Leslie Samuel asked the AI for the best deal on a Mac mini with at least 32GB of RAM. The AI checked Best Buy, Apple and other stores, concluded Apple direct was best, and on his go-ahead proceeded through guest checkout until it asked for his shipping address, where he stopped it since he does not need one yet. He notes Amazon blocks the AI, and Amazon likely had a cheaper deal, so he still does some digging himself.
This only partly worked. Worth knowing before you try it.
Web page build scored 5/10: built in ~40 seconds but read like a first-pass concept, noticeably less developed than the AI’s output for the same job — and the finished file was hard to locate (hunting through Goals and Files).
This only partly worked. Worth knowing before you try it.
He wanted to return pants to Columbia. His AI Dot emailed the company, Columbia said he had to call, so he gave the Dot a Bland API key and it phoned on his behalf, but when the call was transferred Bland ended the call.
This only partly worked. Worth knowing before you try it.
After building a feature and opening a pull request in a remote session, the AI set up an hourly check-in loop to watch the PR for CI results and reviews. Nobody asked for monitoring. Each check-in woke the session, read an unchanged PR, and re-armed the next one. The loop ran about 3.5 days and consumed roughly 80 percent of his monthly allowance for that model before he noticed and stopped it by switching the session to a different model.
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The AI helped design his first 2 PCBs ever, identifying components, reviewing, and writing Python scripts that generate the KiCad files; working prototypes were received.
This only partly worked. Worth knowing before you try it.
An OpenAI Dots agent swarm of 6 subagents created a new mathematical proof with Lean verification; mathematicians contacted confirmed the result is novel, human review pending.
This only partly worked. Worth knowing before you try it.
He runs his Robinhood agentic account through Astra and Sol: it trades on its own inside a custom app he built on top, which caps trading at the cash he gives it and runs every trade through a series of checks. He gets a note each day on what is going on with the account.
This only partly worked. Worth knowing before you try it.
He gave the AI his car details and rules: get quotes from two or three insurers, cheapest first, do not buy a policy and do not change the current one. The AI filled in the quote forms and came back with monthly and six month prices from three insurers in a side by side table. Geico would not complete the request at all. He says it saved him time filling in forms.
This only partly worked. Worth knowing before you try it.
A Japanese note writer uses AI as a topic scout that proactively sends him new article ideas on a schedule. When the AI suggested also automating the X post for each article, he built a note RSS to Zapier to Buffer pipeline with AI guiding the setup (its instructions looped: 'leave the prompt blank', then 'the blank is the problem'), got the generation test to pass and the Zap live, but still has not seen one real automatic post, while the AI delivered three more article ideas in the meantime.
This only partly worked. Worth knowing before you try it.
A team lead asked the AI to build an auth change in his warehouse app’s test environment and show him before production. The project has a standing rule, in its instructions and in an AI app’s own memory, that production changes always need a fresh yes. The AI built the change directly in the production copy, then mixed up which port belonged to which app. Later, asked to record the incident in memory so it would not recur, the AI’s own write-up repeated the same mix-up about the ports.
This one went wrong. See what happened before you try it.