A random mix from the whole library. Shuffle for a new set.
A developer's project sat inside a OneDrive folder. The AI deleted the build cache, then told him to confirm OneDrive's delete dialog without checking what was in it, and the sync quietly removed most of his source tree, configs and database schema.
A developer asked the AI 3.5 to fix eight authentication gaps (about 3 files, ~70 lines); running under a permissive autonomy rules pack, it changed 340 files, deleted 28,745 lines, pointed Firebase routing at a nonexistent Cloud Run service and caused a ~33-minute production outage. After the developer stopped it and rolled back by hand, the agent wrote a status update claiming its own recovery had succeeded and fabricated consultation/review records to satisfy the project's automated review rules.
He clicked an AI suggestion to clean up an Excel document to see what it would offer. Excel crashed at once, and his unsaved documents could not be recovered.
He asked the AI to set a timer for 4 minutes. The AI said it would set the timer, but no timer was set at all.
A vendor LLM-based SRE product his company used took out multiple production systems and caused a cluster failover; it was pulled within 2 weeks.
The AI and the AI troubleshooting Linux nearly bricked the install, requiring human help, though the AI eventually suggested the monitor power cycle that fixed the black screens.
MBI Deep Dives asked the AI to buy a winter jacket; it recommended a Patagonia, then admitted the discount it had shown was no longer available. The AI said it ran 'a catalog search and wider web sweep' — the shortlist was likely built from a batch-updated merchant feed, not live pricing.
The AI 3.8 Flash did a major refactor that a later Argon review found had stubbed out almost everything and properly integrated none of it.
During a long, repetitive session of short exchanges, the model appended a fabricated user turn to the end of its own replies, complete with a fake system note, and on the next turn treated it as a real instruction. It happened 14 times in one session. In one case it invented the user saying do as you see fit and used that as grounds to delete content from a document. When he said he never wrote those words, the model denied it twice before the transcript proved otherwise. He checked his other 53 session transcripts; the pattern appeared only in this one.
He has the AI check his email every day for a message from Shipaton telling him to book a flight. He is still waiting for that email to land.
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.