What people are building with Jev

starmex

@starmexxx

JEV JUST HIT 18K STARS IN A WEEK AND BOOKS ZURICH TO LONDON ON GOOGLE FLIGHTS IN 7.1 SECONDS WHILE CHATGPT OPERATOR STILL NEEDS 3 MINUTES AT $200 A MONTH jev-ultrafast splits every browser decision into operation, click target, and typed text, so one llm request replaces five and the agent finishes the task before the page finishes animating [here's the setup i'd use:] 1. install it git clone http://github.com/browser-use/jev-ultrafast cd jev-ultrafast uv sync cp .env.example .env 2. add the keys typesafe_api_key → .env openrouter_key → .env (for mercury 2.5) 3. wire the models mercury 2.5 → typing (diffusion, milliseconds not tokens) gemini 3.1 → target selection glm 4.6 → fallback text helper 4. build around stages goal → snapshot → operation → target → execute → verify keep the dom snapshot separate from the click so a stale page never restarts the whole run 5. run it uv run jev localhost:8766 → start demo → run automatically 6. monetize it fiverr → "i book any flight, hotel, or form in 10 seconds" at $30 a task telegram bot → travel search on demand at $5 a query b2b → sell it to travel agencies as "one seat replaces one junior researcher" at $500/mo white label → wrap as a "concierge agent" and charge $20/mo per user same reason browser-use's cloud waitlist filled up in a week: one indexed action space, no screenshots, no restarts. http://github.com/browser-use/jev-ultrafast

CyrilXBT

@cyrilXBT

I built agents that run 200x faster and 400x cheaper than the standard Claude Code loop. Best case, not average, on the tasks where a $9 model was doing $0.0004 work the entire time and nobody noticed. Split reasoning from decision making. Let Claude Code write. Let Jev decide. From scratch, the whole architecture is below. follow @cyrilXBT

Kiyoro

@0xKiyoro

OPUS 5.5 IS MAKING YOUR AGENT DUMBER RIGHT NOW AND IT WILL NEVER TELL YOU. JEV CAUGHT IT. The default effort on Opus 5.5 dropped from high to medium. If your agent never set effort explicitly, it is now reasoning at a lower setting than the one you tested it on. No error, no warning, every health check still green. Jev sets effort on every single message, so the requests that went through it never changed. The ones that relied on the default did. That gap is how it showed up. The rest of the migration is loud. Each of these returns a 400 on the first call: > thinking: disabled, now rejected. Drop the field and set effort > tool_choice any and tool, removed. Use auto + strict > computer_20251124, retired. Use computer_toolset_20260801 > editing above a thinking block, rejected. Append only, or drop_block You will find all four in five minutes. The effort change you will not find at all. The cache has its own quiet trap. Hop Opus 5.5 to Sonnet 5 and back and a session that cost 3.32 costs 4.36, +31%, because Sonnet cannot read Opus's reasoning. Change effort at the top of a request or switch fast to standard mid-session and the cache is gone again. Jev picks speed once on turn one and sets effort per message, so it survives. The upgrade is still worth it. $4 / $20 per 1M instead of $5 / $25, cache reads at $0.20 instead of $0.50, and 66.4% on Terminal-Bench 4.0 against 57.9% for GPT-6 Astra. Write effort into every request before you touch the model ID. The default stopped meaning what you think.

Aron | TomorrowLab

@AronTomorrowLab

Jev is actually insane 605,461,046 tokens later: only 47% of the leads in our live campaigns actually fit the client's ICP we used to check ICP fit by sending agents to read every website (Claude Code or Clay). 409 companies took 38 minutes. fine for one campaign, but ofc not scalable for a whole database. now we write the client's ICP once (client.md), and Jev runs every company against it. 100k companies in a few minutes less volume, but better leads (and only relevant replies)

Arthur Marques

@ArthurLabMRP

Benchmarks suck. So I made Jev and Laya play chess instead ♟️ Insomnia project. Repo in the thread

B.AI

@BAI_AGI

📢 TypeSafe AI · Jev Is Now Live on http://B.AI API As the first System One model from @typesafeai, Jev is built for fast, structured decisions inside software. It does not generate text: send app state plus a typed question, and get a typed decision with probability and confidence—no JSON prompting, no output parsing. It evaluates Choice, Score, and Noul questions in parallel, responds in about 70–500ms, and costs $0.042 per million input tokens (output free), making it a fit for ticket routing, moderation, risk scoring, and agent branching. Now available on the http://B.AI API (access via Jev-1.13.0 or Jev-Latest)! 👉 Try now: https://chat.b.ai/chat 🔗 Learn more: https://docs.b.ai/llmservice/models/jev-1.13.0/

MalluCuler

@Farhan60291312

Benchmarked Laya locally on an ancient laptop: 💻 i5-5200U (2 cores) | 12GB RAM | 5400 RPM HDD Kept it resident in RAM and got sub-second (~630ms) typed decisions on pure CPU! 🔥 Great open-weight release by @Nandakishorm1. No GPU or cloud LLM needed for fast routing. #laya #jev

Hugging Models

@HuggingModels

Meet Open JEV DeBERTa V3 Large: a text classification model that doesn't just label, it makes typed decisions. Calibrated, open, and built for real-world trust. Here's why this model is turning heads.

Loutchone

@LiorNsnd

bon un peu de serieux, jev peut etre vraiment utile... pour préparer un workflow de qualification de leads après un audit client, je veux un premier tri de ce que l’agent propose avant de mettre les étapes dans Obsidian. j’ai donné à Jev le contexte de qualification avec les étapes candidates. À lui de choisir pour chacune : retenir, clarifier ou écarter. sur cet appel API, il retient la qualification par téléphone et le rappel des prospects injoignables. Par contre le rappel à J+2 ressort « à clarifier » : le contexte fourni ne fixe aucun délai. L’enrichissement LinkedIn est écarté, il était hors périmètre. la première image, c’est sa réponse réelle avec les probabilités. La deuxième, le workflow qu’on en a tiré, avec le délai toujours à confirmer. dans mon process, je garde la main sur cette présélection : je vérifie et je valide avant de l’intégrer à l’audit. Voilà où je vois l’intérêt de Jev dans mon taf.

MajdAICode

@MajdAICode

Following up on my earlier comment: I mapped out the coding-agent workflow I’m exploring in a short video—Cursor, a separate worktree for each feature, and Jev for narrow decisions 👇 x.com/MajdAICode/sta…

MajdAICode

@MajdAICode

One coding-agent pattern I’m exploring: /ticket-triage asks Jev where to look; Cursor checks the repo; Jev flags possible test gaps after the diff. PHPUnit/Playwright verify behavior, and I review before commit. Video: x.com/MajdAICode/sta…

mindshub

@MindsHub

Jev, TypeSafe’s decision model, joins MindsHub's free tier. API docs: docs.mindshub.ai/inference/deci…

Ram Vinjamuri

@RamV2003

(1/5) trying out jev this week with all the hype. kept it apples to apples vs the fast models it competes with: llama 3.1 8b and deepseek v4 flash same labelled inputs. jev: 235ms median, 82% accurate. llama: 497ms, 68%. deepseek flash was accurate but ~2s genuinely cool. then some odd emergent behaviour: no position or label invariance

Artificial Intelligence Papers

@SciFi

Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents Dongming Jiang, Yi Li, Bingzhe Li arxiv.org/abs/2609.23986 [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙻𝙶]

TypeLLM

@TypeLLM

Jev @typesafeai, but with thinking? TypeLLM can think before producing a type-safe output. It dramatically boosts accuracy without any fine-tuning—surpassing Jev and GPT-5.6 Luna, and coming close to GPT-6 Astra! Open-source: github.com/TypeLLM/TypeLLM

Ishwar

@WesternCube

Instant Claude Code compaction is my favorite use of Jev so far For people going to complain about me letting my context get so long, this was a server redeploy task because Oracle terminated my free VPS with no explanation so it had a lot of moving pieces

Zorawar Purohit

@ZorawarPurohit

Do check out this public repo (github.com/M37Labs/laya-m…) by @m37labs for those interested in understanding Laya-MLX, a new kind of encoder-only architecture and its application. Emerging systems like Jev, Laya can serve as a `System 1` brain in different implementations.

Adam Chester 🏴‍☠️

@_xpn_

Turns out Jev is also good (and sooo quick) at picking out "sensitive files" from a file share 👀

Adithya Thatipalli

@adithatipalli

After seeing so much Hype around Jev, I started using it inside my Hermes as a decision and routing mechanism for a AI video pipeline I was working on But now, I am Changing that thought. Laya is a 421M local decision model by @Nandakishorm1 . Same job as Jev -> choice, score, yes/no with a probability, But it runs on my machine. No API hop. No per-token meter. ~33ms. Weights stay here. For an AI film pipeline, that is the actual bottleneck. >Not “write a better prompt.” >The expensive part is sending the wrong shot to the wrong generator. Before Seedance / Kling / Minimax / Veo / Flux / GPT Image burns a credit, Laya now answers four things in one pass: → which model gets this shot → text-only, character-ref, or first-frame → is the brief ready, or does the enhancer skill run first → how hard is identity lock Sol still writes. Kimi still helps. Video models still generate. Laya only decides. Jev was a good hosted nervous system. For shot routing I don’t need a hosted brain. I need a local one that sits in front of the generate button and says go / wait / switch model. 421M. Local first. That is the router now.

► Alex Rivas | Joseador y Desarrollador ☻

@alex_rivas_v

🥊Laya vs Jev He diseñado un experimento para probar que modelo es mejor, si Jev o Laya, y lo hice poniendolos a jugar ajedrez. La verdad es que ambos son bastante tontos para esto, y no va de mejorar prompts, aunque seguiré probando algunas formas de mejorar las partidas.

Carlos

@alg0agent

Endless possibilities. Dynamic UI generated on the fly by detecting your input intent. Powered by Jev. Check out the demo & repo in the thread

BurstingBagel 🥯

@burstingbagel

Built a jev filter to catch mercenary farmers before TGE. 40k wallets screened in 4 seconds. Community size after: 2. Pre-TGE teams DMs open

Cesar Favero

@cesaremuszka

botei o jev no codexrouter, sinto o trabalho muito mais otimizado, vou criar um harness personalizado para o codexrouter, sinto que da pra fazer muitas melhorias nesse fluxo o jev por enquanto no codexrouter está com poucas funções

CyrilXBT

@cyrilXBT

With a single prompt, the Jev + Claude Code stack can make AI agents 200× faster and 400× cheaper. The secret: agents that run wide not long. I broke down the complete system in 3 visuals. Save this. You’ll want it later. Follow @cyrilxbt for more AI updates.

erKam 🅰️

@erkamyaman_ng

in 2026, why is CLAUDE.md still a suggestion? one @typesafeai Jev request checks every Claude Code reply and edit against every rule you wrote. break a rule, Claude gets it quoted back and rewrites. 348ms per check. 93.3% of broken rules caught on our benchmark.

Chris Goldammer

@floor_per_area

Technical idea, just brainstorming: Make chat responses 10x faster using Jev and pre-structured SQL. Any idea if this could work?

Neha Sharma

@hellonehha

As Jev (@typesafeai) is the talk of the town, and people are having fun finding new use cases for it. I built a real-time demo of smart AI agent routing for anyone working on AI tools, agents, or products. this is what I have done for one of my project. https://youtu.be/c8qV0f4XnRY

Archive

@ArchiveExplorer

Holy crap, who the hell did this?!! Someone open-sourced a Jev-powered second brain for AI agents and lets you plug it straight into your own agent. GitHub's already at 925 stars, repo's called Hermes-Jev-Skills. You just run one install command, and Jev takes over every tiny decision your agent burns premium tokens on. Which model answers, which skill to load, which memories matter, what survives when context gets cut. Give it your computer or browser and it'll pick the next click, but only from actions you already marked safe. It'll sort your inbox into lanes, strip hidden prompt injections out of search results, and triage every message in ~0.4 seconds. Claude Code and Codex support - they threw that in too. No dependencies, no patching Hermes core, and your API key never touches the agent Link's right here: http://github.com/kerpopule/hermes-jev-skills The catch? Jev itself is a paid cloud API. The skills run on your machine; the decisions don't. But hey, a decision costs a fraction of a cent, everything fails open if Jev goes down, and your expensive model finally gets to do only the thinking. Who needs a damn frontier model picking which file to open!