A random mix from the whole library. Shuffle for a new set.
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.
Automation consultant Archit Mittal's post-mortem: his collections agent read a distributor's ageing report from a scheduled export; the night the export job silently failed (accounting machine restarted), the agent re-read the previous, perfectly formatted file and escalated firm-to-cold WhatsApp dunning messages to a long-standing buyer who had paid on time by NEFT. The buyer's 'already paid' reply with the UTR number wasn't matched by the keyword-based dispute classifier, so a colder message went out two days later — and the buyer quietly split their orders to a second distributor before anyone noticed.
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.
In three hours of work with AI, he had a CRM built entirely around his consulting activity. He did not write a line of code, he just described what he wanted.
The AI produced the code plus thousands of unit tests and validation and fuzzing scripts for his tedious conditional-logic side project; it works and is shipping.
At the same accounting firm, staff batch upload client bills and the agent reads each one, decides whether it belongs in payables, receivables, or the cash book, and posts it into AutoCount after a person reviews and amends the lines. The firm has run the setup in production for three months.
When an API call kept failing, his agent retried it by opening a new support ticket each time. By morning there were 47 identical tickets and a very confused support queue.
The AI searches for internship openings and submits the user's résumé on a recurring half-day cycle, keeping applications flowing without daily effort from the user.
A developer runs his own repos and production server under a written constitution file in every repo. Automated runs check production logs daily and file bug reports. When he types one short check in message, the system scans and ranks his open issues, sends sub-agents to fix the top ones, and code deploys to production twice a day without him pushing it. A read only agent on the production box acts as his dashboard, checked through a chat app.
His cafe game sells, though not for big money, and he took it global in one update. The AI translated every screen into 8 languages and the App Store pages were localized too.
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.