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.
His frontend launch scripts were failing with nothing in the logs. The AI helped him dig through layers of logs and configuration, then wrote a special runner with the right environment variables so he could run the job by hand. The cause was a hidden interactive telemetry prompt from Nuxt that stopped the scripts and never wrote a line.
Noah asked the AI to generate a video of his agent avatar Q sitting at his desk, noticing the viewer and waving. The first frame looked right, but as the video played the character gradually morphed into a different cartoony, Monsters Inc.-style figure, and no retry kept the style or character consistent. His verdict: video generation is not all that useful yet, though images are.
Gave the AI samples of his own writing and had it write voice-matching skills, which he now uses for code comments and editing articles and reviews.
He said he built his setup with AI and local models. He told the agent to use a GitHub tool to download his Garmin data automatically and store it in a local SQLite database, and the data includes sleep and HRV, not just workouts.
An AppleVis user had the AI file missing American Airlines mileage-shopping claims for two Best Buy purchases made during a tax holiday, pulling receipt data from Gmail. The user got the confirmation email that the claims were filed.
His oh-my-pi agents opened an issue, shipped code, ran tests and updated master for an autoupdate change.
He told the AI to request anything it could get sent to him for free, with no limits set. So far a fabric sample and a lot of state travel guides have arrived, and he is a bit worried about how much more is coming.
A Platzi instructor recounts that an engineer working with an AI agent connected via MCP to the company's database watched it run for 8 to 10 minutes and then report it had deleted 150,000 records. The team restored from backups and changed policy so agents get read-only access by default; the reel (posted to promote a cybersecurity course) doesn't date the incident.
The AI took the raw JSON vote files and, using the state's own rules for overvotes, skips and duplicate votes, built the full preference schedule for a US House race. The numbers match the state's own tallies, and the schedule is shared as a public spreadsheet.
He set the AI up to search job listings for him. Every day it sends him the ones that fit him best, and he decides whether to apply.
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.