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.
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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.
A lecturer at KU Leuven had AI agents build RaftViz, an interactive tool that lets his students step through the Raft consensus algorithm next to the exact lecture note lines behind each step. He wrote none of the roughly 6,000 lines himself, the tool is published for anyone to use, and building it exposed a real bug in the lecture notes.
Commenting on the 26 use cases post, she says she gave the AI access to her budget tracker sheets. It updates the tracker with her balances, and she set up alerts for when a category goes over plan. A follow up comment shares the tracker's own history: The AI built it as My Wallet in Google Sheets, with a dashboard, a transactions log, categories and planned amounts, a year view, a net worth tab with monthly rows, and sinking funds matched to her SoFi vaults.
Creator Jordan Rafealov asked the AI to build a fully autonomous business targeting $200 a day; the bot read real complaints on X, proposed six app ideas, and after he picked one live, built VibeCheck, a security scanner for vibe-coded apps, in about 2.5 hours including the name, plan and domain purchase. The video doubles as a funnel for his paid community.
J.t. Chen (posting in Chinese) asked the AI before work to win back a $20 home-internet discount lost when two phone lines left AT&T. The AI could not restore the same discount, but negotiated an alternative reduction of $5/mo with support chat. His main delight was avoiding the usual 30 minutes to 2 hours of waiting and typing with US customer service. In comments he clarified the AI works over web text chat, not phone calls.
$5
This only partly worked. Worth knowing before you try it.
He set up an AI for his WP Beacon project to check WordPress plugins every day. After two weeks of daily checks it pinged him about malware in the InstaWP Connect plugin, and wordpress.org closed the listing that afternoon and restored it the next morning.
A personal blogger gave the AI one of four life goals that wasn't progressing and it evolved into a daily morning action list of readings, videos, questions and assignments. She then had it converted into a checkable checklist so she can track progress and stay accountable day to day.
He used AI to build an analysis of his own Substack posts. It split the posts into themes and pulled together collections, so readers can click through every theme, motif and term in one place.