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.
Dan Zhao posted a dark-mode screenshot recommending the AI for auto-insurance shopping, claiming over $1,000/year in savings. The comparison shows six-month premiums: Progressive $1,556 as the new leader vs Costco Connect $2,142.75 and current GEICO $2,324.63.
Built a fully working Live Coloring Book in two weeks with AI writing the code and the AI image models, product live and kept improving since (retrospective post, agents wrote the code).
She keeps an AI side chat called Wish List. Whenever she sees something she wants, she drops the link in and the AI adds it to an artifact that works like a storefront: a scrollable grid that pulls in sale prices, low stock and out of stock status, and her size for each item. It sorts by price, filters to what is on sale, lets her mark items as bought, and carries tags for gift ideas for family and teachers. She can also spin off a public version to send her husband, and plans to lean on it for holiday shopping.
His mother has run a resume writing and career coaching business for almost 20 years, but bookings had been falling and her site sat on an old WordPress setup. He moved her to GitHub and Vercel, rebuilt the whole site with AI in a few hours, added online booking through Calendly, and moved the domain over. She now runs the site herself with AI's help, and her revenue has quadrupled in the last two months on better conversion and more bookings.
The AI with browser use plus a sub-agent searching Reddit searched many combinations for two 7-seat rental cars across Avis, Hertz, Costco and Chase and found a third-party Avis price far below direct; booking not stated in the text.
A business owner's lead profile runs every Monday on its own. It formats his warm leads, finds cold leads that match his criteria across a rotating list of cities and niches, then digs into each business to find what he is missing, owner names and exact contacts, checked against business registries. He gets one standard weekly report: best fit clients, outreach details and the facts to judge each lead.
~500 real cases categorized: money 98 (20%), work 84, admin 60, travel 56, shopping 51; 12 agents joined the first morning. Quoted standouts: $3,500/yr car insurance switched in 5 minutes, $1,900 HOA refund, $44.99/yr forgotten subscription cancelled, PODS booked $576 under quote.
A man gave the AI strict tag research tasks and had it work through the whole ticker list again and again, searching and comparing XBRL tags in SEC filings. The pipeline now pulls 10-K and 10-Q filings for more than 600 companies, maps tags onto about 35 financial concepts, and produces cleaned data. Doing 3 tickers by hand had taken him about 3 weeks and still gave wrong data.
He runs a Shopify based company alone. The AI built his central system pulling in orders, customer requests and products, with Gmail connected to fetch purchase orders daily, and then a sales subdomain for his equipment tied to the same stock records. Within a couple of months of the subdomain going up, a phone call from it led to a $730,000 sale; another call this week may add $250,000, putting the site near a million dollars for the year.
The Nautilus file manager never offered a miller columns view. He forked it and had the AI add the feature, and the fork is publicly available.
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.