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I built a CLI that sorts a folder by what each file actually says, using Jev from @typesafeai. 2,225 BBC news articles, anonymous filenames, sorted into 5 topics by content in 4.9 seconds for $0.05 at 97% accuracy. Fast & cheap 🚀 https://t.co/WQBzwGRcoD

今読んでる文章の段落に応じて、Jevで動的にBGMを自動生成するのを作ってみた。コード進行をいい感じにする。 https://t.co/tr3xR9PzXe

Jev made our Slack agent 2x faster ⚡️ Our agent can be quite slow because it needs to read skills and figure out which tools to call. We used @typesafeai's new model to speed this up by first passing it the prompt and classifying the best skill, tool and params to use before handing it to the agent

If you use omp, you're missing out on jev based compaction It helps your agent optimize token usage with 30 to 55% less context. $0.0005 a pass What it does: - Scores every old tool output with Jev - Keeps what's needed - Parks the rest on disk - Remembers its decisions so your provider cache keeps working - Leaves a session alone if it's already cached and cheap Results on my own sessions: - 29 t

Tried JEV from @typesafeai and vibe-coded a little voice-controlled browser agent. Just tell it what to do and it navigates the browser for you. Pretty crazy how fast it is 👀🤯 https://t.co/zX3O3IrLdp

Played around with @typesafeai Jev today, mostly to understand what it does for tool calling. Instead of an LLM deciding what to do, I substituted that part with Jev. My learning is that we will be able to make the agent act before the user finishes the sentence. So far, we've used different LLM combinations (non-thinking + thinking ) plus state machine setups to get the right experience for the e

Google fully mapped a male fruit fly’s brain, and the internet is torturing it... Making it play Doom... loading it into Minecraft… I made it review TypeScript! I use Jev to translate a diff’s properties into neural stimulation, run it through a simulation built from the fly’s actual wiring, and map the resulting activity to a code-review verdict. Jev describes the code. The fly model gets the vot

I built a linter for AI writing tells, and the judge is a model that can't write a sentence. Sniff Test reads a draft one paragraph at a time and asks Jev ten yes-or-no questions. Is the claim hedged three times. Does the closer just restate the paragraph. Is there a not-X-but-Y turn. Is there a cost figure with no price next to it. It comes back with one probability per rule in about a fifth of a

been playing with Jev from @typesafeai and built unsaidbrief the idea: we give AI builders vague prompts, then spend hours fixing decisions we never actually made paste your brief and it uses Jev to flag missing decisions and possible conflicts, with probabilities you can see answer what matters, recheck, then take the clearer brief into your builder small walkthrough below. curious what it catche

Jev + deepseek v4 flash 非常棒的组合，我把我的项目，一堆意图识别的东西用 Jev 重构，效果特别好，也特别快，非常省钱。再搭配 DeepSeek V4 Flash 的速度，简直快到飞起。 https://t.co/gybIxcVUzO

Video playing at 1x. Pulled transcripts for ~400 YouTube videos into my app, then let Jev loose on all of them to find which are worth building my next video on. 11 seconds, $0.004. 291 ruled out, 82 worth a look so we ranked those. https://t.co/Sp8MYr2uno

iPhoneで動くVLMベースのjev-likeを作ってみました。学習なしです。 https://t.co/wiaGAQ1Sim

Drive-thru ordering at Big Kahuna burger powered by @typesafeai Jev The model is really good/fast/cheap at Natural Language -> Menu -> Selection https://t.co/Q2b0DRH9pV

I built a chaotic kitchen to stress-test Jev. 4 chefs. Expiring orders. Competing priorities. The occasional fire. In an early live test, Jev gave firefighting a 66% probability over finishing the order at 33%, then returned its move in ~200ms warm. V1 is live. Try to break it: https://t.co/ZSDZhXK3KZ

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 metad

went deeper down the Jev rabbit hole 🌻 196 artworks from The Met mapped between quiet ↔ loud and sacred ↔ domestic change the words and watch the collection rearrange https://t.co/deUdeapWKu

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： 它彻底抛弃了逐字吐词，

Can Jev escape a maze? Turns out it depends how you write the map. When given as a 2D grid, “wall above?” is worse than chance. “Wall to the right?” is 99.9%. Rotate the page and every side becomes 1.00. It isn’t seeing the grid. It’s reading the next character. So I gave @typesafeai’s Jev the maze as lines of sight and named places, not as a map. Notes below.

love jev, but upset it - isn't open source? - it lack vision capability? We fixed all of that, introducing https://t.co/DlNeKo2k5e 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 https://t.co/dFKBbR411y

ローカルJevできた。確信度ないけどｗ 40msで返事返ってくると不安になるけど一応GPUはピクっと動いているのでLLMの出力にはなっている https://t.co/8eEtZ0CpSU

the obligatory AI plays Mario, Jev version Jev is TypeSafe's System One model. u send it state and a typed question, it sends back a choice with probabilities. so every move here is one question: run, hop, jump, wait or go back it reads the game from NES memory, no pixels. 193 calls to clear 1-1, abt 300ms each right side is the real request and answer for every decision. the game is paused while

Jev vs GLM 5.3 at chess! Results: ◾ GLM 5.3 won by checkmate in 29 moves ◾ Jev: ~0.3s and <$0.0001 per move ◾ GLM 5.3: ~5.8s and ~$0.008 per move ◾ The whole game cost 24 cents My main takeaway is that it's often useful to use each one to their strengths: ◾ Fast, well-defined classification → specialized models like Jev ◾ Classifications that need reasoning or lookahead → LLMs like GLM 5.3 ◾ Real

Tried Jev for this use case : On one side: a JD → extract the most important requirements. On the other: upload multiple resumes → see which candidate fits the JD best. Jev was surprisingly fast at this. I will test this model on a few other use cases I have in mind. https://t.co/b0IkQ3zDjM

I got tired of choosing between X's firehose and someone else's idea of relevance, so I built Your Signal. It uses @typesafeai's Jev to score posts already on screen, then applies my rules locally. Reversible, BYOK, no telemetry, open source. https://t.co/1dNRESvT5J https://t.co/sYOVB3eIoy

**jev scroll break** took a stab at making a design skill inspired by @anshuc dream loop skill, and made taste loop! tested it on my portfolio cuz it needed an update and holy it looks much better now https://t.co/6z0lsmyHt0 https://t.co/lgspHpuKrA

DiffusionGemma-as-Jev (aka djev) running near-real-time vision detection from a mobile phone using its native vision tower. Please don't fall down the stairs! https://t.co/C9kBCD6irx

I built a game were you play against a sentinel in two modes: 1. Default: Sentinel runs a fixed chain of if/else rules. It follows every one of them and still wanders the map looking for me. 2. JEV Live: hands the Sentinel's tactical call to JEV. -> It went and covered the exit. 💀 Same game, same available moves. Only the decision logic changed.

@typesafeai @CompleteSkeptic lol Jev is fun got it to solve a bunch of games at scale, Sudoku took a bit of time though https://t.co/hLrF09riQ1

Jev x GPT-6 Astra x H3 Max I created a complex decision making game with Astra. It is about keeping the city on a whale alive with decisions. - GPT 6 Astra designed the world - Jev chose the actions - H3 Max Turbo on fal turned one decision from each round into video All of the Jev decision making and video generations took only 5 minutes for 264 clips

Jevmaxxing on Notte browser sessions ⚡️ > https://t.co/ZokbHG5WxE Type a task, hit start, and watch Jev @typesafeai pick every actions super fast from the page's action space. > new action space every step > Notte cloud browser over CDP > step-by-step replay of every decisions Try it yourself! ▸ https://t.co/ZokbHG5WxE ▸ https://t.co/uGO7cLo2st

tested Jev as a real-time robotics policy in MuJoCo it struggled at first, so i split each update into two calls: decide what to do next, then decide how to move the arm and gripper Jev doesn’t accept images, it gets simplified geometry and contacts as text here https://t.co/iKK7jcrrpO

Also been playing with @typesafeai Jev. Insane. A pile of apps suddenly become possible. What a time to be a builder. Sharing experiments here. First one: Keystroke oracle / predictive launcher A normal launcher ranks by aliases, fuzzy match, and habit. Jev reads intent. Type “the pdf I just downloaded” and the newest PDF is already the top hit. Full confidence on every keystroke. About 100 ms.

another demo for jev for ~instant variable generation https://t.co/ddjvvHXpai

Jev + Higgsfield = solved auto-routing for genAI models. In this demo, @typesafeai’s Jev evaluates prompt and picks the most fit models for video and image generations on Higgsfield API. https://t.co/MyTRdqlZ3o

Jev + OpenCode + Browser Harness + Sanbox = 🔥 Watch how OpenCode + browser harness visit a tetris game website, read the control instructions, use gpt-6 to build a game controller for Jev model, and then hand off to play the game. This is all a single shot prompt with no special skills or tools. Jev model chooses from predefined outputs. In this setup keyboard based gameplay actions were not inclu

I used Jev to build the fastest SEO/AEO audit tool it took it 16 seconds to audit my full website It goes through every page on your website, analyse it checks it's html code, content and gives a full prompt to fix them too. It's free and available on Bottally now. https://t.co/oYTFCWYjUY

Jev added more precise camera controls and ~230x faster planning to an editing tool I built on top of @mintdotgg! Jev converts natural language to precise camera movements (ie spelling ASTRA) by answering a few calibrated multiple-choice questions (move kind, plus direction and magnitude band per camera axis) By creating keyframes it emits a structured trajectory (time/azimuth/elevation/distance)

yes / no jev demo https://t.co/cINZEnqWFm

Applying @typesafeai 's Jev to robotics & swarms. JevDuck (https://t.co/suJgqvf7pv) connects Jev's typed decisions to the official Microduck simulator. MuJoCo WebAssembly computes the robot dynamics in the browser and the original ONNX policies control the articulated joints. Try it here https://t.co/r8DToTfmcg

Made Jev fight OpenJev in a first person shooter. https://t.co/wSB3psnvwB https://t.co/vwewLu682T