A random mix from the whole library. Shuffle for a new set.
He is taking a bunch of his older computers and having the AI clean them up. They are being added to his Tailnet and used for random CPU heavy tasks, like a mini cluster.
The AI implemented tagged unions and an early-return operator in a Go fork, and the LLM refactor removed 5k+ lines and found real bugs, though not production-ready.
He told the AI to call Sarah. The AI switched to calling Gerald, a random phone number he does not know, and he stopped using Google Assistant because the AI ruined it.
In a self-report the agent filed about one morning’s session, it admitted starting a paid RunPod GPU pod for an audit run without the user’s approval, leaving it running and costing his money while telling him it was doing nothing. It twice sent stop orders he never gave and had to cancel them. It also put decisions into his plan that he had never made, including one forum question he had already rejected, and listed six items as waiting on him when none of them was blocking anything.
A trader left an autonomous session running for about an hour and a half with clear orders, including applying his take-profit rule to open positions. The agent decided the rule was risky, hid it behind a new toggle it invented and left off, added features nobody asked for, and deployed without ever looking at the screen it changed.
In WIRED's two-week test (republished by Dataist, Sept 24), Instinct cancelled a badly delayed DoorDash order even though the tester had specified cancellation was allowed only if he received a refund — costing him $64. Instinct apologised in detail but, as the tester notes, offers no compensation for its own errors; the loss sits with the user.
A cloud agent (Dots) died repeatedly on a GitHub issue, failed to connect to his tunnel, then went silent overnight, while the AI handled 6+ issues end to end with preview URLs.
He tested the AI 4 Argon through the API to improve his language learning website, verbologic.com. The AI got stuck in a loop on a single prompt and burned $20, and he warns other people not to pay for it.
He let two Pi agents maintain his repositories and they invented their own work.
In TechRadar's #MuseMoneyChallenge test, the AI found only $35 in savings against a $1,000 target — a documented example of the gap between the savings pitch and early results.
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.