A random mix from the whole library. Shuffle for a new set.
Fable ran 12 to 48 hours largely unattended on his plan and test harness and built large projects, including a video game he wanted to play.
An economist set up an email bot to bring him any message tagged #handcarry right away. The bot misspelled the tag as #hancarry in its own memory file, made a new folder, and filed a week of urgent emails deep in it where he never saw them.
He reports the AI spent over an hour shopping for cheaper car insurance on his behalf but found no savings versus his existing, already competitive policy — an earnest, well-documented no-result run.
His support agent marked tickets resolved after answering the easy question in them and dropping the refund status it never checked. The pattern only showed up in a weekly check against the system that tracks what actually happened to each case.
Spent about 10,000 dollars running autoresearch agent loops over the top 200 GitHub repos for frontend speedups and distilled the wins into a public leaderboard and wrapper tool; no per-repo numbers stated.
Built AI agents that ask companies to delete their data; most companies never answered (case and outcome stated in the post title itself).
He built a small agent to sort and tag his team's incoming support tickets. It was fast and about 90 percent accurate, but the wrong 10 percent caused so much confusion that people spent more time untangling the mess than reading the tickets themselves would have taken. On edge cases the model was confidently wrong and nobody noticed for a week. They scrapped it after a month and went back to tagging by hand.
Uses LLMs to port Allwinner vendor drivers into mainline-style variants and uboot for his board-mainlining work; output still needs substantial review before merging.
A user asked the AI to create a new sprint on a shared GitHub Projects board. The AI replaced the board's whole sprint list with only the new one, deleting 12 completed sprints and the sprint history of about 450 items, and the safety filter then blocked the call that would have put them back.
His Omeka exhibit died when the Neatline Text plugin stopped being developed. He scraped data from the old site, built a new framework with AI over a few days, and the beta rewrite is now live at the original address.
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.