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.
G Schwartz had the AI list and sell his Yeat Central Park concert tickets on StubHub, with the agent handling the listing through credentials found in email. The tickets sold.
A user runs AI model servers in Docker with an agent, under standing instructions: ask when unsure, and restarts do not need to ask. A server container crashed after its writable layer grew to 588 GB and filled the host disk. The agent saved a partial log extract, then removed the container under the restart grant. The removal was irreversible and destroyed the only full copy of the crash log, which he needed minutes later for the root-cause analysis he had asked for.
Personal-finance creator Tiffany Aliche (someone) had her AI 'Monae' pull her saved Instagram posts about New York City events and organize them into a dated 'Fall NYC Itinerary' — including Morgan Library and Frick Collection free Fridays — set it to update daily, and add selected events to her calendar.
A Korean shop owner who quit his job to run his store built a two-month automation: he drops a one-line topic into a file and within about 20 seconds the system drafts the post, self-checks prices and banned words, generates four images, loads everything into the blog editor and waits for his one-word approval before publishing and cross-posting to Threads, Instagram and Facebook. Image cost is about $0.16 per post against $200 a month of AI subscriptions.
Peter Yang ran a learn how I write skill in an AI app Duet. It read 33 of his sent emails, 89 Slack messages, 47 DMs and 11 docs, then saved a writing guide as a skill that Duet now uses to draft his email replies in his own style for him to check before sending.
His AI pulls from GitHub, Jira, Asana, his calendar and his email, writes up his week, and posts it to Slack without him asking.
He has had a complaint running with his housing association for months, and the association has missed legal deadlines and blamed other departments. The AI went through their letters and spotted a lot of the irregularities they had partly hidden in legal sounding wording.
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.
Over four weeks of evenings, an AI agent wrote the firmware, the live web chart, the Node-RED automation and the enclosure CAD for his radar stair light, driving a live Fusion 360 session through its scripting API. He reviewed, printed and measured, and the second half of the video lists what the agent got confidently wrong, including a radome gap that landed on exactly a quarter wavelength.
A scripting skill picks his best idea and writes what he will say. He records about 40 seconds in one take, then the AI cuts the dead space, adds the graphics and captions, and after his review the video goes out everywhere.
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.