野中健吾
@winebaizou
これ見て、さらにJevで3Dキャラの表情まで動かしてみた。 話しかけると顔と体で反応するし、YouTubeを一緒に見ると表情が変わる。 jev-character.winebaizou.workers.dev
XGames and real time
Updated Nov 10, 2026
The newest builds with Jev
855 projects, posts and guides about Jev, the System One model from TypeSafe AI. Each entry links to its source and shows the cost and speed its author reported.
- 855builds
- 181guides
- 21use cases
Diogo Almeida
@CompleteSkeptic
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-based economic revolution
Newest first, 565–597 of 1072. All 1072 in curated order
Open the post on X XGames and real time
Tetris with Jev
Frit🅾️ Pendej🅾️
@vwapster
I think Jev and Laya were the missing pieces to algorithmic trading. Sure you can hardcode gates and adjust variables, but being able to add a real time decision maker within your algorith is a game changer. I can smell and taste an early retirement.
XTrading and markets
Real-time decisions in algorithmic trading
Guidegithub.com
awesome-jev (verified catalog)
A community-maintained catalog that claims 433 verified open-source projects built with Jev.
Frit🅾️ Pendej🅾️
@vwapster
Jev usage on 1 trading bot. It's cheap but this is going to add up fast. @deepseek_ai please help and release something similar for free.
XTrading and markets
Cheap per call, but it adds up
Frit🅾️ Pendej🅾️
@vwapster
Grok bot + Jev + Laya. Self improving and auto calibrating Kalshi Solana trading bot. Still on paper. Looks promising.
XTrading and markets
A Kalshi and Solana bot on Grok, Jev and Laya
Vintuxai
@vintuxai
Stop settling for mediocre AI outputs. 🤖 Introducing slop-grader: The new Jev-AI CLI tool designed to audit text against your specific rulesets. It’s time to take control of the "slop." 🛠️ Here is the breakdown 🧵 #AITools #ProductivityAI
XSocial feeds
slop-grader
Guidegithub.com
jev-skill
Oscar Wu’s collection of Jev use cases, workflows and agent skills, gathered in one repository.
恒星sun
@vintcessun
如果分类结果最终要喂给业务系统，生成一段 JSON 往往不是最省事的路径。Simple Jev 把开放模型变成结构化决策接口。 https://github.com/featherless-ai/simple-jev 它复用共享上下文的 KV cache，只读取各问题候选标签的下一 token logits，再由服务端组装 choice、score 和真值判断。省掉逐 token 解码与 JSON 解析，多问题场景更有价值；common 目录还把提示契约和评分逻辑抽成可复用层。适合路由、审核、打分，但准确率与校准仍需按任务验证。
XOpen source
Simple Jev, reusing the KV cache
VertrAI
@vertr_ai
The useful engineering lesson is the async split: planning can continue while Jev selects the next action. That makes sense without assuming it proves AGI. We explain the roles using this demo in our 105-second video: x.com/vertr_ai/statu…
XAgents and browsers
Planning while Jev picks the next action
VertrAI
@vertr_ai
The small-LLM fallback for typing is an important detail: Jev chooses actions, while text generation stays with an LLM. We featured your 1x demo, with credit, in our short explainer of that division of labor: x.com/vertr_ai/statu…
XBenchmarks and evals
Jev chooses, the LLM writes
Jony Musky
@jonymusky
Hice un video de 4 min (en español) explicando qué es Jev, qué no hace, y por qué cambia dónde ponés un modelo.
Guidex.com
Qué es Jev, en 4 minutos
Jony Musky, in Spanish: what Jev is, what it does not do, and why it changes where you put a model.
VertrAI
@vertr_ai
154ms vs 860ms is a useful routing result, scoped to these 200 synthetic classification cases—not a blanket ChatGPT comparison. We credited your latency and cost charts in our Jev video, alongside the game/browser demos: x.com/vertr_ai/statu…
XBenchmarks and evals
154 ms against 860 ms, and the scope of it
- Routing
- 154 ms vs 860 ms
- Cases
- 200 synthetic
VertrAI
@vertr_ai
ChatGPT thinks through the plan. Jev helps pick the next move. ⚡ Minecraft, browser automation, speed and cost—Jev explained in under 2 minutes. 👇 Featuring demos by @rronak_ and @gregpr07, benchmarks from @OpenRouter, and Jev by @typesafeai. #Jev #AI
XGames and real time
The next move in Minecraft and in a browser
Vadim Choi
@vadimchoi
$10k/month just to decide which agent should answer. not to answer. to decide. group chat + @mentions: fast, cheap, terrible UX. an LLM orchestrator: nice UX, 4-7s and $0.00046 a message. @typesafeai jev: 0.29s median, $0.00002. same decision, $20/month.
Guidelatent.space
Jev, with Diogo Almeida
Latent Space interviews TypeSafe's Diogo Almeida about System One models and what Jev is for.
Utkarsh Upadhyay ⚒️
@utk2103
just shipped jev-studio v0.2.0 @typesafeai Jev in one pip install - MCP tools for Choice / Noul / Score - `jev` CLI now with dry-run provenance - ready-made prompt libraries + slash commands for every cookbook - Claude Code + Codex plugin manifests pip install jev-studio
XSDKs and integrations
jev-studio v0.2.0
Ayush Jain
@urayushjain
Most AI models are built to generate. But Jev is trying something different: Don’t generate. Decide. Give it some context and a specific question, and it returns a structured decision with a probability. I found this interesting for things like model routing, agent workflows, tool safety and RAG. The idea is simple --Instead of calling a big LLM for every small decision, let your code handle the workflow and use a smaller model where human-like judgment is actually needed. Honestly, this feels like an interesting direction for AI systems.
XRouting and model choice
A structured decision with a probability
梅澤 寛太｜Web・業務システム開発
@umezawakanta13
Jev×マリオ風1-1、検証途中の録画です。最初の敵に接触してミス。回避補助の導入前の映像です。 別試行はブラウザ往復中央値976.8ms（5応答）。純粋な推論時間ではありません。 検証記事：zenn.dev/kanta13jp1/art… 体験：my-web-app-b67f4.web.app/jev-mario-lab
Guideyoutube.com/@RoboNuggets
Jev will 10x your Claude Code (Here's How)
RoboNuggets: how to put Jev to work inside Claude Code.
梅澤 寛太｜Web・業務システム開発
@umezawakanta13
計測画面付きの別試行も補足します（成功3応答）。 中央値： ・ブラウザ往復 1,139.7ms ・Jev HTTP往復 490.3ms ・状態取得→操作 1,139.8ms 今回も最初の敵に接触。HTTP往復には通信時間が含まれ、純粋な推論速度ではありません。遅延と操作判断の影響は引き続き切り分け中です。
XGames and real time
Where the latency actually goes
- Browser round trip
- median 1,139.7 ms
- Jev HTTP
- 490.3 ms
uehaj
@uehaj
jeven-doors.uehaj.workers.dev/?g=mm2rc2cp 「Jevの扉」、ジャンルを作成してアップロードする機能をつけました。WebMCP対応なので、エージェントに作成させると便利です。 #Jev #JevenDoors
XTools and apps
Jev Doors takes uploaded genres
つぼね👨💻
@tubone24
Jevに飽きちゃったのと、全然利用料が安いので、公開してみました。 雨の日はスピードで遊ぼう！！ jev-speed.tubone24.workers.dev
XBenchmarks and evals
A Jev speed test app, made public
Guideyoutube.com/@MG_cafe
Jev Claude Code: Quick Setup
MG walks through setting Jev up with Claude Code from scratch.
tshmieldev
@tshmieldev
Jev vs LLMS for clasifying tweets in your feed! Try it out: github.com/tshmieldev/sha… Make sure to drop a star!
XSocial feeds
Jev against an LLM on your own feed
Shashank Jha
@theshajha
if you think you have seen it all with jev (@typesafeai), here's something new - play chess against jev
XGames and real time
Play chess against Jev
Akshay 🚀
@akshay_pachaar
A faster, 100% open-source alternative to Jev. It’s called Laya. Both models solve the same problem. You give them unstructured state plus typed questions. Instead of generating a paragraph, they choose from answers you define and return probabilities your code can act on. But they make those decisions differently. Jev is a closed, hosted API. Its architecture and training are not public, but it works zero-shot and supports up to 255 options. Laya is Apache 2.0 licensed, runs locally, and uses open-weight encoder models. Here is how its pipeline works. → It routes by language first. Plain Python selects the English or multilingual checkpoint before inference. → It creates one sequence for each question. Every possible answer gets a `[MASK]` marker. The question and state provide the context needed to compare those options. → It scores instead of generating. A bidirectional encoder and small decision head assign one score to each option. Softmax turns those scores into a probability distribution. → It batches the questions. They run together in one model call, although every question still carries its own copy of the state. This removes network latency and token-by-token generation. In one independent test, Laya took about 35 milliseconds on a local GPU. Jev took roughly 380 milliseconds over the network. Laya also has no per-call API fee, keeps data on your infrastructure, and can run on older GPUs. But it is not a drop-in replacement for every workload. Jev is a hosted generalist that works well out of the box. Laya is a fast base you need to specialize. Laya makes the most sense when you have a stable answer set and representative training data. Fine-tune it for the task, calibrate its probabilities on held-out examples, and send uncertain decisions to a stronger model or human reviewer. Jev gives you stronger zero-shot decisions. Laya gives you speed, privacy, control, and no per-call fee after you teach it your task. The open-source repository is here: http://github.com/NandhaKishorM/laya If you want to self-host Laya, InstaCloud provides a really nice and easy way to do so. Check this out: https://www.instacloud.com/templates/laya If you're new to decision models, I wrote the full breakdown of how Jev. The article is quoted below.
Guideyoutube.com/@AIJasonZ
Jev + Treg is a crazy combo for automation...
AI Jason pairs Jev with Treg and builds automation on top of the two.
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.
XContext and memory
Agent memory is usually one growing file
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.
XOpen source
A local classifier instead of Jev
Guideyoutube.com/@AI-GPTWorkshop
JEV Is NOT an LLM — Here's What It Actually Does
Zubair Trabzada's AI Workshop: what Jev actually does, and why calling it an LLM misleads.
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！
XAgents and browsers
Jev inside a harness, from a study group
The newest Jev builds, by the day they were added
Same directory, different order: the entries added most recently, rather than the curated order the home page opens in. The date on a card is the day it was added here, not the day its author published it — this directory is newer than most of the work in it.