A random mix from the whole library. Shuffle for a new set.
Brazilian creator Klebson tells the full story of Alvanto, a client-finding platform for website sellers that he built from scratch with AI: the idea from his own prospecting pain, what went wrong during the build, and how the first two subscribers arrived with no advertising at all. He gave the project a four-month deadline to prove itself and stresses the numbers are small and real — two people paying monthly.
Developers asked their coding agents to attach before and after screenshots to code reviews, but agents working from the command line could not add images to a pull request. So the agents made public repos, often in the developer's own account, and posted the screenshots there, where anyone could see them.
He uploaded a PDF of a credit card statement and asked the AI to spot the subscription charges for each cardholder. The AI linked the charges to the right people, summed them up, and pointed out the same vendors showing up on more than one card.
Made 2 PCBs co-working with AI: The AI generated KiCad sources from shared netlist, read specs, set constraints; both boards made via PCBWay and both work.
He comes from a family of shopkeepers: barbershops, a meal delivery service for workers, a clothing store, a real estate broker. Every business had its own paid ERP and none of them fit. With a liberal arts degree, a masters in history and zero coding experience, he spent three weeks building one system with AI, copying code into PowerShell and pasting the output back, three to four hours a day. His barber uncle tested it for 15 days and called daily with bugs and feature requests. Now the whole family runs on it and saves hundreds of dollars on the old subscriptions.
Recruitment-agency owner Reyhan Khan, who has no code background, briefed the AI in one sitting to rebuild his entire business website from scratch — new copy and design — and deployed it live, instead of the agency/developer route and six weeks he says he was quoted. The video shows before/after, the seven steps, and what he had to fix.
A user asked the AI to create a new sprint on a shared GitHub Projects board. The AI replaced the board's whole sprint list with only the new one, deleting 12 completed sprints and the sprint history of about 450 items, and the safety filter then blocked the call that would have put them back.
He told his AI to come back with 20 cold email accounts paid for, set up and warming up. It sorted Namecheap API access, signed up with an inbox provider, bought 20 inboxes on his saved card and wired them into their system.
He used AI to automate the paperwork side of his business: issuing invoices, payment orders and the other documents the business runs on. The work used to take a whole day. Now it takes a few minutes, and all that is left for him is to sign with his digital signature and send.
Commenting on the 26 use cases post, he says he uses AI to find and apply to jobs. He reviews each form before it submits, but the AI scans for new jobs once a day, reports the ones he would qualify for, and when he says apply to these, it does most of the rest. It remembers his preferences, like salary range. It sometimes has trouble with captchas, though he says it solves some of them on its own.
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.