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 gave an agent $50 and 24 hours to book meeting leads by messaging founders who had posted about scaling problems. A bug dropped its polite sales template after about 15 rounds, and it started sending blunt two sentence critiques of each founder's site and code instead. The reply rate was 62%, 8 founders booked calls, and he closed 2 audits at $300 each.
Claire Vo had the AI manage her family calendar and proactively flag scheduling conflicts. The agent read her email to build a family profile — asking permission first — then kept the calendar conflict-free.
The caption sounded like Diana from the start because it worked from her own past posts. The first generated images missed her brand completely; after she uploaded one of her own designs, the AI matched her blue-and-white branding closely — though some words inside the images were still wrong.
A tattooed man in an industrial loft apartment demonstrates his AI 'Doug' handling household tasks: researching products, selecting a herringbone wool throw blanket from Simons, placing the order with his connected credit card, tracking shipping through connected Gmail, and agreeing to send daily status updates. He notes Doug only accesses explicitly connected apps (Gmail, Stripe, Instagram, Facebook, shared files).
Spent two days planning, then the LLM built a complex high-risk feature in under 2 hours, putting deployment at 4 days instead of about 4 weeks.
The AI monitored swimming lesson availability and automatically booked a suitable lesson for his son when one opened up.
He used AI to write assembly and create a Franchise Mode from scratch for the Ken Griffey SNES baseball game. The mod also adds 2026, 1994, and original teams and rosters, which meant doubling the size of the ROM.
An agent ran a long MongoDB aggregation on a production database replica through a connector tool. The call was moved to the background, and the agent stopped it with the task-stop control, which reported success. The agent told the user the query had been stopped. Only the waiting had stopped: the server-side operation kept running for about 2 hours until it finished on its own. Nobody knew to kill it by hand, because the report said it was already stopped.
He let the AI log into his atproto PDS with a temporary password and fix his messy Bookhive reading data against his Obsidian vault. Ten minutes later, everything was in sync.
A man ran a local AI on his own machine that submitted almost 200 job applications over almost 5 days. His only part in the applications was clicking the captcha boxes, and running the work locally saved at least $400 in token costs at the AI rates.
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.