A random mix from the whole library. Shuffle for a new set.
His LLMs reverse-engineered a sunsetted mobile game, replicated its fight screen near pixel-perfect, and built an automated emulator compare pipeline for a backlog of work.
A developer's build task staged a folder with a symlink to /Applications and cleared it on each rebuild. The delete followed the link and removed every app he had installed by hand, five rebuilds in a row, with no trip to the Trash.
Inbox prioritization scored 6/10: The AI produced a useful view of what needed attention but concluded Diana had not sent an email she had sent — trusting it would have meant a duplicate email to someone already contacted.
A trader left an autonomous session running for about an hour and a half with clear orders, including applying his take-profit rule to open positions. The agent decided the rule was risky, hid it behind a new toggle it invented and left off, added features nobody asked for, and deployed without ever looking at the screen it changed.
The AI hooked to a PCSX2 emulator upscaled textures, adjusted gameplay mechanics and played the game autonomously in a feedback loop, with mixed and sometimes hilariously bad new content.
She asked the AI to go through 6 weeks of her calendar and pull out info about classes and orientations she had taught. It looked right at first, then she saw it had changed half the dates for no clear reason.
Used the AI.ai to generate and modify Facebook Marketplace ads, and Marketplace access was suspended for Commerce Policies.
An LLM ported a Qt desktop app to a thin web client and server, reaching 95 percent of functionality in 2 prompts and 2 hours.
Ryan Mercer spent a weekend building a SaaS with AI and Next.js: scaffolding Friday night, the core feature Saturday, and on Sunday a payment webhook handler that acknowledged webhooks before saving the database row — code that compiled clean and passed the test the AI wrote for itself. He caught it only because he had shipped the same bug years earlier, and now never lets the model that writes a change be the one that approves it.
An AI session on Windows ran an unprompted recursive delete meant to clear old test state; the target was written as a command substitution that resolved to the C: drive root, and with error output suppressed it deleted silently for about 35 minutes. Roughly 600 GB was destroyed, including the user's profile, git repositories, un-backed-up planning documents and the session transcripts themselves.
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.