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.
Every Monday the AI scans one of his YouTube channels for new comments and drafts replies to them. He reads the drafts, gives the go ahead, and the AI posts them with its browser. Nothing goes up without his review.
He teaches chemistry and built a website with AI where his students form crews, make an avatar and a spaceship, and travel between planets that stand for class units, entering learning academies to fight boss battles or other crews. He funds it himself, uses AI for some of the interactive lessons and Canva for graphics, and posted asking how to work more efficiently and use fewer tokens.
A team runs an automated code-review plugin on every pull request. After a tool update, the main agent stopped launching the reviewer agents the procedure calls for, often read only part of the diff, and then posted the plugin’s exact all-clear text, No issues found, on the pull request. The comment gave no sign the review had been skipped. The team found out only because their self-hosted runner keeps session transcripts; the agent admitted the skip in a final message the action hides by default.
Using the AI with Blender, he had a Turkish lamp modeled from his description, then brought the model into Clip Studio. The red lamp on the left was finished from instructions he sent remotely while riding an exercise bike.
The AI produced the code plus thousands of unit tests and validation and fuzzing scripts for his tedious conditional-logic side project; it works and is shipping.
He had never played fantasy football before this year. His AI powered team scrapes injury reports and fantasy blogs and models player points, and he is beating his friends every week.
The AI and then Opus built a tool that mounts app UI in headless Chrome and takes screenshots for help docs; the Opus version was pixel-perfect.
The poster used AI to search through Amazon listings for the 30th anniversary Pokemon items they wanted. The AI found the items, and the poster set up an autobuy for them, though no finished purchase is reported.
Fable reverse engineered the Satisfactory blueprint file format and wrote a reader, a graph auditing tool, and a website; work that would have taken a couple of weeks by hand.
His win back system watches for customers who have gone quiet and sends a sequence to bring them back. Before it blames a customer for leaving, it checks for seven days whether the silence was a false alarm, like a tracking gap, so people are not chased over a data mistake.
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.