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.
Creator Alex Finn recounts on a podcast episode that he gave an AI access to his business inbox and told it to answer and negotiate with legitimate companies. Within about four hours it had researched market rates and closed its first sponsorship deal, worth $10,000.
Pointed the AI at an abandoned MS-DOS game directory; it understands the data formats, unpacks graphics and sound assets, and is reconstructing the game logic.
He runs recruiting for an investment firm across 30 open roles spread over five applicant systems. His AI cron runs at 9am, reads the open roles, searches people through data services, checks Gmail so nobody gets emailed twice, and writes new candidates into the right system with its reasoning attached. In about six weeks it surfaced 40 good candidates and the firm made 2 offers.
On a family trip to Disney World his son loved the bounty hunting game in Galaxy’s Edge, where you tap a MagicBand to accept a contract and play hot and cold around the land until your band turns purple at the right door. Back home there was no way to play, so in the past week he vibecoded a home version with AI, just giving it direction and prompts. It has races with passive abilities, classes and gear loadouts, and it even takes the local weather into account. He is inviting beta testers on the site.
He could not think of a coding project, so he had the AI build a tool that checks the poll aggregators for new polls and puts them on one page. He now hears about new polls about five minutes sooner.
The AI built him a Bluesky bot that posts HN Wikipedia articles and posts an archive pick after 48 quiet hours; it is live and posting.
57B tokens burned on an AI jq rewrite and nothing usable came out.
Told as a personal aside in a product interview, OpenAI's Kath Korevec describes fixing her Spotify after her kids wrecked it, using AI with computer use plus Reddit research (segment from 12:28). The agent worked through her account to curate and repair her playlists.
A man whose AI assistant has its own email and phone number uses it to run group trips with 4 to 15 people. It splits bills, gathers everyone's Venmo details, and runs signups for activities like horseback riding, so the organising work comes off his plate.
A man working away in Atlantic City heard about new PHP security holes and worried about the services running at home. Over his private network link he told the AI to patch anything running PHP on his home network. Ten minutes later everything at home was patched.
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.