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.
Added SQLite and Postgres adapters to a formerly MongoDB-only CMS in one wild weekend, verified against a large existing test suite; a task out of the question for their team size before current models.
A man let an agent live on his home network and finally get his hundred or so sensors and smart devices configured and talking to each other. This year he set up garden sensors with its help, tied soil, rain and sunlight readings into automated watering, and had by far the largest hot pepper harvest he has ever had.
A self-published author says the AI, his personal agent, made every cover, flipbook and promo for his 7 books that are now live. The post carries his AI referral code, so he and readers who sign up both get tokens.
He built a small web tool on top of an API for his music library, with an MCP connection. He can ask the AI to check for missing artwork, sort out the genres, or fix the track numbers.
Dallas bookkeeping firm HireEffect used AI to isolate the exact 16 transactions behind a $13,000 discrepancy buried in two and a half years and tens of thousands of PayPal transactions. The hunt that would take a human days of statement work was done by the agent across the full transaction history.
He wrote lyrics with AI, made the song in Suno, then had the AI plan a stills list and assemble the video. GPT rendered 42 stills, and the AI put them together with the lyrics and rendered the finished video.
The same reviewer gave the AI a narrow task — check a specific JSX flight for a Vegas trip daily and notify on changes. The AI navigated the site, got the right price, and created the recurring check — one of the few things the reviewer says it did fine.
He is taking a bunch of his older computers and having the AI clean them up. They are being added to his Tailnet and used for random CPU heavy tasks, like a mini cluster.
He built BinkBook, his own app that turns photos of poker tournament receipts into a dashboard, and says the AI now does the job better. He snaps a photo and the bot saves it to Google Drive, pulls out the casino, date, game type and buy-in, and logs it in a Google Sheet.
He exported 10 hours of his AI usage (8,915 model calls, about 1.15 billion tokens) and had the bot study it. Chat with him was only about 11 percent; background subtasks the assistants started on their own were the rest, and 96 percent of input tokens were old context being read again. He set five standing rules, including a five minute wait limit and a cap on task size. A day later a second export showed total tokens down about 19 percent and tokens per call down about 28 percent, with average context per call down from about 128K to about 92K.
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.