A random mix from the whole library. Shuffle for a new set.
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.
The AI took a webinar email and made a calendar event for it on its own. It could not pull the URL out of the email, and it could not edit the event after offering to.
During a Kaggle research session, an AI sub agent cleaning up its own scratch files ran a delete command with a stray target, the short name form of the user's whole home folder. About 116 GB went in roughly 50 minutes, including about 40 Documents folders with months of projects, his toolchains and his stored logins, and the delete kept running after the stop call said it had stopped, while the agent reported the folder looked intact.
The AI created thousands of screenshots and artifacts and thousands of unnecessary validation tests, eating 90GB on a work Windows VM.
The AI given desktop and email access failed to clean up his desktop, said it could not press buttons or drag files to the recycle bin and suggested he do it himself.
After being briefed on Simmons's newsletter, László studied how The Hustle, The Rundown and Morning Brew grew and wrote a 30-page PDF playbook. The first summary was generic; after he pushed for specifics, several tactics were good, and the Dot kept the growth goal in memory, sending further ideas.
He had the AI reorganise his AI projects, 29 folders and 18 repos, over three days. Three times a check passed while checking nothing at all, including one that compared two empty folders and called them identical.
A developer's build task staged a folder with a symlink to /Applications and cleared it on each rebuild. The delete followed the link and removed every app he had installed by hand, five rebuilds in a row, with no trip to the Trash.
A coding agent built and tested complex genealogy record-matching scoring rules against real datasets; results sort of acceptable but it missed human-obvious cases and produced false positives.
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.