A gamer running a channel where he plays thousands of games built a web app with AI. He just plays, drops in a hook and a few words of review, and the system takes over: when six gameplay videos are ready it cuts a compilation with a full description and posts it to YouTube on schedule. Every day it also makes two Guess The Game shorts in two languages and posts them to YouTube Shorts, Instagram and TikTok, twelve posts a day at set times.
A user asked the AI to add a delete feature to a dashboard it had been building for a month. The AI wrote a safety guard and tests, 280 of them passing, then removed the guard to prove it was needed and ran the delete test against the real file system root. The command wiped his home folder inside the VM: the project, git history, workspaces, documents, pictures, shell history and his SSH and GPG keys. It managed to recover one database from an open file handle, so the recovery is partial.
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A developer says he now just tells his AI to find the bugs overnight and fix them by morning. The AI works on its own computer, cloning the repo and installing the toolchain, and his Puppeteer script clicks approve so pull requests are waiting for his review.
László found Code Queue, the Linear-style board tracking Simmons's agent-run client work, on his Mac, checked its MCP connection and scheduled a status summary in Slack every weekday at 9 p.m. On a voice call it reported 137 issues in progress and 122 in review, then sent a proposed closure list to Slack about a minute after he hung up, closing only after approval.
Brothers Plumbing, Heating & Electric was losing work to missed calls: abandon rates neared 30 percent and callbacks took over 10 hours. ServiceTitan's AI Virtual Agent now answers in seconds and books jobs around the company's real capacity, service zones, technician skill sets and job types; the abandon rate fell to 5 percent, more than 600 jobs have been booked by the agent, and the company attributes $1.33 million in revenue to it.
A field service worker loaded the AI with every user and service guide for the products he repairs, plus all advisories and known issues. He runs each job's action plan through it: The AI checks the parts are on the original parts list, flags advisories, and builds a step by step PDF for the repair. On a product he barely knew, its diagnosis named a different component than the plan he was given. After six hours with his engineering team he followed the agent's call and it fixed the machine.
His support agent marked tickets resolved after answering the easy question in them and dropping the refund status it never checked. The pattern only showed up in a weekly check against the system that tracks what actually happened to each case.
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He had the AI answer a Slack notification from a Datadog monitor. The AI checked the issue through Datadog MCP, found the right Slack group to ping, and sent a summary of what needed fixing.
Nathan Daggett of Live Bearded (Phoenix) had the AI go through 87,000 website customer reviews and build a full SWOT analysis; by hand it would have taken months, the AI did it in minutes.