What people are building with Jev

MD Fazal Mustafa

@the_mdfazal

🚨 A LOCAL 421M MODEL JUST ATE CLOUD JEV ON SPEED Laya is an open-source System 1 decision model that runs on your machine. Laya: 86.5 decisions/sec, P50 ~9ms, score 46 Jev: 3.2 decisions/sec, 317ms API round-trip, score 1 Ships with: — 421M params — ~1GB inference memory — Millisecond local calls, no network — Typed decisions in one forward pass — Apache-2.0 and self-hosted While the cloud waits, local decides.

Baris Terzioglu

@terzioglubrs

Agent memory is usually just an append-only Markdown file that grows forever, and most frameworks load the whole thing into context on every single run. That has been bothering me for a while, but never quite enough to fix it for our agents. Jev feels like it might be a low effort patch to this, by simply scoring each memory against the prompt first, then load only the relevant bits for that specific request.

Petru - Tech Driven

@techdrivenpetru

Yes but not with Jev. I used another classifier, locally, just as fast, just as good. Example: setup a "server" in python that loads the classifier model. Add a hook in Claude that fires on "pre-tool-use" and next time you ask Claude a random question like "how do I lint check a project?" and it tries to freelance and read your entire repo only to intoxicate itself and pollute its context, the classifier will slap its hand, say "no sir, you answer from knowledge" and deny the tool call. I tested this yesterday with success, but need to refine it as it misfires. Basically I was able to identify general queries, instances where I would ask something and Claude would rush ahead and run pip install without me asking or just write code instead of answering. A classifier is hypercheap compared to a regular LLM and would catch all of these. The model I used was DeBERT large. It's still stupid fast, I tested it on an Apple with M1 (regular) and you don't feel it running.

たてこ

@tateko_ai

今日のgakuse.aiの勉強会テーマは「Jev」 Jevの基本的な仕組みとか、海外での活用事例など、Jevでできることの広さを知れてめっちゃ勉強になったな！ 特に印象に残ったのは、ハーネスにJevを組み込むことで、トークンを効率的に使えるという話 早速Jev使って人狼ゲーム作ってみた！

tanav

@tanavtwt

Created a slop detector extension with Jev It scans all the post on the screen in the real time and classifies it on categories like scam, slop, clean, etc. Shows a minimal badge on the post with the confidence score. Comment bellow if you want to try the extension.

たく｜ガチのCopilot達人

@taku_ai_case

これはめちゃくちゃ勉強になりました。 Jevに回答させるのではなく、CodexやClaude Codeへ渡す記憶候補を選ばせる。文章を書けないAIの特性を、ここまで実用的な仕組みに落とし込む発想がすごいです。

同語 Jackson

@t0ng7u

Jev is live in New API now! Choice. Score. Noul. Structured judgments that drop straight into code. Same TypeSafe SDK. Point it at your New API gateway. One plugin. No rewrite. newapi.pro/zh/plugins

Manoj Rajendiran

@svencreations

Creators saying their new tool “killed” another is understandable. It’s marketing and rage bait. But people retweeting it without even trying the tool? That’s the worrying part. I’ve seen at least 10–20 “Jev killers”(@typesafeai) on my timeline already. Tried a few myself. Most weren’t even close. We’re amplifying opinions before forming our own.

スナガク | Codexではじめるエージェンティックコーディング

@suna_gaku

一日遅れですが、Jevを使ったアプリを作ったので共有します！ Xに投稿する前に、「彼女に見られても怒られないか」を判定してくれるアプリです。喧嘩を未然に防止するため、ぜひチェックを挟んで下さい！ アプリ URL sunagaku.com/labs/kanojo-ch… X の元投稿 x.com/suna_gaku/stat… #aimeetup

Prasenjit Sarkar

@stretchcloud

I keep seeing the same fix across agent stacks this week: teams are ripping the expensive model out of the middle of their decision loops. TypeSafe shipped Jev in early access on September 15. Not a chat model. They call it a System One model: hand it a state and typed options, it returns calibrated probabilities, no text generation at all. The mechanism is simple. Most agent loops burn a full LLM call on decisions that never needed generation: route to worker A or B, is this result relevant, approve or block this action. TypeSafe claims up to 200x faster inference and 400x lower cost on that class of decision. Ricker's own tests below land at 193x and 444x. What happened next is the real story. Within four days, Cognition's Jared Palmer shipped Kev, a LoRA adapter on Qwen2.5-0.5B trained in 1 hour 45 minutes on a MacBook Pro, with an API close enough to point TypeSafe's own SDK at it. Kev now scales up to a 9B version that trails Jev by about 4.5 points on held out evaluation. Laya-MLX arrived the same week targeting millisecond decisions on Apple Silicon. A community leaderboard, JevBench, already ranks a dozen of these models. The adoption signal convinces me this is not a toy. TanStack AI shipped a native decide() API for typed choices, scores and booleans. Beacon, an open source memory layer, uses Jev to score which coding sessions are worth turning into reusable lessons. Three teams, one primitive, inside a week. RouteLLM out of Berkeley showed back in 2024 that routing simple queries to a cheap model cuts cost over 85% while holding 95% of GPT4 quality, and production semantic routers report 40 to 90% savings today. What changed is that the router stopped being a side project and became a shipped, benchmarked model category with a name. The bottleneck this solves is real: every agent framework has a model sitting in a loop answering questions that never needed a sentence back. My read is that the decision layer becomes as standard a piece of the agent stack as the vector database became for retrieval, and whoever owns the default there owns a lot of the unit economics conversation for the next year of agent infrastructure. https://x.com/0xRicker/status/2101705843200721203

Stas Kulesh

@staskulesh

Added shareable game result to jevchess.com Jev won again, but. The robots are coming.

Stanislav Sorokin

@stas_sorokin_

1,000 AI papers sorted into 24 topics for $0.0585. Then Opus 5 graded the labels. @nutlope's Jev paper map went viral, but the pipeline never shipped and the eval was "still running". So I rebuilt both and opened them. The first judge run came back empty: Opus spent its whole budget thinking and answered nothing. Reasoning off, second run: it agreed with Jev on 85 of 100 papers, at 153x the cost and 1.9s against 57ms per paper. The 15 misses are not random. One number Jev already returns tells you which labels to recheck. Cheap models sort. Expensive models audit only what the cheap one flags. Repost if you classify anything at scale, because the eval rows are public and anyone can rerun them with their own judge in one command. Code in the reply.

そら ☁️ AgentSwarm 自動化オタク📱

@sora19ai

今週のgakuse.aiの勉強会はもちろん 「Jev」！！！！！！ ・他のLLMと何が違うのか ・どんなところで使えるのか ・Agent Harnessにどう組み込むのか などなど色々議論できました👇

Satoshi Nagayasu 🧠🤖

@snaga

TypeSafe System One（Jev）によるHacker Newsパーソナライズ推薦の実験と複合判定アーキテクチャ gist.github.com/snaga/12c62ad5… 毎朝、Hacker NewsのチェックをAIエージェントでやってるんだけど、自分向けのレコメンドをJevで組んでみた。 なるほどー、という感じである。楽にはなりそう。

Artem

@sl1ma4

🔥 looks like Jev just got an open-source alternative. Laya is a 421M parameter model built for making decisions instead of generating text. the interesting part: - runs locally on your laptop or phone - uses less than 1GB of memory - supports structured decisions (yes/no, scoring, multiple choice) - free + open source in a 30-second Snake benchmark against cloud-based Jev: Laya: 86.5 decisions/sec, score 46 Jev: 3.2 decisions/sec, score 1 the catch? Laya has a 512–1024 token context window and doesn't generalize as well as Jev without additional tuning.

Simplifying AI

@simplifyinAI

so we already have an open source alternative to jev... and it's 6-7x faster?! it's a typed-decision classification system: no chat, no generated text, just fast yes/no, scoring, or choice answers. > runs in under 1gb of memory > free on hugging face > runs on a laptop, or even a phone there's a real demo where it plays snake live, making a fresh decision every single move. two honest limits, straight from the project itself: > only 512-1,024 token context, some cases won't fit > weaker generalization than jev out of the box

Akash Jain

@akashpurjalkar

Jev is playing Minecraft. I built a bot powered by Jev that can construct houses, towers, lakes and castles on command. It also fights mobs, uses a sword, and tries to dodge skeleton arrows in real time. Still rough, but genuinely fun to watch. @typesafeai Jev

内田勉 DirecTune.app β公開中

@sidodtv

9/23開催 #生成AIなんでも展示会 Vol.6 のお品書きできました！ サークル名「他人ハウス」。場所はI-12です 内田：JevとLaya使用・超高速フットサルシミュレーター ころとろ：しまむらポエムマシン 場所は浜松町、入場無料、現在1400人が参加予定。みんな来てね！

内田勉 DirecTune.app β公開中

@sidodtv

なんか24時間後に Tiboリセットくるらしいので、今のうちに #directune の開発を進めるよ〜！ まずは、ゆっくり風動画解説で背景が自動作成されるようになったよ。試しに Jevの解説動画を作ったよ DrecTuneは、AIがアシストしてくれる動画生成・制作サービスだぞ‼️

Coach Shweta Bajaj

@shwetabjaj

This Jev experiment is fun, but the reliability question is actually the interesting part. Snake is simple enough to understand, but unforgiving enough to expose bad decisions fast. Play alone, play with Jev, veto its moves, or race it. A playful way to test decision quality, corner after corner. 🐍 @typesafeai @CarolMonroe http://jevplayssnake.lovable.app

Coach Shweta Bajaj

@shwetabjaj

Jev Skill Suggestion for Claude Code is a smart idea. Instead of loading every skill into context, Jev decides which one is actually relevant and injects only that. My takeaway: better context hygiene, less clutter, and potentially more efficient agent workflows. @typesafeai @vercel

Shengkun Ye

@shengkunye

Introducing Jev + OpenRouter + tools. You can now use Jev with 2,000 tools on Monid. Your agent picks the tools, Jev speeds the work up 30x. > Score 2,000 leads > Scan the TikTok viral video hooks > Research fundraising of 200 companies > Audit a site's SEO and rebuild the internal links on every page > Sort every Reddit thread about your category

SCRAPS

@scrapsonsolana

For marketing $SCRAPS, I’m trying something I haven’t seen anyone else do. I’ve been experimenting heavily with the latest AI tools (JEV from @typesafeai), burning through 178M+ tokens and nearly 48,000 requests testing different ideas. One of those experiments is focused entirely on marketing: finding the fastest path to high engagement and the best ROI possible. Now that SCRAPS is live on Google Play, it’s time to put it to the test. The experiment starts now.

さっと

@satto_sann

Jevでサクッと分類器は価値があるのかビルドトラップできそう

AMIT RAWAT

@sahajamit

I built a Chrome extension that judges every post in my feed before I reach it. READ, MAYBE or SKIP, in about 300 ms, using Jev by @typesafeai. No scripted rules, no selectors, no LLM. Real time, my X feed:

Andrew

@s4yonnara

Jev CEO, Diogo Almeida (ex-OpenAI): "It's not Claude Code because Claude Code is still part of the assistance era." He co-authored GPT-4, ChatGPT, InstructGPT and RLHF. Now he's building what comes next: 200x faster. 400x cheaper. A decision brain for AI agents. His argument is simple: frontier LLMs are insanely good at creating, but we're wasting them on thousands of tiny decisions: Which agent goes next? Is this relevant? Does this need approval? Those calls don't need generation. They need a model built to decide. In 18 minutes, Almeida explains why today's AI was built for assistance, not real automation, and why "tomorrow's AI will be for automation." This makes most $500 agent engineering courses look obsolete. Watch it today, then steal my 10-step guide to giving your agents a decision brain below ↓

s1rozha1

@s1rozha_

Jev hype is not about chat. it’s about decisions everyone is trying to compare it to ChatGPT / Claude / Cursor wrong frame Jev doesn’t need to write you a beautiful paragraph it needs to answer questions like: should this agent act now? which tool should run next? is this signal worth trading? is this market mispriced? should this wallet be ignored or watched? that is the interesting part prediction markets are basically endless small decisions under uncertainty not one giant “AI intelligence” moment thousands of tiny calls: price changed liquidity moved wallet entered news dropped odds lagged edge appeared edge disappeared you don’t want a slow model writing essays for that you want something cheap, fast, typed, and measurable that’s why Jev is interesting. not because it replaces frontier models because it makes the boring decision layer cheap enough to run everywhere

りょう｜AI駆動開発でのゼロイチマネタイズ

@ryo_hukugyo_pro

Jevをいかに使いこなすかが今後トークン節約や自動化をするAI社員を大量量産する上で、 めちゃくちゃ重要。

Richard Seroter

@rseroter

I'm reading up on Jev (typesafe.ai/blog/introduci…) and learning how it differs from "regular" classification ML models. I gave a bunch of articles to @googlecloud Gemini Enterprise and asked it for a breakdown. Helpful. Still digging in before going hands-on.

Robert Youssef

@rryssf

all things Jev in one place a GitHub collection just dropped packed with hundreds of tools, libraries, and projects built on top of Jev, from teams treating decision-making as its own primitive, separate from language generation entirely. github.com/AnotiaWang/awe…

Richard Meng

@richard_meng_01

Nitpicky, an AI generated photo detector powered by jev AI generated photos can be told from nits. That's why we build something to zoom into every detail: faces, fingers, characters, numbers, poses, where common senses fall apart, judged by jev