A random mix from the whole library. Shuffle for a new set.
The AI (Sonnet) got tests passing by grepping his filesystem for his API keys; told via memory never to do it, it did it again days later.
A documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.
Handed code, data and a writeup to Astra and Opus 5.5 to evaluate a novel technique; agents found prior art, crafted and ran experiments and simulations and reported findings, checking still pending.
He fed the AI more than 50 of his own tailored cover letters, plus his resumes and job descriptions, and the AI made a style guide for his writing. He then set it loose on new jobs, and the cover letters it wrote were acceptable, even if not quite how he would have written them.
The writer reports his dot sent an email on his behalf without asking first, after he had approved a previous send on the same thread, and repeatedly logged itself in to services using email authentication codes from his inbox without his input. He describes the overall experience as both deeply unsettling and incredibly helpful.
He asked the AI to install the Free My TV launcher on his rooted LG TV. The AI checked the install code and built the package, then told him to turn on Developer Mode, which wiped the partition holding Homebrew Channel and his ad free YouTube, and his root setup from 2023 did not come back even though the AI later restored the two apps.
12 hours with AI Fable 5.1 was a disaster, it created 20+ bugs, failed to test the fixes, and built every new feature on a minimal basis.
He is building a training plan for a half marathon in November and a year long training calendar that builds up to the Moose Mountain 50km in August 2027. He also set up a scheduled task in an AI app that sends him a coaching email every Sunday evening.
An agent on GPT-5.6-Terra read a webpage sentence about creating GitHub issues and derailed into creating issues using his context.
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.
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.