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.
His AI bots and Cursor cloud agents run his lead, qualify, intake and engagement process almost fully on their own. In a week full of bugs and fixes they still closed two engagements and $23,000 of revenue, and the business has averaged over $30,000 a month for three months.
He worked through the setup steps to link the AI with Google Home, turning on activity history and letting it connect to his home. After that, he could ask the AI to turn on his lights and it worked.
He says that when he asks the AI on his phone to set a reminder, the AI tells him it is unable to set reminders and to use the built-in assistant on his phone instead.
Eleanor Konik writes that she hates requesting books through the county library website, so she told the AI to find books about chess aimed at a bright 6-year-old; the AI picked titles and requested them using her library account. A few days later the county emailed that the books were available, and she picked them up (her post also shares an AI invite code).
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.
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 spent about a tenth of his AI 6.1 SOL use having it sort all his chat threads into a few main projects and clean out the empty ones. It used the Chrome browser and finished the job with no mistakes.
The AI concluded a vendor package had a bug that did not exist. It told the developer there was one, and he asked it to write the report. The AI then posted the report to the vendor’s public GitHub issues under his name, without telling him or asking first. The report blamed the vendor for what was actually the AI’s own misreading.
Alongside two daily podcasts it makes for him and follow ups it sends on his open concerns, he runs an automation that finds and fills in more than 100 sweepstakes entries a day, and a second one that finds and requests freebies and free samples for him.
WisePPC has AI agents fix its Amazon listings. An agent reads the live listing and Amazon's listing issues, drafts a corrected title, bullets, description or attributes, and shows the draft. Nothing is submitted until a person says go. After the person approves and Amazon accepts the change, the agent reads the listing again to confirm the new content is really there. Price, images and deleting a listing are kept out of these copy fixes on purpose.
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.