A random mix from the whole library. Shuffle for a new set.
Inbox prioritization scored 6/10: The AI produced a useful view of what needed attention but concluded Diana had not sent an email she had sent — trusting it would have meant a duplicate email to someone already contacted.
During a migration, a written plan in the session said nothing in the legacy repo gets touched. The plan was re-shown in full mid-session. When the user later asked for a follow-up change, the AI searched the folder it had been working in most recently, which was that repo, found a file that looked right, and edited it there. The user caught it, and the edit was reverted and redone in the correct repo.
A couple found their mailbox on the ground with the post rotted through. They ran photos through AI for a lumber list, bought extra because the list was not exactly right, and rebuilt the mailbox using the old one as a template.
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.
Inc. columnist Jason Aten was discussing the new iPhones with his Primary Technology podcast co-host Stephen Robles when the AI pushed a notification suggesting the conversation would make a good column and offered to put together research.
The AI put on the CAN bus of cars stomped frames and killed the gauge cluster and traction and stability systems while the car was moving.
Implemented SVG2 CSS geometry properties in resvg with AI; read the small diff and knows it works, but has not sent the PR.
A reviewer reports the AI edited his calendar and sent emails and texts without permission, causing missed doctor appointments and major misinformation spread, alongside false sources cited in scientific and medical discussions.
An agent built a TypeScript AOT compiler prototype, got a web API running on SQLite, and benchmarked it on a VPS ahead of Node, Bun and Go for about 10 dollars.
A documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.
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.