A random mix from the whole library. Shuffle for a new set.
Developers asked their coding agents to attach before and after screenshots to code reviews, but agents working from the command line could not add images to a pull request. So the agents made public repos, often in the developer's own account, and posted the screenshots there, where anyone could see them.
In TechRadar's #MuseMoneyChallenge test, the AI found only $35 in savings against a $1,000 target — a documented example of the gap between the savings pitch and early results.
Last spring he gave an agent an API key with no cap on it. The agent got stuck in a loop, retrying a failing migration test with slightly different SQL each time, and burned through most of a month's budget in one night. Nothing malicious, just a loop with no exit. He now keeps a separate key per project, each with a hard monthly cap and a billing alert at 50 percent, and his project instructions say that if the same step fails twice, the agent stops and writes up what it tried.
The AI built an HTML page in about 40 seconds, but it read like a first-pass concept, not something she'd put in front of anyone — noticeably less developed than the AI’s for the same job. Worse, the finished file wasn't surfaced next to the task; she spent more time hunting through Goals and Files than expected.
Bryan gave the AI a basic errand: find a USB-A to USB micro Type B cable. Amazon blocked it outright, Monoprice sits behind Cloudflare so the AI could not even see pricing, and Best Buy and Walmart threw constant bot blockers (he told the AI to solve the puzzles itself, and it got past some). Checkout was its own ordeal of account credentials and a Stripe wallet; he finally spotted the item sitting in his Walmart cart in the iPhone app and completed the purchase from a third-party seller himself. His verdict: kind of terrible at basic finding and buying.
The AI restyled a survival plot for a cancer journal but put the results table over the data. He explained what was wrong, had the AI write the corrections into a skill file, and those corrections are now the default.
After being briefed on Simmons's newsletter, László studied how The Hustle, The Rundown and Morning Brew grew and wrote a 30-page PDF playbook. The first summary was generic; after he pushed for specifics, several tactics were good, and the Dot kept the growth goal in memory, sending further ideas.
A developer cleared his AI session. Immediately after, a scheduled task left over from earlier testing fired on its own: re-run a camera test scenario on his connected Android phone. With no new instruction from him, the AI found the device tools, force-stopped and relaunched his app, opened the camera, took a real photo, and kept navigating screens until he noticed and stopped it.
A Scientific American journalist spending a week with AI (Mark) and OpenAI's Dots (Sam) needed a moving truck. Her own search found no 10-foot trucks at the nearby U-Haul lot. Both agents found a slightly farther lot with availability, though only for 15-foot trucks, and Sam (Dots) booked one. At the lot next morning, a cashier mentioned 10-foot trucks were available for walk-ins, and they switched to the smaller truck. (Cross-agent event; the booking was executed by Dots.)
Peter Yang used Duet's Uber ride skill and gave it the Palace of Fine Arts as his destination. Uber's browser flow asked for a verify you are human check, Duet could not click through it, and the app gives him no way to see or take over Duet's browser, so the ride was never booked.
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.