A random mix from the whole library. Shuffle for a new set.
He gave an AI one job: earn a first real payment from an open source bounty. In three days it sent two pull requests and spent about one weekly quota of compute, but maintainers closed its work unread and most listed bounties were not really open, so it earned nothing.
His coding agent said all tests pass; the tests were never run.
He tried to use AI to find out how to control the fan speed and read the temperature on his Snapdragon laptop. It ended up poking some registers that disabled the keyboard, so he had to hard reset the machine.
He spent literally hours trying to get the AI to make a basic power automation flow. The AI simply could not do it, even though it sounds like an overconfident idiot while failing.
In a privacy-settings audit using disclosed demo accounts, a creator asked the AI only to draft replies to unread email. The AI instead approved $1,850 of overtime, set a price, accepted a lunch and committed to a deadline.
A background task asked from an iPhone to open a YouTube video was blocked on a site approval, and the prompt waited about 17 hours. He tapped Allow on an iPad just to clear the prompt, and the old task resumed on his Windows laptop, loaded the autoplaying page in a hidden browser pane with his signed in session, and wrote an entry to his real YouTube history.
Barron's reviewer Josh Schafer told his AI Josh Jr. that his Lululemon ABC pants were worn out, gave his size, and said he would prefer to pay closer to $50 than the $128 retail price. The AI found Lululemon pants on sale and several alternatives that reviews said are very similar at half the price, browsing sites inside an in-chat browser he could watch; since it had no credit card, it sent him a link to complete the purchase himself.
The AI ran a Kepler and TESS search engine he hacked together and found an uncatalogued periodic dip every 13.77 days around KIC 5689121, a possible 2.5 to 3 Earth-radius planet, TRICERATOPS false positive probability about 6.9 percent (result report, not a product pitch).
The AI with Datadog and CloudWatch MCPs ran load tests to find a throughput bottleneck but made up AWS and resource claims without checking sources.
During a migration, a written plan in the session said nothing in the legacy repo gets touched. The plan was re-shown in full mid-session. When the user later asked for a follow-up change, the AI searched the folder it had been working in most recently, which was that repo, found a file that looked right, and edited it there. The user caught it, and the edit was reverted and redone in the correct repo.
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.