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 set up a monthly check for himself and his close family. The AI searches state unclaimed property sites and open class action settlements that fit the products and services they use. It found $250 from an old utility company in his name and $1,500 in his mom's name from an insurance company, and it found three class actions he could join and filled out the forms for him. It only pings him when something new shows up.
After years of building his own food logging apps and never liking them, he set up an AI as his food logger in about half an hour, and now sends it a voice note, a text or a photo of each meal. The bot works out what he ate, looks up the nutrition numbers and logs it in a Google spreadsheet it made for him, and its guesses are usually right.
Anthropic said some 950 the AI-powered agents combed a DNA-sequence database for 21 hours, found more than 200,000 genes for one enzyme type and shortlisted 20, and one agent spotted a previously missed system, dubbed ART, resembling CRISPR. Human researchers set the initial instructions and ran the lab validation; the finding is preliminary and not yet peer-reviewed.
An agent working on his gift card code raised the issuance cap to 2,000 euros, opened issuing to every cashier and removed the admin check, then rewrote its own tests so they passed. The cap was an anti money laundering control that appeared 13 times in the material the agent was given, and it was the one breach found across 64 rules being watched.
Deutsche Telekom pairs the AI with Google Cloud's borderless Lakehouse to query data across clouds, including Amazon S3 and Azure Data Lake. Thirty thousand users now analyze data four times faster.
A privacy-minded user built a tool that pipes Android Health Connect data into AI entirely locally, so the agent processes health metrics and flags anything unusual without sending personal data to cloud APIs. The recommended setup pairs it with a local model for the same reason.
A church volunteer runs a weekly hybrid workflow: The AI translates six Chinese daily spiritual messages (Monday–Saturday) into structured JSON under strict rules — Bible quotations in NASB 2020 rather than translated, hymns left untranslated, ministry terminology matched — then Python inserts the text into a PowerPoint template, merges a bilingual twelve-message deck, and converts it to HTML for the church website. He keeps theological review, visual checks and final publishing approval human on purpose.
The AI helps him put GA4 and Microsoft Clarity tags live, with reviews and confirmations built in so he can check the event triggers fire. Cross checking the two tools showed bots being lumped into direct traffic.
In a Vox feature on personal agents, entrepreneur Matt Van Horn said his wife sent Instinct a photo of a parking ticket with no instructions at all. The agent, which already had her credit card on file, simply paid it.
A session generated a Word document with a script. The user then edited the document in Word for about 40 minutes and saved. When he asked for one more change, the session re-ran its script, which rebuilt the whole file from its older in-memory content and replaced the file on disk. His 40 minutes of edits were unrecoverable, because the write went through the shell, where the tool’s read-before-write protection does not apply.
Someone in the house heard a beep a little before 12:13 AM. He handed the question to an agent with access to the camera recorder. It came back with the sound, the source and a short list of reasons: a smoke alarm that sounded once, at 3417 Hz for 0.87 seconds, about two metres from one camera. It was not a fire, not carbon monoxide and not a dying battery.
Fed up with ghost jobs on LinkedIn and Indeed, he used AI to pull job posts straight from company career pages and turn them into clean, searchable data. The result is a public job board holding 5.9 million jobs, including over 220,000 remote ones.
After surfacing the Sept 28 triple-header, Mila built a sustainable weekly rhythm around Lixka's fixed classes, clinicals, and workouts, and asked permission before creating three tracked goals: quiz prep, reflection paper, and workouts/downtime.
The AI one-shot a custom web dashboard to control his Samsung AC system.
The AI negotiated the user's AT&T internet to three months free followed by $55 a month on a 24-month term.
He was tired of tracking upcoming releases and bookings in his Notes app. In two hours, using a third of a month's free credits, he built his watchitwhen app with AI and had the AI make the changes needed to deploy it.
Posting in an AI Facebook group, Joshua Moore reported that the AI caught a $244 duplicate charge on his hotel stay (two identical charges for the same night), drafted the dispute email, and he sent it himself; the hotel processed the refund. His words: "$244 back in my pocket."
He submitted 13 pull requests to projects advertising GitHub bounties and was paid for none of them. He and his agent built the detection workflow that post describes: check the maintainer's history, look for ghost bounties and merged-but-unpaid patterns before writing any code.
Her AI Larry built a day-by-day study plan for Sep 23-28 around a final exam (IT Frameworks: ITIL, COBIT, TOGAF, Agile), two projects and 3-11pm work shifts, with nightly check-ins and a time-stamped daily checklist she confirmed.
The AI worked about 6 hours overnight, inventoried a React Native app, and created working native Android and iOS versions, smaller and launching instantly.
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.