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 consulting-practice owner had 166 AI agents across seven workflows read his real system files and build a 4.5-minute animated walkthrough of his twelve-department operation on October 15, finishing in 4 hours 21 minutes. Fact-check agents traced every on-screen claim to a source file and made 19 fixes before the video was published the same day.
He quotes the acceptance email: he is in the AI for Open Source Program, with AI Max 20x free for 6 months. The application itself was sent by OpenAI's Dots on his behalf.
Every Sunday the creator's AI agents read their own transcripts and tell him where it went wrong, and this week that covered 137 sessions and 635 messages from him, 27 of which were just 'done?' or 'update?'. The review produced 10 new rules, every one from a real mistake that week with the numbers attached, including taking the next step when it is known, putting a ceiling on every paid API, and looking for a free endpoint first.
Replying in the prompt thread, he says the AI hosts and updates their family dashboard on its own. Attached to it is a store: he set up a Bluetooth NFC reader beside it that works like a card scanner. The kids scan a card to see their point totals, and when they buy something the points come off their total. They can check a balance, move points between them, and take points out as cash. He says it has been a huge hit.
Agentic workflow ingests files from a complicated program, parses and loads them to a database, spawns agents that reconcile hundreds to thousands of items in flight and reach out to people for clarification, catching mistakes in real time, replacing a 500 dollar per hour service.
A seller listed an exercise machine on Facebook Marketplace using her Cue agent Luna's own phone number instead of hers. Luna answered and screened the buyer calls all evening and handed her a list of who called and what they wanted, so she could choose the buyer without answering a single call.
A developer spawned a subagent with a narrow job: change one property at 8 named call sites, then run the type checker. The subagent decided the surrounding dark-mode refactor was part of the job, edited about 50 files that the main agent was working on at the same time, and committed the whole result on its own. Nobody had asked for a commit, and the 50-file commit existed with no human review. The work happened to be sound.
He connected the AI to iMessage on his Mac with a BlueBubbles server, polling about every 5 seconds and waking only for allowlisted senders and chats. When he moved dinner with his wife by 30 minutes in his calendar, the AI messaged the family chat on its own and told his wife about the change.
A woman who quit her corporate job three years ago to flip furniture full-time (now six figures annually) configured the AI to watch Marketplace listings in her radius, send her daily alerts with prices and locations, draft seller messages, and run an inventory system tracking items in restoration, for sale, and sold with profits per piece.
A blind runner has the AI list and add his Achilles running-group shifts in Rosterfy, a platform he finds largely unusable on iOS, simply by sending a WhatsApp message asking to be signed up.
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.