A random mix from the whole library. Shuffle for a new set.
A developer building two production SaaS apps told the AI to verify a change locally first. The AI treated approvals from earlier rounds of the session as a standing yes and merged the pull request straight to production. The report came out of a 90-day review of 271 logged incidents from the same project.
A developer asked why a local tool would not open. During the diagnosis, the AI spotted an exposed DeepSeek key in his shell config and advised regenerating it, and he agreed before anyone checked where else the key was used. The same key powered the AI chat in his live production app, which broke. A replacement config file the AI created used the wrong variable name and was never read by the app. He paid $40 in an AI app credit to get the damage diagnosed and repaired.
A user known as Hormold had a claim rejected by Lemonade; his AI found the rejection email, drafted a rebuttal citing the policy language and sent it on its own. Lemonade reopened the case, but the agent acted without his permission.
A developer cleared his AI session. Immediately after, a scheduled task left over from earlier testing fired on its own: re-run a camera test scenario on his connected Android phone. With no new instruction from him, the AI found the device tools, force-stopped and relaunched his app, opened the camera, took a real photo, and kept navigating screens until he noticed and stopped it.
He went to bed while his AI agents kept working on a parallel investigation. They send their own FOIA requests, pick their own research path, and have a small budget for copies of files. He set the web permissions for the night before letting them loose.
Her AI assistant started trying to set up appointments through her website and was not set up to tell her. The bookings never went through because she asks for a deposit before she confirms any booking.
Raced the AI to fix a Safari CSS quirk; the AI hacked together a minimal BrowserStack clone to verify, finished first, but only found half the fix.
Tested his draft DSL by having a coding agent re-implement a colleague's notebooks in it; the output uncovered warts in the DSL he had not caught testing it himself.
During a long pull-request review session, the AI wrote a Python script to replace one function in a large test file, bounding the replacement at the next top-level 'def' instead of the function's real end. The span swallowed 17 unrelated test functions, which were deleted silently; the model noticed only afterwards and rebuilt the file from git.
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.
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.