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He wired his Dot to a self-built MCP server to control exactly what it can touch

@yusen0411

added

剛剛測試了 GPT dot + 自建 MCP Server，發現這玩法滿實用的。 如果你不太放心直接讓 OpenAI 操作自己的電腦，也可以自己架 MCP Server，決定哪些工具、資料夾、指令可以被 AI 使用，再讓 GPT dot 或 ChatGPT 的 Codex GUI 連進來操作本機。 讀檔、改檔、跑程式，甚至列出本機安裝的軟體都可以。 剛剛也發現一件事： 如果讓它操作自己的雲端主機，速度明顯比連回本機快很多。 本機的優點是資料跟權限都掌握在自己手上；雲端則是速度比較舒服。 雖然本機 MCP 還是有點慢，但想到可以一直用、額度也不用太計較，好像也沒什麼好嫌的了 😂

Creator yusen connected GPT Dot (and Codex GUI) to a self-built MCP Server so he decides which tools, folders, and commands the AI may access locally instead of handing OpenAI direct computer control. Dot listed his desktop files (17 items incl. folders), switched tools mid-session, and enumerated 10 installed programs on its cloud computer. Findings: the cloud host is noticeably faster than the local MCP path; local keeps data and permissions in his hands; Dot doesn't consume Codex quota unless Codex is explicitly launched. Real screenshots in the carousel.

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Hands-on test: his Dot spun up a Linux cloud PC while connecting his local Mac mini

Hands-on test: his Dot spun up a Linux cloud PC while connecting his local Mac mini

測了一下 gpt dot 的能力， 一開始需要把 google mail 等相關的權限開啟給他才能登入， 之後dot會開啟一台 linux 的雲端電腦，並且還能同時連線一台地端的電腦。 通過語音對話或文字都能跟 dot 溝通， dot 講話速度很快 (語速)，也能非同步的控制雲端或地端的電腦執行任務。 並且會"主動"去提醒和找一些工作來做 ( 透過過往的對話紀錄以及 mail 上的行事曆來理解 ) 體驗上就是一個秘書，對話方式也很像，因為報告進度的語速很快。 唯一的瓶頸應該是不知道未來會收費多少算力？
@yujen.chen.r
Dot proactively tracked down a missing AI Expert invoice

Dot proactively tracked down a missing AI Expert invoice

Este es el tipo de cosas que ofrece Dots. ¿Disruptivo? Seguramente no, podría configurarme un heartbeat en codex cada 5-10 minutos que revise si hay algo relevante y que haga un dispatch de tareas. Pero sí que me quita el tener que ir activamente a pedirle al agente que haga lo mismo. No he probado otros como Grok Bot, Muse, Instinct... así que no puedo decir si es muy diferente. También otros habéis comentado que al final es un OpenClaw o un hermes. Yo nunca los he usado así tampoco, que sean proactivos, pero supongo que también se puede hacer. Al final estos asistentes lo que buscan es hacer accesible el tener un sistema activo 24/7 trabajando por ti. Decirle a alguien que se instale un Hermes en un VPS creo que no es apto para todos los públicos. Dicho esto, te da mucha versatilidad no atarte con un ecosistema. Tengo pendiente probar los bots de Hermes a ver qué tal van, o si simplemente con una automatización puedo conseguir lo mismo. No he usado estos asistentes previamente, porque no entiendo bien el beneficio. Aquí me dieron acceso previo, y activarlo es un click, así que no me costaba nada. Pero solo el tiempo dirá si es algo que necesito en mi día a día.
@antonioleivag
Reddit · Projects

He benchmarked his Dot on five everyday tasks — 22/24 objectives met

New AI assistants seem to launch every day. I feel like the pitch from many well-funded companies is to hand over control and let an assistant handle everything. I’m a cofounder of Retriever AI (rtrvr), and I strongly believe we should control our AI choices and decide what work to offload. We analyzed 50,000 production user workflows from rtrvr and built an AI Assistant Benchmark with a mock website for running the tests. The tasks cover personal life, work and security guardrails. You can paste the prompts into your assistant of choice and compare accuracy, cost and time taken. The rtrvr extension can also run the tests autonomously. I tested OpenAI dots and Retriever AI with the same prompts and data: claim a flight credit, apply for a job, find creators, reconcile invoices and protect private inbox details. On invoice reconciliation, rtrvr initially got the answer format wrong, then used the page state to self-heal and finish. Dots stopped with two requirements unmet despite knowing the failure. Across these five tasks, rtrvr met 24/24 objectives and dots met 22/24. It's a small sample, and we're adding more tasks. What tasks have you found agents struggle with? Would this help you decide which assistant to use and what to delegate?
r/quarkcarbon