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.
A small business owner runs a few AI bots that share one Google Drive knowledge base. One built and tested his client assessment flow end to end, from fit call to payment, booking and intake, another reads his calendar and preps his mornings, and the bots also fixed his task list and built a customer slide deck, saving him about two hours in one week.
He points the AI at a folder holding a game ROM and an emulator and gives it a standing prompt: translate the whole game, keep the text on screen, and change nothing about how the game plays. The AI does the translation and tests it in the emulator itself, and he turns the finished work into a patch file so other players can use the translation without anyone sharing the game itself.
The company quotes other firms on its own page, and the AI rechecks every quote it publishes once a day. The check only matched the price inside each quote, so the words around the number could drift and still pass, and their page was carrying a quote the company never said.
He had never played fantasy football before this year. His AI powered team scrapes injury reports and fantasy blogs and models player points, and he is beating his friends every week.
Spent a week's worth of tokens having an agent research industry events, validating dates, finding real prices, filtering junk and deduping, to build his ConferenceFindr data.
Andrew plays casual online chess against a friend he is dead even with, and was about to buy a Chess.com membership for unlimited game reviews. Instead he gave the AI his Chess.com account; it automatically pulls and reviews his game log every day, and the premium upsell lost its reason to exist.
In their apprenticeship, trainees had to log their on the job hours on a paper form and email or text it to the office manager, who then typed every entry into a spreadsheet by hand. He built an app so apprentices can submit their hours directly. Now the office manager just has to make sure everyone submits on time each month. If someone still wants to turn in a paper copy, she can enter it through her admin login, and the app does all the math for her.
The AI fixed a bug that would have made him abandon the distro, but the PR was closed by maintainers and he continues running his own fork with the fix.
LLMs optimized SQLite querying with better indexes for 60 to 80 percent performance gains.
A grade-10 science teacher describes his out-of-classroom reliance in a post asking if he is the only one: he writes the master version of each quiz or exam, then has the AI produce versions B, C and D with the question order shuffled and the calculation values - or even the chemical formulas - slightly changed, so neighboring students get genuinely different papers. The same setup fills his parent-email templates, swapping in each parent's and student's names for questions he has answered countless times. He double-checks everything before it goes out.
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.