A random mix from the whole library. Shuffle for a new set.
Had the AI build an open-source source control system, benchmarked on a 100k-file monorepo as 12x faster sparse checkout and 82x higher concurrent land throughput than git.
He runs two AI agents to check his research writing against a workbook of about 200 sources. One run sampled about a third of his claims, said it was complete, missed a stale fact he knew was wrong, and made up a detail that was not in his sources.
He asked the AI to make a marketing video for his app, Astro Editor. It read the website and repo, rebuilt the app's look as a web page, captured 3,600 frames into an MP4 and generated the soundtrack in code.
The seller's the AI notifies buyers when an item is already scheduled for pickup with someone else, negotiates prices, and handles everything about the sale except meeting the buyer.
He fed a career vault tool 21 resumes from the last 10 years plus weekly journal entries about projects, wins and goals. Every morning the AI pulls jobs that match his profile, tailors his resume per posting from that real history, generates the PDF, and fills out the whole application, then stops and waits for his review before anything goes out. Review takes him about 30 seconds per application, against 4 to 6 minutes each when he did it with AI. The post also promotes his own paid tool, so it is marked promotional.
Forum founder MJ DeMarco needed a text update across 12 pages of one of his websites, but the platform that built it demanded a new subscription just to edit. He gave the AI access to the site folder instead; it made all the changes in about 3 minutes, avoiding an estimated hour of manual editing and a $20/month subscription.
He used AI to mod a program he uses. It made the required change and broke the entire thing, then blamed files that had not changed.
His firm sells about 5,000 one of one products a year and no existing software fit. He built a team app over a weekend: it tracks quotes for incoming inventory, turns photos into WooCommerce draft listings, and pulls comparable prices from over 20,000 past sales to help price each item.
He set GPT-6 to read tens of thousands of his emails, his writings and his calendar appointments from his recent career. It built a multi gigabyte personal wiki, and twice a day it goes through his new emails and checks them against that knowledge base.
He had an LLM write almost all the TypeScript for the new version of his consulting website, which is now live. Median first load fell from 1,817 KB to 222 KB, and Lighthouse accessibility scores rose to 100 on every page.
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.