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My Dot runs my sales prospecting on LinkedIn

@tirthpatel00

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Open ai dot crazy he bhai #dot #openai #trending #ai #SuperIntelligence

Tirth Patel (219K followers, AI researcher/automation builder) posts a first-hand screen demo of his own Dot operating the cloud browser: it navigates LinkedIn, finds named target prospects (e.g. Paul Mathews, Saurabh Raj) for his company Neno, and drafts personalized outreach messages. On-screen text: "AI Just Replaced My Sales Team. Now It Can Handle My Sales Work Automatically." Posted Sep 30, 2026. No sponsored/partner tag in the caption.

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I spent a day poking Dots with sticks. Here's what I figured out.

I got access to Dots and spent much of the day trying to understand what actually runs where, what can happen simultaneously, and what counts as a separate worker. (I absolutely used AI to help me put this together. I don’t know if this audience cares, but I figured I’d disclose that up front since some folks get bent out of shape otherwise) The official material explains what Dots can do reasonably well. I found the execution model much less obvious. Some of this is documented; some is simply what I observed by using a Dot on several substantial real-world tasks. 1. The Dot really does have its own cloud computer I gave my Dot a large document-review assignment involving hundreds of PDFs and thousands of pages. It performed that work on what it identifies as its own cloud computer. This appears to be a persistent computer-backed environment where the Dot itself can do substantial, long-running work. More importantly, this isn’t just a five-minute “agent run.” One of my reviews is now clearly a multi-day job, and the Dot has maintained its place, absorbed side questions, and continued without needing me to reconstruct the task every few hours. That continuity may end up being more important to me than raw speed. 2. Delegated Work/Codex tasks are different While the Dot was working on one project, I had it try to launch a separate legal-research task. The launch failed because there was no available execution environment. Initially I assumed the Dot’s own computer was simply busy. But after the first job finished, the second task still could not start. The Dot then reported the key distinction: Its own cloud computer is separate from the execution targets available to the Work/Codex task launcher. So a Dot’s personal cloud computer is not simply a generic worker that delegated tasks automatically inherit. 3. A connected computer becomes another execution target I connected a spare Linux computer through the ChatGPT desktop app. The Dot could then see: \- its own cloud computer \- the connected Linux machine \- no saved Codex cloud environments I told it to launch the previously blocked task on the Linux machine. It did, and the task entered running state there. I did not have to sit at that machine and manually start a separate chat. I gave the instruction to the Dot, and it dispatched the task remotely. 4. Both can work simultaneously While the delegated task was running on the Linux machine, I gave the Dot a different assignment for its own cloud computer. It confirmed that both were active at once: Dot cloud computer -> Task A Connected computer -> Task B So that is genuine parallel execution across separate computer-backed environments. 5. Background agents don’t necessarily need a computer at all This was the part that got much closer to what I had originally imagined Dots would do. With both computer-backed environments occupied, I asked whether the Dot could create a native background research agent without using either computer. It said yes. I gave that agent a bounded research task and explicitly excluded computer/filesystem use. The Dot then reported that the background agent was running with read-only web/documentation tools and no computer target assigned. At that point, three things were happening simultaneously: 1. the Dot working on its own cloud computer 2. a separate task running on the connected computer 3. a native background research agent using neither computer That is the execution distinction I had completely missed from the launch material. 6. It can also context-switch inside a long-running job Another useful behavior appeared accidentally. While the Dot was deep into a large document review, I interrupted it with a factual question about one specific case. It paused the detailed review, checked meeting minutes and another source, resolved the question, updated its understanding of the case history, and then returned to the packet it had been reviewing. When I asked how it had done that “while continuing” the larger job, it clarified that it had not spawned another worker. It had simply switched attention within the same job and then resumed. So I now distinguish: Parallel execution = separate workers/environments active at once. Background agent = separate non-computer worker running concurrently. Intra-task context switching = one Dot temporarily branches inside an existing job, resolves something, and returns to its prior place. For long-running review work, that last capability is surprisingly valuable. 7. It can keep working while waiting for permission On another assignment, the Dot decided that spawning additional reviewers would accelerate the work, but my rules required permission first. It asked. But instead of stopping while waiting for me to respond, it explicitly continued doing the work itself. That sounds minor, but it matters. An autonomous agent that hits one permission boundary and then stops doing everything is not particularly autonomous. So far, the Dot appears capable of distinguishing: “I need permission to do X” from “I therefore cannot make any further progress.” 8. There is also a kind of manager-level queue I have not found a true native queue where a blocked computer-backed task automatically sits in the launcher until capacity becomes available. What I did find is that the Dot can apparently remember a pending assignment itself, periodically re-check execution targets, and attempt to launch it later. There are limits: \- the target list does not necessarily expose whether a connected computer is actually free \- there is no apparent capacity reservation \- a failed launch does not automatically become a queued Work task So this is more like the Dot acting as the queue manager than a native execution queue. Still, that potentially removes another piece of manual babysitting. 9. My current mental model At this point, I think there are at least three distinct execut
r/dry_towelette99
5 Things I Used ChatGPT Dots For—Real Prompts, Real Results

5 Things I Used ChatGPT Dots For—Real Prompts, Real Results

I gave my ChatGPT Dot five jobs from my actual life. Meet Norm: my AI helper for trip research, microphone shopping, untangling my to-do list, setting up a price watch and requesting a morning AI briefing. This is my hands-on experience with OpenAI's ChatGPT Dots, including readable excerpts from the real conversations—not a simulated live demo. Personal details have been cropped out. Five jobs in this video: 1. Research a family hiking trip and compare the logistics. 2. Compare microphone options and recommend a fit. 3. Organize messy notes into a practical checklist. 4. Set a daily price-check request with a specific threshold. 5. Request a concise AI-news briefing with sources. Travel research was still in progress. Shopping recommendations are Norm's, not my hands-on microphone tests. The price-watch conversation shows a setup confirmation, not a delivered future alert; the morning briefing shows a sample, not verified recurring delivery. No purchases or bookings are shown. What is the first everyday job you would give your Dot? Follow Krish Adavi here and on LinkedIn for practical AI. Builder tools and deeper articles: https://a2zai.ai AI Systems Brief newsletter on LinkedIn: https://a2zai.ai/briefs #a2zai #ChatGPT #OpenAI #AIAgents #AITools @OpenAI
@A2ZAI
ChatGPT dots速報解説。今度こそ「自分専用の秘書」として活躍する未来がはっきり見えました。

ChatGPT dots速報解説。今度こそ「自分専用の秘書」として活躍する未来がはっきり見えました。

ChatGPT dotsがついに僕にもロールアウトされました！ そしたらさっそく、使い始めた初日から、忘れていたメールの期限を先回りして教えてくれたんです。 しばらく使ってみると、使えば使うほどに「自分専用の秘書」の未来が見えました。 ということで今日は、基本の使い方と、実際に驚いた体験を22分で速報解説します。iPhoneでの音声通話も収録しています。 【動画で使用したツール & 配布物】 ChatGPT dots OpenAIによる公式紹介はこちらです。 ▶︎ https://openai.com/ja-JP/index/introducing-dots/ dotsの始め方（公式ガイド） ▶︎ https://learn.chatgpt.com/docs/dots/getting-started 動画内の情報は2026年10月1日時点です。dotsは対象アカウントへ順次提供されています。 【目次】 00:00 使い始めて、笑っちゃうくらい楽しかった 00:54 dotsの基本 02:53 dotsの使い方 05:16 プレゼンだけでは伝わらなかったこと 07:17 メールの確認から、返信の下書きまで進んだ 09:19 Mac miniに置いた仕事の資料を使えた 11:08 作業チャットを動かして、結果を持って帰ってきた 14:01 これまでと同じでは？ 15:31 One more thing：音声通話 15:52 iPhoneからdotと実際に話してみた 20:11 音声通話を体験して感じたこと 21:10 まとめ：僕にとっては秘書 ツイッターとインスタ、やってます。 ▶︎ https://twitter.com/LeoTohyama ▶︎ https://www.instagram.com/leotohyama/ 使用素材 ▶︎ Epidemic Sound:http://www.epidemicsound.com ▶︎ Artlist:https://artlist.io/jp ▶︎ Motion Elements:https://bit.ly/2IyJhAx
@leo_tohyama
OpenAI Dots 實測：把耗時操作交給雲端電腦

OpenAI Dots 實測：把耗時操作交給雲端電腦

耗時的網頁操作一直占著本機？ 這次把批次任務交給 OpenAI Dots，讓它在雲端電腦執行。 從交代需求、生成與下載，到成果交回本機建檔，用一批配樂製作看看工作怎麼交出去、拿回來。 錄影當時，我們用自建 MCP 做任務交接，橋造好了。 後來 Codex 更新，片尾留了一個小插曲。 章節 00:00 認識 Dots 與 24/7 雲端電腦 00:27 把耗時網頁操作交出去 00:44 MCP 交接與電話確認 04:37 Dots 接手，開始雲端製作 05:53 成果怎麼交回 Codex？ 06:50 最後的小插曲 #OpenAIDots #Codex #AI協作
@SyrcusAI