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.
Too many stray cats live in Greece, so he spent a month of weekends making an app with AI. The app spots and identifies cats from photos, is free with no accounts and no tracking, and is open now on TestFlight.
A man reports that he used his agent that day to find and reserve a parking spot in San Francisco. He had earlier used AI to gather his AWS invoices for a reimbursement.
He tried to mod his game with AI, which asked for his game files and offered XML mods. So far not one of the mods has worked.
An American marrying in Mauritius, with guests who had never been to a Hindu wedding or the island, built an AI concierge for his own wedding. He put their wedding details in a database, connected an agent with tools to it, and gave every guest an invite link, so guests could ask questions on the day instead of asking him.
Fable helped find and fix a Motor OS race he had debugged for 3 months; since July most roadmap items are fixed in about a week instead of 3 months supervised.
He was missing Adobe Lightroom's denoise feature for his RAW photos, so he had the AI build it. The app also denoises his standard JPEGs, which Lightroom could not do, and he has a lot of low res, noisy JPEGs lying around.
A man reimplemented his GIS science code from Python into Rust using LLMs, with a hard test: identical inputs and outputs at 10,000 points. Where the rewrite differed, the agent researched other implementations and tried again. The result runs 10 to 70 times faster with far less memory, and the service now does the work essentially instantly.
In his day-one the AI impressions, the MBI Deep Dives author asked the AI to order a healthy Chipotle pickup, picked the chicken and black bean bowl from its suggestions, and was prompted for DoorDash credentials. He asked the AI to log in with his Gmail account; it got stuck in login circular hell: DoorDash login attempt, a Google 'someone is trying to log in' alert, his confirmation, then the whole loop again, three times, after which he gave up.
Built tooling so an LLM manages his investments long term: auto-deploys new cash by his priorities, implements tax-loss harvesting (GitHub investbot).
The AI built him a file manager app for the Amiga's Workbench system. He says it is genuinely useful and powerful, with the best parts of Windows Explorer and OSX Finder.
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.