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Made Jev fight OpenJev in a first person shooter.

Jev will be super helpful for agents to make split second decisions in workflows, data classification, judgment calls, and hundreds of other use-cases in the enterprise. Here's a quick demo with Box and Jev to make that real. The demo pulls an incident report from Box, asks whether it's customer-facing and how severe it is, moves the file into escalate, monitor, or review folders, and sets a meta

Built a tax document classifier with Jev. We ingest thousands of tax documents using an LLM pipeline I built last tax season. I read multiple articles as late as April this year claiming AI fails at tax document classification. Wasn't the case back then, and it's proved wrong again now. Jev classifies 100% of our tax document corpus at $0.001 per page. 34x cheaper and 6x faster than the LLM s

jav @typesafeai - jestem pod wrażeniem 🤩 Cool. Napisałem kolejne demo. Myślę, że teraz to widać potęgę modelu ... i o co chodzi i widać prawdziwą wartość biznesową. Akt I - X-ray: „jedno zapytanie, wszystkie pytania naraz" - dla przykładowej, krótkiej notatki prasowej to 67 osądów w ~1 s za $0.0002; dla dłuższego RAPORTU może być i 145 osądów w 1,5 s za $0.0005. Skoro nie ma tokenów wyjściowyc

I've had access to Jev by @typesafeai for about 30 hours now and I can't stop thinking of the million places it can be used 🤯 Meanwhile, checkout this fun snake game I built where 2 Jev's compete with each other to take the trophy 🏆 Try it out here 🔗 It's open source btw The low latency made it all possible to make such a fun project which requires input in real time 🚀

jev is INSANE. we gave it a library of 11 million+ videos on tiktok & ig and asked it to find viral "bloom nutrition" content. in 20 seconds, it gave us over 384 videos, breaking down the hook, format & angle that made each one go viral. (only costed $0.09 in token usage) jev also finds, watches, analyzes and breaks down viral videos and creators, then writes briefs and scripts for you, based

Jev unlocks SO many awesome new ideas. I built a macOS app that monitors my Downloads folder along with a customisable set of rules. Is the downloaded file an invoice? Move it to a special folder with the correct filename. No other LLM calls involved - just Jev!

Jev unlocked a new way to stop AI agents from spinning. Agents do not always repeat the same action. Sometimes they try different things based on the same wrong assumption. I built a semantic circuit breaker around that. In one stuck run: 12 model calls -> 4 19.9K tokens -> 5.1K

Just hacked a fun little project - Skill Router (Open source) using Claude and Jev @typesafeai Why? I have ~ 90 Claude Code skills installed and use maybe five. The rest are fine, I just forgot they existed, and Claude won't reach for a skill on its own unless you call it. So I built myself a little personal skill-router:

I built a voice controlled computer-use for my mac using @typesafeai's Jev and it's INSANE how fast it is! I can dictate "open the notes app and create..." and the app opens before I even finish my sentence.

Just trying out Jev, I made a Chrome extension that: - Listens to your YouTube audio (optional) - Detects if it gets to a sponsor segment - Skips it ➡️➡️➡️ - All in real-time while costing ~$0.005 per video Prototype project, BYOK, open-source:

pub-sub without sub @typesafeai jev does the routing almost instantly demo link below

Wow, @typesafeai Jev is a game changer for voice-driven UI. Responsiveness really matters for an experience to 'feel' right, and this certainly does the job! Latency here is not optimal given I'm in London and calling out to us-west. But still, super snappy. In this video, Jamcat is using Pipecat to orchestrate a real-time handover between a command agent (Jev) that drives the UI, and voice agen

You can use Jev to assemble interfaces on the fly. Here’s an example I hacked together with Astra. In this demo, Jev turns natural language requests into UI plans in roughly 1 second or less. It’s 6x faster than Gemini 3.8 Flash and GPT 5.6 Luna. Cheaper, too.

The speed and cost is going to enable use cases that haven't been possible before. Here's a quick demo of live decisions of what slide to pull up as I'm speaking, powered by @typesafeai Jev. Also, sorry for the sniffling. I have a bit of a cold.

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: Voice-controlled browser → 11:33 Demo 2: AI memory → 17:27 Demo 3: YouTube predictor

so I gave jev full control of the falcon-9 class rocket full launch to landing no autopilot, no scripted trajectories, no safety veto in the code every decision was made by jev ( you can see decisions at the bottom of the video) and yeah it was able to land the booster and all this with just 245 calls and and $0.04 in API calls for sim I used mujoco and the configurations were 42m boost

I built a trading agent with the all new @typesafeai Jev. You can try it here : Just put your own API key and start playing or clone it from and test it locally. To my surprise the agent was able to take a trade of 2.405 $ETH and took a profit of roughly 0.23%. It is very low but testing a new model in town is genuinely crazy. @CompleteSkeptic did you guys tested your model like this ? Let'

Now we're cooking. Got @typesafeai's Jev + AXe controlling the iOS simulator now ultra fast at a fraction of the cost of using an LLM. This is game changing!

视觉版 Jev 来了 仿照着社区的思路，把候选结果映射成固定标签，prefill 完直接采样，限制输出 token，采用多模态的 qwen-3.5-0.8B 4bit 量化部署在本地，16g m4 mbp 顺利运行 叠甲：这只是在 infer 层小小的复现一下 jev 的形式，肯定不如各种成熟推理框架效率高，也肯定不是 jev 的正在原理，但是作为 inference 的小入门仍旧是不错的 demo 如下 200ms 实现《三色货品分拣》

They say you’re six connections away from anyone on Earth. So I tried Jev from @typesafeai and built this: enter your X handle and someone you’d love to reach. It finds a chain of real follow connections that could get your message there, under 2 seconds! Apparently, Luke Skywalker is just two people away from me. Who’s your impossible person? I can drop the link below if you want to try. @Mark

I just built Easy-Jev, a Jev demo anyone can try right now! Change the inputs and watch the classifications change in real time. I did my master's thesis on classification models so it's fun to see a modernized version of a very useful alg Typesafe are correct in that LLMs are just one approach to intelligence, and I think as time goes forward we’re going to see a hybrid of dif models splitting

I got access to Jev by @typesafeai today morning and I built a cool use case for it Introducing DiffJury - simply paste any public PR link and Jev tells you immediately if it's safe to merge or does it require review ✅ 🔗 Feel free to try it out here - Imagine Jev being able to tell you if you should merge a PR with grounded context of your codebase. that's what we're building at @graphify 👀

i have created moderation guard based on typesafe's jev its fast and accurate live demo you can:

I got access to @typesafeai's Jev model and got it to play Pac-man Jev decides which direction Pac-Man should turn at each junction, given the maze state as JSON, and plays in realtime. Demo link →

I built a browser extension with Jev @typesafeai that can hide/collapse posts on X based on natural language. It's so fast that it's not noticeable and insanely cheap...this must be the future of "ad blockers" and content firewalls.

I made @typesafeai 's new ultra fast model, Jev, generate text, even though it shouldn't, that's fine because I can't read, and it can't write** ** up to 20 words for 0.5$, what a steal

Built an alternative version of @typesafeai but on @cerebras with Qwen 3.8 27b. Similar quality, similar performance, but vastly different cost. TypeSafe was way cheaper, and did beat Qwen on performance. Closest we can get using LLMs I think. Source:

built `jev-review` @typesafeai it's an experimental, local-first MCP plugin that gives coding agents a score quality feedback loop across different metrics. agents call jev while they work, get scored, make improvements, and repeat the loop try below 👇

Jev is actually insane. We benchmarked it, and the results completely change the game for us: > 500 real-time agents > running in parallel > in a 3D environment The preliminary results are crazy: > 500ms average latency > 35 API calls/s > all with a naive implementation We did 0 optimizations! The bottleneck is not Intelligence latency anymore. It's the first System 1 LLM. Crazy times.

Built a model router with Jev by @typesafeai. Jev decides what model fits your request best and the request is sent to that model.

I talk. Codex clicks. Watch the browser go⚡ This video is 1× !!! speed. Built Jev Browser: an open-source adapter for your existing Codex browser tools. Say the task naturally; Codex plans, Jev selects, the browser moves. @sama @OpenAI make this native? @DarioAmodei @hackgoofer

Ran a dumb experiment last night: used Jev by @typesafeai (a decision/classifier model, not a text-gen model) to write C++. At every step I gave it the finite set of grammatically valid next tokens and let it just... pick one. No free-form generation, no hallucinated syntax, purely "choose from this legal set." It's not built for this, that's what made it fun. Got a working piece of C++ out of a

Breaking: Browser Use + Jev = Ultrafast ⚡ Findings flights took 7s and cost only $0.0039 🤯 > new action space every step > DOM state space > small LLM fallback to type (this video is at 1x speed btw) Built a tiny open source browser agent. try it below ↓

Jev VS Fable 5.1 VS GPT-6 Astra: chess typesafe's new Jev V13 isn't an LLM. it doesn't chat, doesn't explain, doesn't write code — it only makes decisions. so we made it play blitz against frontier LLMs. the test: 5+0 blitz. every move is one API call. Jev V13 vs Fable 5.1: • fable outplayed it. by move 29 it was +16 in material and even promoted a second queen • but it kept burning 6-15 secon

I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link →

Jev by @typesafeai works quite well for drone applications. Made this in 15 minutes and only cost 10 cents. Repo:

jev + kernel browser use demo pretty freaking cool! so fast! try it yourself:

I got early access to @typesafeai's new Jev model and built a demo of it playing tetris The generation speed is so fast that it's pushing blocks down This feels like another "this changes everything" moment

typesafe's jev is fun! live demo you can play with:

Testing apps with @typesafeai jev & @opencode will be soooo fast small demo:

i built computer use using @typesafeai ! it is 155x cheaper than opus 5, ~20x faster, and generalizes across OS's more on how it works in the vid & thread below:

On the Jev waitlist? You can use not Jev (but close enough) right here: browser demo

i made an llm from first principles with Jev 29 yes/no questions per character: should the next key be a–z, space, comma, or period? highest probability gets append to it, then fed the updated text back in & repeat an autoregressive loop made out of a classifier

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.

Another brilliant launch for developers: and its 20-200x faster than LLMs because it skips token-by-token generation entirely. TypeSafe AI just launched Jev, > 20-200x faster >40-400x cheaper (w/ output tokens free) > Frontier composable intelligence optimized for decisions So Jev is an AI model built to make software decisions instead of writing text for people. A normal LLM answers by genera

love jev, but upset it - isn't open source? - it lack vision capability? We fixed all of that, introducing A fully open source library which takes any HF model and Jev-ify it, with an API endpoint Now on github, and live in production at @FeatherlessAI

JEV is insane for competitor research! We gave it Resilia's ad library It classified 1,891 ads in 19 seconds for $0.12 costumer journey step + Ad style And a complete deep analysis of the account Coming soon to maxfusion MCP

I got access to Jev and immediately plugged it into Claude Code to see if it actually FIXES the biggest pain of agent workflows: context compaction > result: pretty wild Quick context for those who missed it: Every Claude Code session has a context window that fills up fast with tool calls (file reads, grep outputs, logs, etc). When it fills up, the agent runs "compaction" - asks a separate LL

found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant

jev is INSANE. in 40 seconds it broke down 724 live ads from 37 brands. every hook. every format. offer. cta. awareness stage. landing page mismatch. used 9 cents of tokens. (will be avail in @stealads + mcp)

Inspired by @typesafeai , here is a Jev-compatible public API to play with It runs a comparable open model (Qwen3.6-35B-A3B), and just uses SGLang radix cache to preserve the prefill reuse / really fast parallel systemone generation - 64 tasks in <1s.

TypeGPU + ruNNtime + Jev @typesafeai is a very fun combo :D ruNNtime gives me efficient local inference, TypeGPU lets inference and rendering share GPU resources directly with zero copy. That’s 3 separate NN inferences plus rendering, all happening in realtime Since we control the pipeline, Jev can just sit in the middle and add the semantic bit. camera + mic → Moonshine + YOLO26 + DepthART → J

Jev v1.13 dodges rockets with probability calculation 🚀 @typesafeai's new non-LLM model returns decisions instead of text so we had it calculate a safe tile every 330 ms while rockets fell and it survived 25 of 26 for under a cent Run Jev via API ->

i used jev from typesafeai to build a model router cli you give in a task and current ai subscriptions you have, the jev tells you which agent/model should pick it up pair this with herdr and it will be a killer combo jev was the missing bit that makes this whole thing possible by being a super fast judge

Jev + Kimi K3 for fraud detection! TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07. Video is not sped up, check out the live run! Here was my process: I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds. An underrated feature a

Introducing the Papers with Code MCP server! To celebrate its release, I've asked Claude Code to research the architecture of Jev by @typesafeai using the `search_papers` tool. Here's what it came up with: "If Jev is architecturally anything public, it's a large schema-conditioned bidirectional encoder with parallel label-query heads (GLiNER/ML-Decoder shape) trained with a proper-scoring-rule

got Jev to review my PRs. ~200x cheaper than Claude and it answers in half a second 6 real PRs in the video. $0.00007 each. 1,000 PRs = 7 cents vs ~$14.50 on Opus 5 paste a diff → ONE call to @typesafeai → 14 typed checks come back as probabilities: hardcoded secret, sql injection, touches auth, deletes tests, breaks api, migration, debug leftovers, does the description actually match the diff,

話題のJevで架空のペルソナ150人に製品の導入意向を聞く仕組みを作ってみた。150人ごとに12個の質問をAPIで投げる。 実際にかかった費用は、1.8円でかかった時間は約5秒。十分爆速だけど日本↔︎アメリカのレイテンシが含まれているので本来はもっと早いはず

🚨 I gave the Trump vs Kamala debate a live BS meter using Jev every sentence, both candidates, 5 yes/no questions each 1,191 Jev calls / 1.18M tokens / 415 ms median total cost : $0.0497 same questions for both, clips picked by one fixed rule, not a fact-check

I used Jev to classify 1,018 AI research papers. The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each paper with DeepSeek V4 Flash 2. Send the title + summary + 24 possible topics to Jev 3. Use Jev to classify each paper 4. Visualize everything on The summaries cost $3.99 on @togethercompute. The classifications cost $0.08 on @typesaf

卧槽，JEV 的出现很可能就是自动智能交易的起点。 昨天JEV在AI 圈刷屏，今天 Monad 工程师就用JEV做了一个自动交易机器人。 实时读取 MON/USDC 价格，JEV 不负责写分析报告，也不跟你解释一堆逻辑，它只做最核心的事，判断 Buy 还是 Sell，再给出置信度，然后直接把交易发到 Kuru 的链上订单簿执行。 这一下我突然明白 JEV 为什么要做成一个不会说话的模型了。 交易根本不需要 AI 每 300ms 给你写一篇小作文，它需要的是： 行情进来→判断→下单→新行情→重新判断，而且这一整套循环必须足够快、足够便宜。 而 Monad 现在刚好每 300ms 出一个区块，Kuru 又是链上订单簿。高性能链负责执行，JEV 负责决策，两个东西拼起来，已经有点AI 原生交易系统的味道了。 未来的交易 Agent，不一定需要一个会写研报的超级大模型，它更需要一个能

okay so browser use is a legitimate use case agent-browser <> Jev loop goal: wiki race from 'Coffee' to 'Artificial Intelligence'

Top 5 Jev Alternatives — I have been running a benchmark since this morning on every Jev alternative against Jev. So far, none have beaten it on accuracy but some have slightly less accuracy with better speed. 1. djev — slightly lower accuracy, slightly faster, worthy tradeoff. 2. Simplejev-qwen38-27b — best open source replacement today, but needs a 27B rig (DGX Spark or similar). 3. Reflex-4b — 2-3x faster, ~5% less accurate, Apache 2.0. 4. Decider-2b — ~10x faster, fully local, ~5% less accurate. 5. Laya — 62.5% accuracy, not worth it. Jev is the frontier of this paradigm, but it is losing ground quickly. Benchmark auto-updates at bench.jakecuth.com

ego lite + Jev + DeepSeek Flash = stupid fast. ⚡ 3.71s for 20 Amazon product decisions. GPT-5.6 Sol: 54.45s Same task. Same result. 10/10 on both. Less reasoning. Faster decisions. 1× speed.

Ai is evolving. Jev can be armed at all times. I can speak freely and it knows ( from probabilities ) if im asking my computer to do something or blaberring away at something else. no wake word. speed + affordability + intelligence is getting to the point where an always on ambient jarvis style assistant is possible. Im loving where we are going. CNVS is still the future of vibecoding.

I added Jev to the AI Hedge Fund. We now get frontier-level trading decisions, 100x faster and cheaper than an LLM. How it works: 1 • set strategy 2 • pick tickers 3 • backtest with Jev System now runs in seconds, not minutes.

jev is insane 🤯 Here is Jev playing subway surfers at super human speed, and also playing 50 games at once. cost less than a cent to do this run. Jev does not replace llms like astra or fable, but opens up an entirely new world of capabilities.

Building got faster. Taste didn’t become optional. Framer is where frontier teams like @typesafeai still go when the site has to look like a serious company on day one.

Another useful use-case with Jev from @typesafeai - AI text classification. Very close to @pangram, 6000x cheaper, 5-10x faster Tested here on a @paulg essay!

This is cool. I used Jev to improve my custom memory system. Results: → 94% fewer tokens used → 2–3× faster memory retrieval This was just a quick test, but I believe it opens up so many new opportunities.

If you blink you might miss it Jev + Computer use = 100x faster than any LLM @typesafeai

Had jev, claude haiku 4.5, and gemini 3.5 flash-lite play tetris. Same 200 pieces, same legal moves, real time. Jev: 9200 pts, 75 lines, 300ms/move, 0 errors Claude: 8900 pts, 72 lines, 1.52s/move, 0 errors, $0.48 Gemini: 9000 pts, 75 lines, 1.13s/move, 0 errors, $0.02 Jev wins on score and is doing it 4-5x faster per move than either LLM, for basically free. That gap isn't the model being smart

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI

I rebuilt my job crawler with Jev. The task: start at a company's official homepage, find Careers, and identify jobs that match my profile. Before, with an LLM: ~5 minutes. After, with Jev: just over 20 seconds in my test. Every company organizes its website differently. Jev identifies the Careers entry point, chooses which links to follow, recognizes job pages, and scores each role against my

btw this is what you get if you ask Jev to play Minecraft with GPT-6 Astra. I still can’t believe it

By now you've probably seen Jev on your timeline. Yes, it's an insanely fast & cheap model. But how does it compare against GPT-6 Astra, Fable 5.1, and Deepseek v4.1 Flash? In this video I compare it against top LLMs on 4 distinct tasks. Did it win? Or did it lose? Let's find out. Total Jev bill for everything in this video: $0.49. Chapters 0:00 Intro 1:07 What Jev actually is 2:57 System 1

I trust Jev with my life

Will Jev @typesafeai turn $10.000 into profit? Let's find out! Running decisions for paper trades via

📣 Speaker announcement: @CompleteSkeptic CEO, @typesafeai at Cloudflare Connect Register today:

Jev scoring 3000 kid snacks with multiple criteria on in 28 seconds for $0.11 🤯

完全睡不着了....这个视频让你完全看懂JEV的恐怖能力 我实际演示了一下，用 JEV 和 DeepSeek 做同一个任务：从当天的海量实时新闻流里，为 15 个品牌快速匹配有没有适合借势的公关热点，并打上结构化意图分类标签 结果是：28 秒内，Jev 狂刷完了整整 428 条数据，并完成了全部的目标分解与分类；而同一时间跑在同样任务上的 DeepSeek V4.1-flash，才刚刚完成 6/428 条 速度被拉开了几十倍，答案甚至在你读完第一行字之前就已经返回了.... 为什么会产生这么恐怖的差距？底层逻辑是： • DeepSeek 这种通用模型本质上是在“逐字写文章”： 哪怕你只让它做个最简单的“是/否”判断，它在后台也必须一个词一个词往外推，硬走一遍漫长的生成流程，延迟按秒起步 • Jev 官方定位是“System 1（快决策）”模型，从根上就根本不会写字： 它彻底抛弃

JEV is INSANE. We gave it 400 companies and one candidate profile. In 12 seconds, it predicted which jobs the candidate had the highest chance of getting, assigned a confidence score and detected job-candidate mismatches. All for just $0.0005 It can also score companies, analyse your experience, match you with the right roles and identify the opportunities you’re most likely to get based

instead of generating text, jev from @typesafeai generates structured output this makes it great for classification tasks like model routing, tool selection/search, and guardrails of many forms! it's also ridiculously fast and cheap compared to LLMs doing the same tasks

e2e + jev from @typesafeai ⚡ I'm building an open-source framework for running e2e tests with agents. supports web, mobile (and more!) available soon:

Jev is now in charge of this account's humor

UN TÍO ACABA DE LEERSE LOS 1.891 ANUNCIOS DE SU COMPETENCIA POR 12 CÉNTIMOS Bueno, leérselos no. Se lo pidió a JEV, el modelo nuevo de TypeSafe → 1.891 anuncios → 19 segundos → 0,12 $ Y no es un resumen. Es cada anuncio etiquetado por dónde ataca en el funnel y con qué estilo, más la radiografía completa de la cuenta: en qué invierte, qué repite y dónde tiene huecos Saber exactamente cómo vend

i gave jev ₹1,00,000 to trade nifty intraday at 5x leverage. this isn't paper trading but real money on my real kotak account put to use. we hit our ₹1,000 hard stop today after a pretty green morning. will keep tuning the strategy next week and keep y’all posted

Takie modele jak JEV (System-1) zmienią moim zdaniem bardzo dużo. Obecne modele LLM symulują System-2. Dla mnie implementacja jev to jedna z najświeższych koncepcji ostatnich miesięcy. Niby nic szczególnego (dynamiczny klasyfikator) a dziwi, że tak długo trzeba było czekać na taki model. Uniwersalny klasyfikator, inteligentny "if". W swoich poprzednich wpisach objaśniłem jak to działa. Mam dost

JEV is INSANE. We gave it 700 high-intent leads and personalised outreach messages. In 40 seconds, it predicted how each message would perform, assigned a confidence score and detected lead-message mismatches. All for just $0.09. JEV can also score leads, analyse buying signals, match each prospect with the best message and identify the campaigns most likely to perform based on data. Coming

🔥 卧槽了，jev 简直太适合 codex 用户了，强烈建议 codex 用户去尝试下 jev 因为 jev 非常适合需要快速进行复杂决策和评估的场景，所以和 codex 的 computer use 功能简直是绝配。 直接把 jev 用作判官，指导 codex 快速操作你的电脑，既准确又节省 token。 要使用的话需要去官网申请👇，如何申请我已经录制好视频了，跟着操作即可

thanks to @typesafeai's Jev we now have massively parallel browser-based adversarial testing suite that tries to break each release. and it costs pennies.

卧槽，居然真有比Codex内置computer use更快的操作电脑的方式 我尝试用Codex+Jev打造了一个加强版computer use，我称之为「Jev Use」。比内置的更快更丝滑，token消耗却差不多 下面是我用「添加Mac日历事件」做了一个对比视频，同样的内容，明显可以看到Jev版本整体过程几乎无任何停顿😆

I gave @typesafeai the most important binary in @SiliconHBO history. hot dog or not hot dog Try here: Powered by @render workflows

Controlling Figma with voice using Jev from @typesafeai Total cost from first line of code to recording this video: $0.01 🤯

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 OpenAI 时发现了一个致命矛盾： 通过人类偏好微调（RLHF）训练出来的自回归大模型，存在严重的模式崩溃（Mode-dropping）、过度自信与幻觉，且串行逐字生成的延迟极大，根本无法胜任严肃的机器自动化生产 为此，他们花了两年研发了专为自动化设计的 System 1 基础模型 JEV，核心架构创新非常激进： • 单次并行计算（One Parallel Pass）： 如同当年 Transformer 取代 RNN 一样，JEV 彻底抛弃了逐 Token 串行

First Jev use case published live on Bannerbear! Instant field mapping between template and source, when names are slightly different eg template - photo - name - company_name data source (eg Airtable) - avatar - full_name - business Jev figures it out in one click.

Another crazy @typesafeai Jev example: Predictive spreadsheets Spreadsheets recalculate numbers, not meaning. Jev reads intent. Type "Urgency" at the top of a column and, as you type, it figures out you want each row rated from "no follow-up needed" to "urgent" in ~100 ms.

Claude me integró Jev con Playwright  para buscar usados. Lee unos 26 artículos por minuto y decide qué hacer con cada uno en 406 milisegundos Descartó los que no encajaban con lo que busco, ofertó por los que sí y hasta les mandó un mensaje a los vendedores cuando faltaba algún dato en la publicación. Toda la búsqueda salió USD 0,00085. O sea: con un dólar revisás unas 26.000 publicaciones. Un

jevデモ② 発話内容に応じたスライドの自動表示。 喋った内容にマッチしたスライドを自動で表示してくれます。PCやポインターを触らずにページを表示できるので、これで誰でもジョブズになれるかも？？ 速い・安いだけでできることがこれだけ広がるのかと感動している。

Jev by @typesafeai is now on OpenRouter, in beta. Jev is a System One model. Instead of generating text, it takes your app's state plus a typed question and returns a typed decision with a probability attached. There is no JSON prompting, parsing layer, and nothing to validate against.

jev is insane 🤯 here is jev playing smash bros against itself he is controlling all 4 different characters. and literally deciding whats the best move to play against itself all within a fraction of a second i used over 22 million tokens to play this match and it only cost me a couple of cents... jev does not replace gpt6 astra but the possibilities with its instant response time are endles

Jevで、会話をリアルタイムに観測するセンサーを試作しました。 発言のたびに ・探索 ↔ 収束 ・解消 ↔ 未決 ・未合意 ↔ 合意 を更新し、会議の状況を可視化します ユーザーインタビューのリアルタイム解析などにも使えそう。

i used Jev to classify a youtubers last 100 videos based on how likely it is to sell me something... it analyzed & assigned a sales intent score to each video within 12 seconds & cost about $0.02 i haven't done a deep dive into how accurate the score is yet, but from a quick glance it looks like a super promising classifier model

Jev + SuperX = virality solved ✅ Every post gets 61 questions in ~1s for $0.0004 🤯 > fitted on 9,481 real posts from 207 creators > picks the viral post 2 in 3 times > never rewards reply bait So: write, score, rewrite, stop when it peaks. Free, no signup. try it below ↓

I used Jev to make an audience of 100 personalities to yap to. Each blob has its own personality and makes its own Jev call every time you talk to decide if it’s bored of you yet. Each round costs <$0.01 in credits. Really cool model from @typesafeai !

jev decided to sacrifice a human to save robots when solving trolley problems

Behind the scenes of our launch video… chief of staff @justKDeng keeping our CEO @CompleteSkeptic on track

I build a SQL extension that turns plain english into a WHERE clause with jev @typesafeai WHERE jev(people, 'could work from home') Every row gets judged individually, no index and no embeddings needed. Try it out (don't burn my wallet pls)

had to try something with jev so i tried what you'd expect from me: graph extraction - score every word with semantic significance (1-5) - also tag relevant words with ID - unique list of high scoring words - graph of high scoring words

Small update for today: 1. minirouter/fusion is live. It classifies each request with Jev by @typesafeai, then routes to a handpicked model matched to the task's type and complexity. 2. Codex now lists your presets and our routers in the model picker. 3. Fixed long prompts not caching automatically in Claude Code. BYOK, sandboxes, compute passes and compute yield coming next.

Jev is a new model from the co-inventor of ChatGPT that can use computer tools in realtime right now it's playing Minecraft on its own computer in the cloud on Orgo this is what it looks like with just one Jev, but imagine: what about a thousand? or what about a billion of Jevs? each working together on their own computers in the cloud, discovering new science, running experiments, and designi

The real use-case for Jev

Okay so Jev can actually do computer use really well Without any screenshots, or LLMs and no Pixels leave my mac I dont even read the Dom elements A local CoreML model segments every button and UI element on screen. On-device OCR reads the labels. That text is all Jev gets. It returns a probability across those elements and tells me the best one to click. Then it clicks, re-runs detection,

What if your slides are smart enough that they navigate automatically? 👀 Thank you @typesafeai for providing access to Jev! I had this idea for a few weeks, and Jev felt like the right model for this task. (Sorry for the bad audio)

Function calling without waiting for Enter. Answers only when needed. @typesafeai Jev understands intent and arguments as you type, then executes ahead of time once everything is in place.

Jev enables instant AI suggestions 👏 In this app, 20+ intelligent checks occur every time you edit a cell, immediately providing warnings. No LLMs necessary!

Jevで次の文章予測判断させるロジック作って、 判断特化AIのJevで、AIチャットできるアプリ作ったんだけど、まず爆速だし、入力は$0.042 / 1Mトークンだけど、出力が無制限で無料でした。 出力無料のAIチャットってなんだこれw 「え？これ作れちゃっていいん？」ってなってる。。。 #Jev

whoa this actually worked! Jev lets me control my browser in real time with my voice now > i talk > transcript sent to Jev > jev returns probabilities in ~300ms > browser clicks costs: $0.0002 per decision i'm stunned how fast this is. when i asked it to "go back", it even finished the request before i finished my sentence 😂

昨天出的 Jev 模型 适合高频决策，那岂不又是适合打游戏？ 之前用 GPT-6 Astra 代打杀戮尖塔2 能力强但速度慢。 我刚实测用 Jev 打，行动思考只需 0.7秒， 画面我都没看清它就操作完了。 这超人类游戏速度， 着实又让我震惊瘫坐了😅 语言很难描述这感觉，看视频⬇️

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 below:

I got a rubik's cube to solve itself with @typesafeai 's Jev and it solves it like a person does, 94 moves, not the 22 move optimal solution. Jev isn't an LLM, it just answers one question in ~250ms with a probability. so I put the beginner method in code (the one you'd learn on youtube: white cross, corners, middle layer, yellow layer) and at every step Jev just looks at the cube and picks which

Ask Jev anything. Give it a try at It won't answer. It will judge. Let's see if we can get to 1 million questions. @typesafeai 🤝 @convex work great together. @hmartenjoyer @CompleteSkeptic @justKDeng @mikeysee

arkadaşlar jev'e early access aldım ve test etmeye başladım ve gerçekten çok impressed olduğumu söylemem lazım sadece şu kaydettiğim demoyu bi izleyin lütfen. kesinlikle hızlandırılmış bir video değil. 4 tane jev karşılıklı 101 oynuyor ve oyunun hızına bir bakın. beni takip edenler bilir. yıllardır her yeni model çıktığında bizim meşhur 101 okey oyununu oynatırım. benim kişisel benchmarkl

For those who still didn't understand what Jev from @typesafeai can do, here is the example. Jev can read dozens of structured trading signals and then show intelligent decisions. It's time to make your system actually alive. What a time!

Big AI news... Jev enables this microphone that mutes you when you talk BS @typesafeai

Alright @typesafeai cooked Jev is truly the next era of building w LLMs Added this to Scappa tonight

Acabo de terminar la implementación de @typesafeai + Chromium Headless para que mis agentes puedan navegar por internet a una buena velocidad! En este ejemplo le pido al agente que entre a la página del término "Café" en Wikipedia y navegue por los hipervínculos hasta terminar en la página "Inteligencia artificial" Logra completar la prueba en 20 segundos. Creo que pocos humanos podrían hacerlo

"jev can't generate text" But jev has a lot to say! I gave him a couple hundred common english words to choose from + punctuation

I think the implications of jev on self-driving could be huge

I got access to Jev! It can route website visitors to the pages they need based on what they need help with!

Jev solved delegation

I rebuilt Tesla Full Self Driving with Jev in less than an hour. This model is a total unlock.

JEV CAN DRIVE! 🤯 I hooked up Jev to the raw controls of a driving simulator, this video shows him driving in realtime. Realtime is the kicker here. Asking a model what to do every 50ms then rendering that into a "realtime" video is one thing, but in this simulation I wanted to give Jev the challenge of actually controlling a moving vehicle in a simulation that will never pause while he thinks.

For those trying to understand the difference between Jev and LLMs + how to use it

Jev solved local harness/model routing I use a combination of Claude Code, Codex and Opencode as my local agentic stack and routing to other harnesses was always enforced in the system prompt/rules With a deterministic hook that Claude Code can decide before delegation, Jev helps to route to the right harness/model based on the task, and it's pretty accurate based on the intensity/intelligence o

I was wondering if @typesafeai Jev could generate images so here's what a sunflower looks like according to Jev :

Jev can draw pictures by predicting every pixel in parallel

Jev (@typesafeai) 使って色々プロダクトで使えそうなの考えてみてる。 dndをしたら自動で仕分け先を教えてくれる体験 最初一回はちょっと遅いが、そのあとはサクサク

Silicon Valley predicted Jev a decade ago.

Silicon Valley predicted @typesafeai

got @typesafeai’s new model Jev to play Super Mario Bros. fast inference + structured outputs makes it surprisingly good for real time use cases. I'm excited to see what can be done with these new models!

@typesafeai playing minecraft, costing only 1 cent (150k tokens) for 2 minutes of play automatically knowing to dodge zombies at nightfall with no prompting

I increasingly think future software will combine code for control flow with small neural programs for "fuzzy" judgments. That's what I've been exploring with ProgramAsWeights, which answers the question of where those neural programs come from: they are "compiled" from English descriptions. For example, I combined 30 neural programs with a decision tree to build a course website helper that loo