LLM 相当。 | Articles, tutorials & talksMore guides & websites | |
| OpenRouter 推出 Jev Router，为每次 LLM 调用自动选择模型和推理力度aihot.news | — | Jev 现以 typesafe/jev-router 形式打包上线，OpenRouter 会为每个请求自动选择模型和推理力度。作者用 Pi SDK 构建的支持智能体测试，同一 8 个案例对比固定 GPT-6 Sol 基线共 32 次真实调用，两者全部答对，路由成本低一半以上（$0.008 vs $0.018），中位响应时间也更短（1.5s vs 1.9s）。 | Articles, tutorials & talksMore guides & websites | |
| OpenRouter 推出 Jev 缓存感知模型路由器aihot.news | — | 介绍 typesafe/jev-router：一个由 Jev 和 @typesafeai 驱动的缓存感知模型路由器 Jev Router 为每个请求挑选最佳模型和推理力度，在质量、速度和成本之间取得平衡。 工作原理如下 👇🏻 | Articles, tutorials & talksMore guides & websites | |
| OpenRouter 社区 System One 用例评选aihot.news | — | 1/ 上周，Jev - @typesafeai 的 System One 模型在 OpenRouter 上线。关注度极高。 我们邀请社区寻找最具创新性的方式，将快速且低成本的决策应用到他们的项目中。 当然，我们得让 Jev 来选出 5 位获奖者。 | Articles, tutorials & talksMore guides & websites | |
| OpenRouter 评测 Jev 决策模型速度aihot.news | — | 1/ Jev，由 @typesafeai 推出的决策模型，引发了大量项目和讨论。我们用 Ori Eval 在 OpenRouter 上针对热门 LLM 的评判能力进行了测试。 Jev 比第二快的模型还快 5 倍以上，甚至它最慢的请求也超过了其他所有模型的中位数。 | Articles, tutorials & talksMore guides & websites | |
| OpenRouter：Jev 上线带动新用户aihot.news | — | 问：哪些模型受 Jev 到来的影响最大？ 答：许多实验室的 flash 版本模型。 另外：OpenRouter 上近一半的 Jev 用户在前一周还没用过任何模型。这次发布激起了足够的兴趣，把他们从场边拉了进来。 | Articles, tutorials & talksMore guides & websites | |
| OrcaRouter Chinese introductionwww.orcarouter.ai | — | An overview of the product and available evidence. | Articles, tutorials & talksMore guides & websites | |
| PostHog 玩梗 jev 引共鸣aihot.news | — | posthog 拿 jev 玩梗，感觉就像当年《南方公园》里出现 ChatGPT 那会儿 🥹 | Articles, tutorials & talksMore guides & websites | |
| Privatemode 团队用 GLM-5.3-Flash 将 LLM 变成单次前向的类型化决策模型aihot.news | — | Privatemode 团队展示了一种无需微调的方法，通过编号选项、预填 choice_index: 前缀并读取选项索引的 log 概率，让 GLM-5.3-Flash 在单次前向中输出带概率的类型化决策。 | Articles, tutorials & talksMore guides & websites | |
| Privatemode 团队用 GLM-5.3-Flash 将 LLM 变成单次前向的类型化决策模型www.privatemode.ai | — | Privatemode 团队展示了一种无需微调的方法，通过编号选项、预填 choice_index: 前缀并读取选项索引的 log 概率，让 GLM-5.3-Flash 在单次前向中输出带概率的类型化决策。 | Articles, tutorials & talksMore guides & websites | |
| Project Jev 一周省下 50 万aihot.news | — | 省下五十万，token 用量翻 100 倍，小意思 💅 | Articles, tutorials & talksMore guides & websites | |
| Qué es Jevwww.linkedin.com | — | Spanish explainer of the System One idea, real costs versus an LLM, and what people are building, based on indexing 295 public Jev projects from 269 people between September 15 and 21. | Articles, tutorials & talksMore guides & websites | |
| Rick and Morty 讲透 Jev AIaihot.news | — | 天哪——这该不会本该是我们的发布视频吧？🥹 [引用 @princedoesai]：天哪。 Rick and Morty 给我讲 Jev AI，比任何技术演示都讲得清楚。 | Articles, tutorials & talksMore guides & websites | |
| Scoredocs.typesafe.ai | — | Official scoring documentation. | Articles, tutorials & talksMore guides & websites | |
| SGLang 如何用 Score API 与 MIS 扩展 JEV 类决策模型服务aihot.news | — | SGLang 团队介绍用 Score API 与多条目评分（MIS）服务 JEV 类决策模型：/v1/score 通过 label_token_ids 显式请求标签 token 分数，MIS 在单次请求内复用共享 query 计算并隔离各候选。 | Articles, tutorials & talksMore guides & websites | |
| SGLang 如何用 Score API 与 MIS 扩展 JEV 类决策模型服务www.lmsys.org | — | SGLang 团队介绍用 Score API 与多条目评分（MIS）服务 JEV 类决策模型：/v1/score 通过 label_token_ids 显式请求标签 token 分数，MIS 在单次请求内复用共享 query 计算并隔离各候选。 | Articles, tutorials & talksMore guides & websites | |
| Show HN：杰夫在玩《宝可梦 红版》aihot.news | — | 开发者发布 Show HN 项目"杰夫在玩《宝可梦 红版》"，页面默认静音、取消静音后可听游戏音频，右侧面板实时展示每一步决策与 Jev 的胜率。该项目由 FRIGADE 赞助，FRIGADE 是一款能自主学习产品并在应用内向每位用户提示下一步的 AI 助手，页面同时提供代码查看入口。 | Articles, tutorials & talksMore guides & websites | |
| Show HN：杰夫在玩《宝可梦 红版》jev-pokemon.vercel.app | — | 开发者发布 Show HN 项目"杰夫在玩《宝可梦 红版》"，页面默认静音、取消静音后可听游戏音频，右侧面板实时展示每一步决策与 Jev 的胜率。该项目由 FRIGADE 赞助，FRIGADE 是一款能自主学习产品并在应用内向每位用户提示下一步的 AI 助手，页面同时提供代码查看入口。 | Articles, tutorials & talksMore guides & websites | |
| Simon Willison 评 TypeSafe AI 新形态决策模型 Jevaihot.news | — | TypeSafe AI 上周发布 Jev，作者称其为 System One 或决策模型：接受文本输入但输出浮点置信分数而非文本，仅按输入计费 $0.042/百万 tokens，低于 GPT-5 Nano 的 $0.05。 | Articles, tutorials & talksMore guides & websites | |
| Steerable Reranking: How JEV Solves RAG — notebookcolab.research.google.com | — | Uses Jev as a steerable reranker in a RAG pipeline, explaining where cosine similarity fails and comparing it with LLM rerankers and cross-encoders. | Articles, tutorials & talksMore guides & websites | |
| System One 模型推动自定义 Agent Harness 新浪潮aihot.news | — | DAIR.AI 的 Elvis Saravia 指出，System One 模型正将自定义 agent harness 推向新高度，Jev 之后又出现 Contrastive Language Model（CLM），CLM 比 Jev 快 9 倍，在长周期任务上验证表现更优。 | Articles, tutorials & talksMore guides & websites | |
| Tomer Tunguz 谈 AI 优化 if-then 判断：专用决策器把分类成本降近百倍aihot.news | — | Tomer Tunguz 撰文提出最新一波 AI 正在接管软件中的 if-then 判断原语，Jev 与 SemIf 这类专用决策器以数百毫秒返回结果，成本比传统生成式调用低约 76x 到 209x。作者在自己的 Agent 中替换了约四分之一的调用，在 98 条人工核验的生产邮件线程上，Jev 达到 80%、本地 SemIf 达到 82% 的分类准确率，高于生产模型的 47%。 | Articles, tutorials & talksMore guides & websites | |
| Tomer Tunguz 谈 AI 优化 if-then 判断：专用决策器把分类成本降近百倍tomtunguz.com | — | Tomer Tunguz 撰文提出最新一波 AI 正在接管软件中的 if-then 判断原语，Jev 与 SemIf 这类专用决策器以数百毫秒返回结果，成本比传统生成式调用低约 76x 到 209x。作者在自己的 Agent 中替换了约四分之一的调用，在 98 条人工核验的生产邮件线程上，Jev 达到 80%、本地 SemIf 达到 82% 的分类准确率，高于生产模型的 47%。 | Articles, tutorials & talksMore guides & websites | |
| Trying Jev with a lighting-control demonote.com | — | Japanese hands-on intro that reviews the benchmarks critically and builds a floor-plan demo where natural-language commands dim room lights, showing how the confidence threshold decides between acting and asking. | Articles, tutorials & talksMore guides & websites | |
| Two techniques for working with System One modelswww.seangoedecke.com | — | Layered goals, where a slow loop picks the goal and a fast loop picks actions, plus tournament sampling over batches of options, shown on a Doom agent built with an open-model imitation. | Articles, tutorials & talksMore guides & websites | |
| TypeSafe AI 发布 System One 模型 Jev，返回带概率的类型化决策而非文本aihot.news | — | TypeSafe AI 发布 Jev，一个基于 Transformer 但不生成文本的 System One 模型，通过 POST https://api.typesafe.ai/v1/systemone 接收状态和类型化问题，返回带概率和置信度的类型化决策，提供 Choice、Score、Noul 三种问题类型。 | Articles, tutorials & talksMore guides & websites | |
| TypeSafe AI 发布 System One 模型 Jev，返回带概率的类型化决策而非文本www.marktechpost.com | — | TypeSafe AI 发布 Jev，一个基于 Transformer 但不生成文本的 System One 模型，通过 POST https://api.typesafe.ai/v1/systemone 接收状态和类型化问题，返回带概率和置信度的类型化决策，提供 Choice、Score、Noul 三种问题类型。 | Articles, tutorials & talksMore guides & websites | |
| TypeSafe AI 发布只做高频决策的大模型 Jev，作者实测其分类判断性价比aihot.news | — | TypeSafe AI 推出专注高频决策的大模型 Jev，不做对话和文字生成，只输出判断，速度比传统大模型快20~200倍，成本0.042美元/百万Token且输出Token免费。作者实测预筛任务中Jev准确性第二且更便宜，在并行判断任务上达到最高准确率和最快速度；模型采用RLCD训练方法优化决策校准，可在官网 https://typesafe.ai/ 申请资格，Vercel 已首发接入。 | Articles, tutorials & talksMore guides & websites | |
| TypeSafe AI 发布只做高频决策的大模型 Jev，作者实测其分类判断性价比mp.weixin.qq.com | — | TypeSafe AI 推出专注高频决策的大模型 Jev，不做对话和文字生成，只输出判断，速度比传统大模型快20~200倍，成本0.042美元/百万Token且输出Token免费。作者实测预筛任务中Jev准确性第二且更便宜，在并行判断任务上达到最高准确率和最快速度；模型采用RLCD训练方法优化决策校准，可在官网 https://typesafe.ai/ 申请资格，Vercel 已首发接入。 | Articles, tutorials & talksMore guides & websites | |
| TypeSafe AI 谈 Jev 模型理念aihot.news | — | 我们的座右铭背后有很多含义：Building Prod, Not God。 这项技术将改变世界，但这要靠勤勉的努力和创造力来实现，而不是靠故弄玄虚的诉求。 @a16z 与 @CompleteSkeptic 深入探讨了这一理念以及更多内容。 | Articles, tutorials & talksMore guides & websites | |
| TypeSafe Discord — show-and-telldiscord.com | — | Official TypeSafe community server, where builders share Jev demos in the Show and Tell channel. | Articles, tutorials & talksMore guides & websites | |
| TypeSafe JEV explainedwww.theneuron.ai | — | Plain-language explainer on why software decisions may not need a chatbot, covering the parallel System One design, RLCD calibration, enterprise fit and the strongest counterargument. | Articles, tutorials & talksMore guides & websites | |
| TypeSafe Jev 接入 MotherDuck，文本分类快 50 倍aihot.news | — | TypeSafe 新模型 Jev 以 SQL 函数 prompt_jev() 接入 MotherDuck，文本分类速度提升约 50 倍、成本降至约 1%。10 万行数据仅需 40 秒、花费 $0.50，达到前沿 LLM 准确率，而 LLM 方案耗时 32 分钟、花费 $37。 | Articles, tutorials & talksMore guides & websites | |
| TypeSafe Jev, benchmarked and pricedwww.developersdigest.tech | — | Release-week technical rundown of Jev's primitives, parallel sampler and RLCD training, published workflow evals with their caveats, pricing and the API, SDK and agent-skill surface. | Articles, tutorials & talksMore guides & websites | |
| TypeSafe Jevelopers Discord 首周aihot.news | — | Discord 里正在发生严肃的 Jevelopments，在 @allietheicon 的注视下 👀 [引用 @allietheicon]：TypeSafe Jevelopers Discord 的第一周真是疯狂 | Articles, tutorials & talksMore guides & websites | |
| typesafe-jev-ai-system-one-modelmeetcody.ai | — | MeetCody explainer | https://meetcody.ai/blog/typesafe-jev-ai-system-one-model/ | Spec sheet synthesis | Articles, tutorials & talksMore guides & websites | |
| Where Jev fits in web scrapingdev.to | — | Argues Jev cannot extract fields because it never writes a string, and shows the one scraping job it earns: a gate in front of extraction, with a requests and BeautifulSoup example. | Articles, tutorials & talksMore guides & websites | |
| Where should you use Jev?qiita.com | — | Japanese guide to choosing between rule-based code, classic machine learning, Jev, and LLMs, with the conditions under which Jev is the right tool. | Articles, tutorials & talksMore guides & websites | |
| You could have built Jevsgnt.ai | — | Short illustrated explainer arguing Jev is most likely an LLM that returns a single token, with pseudocode and comparisons to other projects that do the same, so you can reason about it from first principles. | Articles, tutorials & talksMore guides & websites | |
| 作者分享用 logprobs 读取 LLM 选项概率的单函数封装，并扩展到视觉模型aihot.news | — | 作者受 Jev 及 OpenJev、SemIf 启发，实现了一个单函数封装：让 LLM 只生成一个选项字母，再读取 top_logprobs 得到各选项概率。 | Articles, tutorials & talksMore guides & websites | |
| 在 Databricks SQL 中运行开源决策模型 SemIf-OpenJevaihot.news | — | Databricks 发布可导入 Notebook，支持在 SQL 中通过 ai_query 直接调用开源决策模型 SemIf-OpenJev，并返回结构化分类结果与选项概率。该流程借助 Serverless GPU 与 AI Runtime 自动下载模型、注册并创建 GPU Model Serving 端点，三步即可完成部署，示例用于将酒店评论分类为好评或差评。 | Articles, tutorials & talksMore guides & websites | |
| 在 Databricks SQL 中运行开源决策模型 SemIf-OpenJevwww.databricks.com | — | Databricks 发布可导入 Notebook，支持在 SQL 中通过 ai_query 直接调用开源决策模型 SemIf-OpenJev，并返回结构化分类结果与选项概率。该流程借助 Serverless GPU 与 AI Runtime 自动下载模型、注册并创建 GPU Model Serving 端点，三步即可完成部署，示例用于将酒店评论分类为好评或差评。 | Articles, tutorials & talksMore guides & websites | |
| 在人类璀璨的艺术中遨游，如此美妙，谢谢 Jevaihot.news | — | [引用 @ZHO_ZHO_ZHO]：Jev 正在疯狂工作（视频未加速 | Articles, tutorials & talksMore guides & websites | |
| 如何充分利用 Jev：分类模型、Jev 检查与意图驱动软件aihot.news | — | Jev 的分类模型正在刷屏时间线，文章介绍如何把主观问题转化为 Jev 检查，并探讨意图驱动软件的到来。 | Articles, tutorials & talksMore guides & websites | |
| 如何充分利用 Jev：分类模型、Jev 检查与意图驱动软件every.to | — | Jev 的分类模型正在刷屏时间线，文章介绍如何把主观问题转化为 Jev 检查，并探讨意图驱动软件的到来。 | Articles, tutorials & talksMore guides & websites | |
| 对 jev 命名感到厌倦aihot.news | — | 还有人厌倦了 jev 这件事吗？我们的品牌是桀骜不驯、疯狂的，不是"jev"（可能是因为我正处于风暴中心） 它有趣是因为给模型起名 jev 本身就很疯狂 （另外说这个可能也很疯狂，但我们还没有营销人员） | Articles, tutorials & talksMore guides & websites | |
| 小米 MiMo-V3 的 HySparse2 等本周 AI 论文aihot.news | — | 小米 MiMo 团队为即将推出的 MiMo-V3 打造了注意力架构 HySparse2，在 80B-A3B MoE 模型上把 prefill FLOPs 较 MiMo-V2 系列的 Hybrid SWA 降低 5.02x、较 HySparse 降低 2.92x，KV cache 从 12.09 GB 降至 2.69 GB。 | Articles, tutorials & talksMore guides & websites | |
| 开源项目 jevlike 通过逆向工程复现 Jev 类一次通过选项打分模型aihot.news | — | GitHub 项目 jevlike 发布一个独立起步模型，输入一段文本和 N 个文本选项，单次前向即返回每个选项的概率，输入输出形状与 TypeSafe 未公开设计的商用模型 Jev 相同。 | Articles, tutorials & talksMore guides & websites | |
| 快速通道申请通过了，可以玩起来了 😁aihot.news | — | 【引用 @hongming731】：最近几天 Jev 模型很火，BestBlogs 整理了一期专题，可以组队一起学习一下 😆 https://www.bestblogs.dev/explore/topics/typesafe-jev-release | Articles, tutorials & talksMore guides & websites | |
| 把 Jev 用作 Agent 编辑后的模糊 linter：规则筛选与置信度分层实验aihot.news | — | Michael Thiessen 实验将 Jev 作为 Agent harness 中编辑后运行的模糊 linter：把编码指南拆成无需额外上下文和推理的微小规则，并构建合成 eval 加 held out 集防止过拟合。 | Articles, tutorials & talksMore guides & websites | |
| 新模型能力再进化aihot.news | — | 它正在彻底改变新模型能做到的事 | Articles, tutorials & talksMore guides & websites | |
| 用 25 行 Python 实现 Jev：一个本地运行的分类概率模型aihot.news | — | 有人用 25 行 Python 代码复现了 Jev：加载 Qwen3-0.6B-GGUF 模型，对邮件分类任务输出 Legitimate、Spam、Phishing 三个选项的概率，示例中 Phishing 概率为 0.885。作者称这是恶搞博文，强调它不调用 API、不训练模型，只是本地快速分类，并推荐了 OpenJev 等更完整的开源实现。 | Articles, tutorials & talksMore guides & websites | |
| 用 25 行 Python 实现 Jev：一个本地运行的分类概率模型www.nobodywho.ai | — | 有人用 25 行 Python 代码复现了 Jev：加载 Qwen3-0.6B-GGUF 模型，对邮件分类任务输出 Legitimate、Spam、Phishing 三个选项的概率，示例中 Phishing 概率为 0.885。作者称这是恶搞博文，强调它不调用 API、不训练模型，只是本地快速分类，并推荐了 OpenJev 等更完整的开源实现。 | Articles, tutorials & talksMore guides & websites | |
| 用 Jev 和 Pi 构建自定义 harness 的思路aihot.news | — | 刚刚发布了一些关于使用 Jev 和 Pi 构建自定义 harness 的思路。 这是系列的第一篇。 其中一些思路包括 gates、routing 和 verifiers。 但在后续文章中，我计划更深入地探讨更新的思路，并对成本和效率进行基准测试。 | Articles, tutorials & talksMore guides & websites | |
| 用 Jev 整理 2.3K 篇 AI 论文，成本仅 0.14 美元aihot.news | — | 用 Jev 重新整理约 2.3K 篇 AI 研究论文，总成本 $0.14、耗时约 83 秒。Jev 与旧标签（由 DeepSeek V4 Flash 生成）一致率 75%，并找出约 579 处高置信度主题变更；人工抽检 30 处分歧后全部采纳，变更已在生产环境验证。作者认为通过组合 System One 与 System Two 模型可显著改进流水线。 | Articles, tutorials & talksMore guides & websites | |
| 用 Jev 概率 API 拼出的聊天机器人 jevchat：把杰夫变成（很烂的）聊天模型aihot.news | — | jevchat 把 Jev 的概率打分接口当作语言模型使用：每一步让 Jev 在给定问题和已生成回复的情况下，为字母表或 token 列表中每个候选符号返回概率，再由采样器归一化后抽取下一个符号，抽到 STOP 即结束。 | Articles, tutorials & talksMore guides & websites | |
| 用25行Python代码实现Jev：一个本地运行的分类概率模型aihot.news | — | 有人用25行Python代码复现了Jev：加载Qwen3-0.6B-GGUF模型，对提示词中的选项标签取logits并归一化为概率，示例中"Phishing"概率达0.885。作者称Jev本质是接收带选项的提示词并输出概率的分类器，速度快、本地运行、数据不外传。该文为戏仿博客，作者同时给出OpenJev等更完整的开源实现链接。 | Articles, tutorials & talksMore guides & websites | |
| 用豆包工作研究 Jev：从模型判断到软件决策aihot.news | — | 作者用豆包工作的计划模式、目标模式和任务队列完成对 Jev 的研究，产出五主题知识库、24 项声明核查记录和可点回证据的研究观察站。Jev 由 TypeSafe 发布，厂商报告 193.6 倍速度和 444.6 倍成本优势；独立早期测试中 24 份挪威语文档中位延迟 0.32 秒，加入限定词后 ECE 从 0.040 升至 0.116。 | Articles, tutorials & talksMore guides & websites | |
| 网友玩梗：人人都在喊jev jev jevaihot.news | — | 所有人：jev jev jev 我奶奶： | Articles, tutorials & talksMore guides & websites | |
| 豆包工作 /plan 与 /goal 功能获好评aihot.news | — | 用户用豆包工作的 /plan、/goal 及任务队列功能完成 40 份关于 Jev 的研究文件，并整理成五主题知识库和研究观察站。该用户称这些功能非常好用，并为字节团队点赞。 | Articles, tutorials & talksMore guides & websites |
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