What people are building with Jev

Choroshin Alex

@choroshin

"לקוח יקר, זוהתה פעילות חריגה בחשבון הבנק שלך. החשבון יוקפא תוך 24 שעות. לאימות פרטים לחץ כאן." רציתי לשחק עם Jev, המודל החדש של TypeSafe, וגם לבנות איתו משהו שיש בו תועלת אמיתית. אז בניתי ב-Skills IL כלי שבודק הודעות כאלה: מדביקים את ההודעה, מ-SMS, מוואטסאפ או ממייל, ומקבלים

Saksham Sharma

@code_saksham

And just when I thought I have a crazy idea with JEV that i will open source then this happened. Please @typesafeai fix it ASAP, the curiosity inside me to build that thing is insanely high

Siim Haugas

@siimh

scoreboar v8: 128 MB instead of 597, about 30 ms a post instead of a second. and more accurate. guessing which of two X posts did better for its account size: 61.4%. grok 4.7: 55.3%. jev: 52.5%. free, in your browser. github.com/Siim/scoreboar…

Chase

@0xChaseTM

Jev Engineers just released Jev, and the architecture is simple: state goes in, auditable decisions come out I mapped the decision layer into a field blueprint: what Jev reads, what it returns, and how an agent uses those answers in a real loop: step 1 → give each layer a job. The LLM plans and writes. Jev chooses between bounded options. Code carries out the action. step 2 → build the state before asking for a decision. Include the goal, evidence, available actions, previous actions, and what the environment looks like now. step 3 → define the questions in code. Choice selects an option. Score rates it. Noul estimates whether a condition is true. The answers are typed, so the runtime can use them directly. step 4 → ask independent questions together. One state snapshot can support routing, risk, and relevance decisions in the same pass. step 5 → use the answers to route work. A lookup, a complex reasoning task, and a tool call don’t need the same model or execution path. step 6 → add a gate before the tool runs: allow, ask, or deny. A high-confidence answer still has to pass the action policy. step 7 → execute the selected action and check the outcome. Record what the tool actually did, including failures. step 8 → update the state with that result. Otherwise, the agent’s next decision is based on a world that no longer exists. step 9 → apply the pattern where small decisions repeat: model routing, tool guardrails, inbox triage, research classification, browser actions, and context compaction. step 10 → judge the whole task. Track whether the agent reached its goal alongside cost, latency, approvals, and failed actions. the result: you can trace an action from the evidence Jev received to the decision it returned and the outcome your tools produced. Save the Jev Decision Infrastructure blueprint for your next agent build ↓

YZ

@robot_yz

使用 #Jev 做了一个填写外链信息的演示。 眨眼的功夫就找到入口，并准确的把已知信息填写完了。 准备把之前的Submit Agent改成使用JEV模式。 关注github，在评论区

Harsh Singh

@haaarshsingh

i created a 404 page that fixes your typos for you, and redirects you to the correct page built with @typesafeai jev try it out: kobra.systems/cmr-table

Justin Youens

@youens

This is the neatest example of Jev I've seen. But didn't see a demo link, so here you go: emogee.youens.com

Leaf Yeah!

@leaf_sanren

啊啊啊啊啊🎉🎉！JEV 黑客松已经出来啦！！！ 同时，我也见到了把 Jev 用得最对的一个项目！！ Jev 这模型最大的特点： 只会判断，一个字都写不了。所以用它的关键，就是别让它干写字的活。 这个开源的「聊天副驾」就很聪明： 你在微信里收到一句"在吗"或者"方案再想想"，它先让 Jev 一秒判出对方真实意图、危险等级 1–9、该马上回还是先晾着； 写回复这活交给另一个会写字的模型起草 3 条，最后再让 Jev 排个序。 判断归判断模型，写字归写字模型。 且它最好的一点：它永远不自动发送。 搞得我都想去闲🐟搞几台吹灰的安卓机啦！ 期待后续也能拿下 iOS、macOS、鸿蒙系统！ https://x.com/AYi_AInotes/status/2102605821456814286/video/2

Erik Kokalj

@erik_kokalj

Another cool demo, specifically for Jev Omni: x.com/erik_kokalj/st…

Artem

@S0RATNIK

Немного упоролся в Jev, но так у меня всегда бывает, когда на чем-то гиперфиксацию поймаю. Вот, например, собрал подборщик нейросетей на базе тех, что есть у OpenRouter, под ваши задачи. Просто пишите, что хотите - получаете подборку нейросетей.

Utkarsh Maheshwari

@utkarshgsuv

Is the Jev hype real, so I made to test this on my movie recommendation engine, which is getting crazy results on Quen Re ranker , I used an open-source alternative of Jev , Laya. But unfortunately Quen is way better, dekhlo! @typesafeai #jev #Jev

Spikez 99.9%

@0xSpikez

Jev Founder, Diogo Amogo, just released 12-page PDF on building a Jev Harness for coding agents this is a 10-step blueprint on how to make your coding agents 200× faster and 400× cheaper: step 1 → meet Jev: the LLM writes, the harness executes, Jev decides what each turn sees, where it routes and whether it runs step 2 → ask the question that breaks every agent: how would you design one if LLMs had no KV cache? step 3 → stop routing blind: Opus → Sonnet → Opus costs 6.19 vs 4.15 for pure Opus, because handing back reprocesses the whole context step 4 → follow the tokens: reading and searching take 56.2% of tool turns and 46.5% of tokens. Writing code is under 10% step 5 → score every chunk per query: hide, short summary, long summary or full. Compress after the question, not before step 6 → disclose tools in tiers: one-line snippets for 100s of tools, schema on demand, docs for one-off queries. Batteries stop costing context step 7 → load instructions by condition: touching *.tsx loads the style guide, billing/ loads its gotchas file, and compaction can't erase either step 8 → route by trust, not just difficulty: secrets and infra stay on first-party frontier models, public docs go to the cheapest one step 9 → share one retrieval pass: cross-model review, eval generation, ELI5 explainers and live progress pages all run read-only in the background step 10 → gate every command: programmable allow / ask / deny policies that read a script before it runs, not just its name Send this PDF and the article below to your Claude Code or Codex instance and start shipping 200× faster.

bays wong

@dennis_huangbei

使用 JEV 玩贪吃蛇、打坦克，弱爆了，刚好前端时间用 astra 写了一个纪念碑谷的关卡foldedrealm.gameai.club，来玩个找不到 dom 的 3d 游戏，能成功算你厉害。截止发帖，已经玩了 15 分钟了，第一个机关都还没正确打开

lemon👑

@lemonDefi1

Laya just mogged Jev at Tetris. The interesting part isn’t just that a local model won. Laya is an open-weights System One model running locally on a 16GB MacBook Air. Jev, meanwhile, is cloud-based and built with Grok 4.7.

Blue007｜Crypto × AI

@blueoli007

JEV integration is not another demo. It feeds live data to one ID with a hard risk cap. The trace after execution is the moat — not the launch graphic.

Xiang Wei

@xgwei

Great minds! I've been building a stylized SF with Opus too, just without the pets. My cars follow rules on the real street network, lanes and traffic lights included. Jev driving pedestrians is tempting though. How many agents can it run at once?

web5kol

@web5_kol

Jev微信聊天助手已开源，可作为手机端对话副驾，帮助用户分析微信群聊和私聊意图。 Jev通过无障碍服务读取屏幕（非侵入式），结合AI模型判断对方真实意图、危险等级，并生成3条候选回复，支持一键填入但不自动发送 x.com/li9292/status/…

emil

@esnx_xyz

you know how x feed is blasting you with promos, politics, rage baits, etc? so i made a (tamper)monkey script that uses jev to classify and hide types of posts you don't want

Jeetendra

@jeetendrayd

Built a walkable 3D street view of where SF/Bay Area startups & VCs actually cluster, every building named, every classification made by Jev.

Kelbie | Sovran

@KevinKelbie

I made a tool with Jev that reads my whole codebase to answer one question: what should I be looking at? Every chunk of code in your project is scored for relevance.

ask

@s5ststtt

買い物コンシェルジュを作っていたら、思ったより面白いものになってきた 最初は 「野菜や食品が今安いか？」 を公的統計＋Jev＋LLMで判断する仕組み。 でも敵対的レビューをかけて改修していたら、本質が変わった。 今作っているのは EvidenceとActionの間に“科学的な昇格条件”を置く仕組み に近い

spect

@spectnfa

The founder of Jev just dropped a 1-hour masterclass on how Jev actually works LLMs → Decisions → Verification → Coding Agents 0% → 0:40 - why LLMs alone aren't enough 25% → 20:27 - the 3 primitives behind Jev 50% → 26:15 - turning huge AI tasks into tiny reliable decisions 75% → 45:04 - real systems: data, agents, verification 100% → 56:33 - the future of coding agents, memory and subagents Most people are still learning prompts. This explains the layer that decides what the model should do, checks whether it worked, and decides what happens next. Basically: how you go from “AI chatbot” to software you can actually trust to run itself. And it's explained by the guy building it. Bookmark this. Watch the full hour tonight. Then read the guide below and steal the architecture for your own agents ↓

Raph Guilhem

@raph_guilhem

Opus 5.5 + Jev = Automatic scoring of 120 competitor ads, shortlisting the 10 worth adapting, and creating versions of each. Both models came out in the recent days, so I tried them together on a marketing use case, playing to what each one is built for: → Claude Opus 5.5 built the pipeline: it found the competitors, ran the http://Pletor.ai workflow "Meta ads spyer", wrote the scoring script and built the interface. → Jev made the judgement calls on every ad The use case: I picked Grüns, the greens gummy brand, as a test case. I pulled 120 ads from 12 of its competitors on the Meta Ad Library, and had Jev score each one on 7 questions: hook, clarity, message, concept fit, tone, angle, health claim risk. Code then combined the scores and picked the 10 worth adapting, and created them, via Pletor MCP.

FrankD

@FrankDa18249347

支撑这个交易机器人的Jev账户快没钱了，我准备把机器人关停后拿最后的Jev额度做一个实验，在polymarket上实盘反买。因为这个机器人亏钱亏的实在是太稳定了，甚至没有一丝起伏，仿佛每个决定都是奔着击穿账户去的，所以我也不打算搞什么dry run了，直接实盘小金额开怼，看看把剩下额度跑完结果如何

Mnimiy

@Mnilax

tool which made me over $2,000 is open now my Jev + Grok Bot copytrading bot finally has a front door 50k+ lines written. 270+ contributions. four weeks of it. every buy walks the same floor before it can become yours: > watch only the wallets you picked > match the move against the rule you wrote > check it on two providers first > hold it to your caps, allowlist, slippage and expiry the full build is in the article below.

Gabriel Pauli

@gabrielpavli

Working on a simple tool that uses JEV to help organise my bookmarks. The UI is awful and it’s nothing crazy, but it’s been nice to learn about JEV and how it works.

Ed Plese

@edplese

Jev but for images? I built a local model to try it out! 63ms for a 512x384 image and fast enough for 15fps video. Birthday 94%. Golden retriever 78%. Sprinkles 93%. Candles lit 67%. Dog about to eat the cake? 52%.

Coach Shweta Bajaj

@shwetabjaj

What stands out to me in the Jev + Grok Bot setup is the division of work: Jev decides. Grok Bot acts. Human stays in control for anything irreversible. That’s a much cleaner agent architecture than asking one model to do everything. Cheaper, faster, and easier to reason about. @typesafeai @grok

kayato

@_kayato

Jev使ってネタバレ防止のChromeアドオン作ってみた。 例） ガンダムシリーズの投稿は見たいけど、閃光のハサウェイの新作のネタバレは見たくないというシチュエーションでのデモ 結構いい感じに動いてる…！これなら不意打ちのネタバレくらわないのでは…！ @typesafeai #Jev

delost

@thedelost

Jev has been blowing up lately. If you already have API access but don't know where to start, just copy this checklist: 1. jev-browser Drives a real headless browser through MCP, CLI or a library. Jev picks one action per step from the page's clickable and typeable elements, and scores whether the goal is met or the run is stuck. https://github.com/jkudish/jev-browser 2. jev-browser-agent Per-step browser decisions at about a second. One request asks "which operation" and "which target" together, and confidence under 0.6 escalates to a bigger model or to you. https://github.com/smartdio/jev-browser-agent 3. typesafe-jev-bridge Zero-dependency OpenAI-compatible bridge. Use typed yes/no, choice and score judgments from Claude Code, Cursor, Cline or any OpenAI SDK, over CLI or HTTP. https://github.com/RevocGG/typesafe-jev-bridge 4. pilot-typesafeai-jev Jev inside LangGraph and Deep Agents. One request asks three questions, plain code picks the route, and only the winning route spends chat model tokens. https://github.com/jyje/pilot-typesafeai-jev 5. building-with-jev-skill An agent skill for writing programs that call Jev: question design, state structure, thresholds, and how to diagnose a question that answers wrong. https://github.com/dbreunig/building-with-jev-skill 6. jev-skill Guided setup plus evaluation workflows, with a read-only presence check and no silent provider switches or fabricated probabilities. https://github.com/wuyoscar/jev-skill 7. awesome-jev Source-backed catalogue of what people actually shipped, sorted by category, with limitations noted per entry. https://github.com/yibie/awesome-jev 8. awesome-typesafe-jev Field guide with SDKs, live demos, agent tools and independent evaluations, including where the vendor numbers came from. https://github.com/AbdelStark/awesome-typesafe-jev 9. awesome-jev-by-typesafe Evidence-backed use cases, patterns and starter code, with the 70 to 500 ms latency claim marked as vendor-reported. https://github.com/Anil-matcha/awesome-jev-by-typesafe 10. awesome-jev (cobanov) The widest catalogue right now: SDKs in Go, Java and JS, MCP connectors, guardrail experiments and studies. https://github.com/cobanov/awesome-jev Copy these complete Jev blueprints - then read the full Jev setup below ↓

silentguy

@silentguyy66

Grok Bot does the job, Jev decides where the job goes next That's the ai company split: a work model and a decision model In this free 22-min tutorial they walk through jev, typesafe's "system one" model that makes decisions instead of chatting You ask one question, Jev answers from a fixed menu: noul, choice, score The demo: customer messages in, frustration score out. usable right in code Not another chat model, it's the fork in the pipeline Grok Bot keeps generating Jev keeps deciding Humans keep the approvals The teams wiring in that third layer are quietly becoming one-person companies Save this before your competitors put a decision layer between their bots

Geek Lite

@QingQ77

把本地 MLX 推理的 Laya 模型和托管 API 的 Jev 放在同一个 T-Rex 跑酷赛道上对打，用延迟、存活和回放数据对比本地推理与云 API。 github.com/virajbhartiya/…

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

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.

امیرحسین اسلامی

@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 👀

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

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:

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

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 😬

Timothy Kassis

@TimothyKassis

We built a really nice free tool on top of @typesafeai Jev to see how rigorous an academic paper is.

Yannis

@yannnis

Enough is enough! Is it that LinkedIn sucks or what? Let's settle this. I go viral on LinkedIn for slop (attached) Then I spend 2 days to build a JEV product to scan Reddit for leads. And get tumbleweed!?! How??

Ackerman

@Yarilo7brigada

98% of my feed is junk. Now I don’t even see it I built a filter that reads my feed for me. It runs on Jev - a model that doesn’t generate text; it just makes binary decisions, in milliseconds and for pennies. For every post, it evaluates four questions: Is it relevant to my niche? Does it provide real value? What format is it (breakdown, news, ad, meme)? And does it make loud claims with zero proof? Out of 1,000 posts, only 20 remained. 97 milliseconds per post. 1.2 cents for the entire morning. The most frustrating takeaway, nearly half of my feed was ads and memes, not the creators I originally followed for substance. An hour of mindless scrolling turned into five lines over breakfast.

AI Insider

@TheAIInsiderN

JEV + OPUS 5.5 IS INSANE. I built an AI system that analyzes an X post before you publish it. Paste any X link. Press Start. Get a virality score in minutes. The entire production version was built in 9 minutes. Here’s what happens: Jev analyzes 800 viral posts as a live baseline Groups them by hook type: demo, launch, proof, contrarian take Runs 12 structured checks: hook, numbers, media, CTA and more Finds the 5 most similar viral posts Opus 5.5 explains why those posts spread Then your post receives: Virality score out of 100 Estimated likes and views Percentage of the viral baseline it beats Direct comparison with similar winners It works on drafts too. So instead of posting and hoping, you can identify weak hooks, missing proof and unclear CTAs before anyone sees them. Hover over any result to inspect the original post, its media, every check and the final score. JEV DECIDES FAST. OPUS EXPLAINS WHY. #JEV

Avid

@Av1dlive

Jev + GPT-6 Astra just built the most TERRIFYING AI trading setup on the internet... [this article covers 90% of what is required to build quant-level systems] /1 GPT-6 Astra reads the order book, the tape and 3 correlated futures /2 Jev turns the signal into a trade and checks the risk limit /3 computer use clicks the order screen, no broker API needed /4 the full loop runs in 6 ms, signal to fill steal this setup in the article below👇

Proliquid

@proliquid_xyz

Did you know that we have @typesafeai Jev AI giving you the sentiment on the news? Don't miss the next ethereum:0xfaba6f8e4a5e8ab82f62fe7c39859fa577269be3 like move. One click long on bullish news only on app.proliquid.xyz/terminal 🦈

Jetwani Avinash

@jetwaniavinash

First thing I built on Jev: a memory gate for Claude Code. Every message gets one question: is this worth remembering? 0.3s later it's either skipped or saved as one line in JEVMEM.md in the repo. Open source, link in my pinned post.

Prasad Pilla

@prasad_pilla

AGREED. team built an internal tool using Jev to qualify candidates, and Jev itself is genuinely impressive at what it’s designed todo. but recruiting is hard. there are too many variables that don’t fit neatly into a rubric: context, trajectory, ownership, quality of work, potential, and the signals you only catch by actually digging into someone’s work tbh https://x.com/idleshubh/status/2102429846660079971?s=20

morph

@morpphhhaw

TypeSafe founder Diogo Almeida introduced Jev, and the field I would inspect first in any agent built around it is not model. It is state An agent can have room for a long conversation and still miss the one line that matters. A refund request needs the current order, the charge record and the policy that applies today. It does not need twelve earlier attempts to sound helpful Here is the state packet I would put in front of Jev: step 1 → name the decision the code needs to make now, not the entire job the agent was given step 2 → pull facts from the system of record: order status, amounts, timestamps and permissions step 3 → include the customer's actual words as evidence, without asking Jev to reconstruct the whole conversation step 4 → state the constraints that can change the route, such as a refund window or an account hold step 5 → build the available actions from live code, so a closed account never appears as a valid destination step 6 → put the question in the question field. State is evidence, not a second prompt hiding instructions step 7 → leave the sums and date arithmetic to code, then send Jev the result it needs to judge step 8 → keep the exact packet with the answer, because you cannot debug a decision from its label alone The point is not to compress everything until it looks clever. The point is to make it obvious which piece of evidence could change the answer The document below shows that packet on one page. The article goes further into shaping state without turning it back into a transcript

OrcDev

@orcdev

TanStack AI just shipped subagents. Jev picks which models / agents should run for the task, in parallel, in sequence, or mixed. Best Jev use case I've seen so far. TanStack team cooked. 🔥

rewind

@rewind02

built a full production app with opus 5.5 and jev... fed it a url, it screenshots the site, scores it against a list of known patterns, and ships a shareable report here's the actual pipeline: - gemini flash takes the screenshot, that's the only vision step needed - jev scores it against a fixed list of criteria in a fraction of a second, no text generation, just structured ratings - opus 5.5 writes the entire app, front to back, off a single planning prompt - product os turns the plan into a spec, then a roadmap, then working code, task by task - code review, security audits, and testing happen automatically as it builds, not after - a deploy checklist gets generated too, walks you through auth setup, hosting, env variables, step by step - cost to run per scan lands in fractions of a cent, both models chosen specifically because they're cheap at their job what actually makes this work: opus 5.5 handles everything generative, jev handles every decision that doesn't need generation, and neither model gets used for a job it's not built for that split is why the whole thing runs on pennies instead of frontier-model pricing pair that with a structured build system instead of one long freeform prompt, and a non-coder ships a live, secured, production app in an afternoon, not a sprint

aiasssistantstore

@aiassistantstor

CLM-8B Just Dropped: The New Open AI Model That Claims to Be Up to 9× Faster Than Jev for Agents

Abdullah

@Abdullah_Ops1

التجربة الثانية مع JEV 👇 سويت له Vibe Coder Launch Inspector تحط رابط مشروعك يفحصه ويطلع لك هل جاهز للإطلاق ولا يحتاج شغل يعطيك Score + المشاكل + الأدلة + وش اللي يحتاج تعديل والأحلى كل مشكلة معها Fix with Codex يطلع لك برومبت جاهز ترسله لكودكس #AI #JEV #Codex #VibeCoding

apolinario (poli)

@multimodalart

congrats on the release! i've included xor on the Decision Index 0.2 benchmark of jev-like models we don't have a vision benchmark yet, but the text-only version got 8th place, strong in arts & human taste x.com/multimodalart/…

PeterY

@yudeewxy

用JEV + Codex + TG Bot，开发了一个小工具，用于实时判断9个coin的价格走势和概率。交易信号来自前几个月开发的trading信号平台。同时给这个Bot授权了1万刀，看看他围绕这些信号来实时交易的表现会怎样。 开发过程： - JEV开通，获取API，配置到本地 - 配置TG Bot - 连接Binance和OKX的API - 信号聚合平台：http://pytrading.vercel.app - 或者找到trading view或者自己的一套交易因子 - Bot配置钱包，授权10K usdt，在OKX上进行交易 JEV的优势是：快速高效的反馈价格及进行交易行动、相比LLM大量节省Token

apolinario (poli)

@multimodalart

congrats on the release, i've added it to the jev decision index 0.2, it's the #2 open weight jev-like model 🥳 and the strongest in chess and for its size x.com/multimodalart/…

ImRobot

@th3nolo

Been building an agent-agnostic permission gate: Jev (TypeSafe) + hooks. Many harnesses copy Claude Code's hook format. Some barely have hooks at all. This makes me happy: agy in YOLO mode. chmod -R gets blocked, the agent asks me, I say yes, it runs once. Again? Blocked again.

Cartwright

@CartwrightApp

Built Cortex, a local MCP server: Claude plans, a fast layer does the clicking. Same Mac-app task: computer use 23 s → Cortex 0.8 s One action: ~5 s Claude round → 0.25 s, no model 24 tools: browser, Mac apps, GitHub search, safety gate Measured on my Mac. @typesafeai #JEV

TactiX Trading Panel

@TactiXPanel

The Jev-powered AutoScalper passed all Tests during the Testnet Live Test with an average win rate of 82% trading on the 1m chart, using a 120 candle lookback window. So 2h structures get scalped top-to-bottom and bottom-to-top Now its time to aim for mainnet deployment!

Awais.

@abbas_kazmi066

plenty of use-cases for JEV. here's one: Hover Explanations. This simple demo testing cost me around: $0.006. Definitely very cheap and fast.

Cuth

@ItsCuthulhu

Now that I've collected so much data on System One models on my DGX Spark. Lets see if I can beat Jev on size, speed, and accuracy. It will be MIT for anybody to build on. Everything I do on here is open source.

Browser Use

@browser_use

Luna (planner) -> Jev (actor) playing poker and winning

Chris Brownridge

@chrisbrownridge

another @treg_ai /Jev demo to analyze data SUPER fast used treg to pull meta ads and then Jev to teardown the landing pages so you can understand where brands are sending traffic to categorizes the type of page, offers they're using and language used to sell also shows ad creative launch velocity, creative mix takes a few seconds to do it all end to end.

OpenMed

@OpenMed_AI

Four typed questions across four authored fictional notes, labels written before the calls. Jev 16/16, Laya 13/16. On the medication-conflict question alone: 4/4 vs 2/4. A useful diagnostic, not a clinical accuracy estimate.

Ricker

@0xRicker

Jev Engineering turns a static agent workflow into a graph that can rewrite itself while running. the video is basically the problem at scale: hundreds of routes → thousands of crossings → different agents → different tools → different confidence levels Jev Engineering doesn’t control every step. it controls the crossings. when two paths compete: → score both → kill the weak route → reroute the task so instead of one fixed chain, you get a live braid: state → decision → parallel paths → crossings → Jev → next state that’s the point of Jev Engineering: more parallel execution without letting the system lose the objective. the agents create the paths. Jev decides which paths survive. full breakdown below ↓

Dain

@Dain0x

A beautiful pattern can still be noise. Here’s a JEV × Opus 5.5 workflow I’d test: → A data pipeline identifies candidate signals. → JEV selects which candidates to investigate using defined criteria. → Opus helps explore those candidates and propose explanations. → Statistical checks and human review test whether those explanations hold up. The critical question: what evidence would change our mind? That question belongs inside the workflow, before a promising pattern becomes a confident conclusion. This animation illustrates the concept. The particles and activity are simulated.

frevana

@frevana_ai

Jev made competitor ad research 30x faster and ~90% cheaper. Typed “game” as the category. 20 seconds later: 294 top TikTok ads across 68 brands analyzed. Jev: 1/ pulled the top ads from the past 180 days 2/ scored every ad on stop-scroll, hook, format + trust 3/ clustered them into creative patterns 4/ classified winners, watches + dogs 5/ built a leaderboard of what’s winning 6/ recommended what to make next 20 sec · ~$0.02 For gaming: Winning hooks: offer/promo, challenge accepted Winning format: gameplay + UGC This is what we’re building at @frevana_ai with Jev: decode your category’s ads → know what to make next, in seconds. Try it free. Link in the first comment 👇

nicolay

@nicolaycz

Jev is the semantic if in your agent loop. LLM writes. Jev (TypeSafe System One) answers typed questions on the same state (Noul, Choice, Score) with calibrated probs. Your code branches. Pull the cheap decisions out of the LLM. Early access: console.typesafe.ai

leiro

@leiroops

the lead engineer at a research company used Jev + GPT-6 Astra to build a system that analyzes 12,000,000 source updates every 6 hours the system processes massive streams of information, a process like this used to cost $1,000,000+, now Jev engineering does it for $200 the first version relied on GPT-6 Astra to interpret every result it worked, but processing millions of routine decisions through a large model created unnecessary latency and huge inference costs then the engineer rebuilt the decision layer around Jev instead of explaining every source, Jev reads the saved task state and decides what should happen next: Research, Write or Review Astra handles the open-ended work: comparing evidence, resolving contradictions and preparing the client brief code controls the queue, permissions, budget, retries and files at the core of this system is the same architecture I break down in the article: Jev routes the work, GPT-6 Astra handles the open-ended tasks, and code keeps the process running continuously the full step-by-step build of a client research process that runs 24/7 is below ↓ would you let AI decide which findings deserve further research?

gabidev

@GabiDev98

Here is another use case for Jev in DeFi. Vault risk classification. I pulled the signals that matter for Morpho vault risk (allocations, LLTV, utilization, idle, oracle, curator, APY sanity, liquidity and more) across 50 @Morpho vaults on Base, then asked Jev to score each one LOW / MEDIUM / HIGH / EXTREME. 50 judgments in 22.6s, ~452ms avg, ~60% median confidence, around $0.006 in costs Result mix: 30 MEDIUM - 12 HIGH - 8 EXTREME - 0 LOW. Jev also ranked curators and vaults by trust, who looks safest, who looks riskiest. Claude, Codex and other models can do this as well but it takes much more time and money.

keno

@kenonews

JEV can’t see images. So how do you give it eyes? What’s possible? What can run locally? How far can you get for free? The answers (and the catches) are in the full breakdown below:

Hassan

@nutlope

Just trained Tev1 0.8B, a tiny Jev-like classifier. Here it is running completely locally on my mac with @ollama & classifying some tasks. It's extremely fast: only ~50ms E2E latency. Video is not sped up! Releasing weights & benchmarks very soon so you can try it yourself :)

Viv

@Vtrivedy10

Jev for RAG in almost all cases you trust the semantic matching capability of Jev more than dot product similarity very useful as the direct similarity metric in small data cases and a great reranker with big data

Jarek

@jarekceborski

I rebuilt CAPTCHA with Jev The browser measures how you fill in the form. Server turns that into a few plain sentences, and Jev decides: person or bot. Blog post with source code in the comments: