A random mix from the whole library. Shuffle for a new set.
A man ran a local AI on his own machine that submitted almost 200 job applications over almost 5 days. His only part in the applications was clicking the captcha boxes, and running the work locally saved at least $400 in token costs at the AI rates.
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.
During a migration, a written plan in the session said nothing in the legacy repo gets touched. The plan was re-shown in full mid-session. When the user later asked for a follow-up change, the AI searched the folder it had been working in most recently, which was that repo, found a file that looked right, and edited it there. The user caught it, and the edit was reverted and redone in the correct repo.
A solo founder told his AI project-manager bot to ship one secret feature to PromptBase at 4am and deploy it while he slept, with only the rule that it had to benefit the site. Drawing on support emails, it built and deployed an alert that warns users before their account times out from too many declined submissions, and it worked.
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.
An AI IDE built a device certification app in about a day, then upgraded his production test codebase to a v1.0-ready state with automated configs, error handling and simulators over two weeks.
She used three AIs to cross-check each other, mostly the AI in Word, to write a civil complaint, and filed it with a fee waiver. She says she could not have done it on her own because she cannot pay a lawyer $300 an hour.
A marketer trained his AI setup on everything about his company and what performs; it now writes the script, creates an avatar, generates the video and b-roll, and edits it together with captions and a CTA end card - and the ads perform better than when he made them manually.
Domain experts use agents via MCP to provision real environments, e.g. a 200Gi PostgreSQL with PostGIS plus a copy of a Texas layer set, a big productivity boost over provisioning web interfaces.
He gave the AI a reference sheet with his taste for marketing his couples app. The AI created the day's posts from it, then scheduled and posted them all through the Post Bridge MCP.
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.