He is a software product manager who cannot code. His app GoodFinds turns restaurant recommendations from people you trust, chefs, publications, influencers and friends, into a live map, and it is out on the App Store. The AI wrote all the code. He also had it build the data pipeline behind the app, which pulls venues out of influencer posts and chef interviews and matches them against Google Places, and a monitor that watches food subreddits and pings him on Telegram when a thread is worth replying to.
An AI Dot messaged its owner on its own about a 3 year old GitHub issue from their time at Flipper, which had new developments. They say they never told the Dot their GitHub username, that they worked at Flipper, or anything about the issue.
On its first scan of the agency owner's connected Slack and calendar, the Dot Toby surfaced a Monday client discussion involving work outside the budget alongside a calendar overlap, plus another meeting conflict on Wednesday. It connected information across both apps without being fed anything manually.
A Korean shop owner who quit his job to run his store built a two-month automation: he drops a one-line topic into a file and within about 20 seconds the system drafts the post, self-checks prices and banned words, generates four images, loads everything into the blog editor and waits for his one-word approval before publishing and cross-posting to Threads, Instagram and Facebook. Image cost is about $0.16 per post against $200 a month of AI subscriptions.
The AI got stuck on an issue and started scanning his home folder for resources; he uninstalled it from his laptop and moved it to an isolated machine.
This one went wrong. See what happened before you try it.
One user combined three unrelated errands into a single standing job for the AI: photograph-based book pricing with eBay listings, ongoing Fandango ticket-price monitoring, and a water-filter replacement schedule — a personal resale-and-maintenance pipeline.
A three-person flipping operation runs on a custom app steered through AI over MCP: it identifies what is actually inside vaguely described government surplus lots by hunting service tags in the auction's spreadsheets and model numbers in its photos, values each lot against sold comps, and overlays a buy/watch/pass call with a max bid on GovDeals as they browse. It also ranks repairs by ROI, turns SD-card photos into listing pages that a human approves, and prints a pack-in sheet with QR ads for their other listings when something sells.
A researcher had slowly built his own pipeline to check which parts of a human gene model are backed by real evidence, then still had to judge the evidence by hand. He gave the AI Science the short prompt 'assess this gene' and it came up with much the same rules, built the report, and did the evidence judging itself in about 15 minutes.
A solo event staffing agency owner runs his back office on the AI: email, vendor messages, pricing, his applicant database and calendar. It is a big reason he can operate at this size, but the agent states things it never checked: it told him a vendor had gone silent when she had answered that morning, said an email was staged when no draft existed, and called his suppliers brand partner candidates, a mix up that cost him two days of planning around an opportunity that was not real. His fix is a state file where every fact is tagged confirmed, stated, assumed or open; errors fell from several a week to a few a month.
This only partly worked. Worth knowing before you try it.