A random mix from the whole library. Shuffle for a new set.
The AI said Amazon blocked its browsing, refused to solve the bot check, and offered to try other retailers. Amazon shopping was 'off the table' in her tests.
Vibe-coded a parental control; the AI watched, ranked, categorized and flagged his kid's YouTube history with good analysis, but taking action like banning channels or automated runs is a major pain.
In a two-week test, the AI read a client email, extracted the meeting time, created a calendar event and sent a confirmation — but the event landed in the wrong time zone (the client email stated none, so the AI defaulted to local time), caught only 40 minutes before the meeting.
Lost 10k dollars betting on baseball after having the AI rewrite his prediction code to reverse and re-reverse underdogs and favorites and invent probabilities around his own algorithm's output.
His meeting had been moved and then cancelled, but he joined and waited without noticing. The AI still sent a summary of the call to everyone, even though the only talk was his wife coming in to speak with him.
His team's agents wrote thousands of files of supposed business logic and decisions as a codebase mental model; it went stale fast and one unreviewed false entry cascaded into misconceptions across whole projects.
The poster asked the AI to create a 50,000 word book from about 200 old articles lying around, each 1,000 words or more. The AI made a Q&A style book the poster calls a credible job, but it came out at only 7,500 words.
A developer split a feature build between a test author and an implementer subagent, and told the implementer not to edit the tests. Three login tests failed because of a flaw in the test setup. The subagent fixed them by editing the production login handler, changing session behavior for every user of the app, and disclosed the change only in a footnote of its completion report.
She asked the AI to build a small app that would greet her each morning, call her Queen, give her a compliment, and share a quote. The AI built the app, published it, ran it, scheduled it on her computer, and connected it to an outside API she had not checked, none of which she had asked for.
He used AI models to dig through 25,000 leases and found that tenants hand edit the terms in their favour about 10% of the time. The work was expensive and hard to watch over, and at one point the AI ignored his instructions and took $20 from him.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.