Jev computer use and browser agents

Jiaqi Gu

@droidqw

github.com/droid-Q/jev-sk… ，基于 jev 的 skills 路由

Chris Adcock MD 🍊💊

@ChrisAdcockMD

Took Gregor’s Ultrafast idea and wired it into Grok Bot. @bot @OpenRouter @typesafeai @gregpr07 Your bots can now use Jev to drive the real Chrome on the machine instead of slow look-and-click. Drop in the API key you already have (OpenRouter or TypeSafe), and it gets going. It also walks your existing bot workflows and flags which decisions Jev can take over — the quick yes/no and “pick one of these options” calls — so you’re not guessing where it helps. Share link if you want to try it: https://x.ai/bot/sM_Xi4OF09cGU8KGyLvlC

Izzuddin

@Izzuddin_Shafi

1/8 Saw Jev from @typesafeai on my feed, so I made it play Pokemon Showdown. Codex built the harness. It was damn fast. Its choices were a mixed bag. Full match, video 1/2. This is a saved replay with decision data, latency and added reading pauses.

Ranjan

@manofsteel3129

built askjev on typesafe jev for all-site navigation with claude you talk to claude in plain english and askjev runs your real browser on any site. jev decides every next click — open pages, switch tabs, scroll feeds, fill forms, run multi-step goals without you babysitting the DOM. mcp server + chrome/brave extension. auto-connect once, then stay in chat while the browser moves. claude handles the conversation. jev handles the decision on each step. askjev is the hands on the web. install: load the extension → paste your typesafe key → auto-connect → restart claude → talk example: use askjev, open http://x.com and scroll my feed and find the best posts http://github.com/ranjan2829/AskJev npx -y askjev-mcp

Jason Lu

@jasonlu_ai

JEV is changes the world of E2E testing! Same eBay test flow, completed-run medians: Jev: 47s / $0.0067 GPT-5.6 Luna: 62s / $0.0277 Claude Sonnet 5: 79s / $0.4062 Try jev-e2e. github.com/perixtar/jev-e…

Aditya Singh

@xyz04274951

I made this for fun. Wired a mic over Fusion 360: click, speak, Fusion runs the feature. @typesafeai ‘s Jev only decides if the utterance is a command.

Milind S

@milindlabs

Aaaaaand this is now open-source here: A tiny AI pointer companion for your Mac driven by JEV or Gemini Live - Bring your own keys - Local OmniParser running on CoreML - Jev drives the pointer - Ctrl + K to type a task Voice mode next if people want it! https://github.com/milind-soni/tiptour-macos

codila

@0xCodila

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open @typesafeai , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

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。

Versun

@VersunPan

刚看了下这个jev wechat项目，模型是用 jev + deepseek 通过 Android 无障碍服务读取App 的节点树，兜底是用截图 + 本地 OCR 上下文只有最近 10 条消息 微信能监测到，有封号危险

GOGOGO

@GodName794

第一版： 让Jev模型自己决定怎么走，显然Jev败北； 第二版： 算法优化 使用预判攻防筛选落子，再让Jev决定走哪一步，Jev最终打败了我！ 这次经历让我更清楚地看到：表现聪明，和做出可靠判断，是两件需要分别验证的事。 我怎么看 AI？ AI 很擅长从复杂信息里提取规律，在信息不完整、要求不够明确时给出判断。但它给出了选择，甚至给出了很高的概率，都不等于它已经算清楚后果。 这盘五子棋就是一个例子：Jev 能选择落点，却没有独立证明自己看到了所有关键反击。给它补上试走和检查，整个系统才更可靠。所以评价 AI，应该看它在具体条件下做对了什么，而不能只看它说得多像专家。 我怎么看算法？ 算法是把判断方法明确写下来：先检查什么、遇到什么情况怎么办、怎样验证结果。 我们这套程序的优势，是能重复、稳定地执行这些步骤，还能给出可核验的杀棋路线。但它的局限也很清楚：搜索只覆盖有限范围，评分规则是人为设定的，候选筛选也可能漏掉好棋。 另外，AI 本身也是由算法实现的。这里真正的区别，是“人为明确写下的规则与搜索”，和“通过训练学到的判断”。 由此能得出 Jev 擅长什么？ 这次实际验证的是：Jev 能接收棋盘、候选和战术描述，快速返回一个明确选择及概率分布。 因此，它适合充当软件里的“候选判断环节”，把描述性信息转成程序可以直接使用的选择。 但我们还没有证明： Jev 擅长独立进行多步博弈； 它比程序直接选最高分更强； 它给出的概率能准确预测胜负。 所以，我会把它定位为可嵌入程序的判断组件。它到底增加了多少价值，需要通过“有它”和“没有它”的对照来衡量。那次赢棋，是整个系统的成果，不能全算在 Jev 头上。 对于 AI 和人，最有启发的是什么？ 第一，好判断往往来自好的检查过程。人也会只看到眼前收益，忽略下一步反击。重要决定前，多问一句“如果对方这样回应，我怎么办”，常常比增加信心更有用。 第二，把能算清楚的部分交给计算，把尚不确定的部分明确留下来。 不要因为一部分经过验证，就把整个结论都当成确定。 第三，工具的价值要靠比较，而不是靠名气。 如果去掉 Jev，效果一样甚至更好，就应该承认；如果它能改善选择，也应该用结果说明。 对人而言，最值得保留的角色，是确定目标、选择取舍、检查证据，并承担决定。AI 可以扩大我们考虑问题的范围，但判断“什么才值得做”，仍需要人的参与。

Min(building Moss, hiring now)

@zeroxmin

Great article from Hrishi! So despite all the hype, JEV is a great observer for agent runs (progress, time/cost-to-finish, trace labeling), but not a security gate, and not yet a laziness detector. Still sounds good to plan JEV into our roadmap and explore some use cases for onchain agents!

よたん

@yo_ta_n

Difyで作った身内用LINE Bot。家族だけはLINE ID判定して自宅のライトやエアコン操作がチャットから行えるように対応できた。極力コストを減らすため分類器はJev、チャット応答はLunaを選択。#Dify #Jev #ChatGPT #LINE

DaBin.Binah

@yedabinbinah

JEV语音控制mac，指令刚说完，任务就执行完成了，这个模型泛决策能力很强，效率极高 JEV目前是申请制，所以大家先去申请，用不用再说 申请链接：typesafe.ai #JEV

Vincent.seo

@xunaoo

Jev 가 우리 로직을 대체할 수 있을까요? 아닙니다. 대체가 아니라 로직 안으로 들어앉습니다. 제 브라우저 에이전트에 붙여 봤습니다. 판단 한 단계가 2초에서 0.2초, 모델 호출이 열 번에서 두 번. 그런데 정확도는 6%p 낮습니다.

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…

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

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.

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.