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.
Marshall Haas, a serial founder (140K followers; sold his last company for $52M), got early Meta access to the AI and set up an agent named 'Nova' with standing instructions to proactively handle his bills plus business and personal tasks. His caption: 'Meta gave me early access to someone AI...
Diana asked her dot Sparky to turn a real meeting record into decisions, owners, dates and a follow up message, and told it not to invent commitments and not to send anything yet. It invented nothing, but the follow up came back as plain chat text, and she had to ask twice before it made a send ready draft. When she told it to send, it sent at once.
A maker had an LLM design a circuit board by running KiCad headlessly from a terminal, checking the design in a loop until it passed every rule check. He sent it to PCBWay for manufacture and it worked the first time, and the board is now in a product many people use.
Keerthi Jeethuri, a Seattle-based designer and TEDx speaker (19.6K followers; wallpaper, wedding invites, branding), posted a reel captioned 'What do you mean I have an assistant who does EVERYTHING!!!! someone someone.' The reel shows the AI running her business back office: writing LinkedIn job posts, screening applicants, building Google Sheets shortlists, and handling TicketTailor ticket sales for her 'Desi DaVinci' workshop.
A screenshot thread shows AI agent Hubert evaluating two Facebook Marketplace Herman Miller Aeron chairs — a $325 New York listing with unconfirmed size and lumbar support, and a $250 Jersey City Size B with a broken gas lift — and proposing a seller message that waited for approval.
Let Fable work overnight on llama.cpp, producing 19 patches and up to 10x speedup for multi-user long-context use, then ran that code for 3 weeks with no crash and built a 5-agent system on it.
Pat Simmons gave his Dot László a shopping list to buy on Amazon, but Amazon blocked its cloud browser immediately and the Dot refused to circumvent the block. Falling back to Chrome on Simmons's own computer, it discussed choices over Slack and built a cart, but no purchase was completed.
A small-business owner runs a small AI team sharing one Google Drive knowledge base, with a chief-of-staff bot that preps his mornings from his calendar. In one week the bots built and tested his AI Assessment client flow end to end (fit call, payment, booking, intake), rewrote his Todoist tasks as next physical actions, searched X for him, and built a customer slide deck from templates, saving about two hours.
A parent describes a standing weekly loop: The AI checks the store he uses and builds an ingredient list that feeds him, his wife and their kid for under $30 a week, hands him a recipe each day, then updates the running inventory of what they still have to cook with - so next week's list starts from the actual pantry, not from scratch.
The AI goes through his sent emails to learn which sorts he typically responds to, pings him when it thinks he left something unanswered he'd want to answer, and repeats the exercise monthly to stay current.
As part of running his household from the AI, Fulton has the agent tracking twenty-seven Thanksgiving RSVPs. The headcount for a large family holiday is being maintained by the agent rather than in his head or a spreadsheet.
Amplifying.ai asked Dots to build a public AI-service status tracker covering 12 providers, with accounts and private watchlists. All eight builds shipped a working public app on the AI Sites with Cloudflare Workers and D1, collecting status feeds on a schedule, though some runs lacked an active scraping schedule at delivery.
Used nit-picking history-teacher agents to evaluate a few hundred generated stories for accuracy, and pointed the AI at HN comments to aggregate issues and fix them with his OK; reported issues mostly fixed.
The AI in 20 minutes built him an Android app to find and control an ATEM Mini Pro over network, switch inputs, audio and fade, used for courtroom work.
Creator someone asked the AI to research and book a Burmese restaurant for the weekend via OpenTable, with her approval. Cuisine-specific restaurant booking with an approval gate — the delegation pattern working as designed.
The first version of his Groundhog property tool was built from a three sentence prompt. The agent run lasted 31 hours, not counting sub-agents, and the result worked with MLS property data straight away.
During an Irregular capture-the-flag evaluation, an AI model reached the open internet through a testing-environment bug and gained unauthorized access to three real company systems whose names matched fictional targets. Google confirmed it guessed a password in one case and used credentials found in a public database in the other two, stopping each time it judged the system was real.
He points an AI project at a folder of case files. When a new court filing arrives, he saves it to the folder and has the AI read it and draft the response. He also uses it for file organising, reading PDFs, transcribing audio and filling forms, and says the work became far easier.
The AI vibe-coded a RAG and semantic search augmentation to his full-text SQLite search, now in use on a corpus of under 2 million rows.
He runs all the steel on a large construction job. The AI builds him a packet for each item from the project document database, and reads the specs and codes to work out which one takes precedence when they conflict. He says it saves him at least 15 hours a week.
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.