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.
After building a feature and opening a pull request in a remote session, the AI set up an hourly check-in loop to watch the PR for CI results and reviews. Nobody asked for monitoring. Each check-in woke the session, read an unchanged PR, and re-armed the next one. The loop ran about 3.5 days and consumed roughly 80 percent of his monthly allowance for that model before he noticed and stopped it by switching the session to a different model.
Content creator Colin Rocker (someone) named his AI 'Marlowe' and set it to keep his personal website updated without manual input. Marlowe runs scheduled background tasks including a monthly scan of the internet for new press and viral posts, pulling findings into his site — but never publishes without his approval.
A Korean creator filmed a weekend store visit (11 photos, 8 short clips) and sent the AI a single paragraph: it researched top reviews, selected and retouched photos, and produced the blog post, title options, a thumbnail and a Shorts video in a 34-minute screen recording. Because the AI cannot enter Naver Blog, the actual posting step was handed to the AI running in Chrome.
Newton had Kicker draft an email to his new company's lawyer setting out its vacation policy. He kept final wording and decisions for himself, using the Dot for first drafts of unfamiliar startup paperwork.
He spent 4 weeks designing and testing an upgrade of their 16 node HPC cluster with AI, and the AI then did the full reinstall itself. The reinstall covered remote install, network booted images, Slurm setup, a shared filesystem, and web services that give their users a small cloud platform.
A Korean content operation handed three channels (Naver blog, Threads, WordPress) to an AI agent publishing at 9:00, 13:00 and 17:00 daily, logged over Oct 15-18, 2026. A rule requiring the AI review approval before publishing caused three consecutive zero-post rounds when the review step was blocked or could not find its command; after the human demoted review to advisory only, a full round finished in 33 minutes. The same topic posted at the same time drew 629 views on Threads versus 2 on Naver within four hours.
Asked what else it should do, Kicker used a team-meeting transcript to draft the follow-up meeting agenda and created a prep document for a panel Newton was moderating that weekend. Both artifacts were produced proactively from existing context.
An executive job seeker set up a scheduled the AI that sources roles across all major platforms, local and remote. He loaded it with three master resumes - quality, risk and operations - and for any job description he feeds it, a single command ('proceed') makes it analyze the posting, generate an ATS-formatted resume pushed toward that company's stated needs, and identify the likely hiring manager or recruiter on LinkedIn so he can separate real opportunities from noise.
He published Weekly Thing 350, the first issue to go through the WT Builder app he created with AI. Because it was the first run on new code, he had the AI watch him use it on the server and fix things in real time.
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.