Updated Nov 10, 2026
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.
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, 498–531 of 1072. All 1072 in curated order
Just AI
@thelazyyogii
上次说的 Jev： 不会陪聊，只负责拍板。 这几天又冒出个 Laya。 活差不多，路子不一样。 一起想成： 流水线上专门判题的人， 不写长文，只答「过不过 / 归哪类」。 Jev = 外面请来的裁判 开箱就能用，按量付钱， 选项多、材料长时更稳。 适合懒得自己搭、先要结果的人。 Laya = 自己厨房里养的裁判 权重能下，Apache 开源，本机跑， 更快更省隐私。 但要自己微调；选项一堆时容易糊。 适合有机器、要数据不出门、愿自己调的人。 一句人话： 不是谁取代谁—— 要省事找 Jev，要自己掌控找 Laya。 都是选择题机器，不是聊天搭子
XOpen source
Jason Torres
@jasontorres
I launched a chatbot yesterday and used JEV as a 17-in-1 router, filter, classifier, intent judge for efficient tool calling. Then further downstream is a relevance/context loop system. Worked really well. No more unwanted spams and prompt injections.
XTools and apps
Amirhossein seghatoleslami
@AHSeslami
اولین تجربه‌ی خودم با JEV 👀 این تجربه برای ۳ روز پیشه؛ وقتی JEV هنوز توی حالت Waiting List بود. من کلاً هر ابزاری که سخت‌افزارم بتونه بکشه رو تست می‌کنم 😄 برای همین توکن JEV رو از OpenRouter گرفتم و به Grok Bot دادم. ماجرا چی بود؟ از GITEX کلی ویدیو گرفته بودم و نمی‌دونستم کدوم‌ها رو اول منتشر کنم. از اون طرف هم برای هرکدوم کلی کاور طراحی کرده بودم و واقعاً حوصله انتخاب و مرتب‌کردنشون رو نداشتم 😂 به JEV گفتم: «خودت انتخاب کن، دسته‌بندی کن و فولدربندی‌شون کن.» در کمتر از چند دقیقه همه‌چیز رو مرتب کرد و حتی پیشنهاد داد کدوم ویدیوها رو اول منتشر کنم. Grok Bot هم بقیه‌ی توضیحات و تحلیل‌ها رو انجام داد. برای اولین تجربه، واقعاً از JEV راضی بودم 🔥 هنوز Laya-Max چینی رو هم تست نکردم؛ نصبش هنوز تموم نشده. اونم که آماده شد، تجربه‌ام رو می‌گم 👀
XRouting and model choice
Arthur Marques
@ArthurLabMRP
was trying to figure out how to make these 2 models fight each other, so I put jev vs laya in chess lol script picks the 10 best moves, captures, and moves that put pieces under attack. still wip but jev feels way smarter
XGames and real time
Morgan
@morganlinton
It has been a really interesting experience to build an eval suite for System One Models like Jev. My first two attempts didn't work out. But I learned a lot in the process, and since Jev is so fast, and cheap, it's a lot easier to run quick tests than with an LLM. I'm on attempt number three with this eval suite, and thought I'd share more about it. The eval suite is called @VulcanBench Verdict, and here's the high-level on how it works:
XBenchmarks and evals
Guidegithub.com
A source-backed project directory, with a GitHub review workflow that uses only Jev.
Akshat
@akshatnerella
I vibe coded more fun apps using @typesafeai AI jev on the backend. My fav one is probably JevedIn, which is an extension for linkedin that automatically detects and blocks AI slop (thank me later). But I am lowk afraid of my linkedin feed being empty at this rate lol. Source code: JevedIn: https://github.com/akshatnerella/jevedin JevTube: https://github.com/akshatnerella/jevtube (currently building jevvit for reddit lol) comment what you want me to build next! #TypesafeAI #jev #classificationmodel #extensions #aislop
XTools and apps
Thinh Le
@thinh_lvv
Can someone make Laya and Jev work? 😅 I created simple Flappy Bird game and let Jev and Laya control it. But it look weird 😬
XGames and real time
Nailthy Tang
@nailthy62
jev is insane 🫣 it makes realtime virtual try-on hauls possible. built this experiment for Drape with @typesafeai > i talk > jev reads transcript + what i'm wearing > picks from my closet > changes my outfit in realtime cost: $0.0011 per decision time: ~620ms per decision imagine getting ready like this:
Guidegithub.com
OmniJev’s list of the papers, open reproductions and independent evaluations behind System One models.
Justin Schroeder
@jpschroeder
I rebuilt Tesla Full Self Driving with Jev in less than an hour. This model is a total unlock.
Stefan
@heystefan_
when a designer gets access to Jev
Sarah Drasner
@sarah_edo
🎇 I made a small Chrome extension: a side panel that drives any site's WebMCP tools with Jev! When you type, on every keystroke it picks the relevant page's tool, fills in the arguments, and tells you how sure it is. Here it is grocery shopping:
XRouting and model choice
Pick
Guidegithub.com
思维怪怪’s source-backed radar of the open-source ecosystem, in English, Chinese, Japanese and Korean.
Jon Kraayenbrink
@kraayenJon
Jev repos blowing up right now. I made a list of the repos people are using to build with jev. 👇
Aayan
@aaayandev
Jev (@typesafeai) is so insane & cheap for search!! > 6000+ @ycombinator Startups indexed. > Sub 1 second search results. > 90M tokens & $2.7 in total testing costs. Search any startup in a second, in any way! - Color - Niche - Your Competitor - Age - Image - etc... > watch the entire video, it's so freaking cool omg! > this is the coolest thing i have ever built for fun! (worked on it for 2 days straight!)
Charlie Hills
@charliejhills
Jev is blowing up right now. I made a list of the best things people are building with the Jev API: 1. jev-ultrafast — browser agent http://skills.sh/browser-use/jev-ultrafast 2. fast-jev-compaction — context compression http://skills.sh/tamaratran/fast-jev-compaction 3. json-render — generative UI http://skills.sh/vercel-labs/json-render 4. typesafe-mcp — plug into any client http://skills.sh/itsmostafa/typesafe-mcp 5. jev-mcp — judgment toolkit http://skills.sh/jkudish/jev-mcp 6. SemDecide — CLI classifier http://skills.sh/sharziki/semdecide 7. jev-codex-router — model routing http://skills.sh/0xNatoshi/jev-codex-router 8. Winnow — context garbage collection http://skills.sh/GhalebDweikat/winnow 9. jev-review — code review triage http://skills.sh/devagrawal09/jev-review 10. Blink — repo navigator http://skills.sh/ellipsis-dev/blink 11. agent-desktop — desktop automation http://skills.sh/lahfir/agent-desktop 12. typesafe-mario — plays Super Mario http://skills.sh/fhshaik/typesafe-mario 13. jev-drone — drone control http://skills.sh/RomanSlack/jev-drone 14. OneVOneJev — browser FPS http://skills.sh/emrickgarrett/OneVOneJev 15. jev-trader — HFT market making http://skills.sh/jarrodwatts/jev-trader 16. Prism — liquidity signal detection http://skills.sh/irfndi/prism-liquidity-agent 17. neo4jev — knowledge graph traversal http://skills.sh/jexp/neo4jev 18. jev-curate — training data screening http://skills.sh/AkashPriyadarshii/jev-curate 19. Canny — completion verification http://skills.sh/qkal/Canny 20. killmyidea — startup idea scoring http://skills.sh/monteduro/killmyidea
Guidejev.directory
A community directory of systems built on Jev, listed by the Noul, Score and Choice questions they send.
Wayne Nilsen
@waynenilsen
Jev to unslop Jev as slop linter
XSocial feeds
Tomohisa Ota
@toowitter
Jevを試すなかでいろいろ気づいた点をルールベースに落とし込んだunawair v3エンジン、外気温への応答がかなり改善してる。 AIにわかるように自然言語でいろいろ説明して、ルールも簡略化していった結果、普通にルールベースで書けてしまうというオチ。
XRobotics and devices
Anand Prasad
@theanandprasad
you can turn any low-cost open-source LLM into a Jev like fast decision model without training it. Here's how to do it: An LLM doesn't write text directly. At every step it scores every word in its vocabulary for how likely that word is to come next, and then it picks one. Those scores are called logits. Suppose you want to know whether a customer email is angry. The usual way is to ask the model, wait while it writes a paragraph, and then try to pull a yes or no out of the response. The trick is to never let it write anything. Give it the email content and the question, and end your prompt with "Answer:". Your full prompt will look something like this: [ Message: "Third time I've contacted you. Still broken." Is this customer angry? Answer yes or no only. Answer: ] The model reads the prompt once and scores every possible next word. You look at two of those scores. "yes" scores 8.1 "no" scores 5.9 Convert those two scores into probabilities and you get 90% yes and 10% no. (Use softmax function -- google it) Your code then uses that number directly. If it's above 0.8, escalate the ticket. Congrats, you have created your own Jev! Here’s why it works: 1. It's fast. Reading a prompt happens in one parallel pass. Writing is the slow part because the model writes one word at a time. Here the model never writes. 2. It's cheap. Output tokens are the expensive ones, and this produces none. 3. There's nothing to parse. You always get a fixed output, and never a paragraph. 4. You get a confidence level along with the answer. A 0.55 means the case should go to a human or a bigger model. A 0.98 means you can act on it. I tried running the code on llama 3.3 70b (via openrouter) and the accuracy was 21/22 (95%). Cost per query: $0.000013, latency: ~500ms
XOpen source
Guideawesomejev.com
A directory of projects built on Jev: repositories, SDKs, integrations, demos and benchmarks.
Clawby
@openclawby
Clawby 更新： 1. 资产评级: 技术面 / 流动性 / 情绪 / 风险四个快变维度由 Jev（TypeSafe System One 决策模型）每 10 分钟按最新行情与资金流重评，亚秒级校准打分。并且基于clawby的skills + claudecode 完成每周的定时分析报告，设计到前100非稳定币的主流币种。 2. 趋势分析：六个时间框架 × 五个指标，逐一与 BTC 对照。使用Jev System One决策模型，每分钟都进行分析打分，找到所有的独立信号。 3. 交易信号：覆盖meme，加密二级市场和美股三个板块。每分钟通过Jev模型进行快速分析，找到买入，卖出和观望信号。 4. 资金流：市值前 100 的币，主动买入减去主动卖出，8 家交易所聚合、13 个时间窗口——钱在进哪里，又从哪里出。 5. 链上市场：聚合了FOMO接口，对CA和钱包进行快速的深度分析，相当于一个链上快速检测工具。 https://app.openclawby.com/
Wahiduzzaman Hridoy
@hridoy43
Built a stock market scoring and decision engine and invested for the first time! 🚀 It includes portfolio-aware suggestions. Used @typesafeai's JEV for non-mathematical data analysis. Open to the following positions btw. 👨💻 Senior Front-end | Product | Mobile (RN) Engineer.
XTrading and markets
ギガビット@ゲームつくるひと
@gigabit_million
これGoogle検索でいいのではと思いながら作ってたけど、Chrome拡張機能でJevで意図で検索できるの作ってる人がかなりいたみたいでJevはGoogle検索より良いケースあるみたい。Jevのユースケースとして良いやつだった
Akshay 🚀
@akshay_pachaar
Guidex.com
Akshay Pachaar’s X article on Jev as a millisecond decision layer, and where it sits next to an LLM.
げま｜個人開発
@gemama0
Jevでノートの自動タグ付けシステムを作った🎉 Obsidian使っている人はぜひ👇
XSearch
大伟｜AI × Web3
@david1989_zhu
这条新闻最值得警惕的，不是“24小时覆盖Vercel约13%的付费团队”，而是把覆盖率直接当成产品价值。按报道，Jev不写文本，只做判断；这类产品切中的，是开发流程里高频、重复的决策节点。 我没参与Jev落地，下面是基于团队交付经验的判断。当时看到这个数字，我原来会先问：模型到底有多强？后来逐渐发现，团队持续付费更看重一件事：它能否稳定减少人工审核和返工，而不是演示时答得多漂亮。 现在我会先定清决策边界、误判后的回退方式和单次调用成本，再看它是否进入关键链路。“覆盖”来得快是好信号，但留得住、算得过账，才是商业闭环。
XBenchmarks and evals
BabaVT
@cruzex100
This is pretty good.. My smoke test earlier: Accuracy: Jev 0.727 Soft accuracy: Jev 0.580 · Laya 0.471 Calibration (ECE, lower is better): Jev 0.144 · Laya 0.213 Latency: Jev ~710ms · Laya ~30-40ms Cost per decision: Jev ~$0.0004 (API) · Laya ~$0 (self-hosted)
Guideyoutube.com/@nateherk
Nate Herk compares speed and cost against ordinary models, then builds an X feed classifier and a real-time paper-trading prototype live.
Ramarao Kadiyala
@broadrangeAI
Reviewing Jev: fast, structured decisions for AI workflows. Vendor figures: 70–500 ms, $0.042/M input tokens, $0 output. My takeaway: pair low-cost decisions with clear controls and measurable outcomes. Full post: linkedin.com/posts/activity… #AIEngineering #BroadrangeAI
BlanPlan
@blanplan
jev 真的火，确实有一些场景可以用得上。所以我真实跑了一下它的一个比较火的场景，操控浏览器，在我们给AI员工提供的云机上真实跑。github.com/browser-use/je… 结论自己看看。
XAgents and browsers
佐藤 裕二／さいたまのWEB広告運用者
@Ysato_su04
これができたら衝撃的。 JevがMeta広告ライブラリから長く回っている勝ちパターンを自動抽出、Google検索語句まで分析して不要クエリ除外、LPとのズレまでスコアリングしてくれる。調査から改善まで一気通貫で、人が何時間もかけていた工程を3ドル未満で済ませるのは衝撃的。 広告運用者として実務で感じるのは、クリエイティブ疲れやオーディエンス重複の見落としがROAS低下の主因になること。 このツールなら仮説立案が高速化され、PDCAが30倍速になる可能性大。
XAds and marketing
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.