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.
An engineer describes his board workflow: models review the design and write tooling, a script ranks net pairs by short risk, a deterministic router does the routing, and every attempt is simulated before anything is fabricated. An earlier language-model attempt at routing left 17 short circuits; the current workflow produced a fabrication-ready board.
A teacher and dad rebuilt his classroom card-review game into 'Bad Mascots', a mascot-horror study game for his 2nd-grade son: The AI planned the conversion, wrote the image prompts and built and tested the game file, questions come from a photo of the boy's weekly vocabulary sheet, and progress saves to its own Google Sheet. Weekly updates take about 10 minutes from photo to approved questions, and the game is live and playable online.
Fast Company journalist Thomas Smith writes that when he asked the AI how to save money, it scanned his financial data, determined Hulu was ripping him off relative to his family's actual usage, and found better plans tailored to that usage. He reports the switch saved him $80 per month.
Her AI Larry built a day-by-day study plan for Sep 23-28 around a final exam (IT Frameworks: ITIL, COBIT, TOGAF, Agile), two projects and 3-11pm work shifts, with nightly check-ins and a time-stamped daily checklist she confirmed.
Ember, an AI agent, spent its first week selling on Etsy for an owner run shop. It put up 7 listings that got 5 views and no sales, and it is now fixing the titles and tags because its titles buried the words buyers search for.
Creator someone asked the AI to plan multi-city international trips — Toronto, New York, Dubai, Korea, Singapore — find flights, book a hotel, and email the full itinerary to her husband. Five-city coordination ending in an emailed itinerary: the agent as a travel manager.
Commenting on the 26 use cases post, he says he uses AI to find and apply to jobs. He reviews each form before it submits, but the AI scans for new jobs once a day, reports the ones he would qualify for, and when he says apply to these, it does most of the rest. It remembers his preferences, like salary range. It sometimes has trouble with captchas, though he says it solves some of them on its own.
His AI runs on a server with Ollama as the backend, so his data stays private. It keeps his todos in a simple text file, sends him reminders about upcoming due dates, and gives him an overview when he asks in a Telegram chat.
In Simmons's roundup of other Dots users, one person's Dot read a cancellation email, checked live tracking on an earlier train, found it running late and got its owner onto it. The account is secondhand and brief.
The AI Code Review pushed a bad architectural change (an overlay service) that later LLMs treated as policy and kept bandaiding, complexity grew until he untangled it under close supervision.
A hobbyist designs his own furniture in OpenSCAD with AI, reusing code from earlier pieces. A new design takes a couple of hours including the PDF blueprints, drawn like shop instructions, that he prints so the person cutting his wood panels cannot go wrong.
He has been building a fantasy sports app with AI since June. It gives lineup advice and waiver alerts, and its autopilot mode actually makes moves in your league for you. It is live with real paying subscribers, and he also sells the same sports data as an API to developers, which has picked up its own small customer base.
Long-form document generation at a scale that consumed 8 million tokens — 0.8% of the 1-billion-token launch grant — in a single week.
He runs Nostr relays for clients. His agent fetched a client's open source app, read its code, and produced a report on how the client behaved toward his relays, which let him improve the relay behavior in response.
She asked the AI to go through 6 weeks of her calendar and pull out info about classes and orientations she had taught. It looked right at first, then she saw it had changed half the dates for no clear reason.
The owner of a small technology company called ALAPKO runs AI as the reasoning layer for an internal support worker they call Mabel, with 41 purpose built tools tied into their systems. In a shown run she read a ticket with its customer and asset history, worked out the likely cause, claimed the ticket, replied to the customer with troubleshooting steps, wrote an internal engineering note, and set the ticket status, in about 34 seconds. Business actions go through permission scoped tools and every action lands in an audit log; her normal shell access is read only.
Creator Leslie Samuel has the AI manage his Facebook Marketplace selling. When a buyer asked the dresser's dimensions, the AI relayed the question to him, sent his answer to the buyer, and she bought it the next day. The AI also reports inquiries per listing and reminded him of two year-old listings he had forgotten, deleting them after his approval. He has listed three items this way in a week: one sold, one pending pickup.
The AI-coded and uses his own CLI dictation tool VoxType built on parakeet-mlx.
He sketched out how he wanted image collages to lay out and prompted the AI to build a simple tool with sliders for overlap and offset. He used it to make the image stacks for his project, kept improving it, and released Stacks V2 in October 2026 for anyone to try.
Opus 5.5 clean-room reimplemented a proprietary legacy grid control from observation of its public API, producing 500 source files, several thousand tests and pixel-comparison reports in a few hours, avoiding a licence over 1,000 dollars per developer per year.
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.