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.
A traveler planning a trip with AI booked his own flight and hotel, but Dots noticed the shuttle was missing, proposed it with the details they had discussed, and completed the booking form on its cloud computer - after asking him to confirm the same booking three separate times.
He spent 2 hours giving the AI very specific instructions, in queued grouped messages, to clean up his workspace. Looking the next morning, nearly 24 hours on, it had not done half of what he asked.
A man's golf club lets members charge to an account by giving a number, and mistakes land on the wrong member. The AI built him a watcher that opens his club statement every morning at 8:20, spots new charges by their ticket numbers, and texts him each receipt with the running balance. In two months it caught a 25 dollar and a 115 dollar charge from other members that he would never have spotted.
Filing travel reimbursement, the author realized he never got his hotel receipt. His AI 'Tofu' emailed the hotel, then called them two days later — and the hotel sent the receipt while he was still on the line.
WisePPC has AI agents fix its Amazon listings. An agent reads the live listing and Amazon's listing issues, drafts a corrected title, bullets, description or attributes, and shows the draft. Nothing is submitted until a person says go. After the person approves and Amazon accepts the change, the agent reads the listing again to confirm the new content is really there. Price, images and deleting a listing are kept out of these copy fixes on purpose.
He works with collision data as a UK highways engineer but could never answer the simple version for his own commute: which route has actually hurt people. He built SafeRoute with AI, which plans a route and scores it on five years of collision records; colleagues then asked for the same data as branded evidence reports for committees, which the tool now produces in about 30 seconds instead of a day of manual work.
LLM made human-like fuzzing sessions for his text editor; local Qwen babysits, investigates, writes repro and files ticket; bugs found after 1-2h runs.
Quitalashawn told the AI to scan her inbox for unanswered brand-collaboration emails, found 277, drafted and sent the replies, then continued negotiating back and forth with brands on her behalf.
Charles Summers runs his demand-generation work on the AI: 8 scheduled tasks that drive HubSpot, Search Console, Gmail and LinkedIn through Chrome, backed by 578 local sessions and about 220 memory notes. Ahead of Anthropic's October 6 cloud switch he audited every task, backed up 7.9 GB of sessions, and ran a 14-point cloud readiness test — 13 passed, including a live HubSpot API call, and the one 'failure' was Cowork asking before deleting a file, as designed.
Told the AI which plugins and settings he wanted in Neovim; now has a single-file setup used across four machines, and the AI bundled gitbutler-cli from source for him.
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.