Jev computer use and browser agents

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！

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👇

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

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。

Ranjan Kumar

@ranjankumar

𝐖𝐡𝐞𝐫𝐞 𝐉𝐞𝐯 𝐁𝐞𝐥𝐨𝐧𝐠𝐬 𝐢𝐧 𝐚𝐧 𝐀𝐠𝐞𝐧𝐭 𝐇𝐚𝐫𝐧𝐞𝐬𝐬 (𝐍𝐨𝐭 𝐚 𝐌𝐨𝐝𝐞𝐥 𝐒𝐰𝐚𝐩) Your agent harness needs a decision model. Where you place it depends on one brutal fact: Jev's ordering is trustworthy. Its confidence numbers are not. Most placement guides skip this distinction entirely. They tell you to pick a threshold and execute above it. That works if you actually have a probability. You might not. 𝐓𝐡𝐞 𝐜𝐨𝐫𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦: A model can rank beautifully and lie about magnitude simultaneously. When you gate execution on if confidence >= 0.85: approve_transfer, you are trusting a number that was never calibrated to your prevalence, your cost ratio, or your queue. Move the threshold up and you trade recall you never measured for precision you cannot state. You have no idea how far along an unmarked axis you moved. 𝐓𝐡𝐫𝐞𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬, 𝐭𝐡𝐫𝐞𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐧𝐞𝐞𝐝𝐬: Routing and ranking consume ordering only - which answer is best matters, the number beside it does not. Gated execution consumes magnitude - the threshold is your boundary and it must land on a calibrated scale. Relative logic like if top1 - top2 < 0.1: escalate consumes differences - and rescaling the score axis will flatten margins unevenly, breaking your code. The sepsis alert systems of 2020 learned this hard way. Michigan switched off alerts when COVID shifted patient prevalence beneath a fixed threshold. No weights changed. The denominator moved, the promise broke silently, and nurses drowned in false alarms. 𝐓𝐡𝐞 𝐟𝐢𝐱 𝐢𝐬 𝐬𝐞𝐪𝐮𝐞𝐧𝐜𝐞, 𝐧𝐨𝐭 𝐭𝐮𝐧𝐢𝐧𝐠: First question: is this number admissible as a probability at all? Run a calibration study on a few hundred labelled cases from your own queue. Samuel Sacco's measurements from 18 September and Adil Muhammad Pervez's 8,000 judgments both reach the same conclusion: fit your own map. The weights are shared across every account by design - there is no per-customer adaptation - so a deployer-side calibration map is your only mechanism. 𝐒𝐞𝐜𝐨𝐧𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: given that the number is calibrated, where does your cost ratio put the line? That part of the familiar advice still works. Run them backwards and you are tuning a dial with no markings. Read the full analysis on calibration, harness placement, and where Jev actually fits: https://ranjankumar.in/jev-system-one-model-agent-harness-placement Follow for more practitioner insights on agentic AI systems and production AI engineering. #AgentiveAI #AIEngineering #SystemOne #Calibration #MLOps #DecisionModels #HarnessEngineering #Jev

Doge 🐶

@pprownets2023

💡 ยกระดับ AI Agent ให้เก่งขึ้น 100x ด้วย Jev Engineering (Decision-Making Brain) เคยเจอปัญหาไหม? เวลาทำ AI Agent แล้วสั่งงานด้วยข้อความธรรมดา (Free-form Prompting) มักได้ผลลัพธ์ที่ไม่แน่นอน ควบคุมยาก และนำไปต่อยอดในระบบแบบอัตโนมัติได้ลำบาก 🤯 เอกสาร JEV Engineering ได้เสนอแนวคิดในการแก้ปัญหานี้อย่างน่าสนใจครับ: 🔹 1. Jev คืออะไร? Jev คือ "สมองส่วนตัดสินใจ" (Decision-Making Model) สำหรับระบบ Agentic Systems ที่ไม่ได้เน้นการคุยแบบ Free-form Chat แต่เน้นการแปลงข้อมูลสถานะระบบ (Structured State) ให้กลายเป็น Typed Outputs ที่ชัดเจน เช่น ช้อยส์การตัดสินใจ, คะแนนความเกี่ยวข้อง หรือคำสั่ง ใช่/ไม่ใช่ พร้อมระบุค่าความน่าจะเป็น (Probability) 🎯 🔹 2. ทำไมถึงทำให้ระบบเก่งขึ้น 100x? การแยกส่วน "คิดตัดสินใจ" (Decision) ออกจาก "การลงมือทำ" (Execution) ช่วยลดความคลุมเครือ และทำให้ระบบเสถียรขึ้นอย่างมหาศาล โดย Jev จะส่งผลสูงสุดในจุดที่มีการตัดสินใจถี่ๆ (High-frequency Decisions) เช่น: 🔍 Research Evaluation: ประเมินความน่าเชื่อถือของแหล่งข้อมูลอัตโนมัติ 🛠️ Action Selection: เลือกใช้ Tool หรือส่งต่อ Agent ให้ถูกตัว ✍️ Content & Draft Refinement: วางแผนและเกลาเนื้อหาเชิงลึก 🚦 Human-in-the-loop Gating: ตัดสินใจว่าเคสไหนต้องส่งให้มนุษย์ตรวจต่อ 🔹 3. สรุปสถาปัตยกรรม 3 ขั้นตอน (How it Works) Input State (01): ใส่ Goal, Context, Rules และ Actions ที่ทำได้ Jev Brain (02): ประเมิน เหตุผล วางแผน และคืนค่าเป็น Typed Decision Orchestrator (03): นำผลลัพธ์ไป Validate และ Execute ร่วมกับ Agent, Tool หรือ Human สดๆ 💡 สรุปสั้นๆ: ถ้าอยากให้ AI Agent ทำงานอัตโนมัติได้อย่างแม่นยำ เลิกส่ง Free-form Prompt สั่งงานตรงๆ แล้วเปลี่ยนมาใช้ Structured State + Typed Decisions เพื่อการขยายระบบที่นิ่งและน่าเชื่อถือครับ!

Pydantic

@pydantic

Whichever new model has you most excited, Pydantic AI has you covered. Claude Opus 5.5 from @AnthropicAI and GPT-6 Sol and Luna from @OpenAI, supported on launch day. And Jev from @typesafeai, not even a week old. Upgrade, then change one string. github.com/pydantic/pydan…

Anatoli Kopadze

@AnatoliKopadze

6. Opus 5.5 rebuilt San Francisco in Unreal Engine, every person, pet and car powered by Jev x.com/MatthewBerman/…

orvian

@heyorvian

this is absolutely crazy! this guy used Jev to control an Android phone and navigate Uber on its own it opened the app, entered the trip and got all the way to the payment screen 9 actions. around 21 seconds people are already doing crazy stuff with Jev

starmex

@starmexxx

JEV JUST HIT 18K STARS IN A WEEK AND BOOKS ZURICH TO LONDON ON GOOGLE FLIGHTS IN 7.1 SECONDS WHILE CHATGPT OPERATOR STILL NEEDS 3 MINUTES AT $200 A MONTH jev-ultrafast splits every browser decision into operation, click target, and typed text, so one llm request replaces five and the agent finishes the task before the page finishes animating [here's the setup i'd use:] 1. install it git clone http://github.com/browser-use/jev-ultrafast cd jev-ultrafast uv sync cp .env.example .env 2. add the keys typesafe_api_key → .env openrouter_key → .env (for mercury 2.5) 3. wire the models mercury 2.5 → typing (diffusion, milliseconds not tokens) gemini 3.1 → target selection glm 4.6 → fallback text helper 4. build around stages goal → snapshot → operation → target → execute → verify keep the dom snapshot separate from the click so a stale page never restarts the whole run 5. run it uv run jev localhost:8766 → start demo → run automatically 6. monetize it fiverr → "i book any flight, hotel, or form in 10 seconds" at $30 a task telegram bot → travel search on demand at $5 a query b2b → sell it to travel agencies as "one seat replaces one junior researcher" at $500/mo white label → wrap as a "concierge agent" and charge $20/mo per user same reason browser-use's cloud waitlist filled up in a week: one indexed action space, no screenshots, no restarts. http://github.com/browser-use/jev-ultrafast

CyrilXBT

@cyrilXBT

I built agents that run 200x faster and 400x cheaper than the standard Claude Code loop. Best case, not average, on the tasks where a $9 model was doing $0.0004 work the entire time and nobody noticed. Split reasoning from decision making. Let Claude Code write. Let Jev decide. From scratch, the whole architecture is below. follow @cyrilXBT

Kiyoro

@0xKiyoro

OPUS 5.5 IS MAKING YOUR AGENT DUMBER RIGHT NOW AND IT WILL NEVER TELL YOU. JEV CAUGHT IT. The default effort on Opus 5.5 dropped from high to medium. If your agent never set effort explicitly, it is now reasoning at a lower setting than the one you tested it on. No error, no warning, every health check still green. Jev sets effort on every single message, so the requests that went through it never changed. The ones that relied on the default did. That gap is how it showed up. The rest of the migration is loud. Each of these returns a 400 on the first call: > thinking: disabled, now rejected. Drop the field and set effort > tool_choice any and tool, removed. Use auto + strict > computer_20251124, retired. Use computer_toolset_20260801 > editing above a thinking block, rejected. Append only, or drop_block You will find all four in five minutes. The effort change you will not find at all. The cache has its own quiet trap. Hop Opus 5.5 to Sonnet 5 and back and a session that cost 3.32 costs 4.36, +31%, because Sonnet cannot read Opus's reasoning. Change effort at the top of a request or switch fast to standard mid-session and the cache is gone again. Jev picks speed once on turn one and sets effort per message, so it survives. The upgrade is still worth it. $4 / $20 per 1M instead of $5 / $25, cache reads at $0.20 instead of $0.50, and 66.4% on Terminal-Bench 4.0 against 57.9% for GPT-6 Astra. Write effort into every request before you touch the model ID. The default stopped meaning what you think.

MajdAICode

@MajdAICode

Following up on my earlier comment: I mapped out the coding-agent workflow I’m exploring in a short video—Cursor, a separate worktree for each feature, and Jev for narrow decisions 👇 x.com/MajdAICode/sta…

Artificial Intelligence Papers

@SciFi

Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents Dongming Jiang, Yi Li, Bingzhe Li arxiv.org/abs/2609.23986 [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙻𝙶]

Cesar Favero

@cesaremuszka

botei o jev no codexrouter, sinto o trabalho muito mais otimizado, vou criar um harness personalizado para o codexrouter, sinto que da pra fazer muitas melhorias nesse fluxo o jev por enquanto no codexrouter está com poucas funções

CyrilXBT

@cyrilXBT

With a single prompt, the Jev + Claude Code stack can make AI agents 200× faster and 400× cheaper. The secret: agents that run wide not long. I broke down the complete system in 3 visuals. Save this. You’ll want it later. Follow @cyrilxbt for more AI updates.

Neha Sharma

@hellonehha

As Jev (@typesafeai) is the talk of the town, and people are having fun finding new use cases for it. I built a real-time demo of smart AI agent routing for anyone working on AI tools, agents, or products. this is what I have done for one of my project. https://youtu.be/c8qV0f4XnRY