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 newsletter, close to 9,000 subscribers, is run day to day by four AI agents with set roles: one oversees, one edits and manages, one builds, one dispatches the work. He still approves anything that goes out, and the agents refresh their own context from checkpoints instead of slowly forgetting across days.
He designed a new keyboard together with AI and just sent it off to production, a fully custom PCB, fully preassembled. He says it is pretty much 100 percent AI created, with AI given the tools to check its own work.
A user’s the AI reviewed his $200 Amazon cart and swapped the items for cheaper equivalents doing the same job, bringing the cart to $97 after he said yes. He posted that the single task paid for the subscription.
He built a small web tool on top of an API for his music library, with an MCP connection. He can ask the AI to check for missing artwork, sort out the genres, or fix the track numbers.
He says he had the AI call his carrier to cancel the data plan on his iPad. The AI called him in when the agent needed a human to confirm. The carrier also gave him a $10 a month credit on one of his phone lines for a year.
He filmed about 4.5 hours at a village event weekend with no plan to edit it. Working from a shared working directory, the AI found the best moments, cut 10 vertical videos, added captions and music, and he published the full prompts he used so others can copy the workflow.
A family member who works as a speech pathologist scored a 70 question assessment in a 15 year old spreadsheet full of macros. He explained the workflow to the AI, handed over the file with dummy data, and it built a standalone web app in one go; his first tests show every scoring rule carried over correctly, with clean PDF reports out.
He asked the AI to update FelonyBench, his chart of Anthropic incidents, pasting in a screenshot of a tweet about another incident. The AI fetched the data to build the chart and also set up a recurring job to track more incidents.
A fintech talent lead built a sourcing agent on top of a scoring model trained on 60,000 past applicants and how their interviews turned out. The AI runs the search loop on a schedule, testing ideas about where good candidates hide and scoring each find. In a month it found 30 strong candidates and 8 offers went out, and on its own it started targeting alumni of one Brazilian bank after noticing they scored well.
He merged five scattered file messes into one, then the AI and an LLM wiki in Obsidian reviewed and cataloged everything. He says knowing what is in it and how to get it back matters more than tidy storage.
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.