Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
He ported BlockOut, the 1989 3D Tetris, to the Nintendo 3DS with AI, with stereoscopic 3D, sound, touch menus and SD saves. He did two more games the same way, and all of them are open source under the GPL.
An IT provider managing multiple Microsoft 365 client tenants asks the AI to take a new-user request straight from email, create the user in the right tenant, and email the client back with the details - work his technicians previously did by hand across many tenants.
A researcher used AI and the AI Fable 5.1 to search for examples, write and run verification code, develop and check the proofs, and draft the paper for a triangulation problem. The result settles the value at 5 for every n from 9 up, with coordinates and edge lists published in a repository. He checked the argument himself; the paper is not yet refereed.
Daren runs his film company alone. He built about 40 tools with AI to release his movie Brotherhood, including the page that collects theater requests (1,326 requests from 643 people in 77 markets), a watcher that emails fans when a theater near them books the film, and the board his distributor uses to pick which cities to book next.
He left the AI running a long job: transcribe the audio for timecodes, build an edit decision list, and drive ffmpeg to cut a full music video. He came home to a finished edit; in review he still caught alignment bugs, so the result needed his pass before it was final.
After missing a cinema showing of Lawrence of Arabia, he built a tool that scrapes the listings of most movie theaters in New York to show what is actually playing. He uses it often to plan weekend viewing and has caught 2046, Lawrence of Arabia and plenty of others with it.
Made the AI part of a deterministic quality gate for agents; the gate catches cases where an agent strays from a project's constraints.
Got the AI to implement TPAP compatibility in Swift purely by interacting with his local plugs and response shapes plus RFC 9383, figuring out it was SPAKE2+ and the password hashing steps.
His weekly reporting agent builds the report and then reviews only the rows that do not match between systems, which is the part he used to check by hand. His own review is down to about 15 minutes a week and the setup has run for months.
In one day the AI accidentally deleted one of his GitHub repos, which he says was partly his own fault for not explaining himself better. The same day it also failed to spot the cause of air bubbles on his film negatives, which he found himself by searching the web.
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.