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.
A man connected his local agent to Monarch Money through an open source MCP project, with some AI help on the login. The agent can now pull his transactions and look at spending patterns, and the AI generates a daily report from it.
Kilsonmoon sent the AI a photo of a neighborhood water-pipe cleaning notice: The AI identified when his street would be cleaned, added it to his calendar, set a messenger reminder for the day before, and relayed the precautions — close the valve, avoid running the washing machine. It also found the full schedule on the water utility's website and registered ongoing monitoring so future notices trigger automatic alerts.
He told the AI his AT&T bill was high. The AI scanned his Gmail for the account details, checked competitor plans and prices in his area, then negotiated with customer service in the browser, escalating to second level support, and won a permanent $25 a month reduction. His part was one prompt and confirming the final offer.
A man runs a word game site where the AI invents a new word game mechanic every day and publishes it with an example. He says he does nothing; the daily game appears on its own.
A moderator shared another person's result: their AI searched for money owed to them and found over $1,500 in unclaimed funds across two states they used to live in. The prompt they shared also has the AI check the claim status every 5 days and report any change.
His new hardware device was faulty and the community had spent days looking for a fix that did not need a replacement. He plugged it into his computer, asked the AI to fix it, went to dinner, and came back to find it fixed.
Had a frontier LLM decode an old Director game and port it to run in a web browser in about 2 hours, reusing existing OSS libraries.
Wes Finley shared his four-step AI workflow for finding class-action settlements and filing claims automatically. Commenters chimed in with their own results, including $400 payouts.
He gave the AI a profile on his Synology NAS, linked over Tailscale by SSH, with one hard rule: never delete anything without his clear say so. The AI went through his large MP3 collection folder by folder. It renamed files, removed duplicates, found album art, fixed tags, and moved songs that were filed wrong, like songs in an 80s folder that were not from the 80s. It also relinked a playlist after he had moved all of its tracks into other folders.
He set the AI up to search job listings for him. Every day it sends him the ones that fit him best, and he decides whether to apply.
A traveler planning a trip with AI booked his own flight and hotel, but Dots noticed the shuttle was missing, proposed it with the details they had discussed, and completed the booking form on its cloud computer - after asking him to confirm the same booking three separate times.
A researcher had a language model transcribe a digitised volume of Sophie Germain's incoming correspondence at the national library, and published the transcription. Working from it, he spotted a mathematical note written on an 1814 letter cover and is asking who wrote it.
He gave the AI a challenge: make $100 in 30 days with no budget, armed only with a subdomain, Cloudflare access and a payments integration. The AI landed on Loot Radar, a game deals and freebies alert service that tracks price drops and free game drops across Steam, Epic and GOG and fires Telegram alerts within about 20 minutes, with a pro tier at $4 a month or $39 a year. The site is live and taking sign ups, payments wired. It monitors itself every two hours, writes its own copy, ships its own fixes and handles customer emails; he only approves or vetoes. The $100 itself had not landed yet on day one, so the outcome is partial.
He built Tidehook with GPT-6 Astra and generated art. Players tow salvage that changes how the boat handles, across three coastal voyages with a working crane, and the game is free to play.
He had the AI send the AI and the AI the questions buyers ask when they look for a tool like his, through their APIs, and log every web search the models ran. Then the AI wrote SEO pages for those exact questions.
Made an AI sysadmin bot with root SSH that runs hourly health checks, Monday 6am updates with rollback, firewall, nginx and site fixes he triggers from his phone.
A daily routine on Sonnet reads his email, answers the routine messages from templates, and puts the rest on his todo list. When a task needs more than an hour, the routine blocks time for it on his calendar.
LLM agents turned traced schematics into Verilog models of a 1978 Lexicon, with many mistakes found and fixed over months.
Micro.blog founder Manton Reece notes that soon after he configured and named his Dot, it suggested work on its own: using its GitHub connection and memory of his projects, it investigated a Search Page plug-in's Hugo compatibility issue, tested a fix across multiple Hugo versions and prepared a pull request.
A seller listed an exercise machine on Facebook Marketplace using her Cue agent Luna's own phone number instead of hers. Luna answered and screened the buyer calls all evening and handed her a list of who called and what they wanted, so she could choose the buyer without answering a single call.
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.