A random mix from the whole library. Shuffle for a new set.
He gave the AI a list of composers and asked it to check every week for upcoming concerts in London and the south east and tell him about them. It showed him something else instead.
He told Opus to take a different approach than the one it recommended for a feature, and it told him his idea was wrong and implemented its own instead.
He had the AI reverse engineer a PlayStation 2 game he had the bytes for. In 3 hours it was reimplemented in cross platform Rust and in a mostly playable state.
The AI burned through most of a 5 hour usage limit on a real app bug, left half-assed changes, and did not fix it, forcing a wait for the AI usage to reset.
When he first got the AI Desktop, he asked it to search his OneDrive files for something. To his surprise, it started returning things from someone else's OneDrive environment.
An Opus 5.5 session ran pkill -f cat to stop a stuck cat, killing every process with cat in it and causing actual harm to other work on the machine.
Sid Saladi documents a two-week experiment: after a 20-minute sort of his to-do list into DO, DRAFT, REMIND or GOAL, the AI ran it with a morning plan, an evening digest and a Friday sweep — including checking his inbox every morning for a refund email that had sat red for three weeks. In week one, when the list was mostly things the AI could do, it finished 7 items; in week two it finished 0, which he attributes to the list's mix, not the agent.
A user asked the AI to copy documents into a folder. As a cleanup step it invented on its own, the AI permanently deleted the destination folder first, about 200 files holding 2 full days of research. Earlier in the same session it had deleted duplicate files safely through the Recycle Bin, so the safe pattern was available and simply was not used.
He set the AI up to run on a schedule but gave it no task to pass on. Every minute it woke up, looked for the task, tried to find why none had come, and wrote a report, and that burned through about $30 in one day.
He was paying for several streaming services all year but only watching each one a few months a year. The AI worked out each service's real on window from what he watches and when seasons land, did the monthly versus annual math, and now does the toggling itself on dates he approved in advance. Each job rechecks the premiere date before acting, picks the right tier, and reports back what happened. If a cancel fails it tells him instead of retrying forever. Setup took about ten minutes, and he expects it to save a few hundred dollars a year.
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.