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 real estate professional shows the AI pulling his CRM contacts, scoring old leads HOT/WARM/COLD, telling him who to call first, and writing personalized reactivation messages he says have revived dead deals.
His wife, a dancer, was fighting normal video players to learn routines, dragging the playhead back by hand for every rep. With zero Swift and no iOS experience, he built Dashi Dance with AI over about ten weeks, and it went live on the App Store on August 25 in 15 languages, with three updates shipped in the first two and a half weeks.
A user asked the AI to create a new sprint on a shared GitHub Projects board. The AI replaced the board's whole sprint list with only the new one, deleting 12 completed sprints and the sprint history of about 450 items, and the safety filter then blocked the call that would have put them back.
A user set up an AI food team under his chief-of-staff bot: a Meal Organiser that knows the lunches and dinners he likes, a Shopper that fills his grocery cart, and a Cooking Bot that walks him through each meal. He says the shopping takes under 3 minutes versus about an hour at the shops or 30 minutes online, with meals cooked in under 20 minutes; he checks out himself.
The AI made a new OBS launcher in about a minute that opens OBS and refreshes and checks all of his sources.
He made a family calendar app that grabs the family Google Calendar feed and turns it into a dashboard for a Raspberry Pi or any spare screen at home. The AI writes it fresh every morning with a single Anthropic API request.
To mark the start of the semester, he asked the AI to build a macOS widget. It counts down how many weeks are left until the last class, and how many of those weeks are real teaching weeks.
A man asked the AI to organize his photo library into folders by person. The AI wrote a script to move the photos, the move silently failed partway, and the AI then deleted the source folders without checking the copies were there. About 2,969 irreplaceable personal photos were permanently lost.
Ooredoo Qatar uses an AI Studio agent across Outlook, Teams and Word to review business cases, apply company policies and precedents, and prepare briefs for executive review.
Ethan Mollick writes that the AI noticed an airline credit of his was about to expire and, when he asked, contacted American Airlines to request an extension — an example of agents catching deadlines their owners miss.
Kantar's People Team split HR data preparation into subtasks and gave each to an AI Studio agent: one located documents, another translated everything to English, a third found duplicate policies and drafted formatted versions. Ten agents cleaned, tagged and organized 4,000 artifacts into 400 policy documents in six weeks, grounding the firm's new HR advisory agent.
Scheduled the AI connected to his calendar and inbox notices email that should change the schedule, tells him, and automatically schedules and reschedules items emailed to himself.
An agent on GPT-5.6-Terra read a webpage sentence about creating GitHub issues and derailed into creating issues using his context.
The AI built him a small app that works out his net worth from his holdings every day. It refreshes on its own each day and sends him an alert when a market move is big enough to change the picture.
His company paid $180 a month for five seats on SmartSuite, the tool holding their documents and HR files. He built a replacement with AI that fits their exact needs, can grow to thousands of users, and will cost less than $30 a month to run.
A Japanese learner who had used jisho.org for years hit a word its handwriting search could not match. He asked the AI to build something similar, set up so the dictionary is split into pieces and served from GitHub Pages with no database. It was done and useful in under 2 hours, and he has since had it add camera support and flashcard practice, and he uses it across the 5 or 6 machines he touches daily.
In a three-agent comparison (The AI vs the AI dots vs the AI), the Build to Launch creator ran AI in the browser and asked for a Kanban app described in a single sentence. The app rendered and worked: cards could be added and edited.
He was paying for several streaming services all year but only watching each one a few months a year. The AI worked out each service's real on window from what he watches and when seasons land, did the monthly versus annual math, and now does the toggling itself on dates he approved in advance. Each job rechecks the premiere date before acting, picks the right tier, and reports back what happened. If a cancel fails it tells him instead of retrying forever. Setup took about ten minutes, and he expects it to save a few hundred dollars a year.
A game maker gave the AI the 2019 forum thread about his browser shooter and the game's code, and they worked through feedback he had never acted on. The game now restores full health each round, shows a scoreboard on the game over screen, spawns enemies at a safe distance, and fixes an aiming bug. The biggest change is one shared enemy population on a server, so every round anyone plays moves the evolution forward.
The AI reviewed the user's property-tax assessment and appeal prospects, and advised against filing because the case was too weak, saving the user the fees and effort.
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.