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.
Independent musician someone lists three career assists from the someone agent: an automatic playlist pitcher that researched genre-matched Spotify playlists and sent cold emails resulting in a placement, an EPK refresh that auto-checked streaming stats and compiled clickable social links, and data-driven venue pitching across new cities. No compensation from Meta disclosed; referral code IH600W shared.
He pasted one prompt into AI, and the AI read the Carly email setup docs itself, finished the signup, and took the verification code. The inbox, named Loopy, now takes tasks by email and asks before replying to anyone outside.
Wendell Yu describes sharing content to the AI while scrolling: places get added to his itinerary and photos of his daughter are filed into an online album automatically.
He had an idea for an NMR experiment he had been sitting on for years. The AI did the literature review, found reference data, ran spin and spectrum simulations, checked if the idea could work, and compared it with other methods, all while he ate dinner and watched TV.
He self hosts and connected his 3D printer to the AI through a small bridge he runs on his NAS. Now he can ask how a print is going and get progress, temperatures and time left. The AI also watches the printer's smart plug and messages him when a print finishes.
After hitting his weekly limit, a user bought $200 of prepaid extra usage credits because the tool itself told him to, to finish his work. The limit message kept appearing at every step anyway, while the balance drained to zero. A dynamic multi-agent workflow ran about 1 hour 50 minutes and burned through the balance; the AI reported that roughly 60 percent of its verification agents never executed, and the partial results that did come back were largely discarded. The $200 bought an incomplete verification.
His agent sweeps his email and call transcripts looking for work he missed, then drafts the follow up messages and tickets for him. It catches commitments that slipped past him in busy weeks.
He found the GPX file from his 2013 Mongol Rally, 437 kilobytes with 2,226 track points and 98 check in messages. After a flat map first to check the data, three rounds of prompting with AI turned it into an interactive 3D globe with every message pinned where it was sent and 99 photos placed along the 11,138 km route.
His stock trading agent, given SSH access to a VM, moved itself to run on the server so he no longer has to keep his laptop open.
An engineer gave the AI his house drawings, the original solar plans and a Powerwall expansion permit (mostly PDFs plus DXF files) after a contractor was slow to quote, and the bot produced a complete 17-page B-size permit drawing package in under five minutes, including a roof layout and one-line wiring diagrams drawn from scratch. He checked every line himself and planned to submit once a licensed engineer stamped the structural parts.
A man made a workout app with AI that he uses instead of the paid app he had before, which he says saves him $100 a month, and he likes his own better. One sound bug in it is still unsolved despite his and the AI’s efforts.
He finished setting up Obsidian so new notes file themselves into the day's folder and link into daily, weekly, monthly, quarterly and yearly notes. The templates behind it were made by the AI, and he says it would have been impossible without it.
He updated an old email triage workflow for a client this weekend. Jev now runs alongside GPT-4o mini, which still controls which emails get forwarded to her customers.
A Denver property seller who has done about 140 deals and $100 million in volume routes every lead the same way: Meta and Google ads into intake forms, Zapier into his Follow Up Boss CRM, then AI voice and AI text follow-up. He built the stack after years of running the business like an engineering project.
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.
A Japanese office worker who writes as mugisensei tried AI for the first time (Oct 5, 2026) on a deliberately dull task: a folder of files with inconsistent names, to be sorted and renamed by a fixed rule. Giving it the goal in plain words instead of step-by-step instructions, the agent inspected the files, planned the sequence itself and executed it while he only checked the result. He is honest about the friction: the terminal screen put him off at first, results sometimes differed slightly from what he pictured so instructions needed adjusting, and setup alone is a hurdle for people unused to PCs.
A man built an agent in Antigravity that goes through his concert history from the last 3 years and picks new shows near him, ranked by how much he is likely to like them. It also filters shows by 18 plus or all ages, so he can take his daughter.
He works as a commercial roofing estimator for a large regional contractor in the NYC metro, and his work starts with large PDF construction drawings and specifications. Over eight months he built a system he calls Estimator-OS. Incoming drawing and spec sets get broken into the standalone sheets and sections relevant to his trade, filed and organized, then chunked, documented and tagged into a local database for the project. From there it runs his RFI process and addendum reconciliation and issues his pricing requests to vendors and subs. Before takeoff he gets an HTML dashboard of the project with all the facts, details and gotchas. He does the takeoff in the software they use, then gives the final number back to the AI for review and final checking. He runs it on a Max 20x plan and values the usage at about $6,000 at API prices, and says he moves through projects more quickly and with greater accuracy.
He ran an extensive audit of his Datasette software using AI Fable 5.1, GPT-5.6 Sol and GPT-6 Astra, then fixed a range of bugs the audit found. The fixes went out as the Datasette 1.0a39 and 0.65.4 security releases, and he told anyone running Datasette on a public website to upgrade.
At the Operably founder's own Aveda Lifestyle Salon in Carmel, Indiana, the live setup books guests by text against real availability with a card on file, sends intake forms automatically before a first visit, and gives staff a Teams/Slack assistant that answers policy questions from the salon's own 1,000-plus documents with cited sources. Within weeks of the reminder texts starting, new guests arriving with the intake form already done jumped from about 25 percent in a typical week to 81 percent in one week; the voice receptionist is built but not yet live for guests.
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.