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Dünya kupası Sinematik evrene dönüşür ise 😎 Vikingler Brezilya'yı ezdi geçti.

Mnie śmieszy 😂

@VietLook8rq gestomeme 点 com

@waylybaye region 跟 时区吧

我就猜到 A\ 肯定会用时区做指纹，所以我很早就把 Mac/iPhone 的系统时区改成新加坡了。

咸鱼卖什么，已经有人都归纳好

我怎么早没看到这张图？ 总结的太好了。

@grgerwcwetwet 打工人别试 钉钉检测出来直接给管理员发通知

iPhone 改定位，以前要么连电脑，要么折腾越狱。 现在有个新方案，直接在手机上就能搞定苹果的网络定位。 最近 GitHub 上有个很火的开源项目 wloc，通过修改 Apple 的 WiFi/基站网络定位结果，实现 iPhone 网络定位切换到世界各地。 GitHub：github.com/Yu9191/wloc

@huangjinbo 确认可以吗 走代理应该也就是出口 跟转发的特征 还有入口那个域名吧？

为了解决这个问题，准备在VPS 上采集“docker-compose + LiteLLM + Postgres + Caddy 自动 HTTPS”搭建一套仅供自已使用的中转站，把 OpenAI、Gemini（Google One会员送的）、ollama Cloud 订阅集中在一起。同时为了减少被封的可能，使用 Tailscalle 把所有设备组成一域，统一出口出去。思路来自某位推友的方案（截图）。

@codewithantonio 👍

@bigdatabong Custom one built from scratch!

For the next tutorial, I will teach you how to build a collaborative browser automation workflow builder with endless possibilities - here i instructed the agent to configure a new Porsche 911 and send me an email with the configuration link 😎

giffgaff 卡海关被查扣，上新闻了，2026年6月

@xiongchun007 谁家的cli ， cc的可以吗

就 Coding Agent 而言，JetBrains 家族原生 AI 面板通过 ACP 协议连接CLI Agent 实现的交互效果，可以说独步全球，无人能超越。小小一个执行结果信心整合面板，显示了多个非常实用的细节。 当然，那种闭着眼睛无脑制造电子垃圾的 VibeCoding 大仙是体会不到的。

@billtheinvestor 假的吧 没看到

Google AI Studio 正式开放了每分钟 100 万 token 的免费额度。开发者现在可以直接调用顶级的算力资源，无需绑定信用卡，也不需要支付任何月费。通过这种方式，你可以零成本获取原本价值数千美元的计算能力。如果你开发高并发、长上下文的项目，可以按以下路径接入： 1. 直接访问 Google AI Studio 平台。2. 获取官方 API Key。3. 在代码中配置请求频率（Rate Limit）以适配每分钟百万级的调用规模。这种无需订阅即可使用的策略，极大降低了大规模模型应用的开发门槛。

OpenClaw is now on iOS + Android 🦞 📱 Native mobile apps, finally 💬 Agents in your pocket 🔔 Channels, tasks, replies on the go Run agents from wherever your thumbs are. iOS: apps.apple.com/us/app/opencla… Android: play.google.com/store/apps/det…

@LufzzLiz @YuLin807 高级

@bigdatabong @YuLin807 整完了都😂

哈哈，早上看到 @YuLin807 发帖老外们做出类似漂亮的动图，我尝试了下用动图总结下我这个文章的项目，没想到很快就出来了 QingYue快来看看，效果可以不，可以我就整个skill 开源出来🐶

everyone is talking about agent loops, harnesses, and self-evolving agents. but almost no one is talking about the actual hard part: you cannot run a company on one giant agent with every tool, every file, and no accountability. that's not autonomy. that's a fog machine. here's how we're building an agent company OS inside Matrix. — the stack: Workspace Brain → Matrix Runtime Orchestrator → Department Verticals → Department Lead Agents → Worker Agent Pool → Proof / Check-in Loop Matrix is not a chatbot. it's an operating system for autonomous work. — the workspace brain is the company boundary. it gets loaded with the things a real company actually runs on: → product docs → codebase context → chats, files, goals → operating rules → prior runs + examples of good work → approvals, memory, skills this isn't "context." it's the shared operating layer. it knows what the company knows, what it's trying to do, who owns what, what good looks like, and what must be proven before work counts as done. — on top sits the Matrix Runtime. it coordinates wake, cron, department messages, OKR state, permissions, worker dispatch, proof ledger, memory updates. under the runtime, work is organized into departments. a department is not a chat thread. it's a long-running agent with identity, memory, skills, goals, history, tool boundaries, taste, and accountability. Founder Strategy. Product Engineering. Growth. Ops. Research. each one has a lead agent that decides what happens, reads the relevant Memory Skill, breaks work into scoped tasks, and picks the right execution seat. — sometimes that seat is a native Matrix worker. sometimes Codex. sometimes Claude Code. sometimes a browser / computer automation worker. the point is not "one model does everything." the point is: → the right agent → with the right context → inside the right boundary → using the right tools → with a clear definition of done — this is why scoped workers matter. a "do everything" agent is too vague. but: → a release worker with repo context, tests, and approval gates → very good → a Codex worker scoped to one patch and one validation path → very good → a Claude Code worker doing deep repo analysis → very good → a browser worker with a specific flow and proof requirement → very good narrow scope reduces drift. Memory Skill keeps narrow agents from going blind. proof prevents fast output from pretending to be progress. — that is the loop: Workspace Brain → Department Lead → Worker → Artifact → Proof → Check-in → Memory Skill update every cycle, the company gets smarter. that's the real self-evolution. not a single agent rewriting its own prompt in a void — but a whole org compounding through proof. — each workspace is an isolated agent company. its own brain, departments, memory, workers, proof ledger. workspaces can talk when needed. but context should not bleed by default. isolation is not a limitation. it's what makes the system usable. — once a department pattern works, you fork the pattern — not the raw context. you still customize memory, examples, approval gates, tools, voice, definition of done. but you're not starting from zero. you might already have 70% of the OS for that kind of work. — what this actually changes: a small team of strong operators can now run surfaces that used to require entire departments. but only if the agents are actually good. and good agents don't come from connecting more tools. they come from source material, taste, iteration, narrow scope, workflow design, proof, memory, and human judgment. vague agents just create vague output faster. Matrix is our attempt to build the opposite: an agent company OS where autonomous work has structure, memory, ownership, and proof. the loop is the product.

this is f*cking dangerous someone just open sourced the entire "LOOP ENGINEERING" framework for free build a hedge fund printing alpha 24/7 by feeding it into claude code with my article below bookmark before someone takes it down

@karankendre 没有看到使用入口啊。

Nobody saw this coming Japan is now officially in the AI race and they’ve released a model that performs on par with Claude Mythos