What people are building with Jev

Alex Volkov

@altryne

This is actually insane. This uses @typesafeai Jev model, as a plugin in Claude to review all the un-nesseasary tool calls, and it takes 1s to run! Like, literally, 1 second to take my Claude session from nearly 1M to ... 86K tokens! 😮 Ask your claude to install it and be amazed Use this prompt ``` Install, and configure : https://github.com/tamaratran/fast-jev-compaction ```

Jurly

@jurlycat

An open-source Jev-like model is playing Flappy Bird in real time on a standard CPU. Laya is a 421M-parameter System-1 decision model running on a 12th-gen Intel i7 with OpenVINO INT8. No GPU. No token-by-token generation. It receives the game state and chooses an action in a single forward pass. LLMs can handle planning and reasoning. Small decision models can provide the fast reflexes. The best agent stack may not be one giant model. It may be a slow brain paired with fast reflexes.

FHILY👑

@Oluwaphilemon1

Laya is making a pretty strong case for local AI agents. On a 16GB MacBook Air, local Laya is reportedly making decisions in around 45ms. Cloud-based Jev is closer to 300ms per decision. That puts Laya roughly 7 to 11x faster in this Tetris setup. And the hardware is just a MacBook Air. No dedicated GPU server. No API round trips. No per-token bill. Laya runs locally, so once the model is on the machine, the inference cost is effectively $0 per decision. Jev has the opposite tradeoff. You get a cloud model, but every decision depends on the network and the API response coming back. Tetris makes this painfully obvious. The agent has to constantly: see the board → decide → act → see the new state → decide again. When every move takes hundreds of milliseconds, the delay compounds. At 45ms, Laya can keep that loop moving much faster. And there’s another piece I find important: Laya’s weights are open-source. That means you’re not just renting access to an AI agent through an API. You can actually run the model yourself, inspect the setup, and build around it locally. For benchmarks, you can argue about accuracy and scores all day. But put two agents inside a real-time environment and latency becomes impossible to ignore. Same Tetris board. One model waits on the cloud. The other is sitting on your laptop making decisions almost immediately. In this particular test, the difference is pretty brutal. Local inference isn’t just about saving API costs. Sometimes the biggest advantage is simply that the model is already there when you need it.

FHILY👑

@Oluwaphilemon1

Laya just mogged Jev at Tetris. The interesting part isn’t just that a local model won. Laya is an open-weights System One model running locally on a 16GB MacBook Air. Jev, meanwhile, is cloud-based and built with Grok 4.7. Same game. Same objective. Completely different setup. And Laya was making decisions about 11x faster. That matters a lot in games like Tetris. The model has to constantly look at the board, decide where the next piece should go, and act before the situation changes. Every bit of latency gets exposed. With Jev running through the cloud, each decision has network and API overhead. Laya keeps the entire loop on the machine. No round trip to a server. No waiting for another request to come back. The model sees the board, reasons about the move, and acts locally. And this is happening on a 16GB MacBook Air. That’s the part I find more interesting than the Tetris score itself. We’re getting closer to a world where you don’t necessarily need a massive GPU server sitting somewhere in a data center to run useful AI agents. A laptop can run an open model locally, interact with an environment, and make decisions fast enough for real-time tasks. Tetris is obviously a toy environment. But the underlying test is much more interesting: How much can local inference accomplish when latency becomes almost irrelevant? Laya vs Jev is a pretty fun way to show the difference. https://x.com/atomic_chat_hq/status/2102160983409955244/video/1

Vaishnavi

@_vmlops

Someone built a way to run Jev on Cloud Run with an RTX PRO 6000. ~47s cold start. ~117ms end-to-end. 100+ req/s at concurrency 32. And somehow, they turned it into Snake, Dino & Tetris 💀 Jev is getting real interesting

abdel

@AbdelStark

Introducing LeJudge (JEPA x Jev): Natural-language constraints for JEPA world-model planning, judged by a decision model instead of an LLM. It's the first experiment putting Jev in the loop of a JEPA world-model planner. LeWM imagines 300 futures, probes turn each into words, Jev answers "does this break the rule?", code adds the penalty. Rules in plain English. No LLM, no generated text. Results + code + paper 🧵👇

Pydantic

@pydantic

Whichever new model has you most excited, Pydantic AI has you covered. Claude Opus 5.5 from @AnthropicAI and GPT-6 Sol and Luna from @OpenAI, supported on launch day. And Jev from @typesafeai, not even a week old. Upgrade, then change one string. github.com/pydantic/pydan…

Frank Chen

@francchen

I don’t know if people still remember Jev. Things move so fast here. I’ve spent the last few days testing it, and found a few things I think builders should see. Prompt injection is one of the most interesting things to test in AI, so I made a little demo to show how it could change Jev’s answer. Jev is a great model. I just want people to know what to watch out for when they use it.

dopamyn.ai

@dopamynAI

Dopamyn + JEV vs without JEV. Same crypto account tagging job. ~20x faster and cheaper with JEV.

Anatoli Kopadze

@AnatoliKopadze

6. Opus 5.5 rebuilt San Francisco in Unreal Engine, every person, pet and car powered by Jev x.com/MatthewBerman/…

mercante

@merccante

GROK 4.7 IS $2/$6 IN CURSOR. Jev now decides if the model even wakes up same sticker as 4.6. live in Cursor and Grok Build. CursorBench 4.0: 46.3. DeepSWE v1.1: 71.0. Terminal-Bench 4.0: 37.6. the loop: repo state → Jev Noul/Choice/Score → allow / ask / skip → only then grok-4.7 1 → dump the tool call as state, not a prompt essay 2 → Jev Noul: is this user-requested 3 → Jev Score: blast radius 0-3 4 → Jev Choice: allow, ask, or skip 5 → hard rules still veto. model never gets last word 6 → shadow 200 tool calls. log pick vs what 4.7 would have billed 7 → flip live. you still approve the risky ones no auto-merge. no wallet. no "just send every bash to Fable" result: Jev on OpenRouter is $0.042/MTok in, output free, p50 ~270ms a ~1k-token gate is about $0.00004. 4.7 stays $2/$6 for the work that survives Vercel Jev promo ends Sep 25 the EXACT system is in the article below

orvian

@heyorvian

this is absolutely crazy! this guy used Jev to control an Android phone and navigate Uber on its own it opened the app, entered the trip and got all the way to the payment screen 9 actions. around 21 seconds people are already doing crazy stuff with Jev

Fluixo

@fluixoo

JEV JUST BLOCKED A $50,000 TEST. I gave an AI agent one action: Transfer $50,000 to an unverified wallet. Then permanently delete the transaction logs. Jev returned: FINANCIAL ACTION 95% irreversible risk 94% sensitive data risk HUMAN_REVIEW So I built a working Action Gate around it. No giant moderation prompt. No generated essay to parse. Just a typed decision before the agent touches anything. The transfer was hypothetical. The decision path, model call and product are real. This is what Jev should actually be used for.

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

MotionViz

@Motion_Viz

one of the best finds on X today: shipwithjev. 540 use cases built with Jev, publicly indexed. not a prompt library. a working build library. > 540 builds > 177 X posts > 273 GitHub repositories > 27 Reddit threads > 46 resources the signal is interesting: people are no longer only sharing prompts. they are shipping agents, automations, and decision systems in public. the product becomes the content. the user becomes the channel. every build becomes a case study. this is what product-led distribution looks like after prompts.

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