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Jev AI Guides cases
37 public posts filed under Guides. English summaries on this page, with a link to each case.
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@ArchimedesLore describes a Jev decision pipeline: STATE, typed questions, Jev System One, a 0.85 gate, then deterministic code.
WHOEVER LEAKED THIS JEV DECISION PIPELINE SHOULD BE WRITING THE RFC I thought routing an agent through five steps meant five prompts, five parsers, and five pla…
Quoted @0xCodila
@ericosiu argues Jev Images is underused for marketing: AI search visibility, ad creative gaps, CRO audits, pain-point reports.
Jev Images is a BIG deal for marketing, and it's being slept on. Scroll down for an unfair advantage. Some use cases: • AI search visibility report • Competitor…
@baboonzero uses Jev, TypeSafe's System 1 model, to decide where a teaching explanation starts, ends, and stands alone, in a Super IC write-up.
I have more than a hundred hours of recorded teaching. Finding useful two-minute clips means deciding where an explanation starts, where it ends and whether it…
@Asteri_eth listed ten GitHub repos already shipping on Jev, from a browser agent to a Claude Code bridge and a LangGraph router.
People are already shipping a browser agent, a Claude Code bridge and a LangGraph router on top of Jev. Here are all 10 repos. Got API access and no idea where…
@_avichawla explains standard RAG (chunk → embed → retrieve → optional rerank → LLM) versus adding Jev into the RAG path, with an animated explainer video.
RAG vs. Jev + RAG, clearly explained! In a standard RAG setup, documents are split into chunks, converted into embeddings, and stored in a vector DB. When a que…
Quoted @_avichawla
@Sumanth_077 explains jevgrep: Jev judges which folders, files, and declarations are relevant, then returns source excerpts for the coding agent.
Jev for semantic code search! jevgrep is a CLI for coding agents that helps them find relevant code when they know what a behavior does, but not where it lives…
Quoted @Sumanth_077
@PageIndexAI shows long-document search with TypeSafe Jev and PageIndex, with no vector database and no embeddings.
Inspired by @EGafni’s Twitter thread on combining Jev with PageIndex. We show how to build long-document search with @typesafeai Jev + PageIndex. No vector data…
@N01ennn maps TypeSafe’s agent docs into nine Jev patterns under one rule: the LLM thinks, Jev decides, code acts — routing, guardrails, gating, triage, and more.
this is pure f*cking treasure TypeSafeAI engineers spelled out in their docs exactly how Jev fits inside an agent, and these patterns are perfect one rule behin…
Quoted @N01ennn
@Av1dlive points to one article they say contains the code, architecture, and diagrams needed to start building with Jev and follow the build.
everything you need to start building with jev, in one article. code, architecture, diagrams... everything you need to follow the build and make it your own. ht…
@0xRicker describes “Jev Engineering” as turning an agent stack into a control system and moving the expensive model out of every decision loop. They cite tests up to 193x faster and 444x cheaper, and say the model should not decide everything.
Jev Engineering is what turns an agent stack into an actual control system and moves the expensive model out of every decision loop. and up to 193x faster and 4…
@markjaquith says this post is their new shortest explanation of what Jev is.
This is my new shortest “what is Jev” explanation
@akshay_pachaar explains LLMs versus Jev: the difference is not that Jev generates faster, because Jev does not generate text. A traditional LLM produces an answer one token at a time.
LLMs vs. Jev, clearly explained! TL;DR The key difference is not that Jev generates faster. Jev does not generate text at all. A traditional LLM receives contex…
@0xCodez quotes TypeSafe founder Diogo Almeida, formerly of OpenAI, saying the next era is not Claude Code or Codex with a human in the loop, and describing Jev as what comes next: about 200x faster, 400x cheaper, and, in his words, zero hallucination.
Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code or Codex era, they are still part of the assistance era with human in the loop - JE…
@jonymusky made a 4-minute video in Spanish explaining what Jev is, what it does not do, and why it changes where you put a model.
Hice un video de 4 min (en español) explicando qué es Jev, qué no hace, y por qué cambia dónde ponés un modelo.
@DataChaz calls Jev a possible “Internet moment” and repeats TypeSafe’s reported figures of up to 193x faster and 444x cheaper versus Claude Fable 5.1 and GPT-6 Astra in tests. They point to a 10-page article by @0xCodila on what Jev is, how to use it, and where the claimed 100x advantage comes from.
Jev might genuinely be an “Internet moment” for AI. TypeSafe reports up to 193x faster and 444x cheaper performance in tests with Claude Fable 5.1 and GPT-6 Ast…
@0xRicker argues Jev could be the missing control layer for agents, routing actions in milliseconds instead of spending 5–20 seconds and an expensive LLM call on every next step. They link an article on how.
Jev could become the control layer AI agents have been missing. Instead of spending 5–20 seconds and expensive LLM calls deciding every next step, it can route…
@MKhordoo says classifiers already existed, including BERT, DeBERTa, GLiNER, and fine-tuned heads, and argues Jev is the missing piece: labels defined in code, a typed answer plus confidence, and no extra LLM call.
Yes, classifiers existed. BERT, DeBERTa, GLiNER, fine-tuned heads... Jev is that missing piece. Labels defined in code, typed answer plus confidence, no extra L…
@zodchiii quotes the Jev founder, formerly of OpenAI, calling Jev the biggest breakthrough they have worked on and saying it is beating everything. The post says he explains in 5 minutes why older LLMs were good at talking and bad at deciding.
Jev Founder (ex-OpenAI): "I believe JEV is the biggest breakthrough we've ever worked on This sounds too good to be true but it's beating everything" In 5 minut…
@moritzkremb posts a full Jev tutorial: what it is, how to build with it, API setup, then demos of a voice-controlled browser, AI memory, and a YouTube predictor.
Full Jev Tutorial What it is, how you can build with it and what new applications it can unlock → 0:00 Intro → 0:34 Jev explained → 4:06 API setup → 5:59 Demo 1…
@MatijaSosic posts a 45-second TL;DR of Jev. They say the core idea is simple, but another video made it hard to understand.
Here's a 45-second TL;DR on Jev. I find the core idea beautifully simple, but the video made it really hard to understand. Hope you find it helpful. https://t.c…
Quoted @CompleteSkeptic
@sydneyrunkle says Jev from TypeSafe returns structured output instead of generated text, which suits classification such as model routing, tool selection, and guardrails, and that it is fast and cheap compared with LLMs on those tasks.
instead of generating text, jev from @typesafeai generates structured output this makes it great for classification tasks like model routing, tool selection/sea…
@hot_town posts an explainer covering how Jev differs from an LLM, how it works, real-life examples, and when to use it.
Jev is here. - how is different from an LLM? - how does it work under-the-hood? - what are some real life examples? - when should I use it? I answer all that be…
@SUOHA_AI posted a 50-second video explaining Jev and its architecture: a System 1 model from Diogo Almeida after two years, one parallel pass instead of token-by-token generation, RL for calibrated decisions, and typed outputs. They say the video also shows an 8.5-second autoregressive delay next to a real-time minigame, referencing a @MatijaSosic demo.
很多人没看懂爆火的JEV是什么？ 所以我做了一个 50 秒的演示视频，带大家把海外刷屏的 JEV 以及它背后的底层架构看透👇（见下方视频） JEV 是前 OpenAI、参与 ChatGPT / RLHF 的 @CompleteSkeptic（Diogo Almeida）创办 @typesafeai 后的第一款模型 他在…
Quoted @SUOHA_AI