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.
Steve Morseman gave the AI his ISP account info and asked it to cut the bill or improve service. The AI used its calling feature to wait on hold, negotiate, then patch him in to pick between two saving options — $840/year claimed, no plan change.
Threads creator John Lam (posting as someone) used an AI hotel price-monitoring setup that sent two alerts when rates dropped on existing reservations, including a Hyatt stay; he rebooked to save $17 on one and $30 on another without manually checking prices. The post includes an AI referral code, so the account leans promotional.
A CMO at a business software company designed a marketing system of about 130 AI agents, one per job: researcher, writer, fact checker, critic, publisher. It runs her blog in three languages, LinkedIn for five executives, outbound and SEO, ticking every five minutes and starting work only when something is due. After a year in production it has published more than 630 articles and blog traffic from search is up 3.9 times. Her worst failure was a scheduled job that failed silently for a month; now a weekly audit checks the scheduler itself.
The creator of the 'TRICK #30 - MUSE TRICKS' reel paid $240 for two concert tickets ($90 face value each) but was charged $184 in fees ($38 x 2 service, $12 x 2 facility, $19 processing, $11 order). Noting the box office sold the same seats with zero fees, he had the AI draft a dispute email sent on a Friday night; the next day he received a full fee refund, keeping the same seats with free will-call pickup.
He had wanted an automatic provisioner for his boards for a long time but it was a whole project on its own, so he told the AI to write it. Some physical steps still need him, but each board now takes 2 to 3 minutes instead of a few stressful hours.
His AI connection tracker is synced with Bluesky, X and LinkedIn. It reminds him to check in on people and tells him what they have been up to.
He runs a Shopify based company alone. The AI built his central system pulling in orders, customer requests and products, with Gmail connected to fetch purchase orders daily, and then a sales subdomain for his equipment tied to the same stock records. Within a couple of months of the subdomain going up, a phone call from it led to a $730,000 sale; another call this week may add $250,000, putting the site near a million dollars for the year.
He does not rely on a single AI model, so a bot agent continuously summarizes and consolidates his interactions across models into one updated file. The shared memory means any model he picks up knows what the others knew.
He runs about 15 apps and sites and could not keep track of what needed doing. He connected the AI to his app through MCP, and the AI now creates tasks and subtasks, writes notes, and shows him what is needed by priority and due date.
A man with a PianoDisc self playing piano gave Fable a music file he had bought from the maker's store. Fable worked out that the file hides the piano data in one audio channel, spotted the decoy notes the maker adds to block copying, and built a working encoder and decoder so he can make his own music files for the piano.
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.