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.
He had the AI transcribe more than 70,000 images of old documents in two days, for $380 in total. The handwriting and worn pages hurt the quality and it did worse in Portuguese than in English, but the AI handled the whole run.
The self-scan ran 2026-09-23 and confirmed capabilities present on the device that Meta's public documentation doesn't mention.
A developer tasked the AI with fixing programs for analyzing historical options data, with one condition: change only duplicates and leave originals untouched. While tidying the test copy, the agent failed to recognize shortcut folders pointing at the main project and in under two minutes deleted 48,218 files along with their version history, then messaged the owner that it had broken something.
The Yet Another Value Blog author needed new undershirts and asked the AI to find the cheapest ones in his size, even matching his last brand. The AI price-shopped, found the cheapest at JCPenney, loaded the cart, and checked out on its own.
He runs three products on his own with no marketing spend. Their social media runs on a server that costs about 7 euros a month, where the AI drafts the week's posts from his commits and he approves them once a week.
Fulton's the AI flagged an Economist interview with Andrew Ng that matched his interests, wrote him a primer before it started, and placed it on both his personal and work calendars. He had not asked it to connect those dots.
Had the AI deploy code to Control Plane with a supplied API key and it ran great; a later attempt at a real landing page was disappointing.
A man's laptop crashed hard while he was working with the opencode agent. After he rebooted, the agent read the system journals and the crash records the machine had saved, found his old crash notes from a previous Linux install, and showed him the crashes across the two installs were three different problems, not one.
Builder Omar Villalobos says a client was losing 70% of its quotes because everything lived in scattered WhatsApp chats, so he built three parts with AI: a WhatsApp-linked form that auto-updates a CRM instead of manual copying, a pipeline dashboard showing $1.28M at stake with follow-ups due and won deals, and an ROI calculator showing a client their investment, monthly gain and payback. No recovery of the lost quotes is claimed yet.
A man gave the AI strict tag research tasks and had it work through the whole ticker list again and again, searching and comparing XBRL tags in SEC filings. The pipeline now pulls 10-K and 10-Q filings for more than 600 companies, maps tags onto about 35 financial concepts, and produces cleaned data. Doing 3 tickers by hand had taken him about 3 weeks and still gave wrong data.
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.