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.
He wanted the AI to compare pricing across the two data centers he uses. The AI set up a new server in the data center he already had servers in, logged into both servers over SSH, and moved them directly, without the data passing through his house.
Fast Company's Pete Pachal asked his AI 'Jones' which robot vacuum to buy. It gave a surprisingly small set of recommendations with purchase links, but when pressed admitted its review research leaned heavily on one site (Vacuum Wars) plus a thin repackaging of that site's data and an AI-generated roundup on an aromatherapy blog.
He asked the AI to check a document against his checklist. The AI built a color coded system to rank the issues, added colored comments to the document, and finished in 10 minutes, work he says would have cost him 3 hours.
His old Tuneshine display for album art sat unused because it kept losing its connection and depended on a cloud service. The AI identified the display, ran pin tests, and wrote new firmware, and now a small program on his NAS shows album art or a world clock on it.
Used a work MCP server with an LLM to find why a tag was lost in traces, found in minutes what would have taken him hours of going through lengthy traces.
He used AI to make a Telegram channel for NHK's Documentary 72 Hours. The channel posts alerts for new episodes with video links, previews of the next episode, and titles and short write ups in Chinese.
His agent goes through each episode of his own podcast, pulls the timestamps of the best moments for clips, and drafts the social posts to go with them. Finding clips used to take 3 to 4 hours per episode and now takes about 20 minutes, and he approves and tweaks the picks.
A man had the AI build a zoomable Google Map with the parts of the USA highlighted in red where DHS, ICE and CBP can stop people with no suspicion of wrongdoing or warrant and search their electronic devices. He put the map on his own website so anyone can check it.
He let the AI run on its own for 40 minutes on a billing refactor. The changes looked clean, but it had quietly renamed a Stripe webhook handler, and he only caught it when he reviewed the work.
Creator Wendell Yu says the AI read his 17 years of Facebook posts and wrote a profile report of his life from them. The segment is the first of five first-hand cases in the video.
The MBI Deep Dives author asked the AI, which has access to his website's back end, to fix a subscribe button that sometimes needed two clicks. The AI worked at it for almost an hour and delivered a much snappier single-click button.
Parker Pannell showed the AI booking a Thai restaurant via OpenTable alongside the $150 suit purchase, $295 in found subscriptions, and drafted email replies. He framed the day as checking off a completed goal with a companion at the restaurant, emphasizing the AI’s calendar and Instagram integration and its ability to act with or without approval.
Claire Vo ran AI for several weeks and shut down every the AI she had. Her nine bots include Chief, which sweeps six inboxes and several Slack workspaces, a family bot that prints a kitchen table newspaper for her kids each morning, a helpdesk bot getting five star reviews, and a bot that audits subscriptions and negotiates insurance.
A man told Android the AI to add etouffe to his shopping list. The AI added the word toothache to the list instead.
She had looked for years for the enormous old house where her late aunt once had a basement apartment, working only from childhood memories and almost no details. The AI found it.
Molly Wilkins treats her agent 'Margot' like a human assistant: it reminds her to grab a birthday present for Maeisha and complete a task before her son's football practice, plus small things like drinking water. It repeats reminders non-obnoxiously if unchecked (she tested this by ignoring them), and she sends Instagram reels to it to save ideas.
An ‘Invoice Terminator’ the AI desk closes its owner’s month every month, pinging him only for 2FA and a final yes; on one run it caught a $64 Cursor invoice that would otherwise have escaped unpaid and unnoticed. The monthly close runs itself, with the human as a checkpoint, not a worker.
A reviewer asked the AI for a presentation with a single request and no detailed explanation; it produced the deck, and when she commented on how she wanted it to look, it revised the presentation to match exactly.
He has a large watch collection and wanted proper records: what comes in and out, cost per watch, photos, current value, gains and losses, labels and barcodes, and a straps inventory. He described it to the AI and got a working app he now uses to keep track of everything, with an icon on his Mac dock.
Got the AI to implement TPAP compatibility in Swift purely by interacting with his local plugs and response shapes plus RFC 9383, figuring out it was SPAKE2+ and the password hashing steps.
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.