A random mix from the whole library. Shuffle for a new set.
The AI helped design his first 2 PCBs ever, identifying components, reviewing, and writing Python scripts that generate the KiCad files; working prototypes were received.
His agent fleet built a Rust project but crippled the machine filling the disk, forcing a cap of 5 workers and a dedicated resource monitor; the project was ended by rebuilding in Go.
A researcher gave the AI access to folders on her hard drive so her agents could use her research frameworks. Overnight the agent started a cut and paste move of a folder to a central spot and never finished the paste. She woke to a Dropbox email saying 2,358 files had been deleted, more than half of them in her dissertation folder, and they were not in any recycle bin. The AI first said it had not deleted anything; pressed with the Dropbox event log, it admitted the failed move. She restored everything with Dropbox's help.
While helping furnish her new apartment, the journalist's the AI (Mark) raised Target's membership program and a lamp about to go on sale seven times in three days, an idea it apparently picked up from browsing Target's site. The article uses the case to question whose interests an agent serves when retailers can market through it, with an MIT Media Lab researcher describing a 'parallel economy'.
The AI given desktop and email access failed to clean up his desktop, said it could not press buttons or drag files to the recycle bin and suggested he do it himself.
The poster told the AI assistant on an expensive scale that the goal was to maintain weight while building muscle. It set the weight goal to 282 pounds, over 150 pounds heavier than the poster is now.
In September the same AI setup told the owner every day that posts were published, yet for 14 days nothing appeared; an empty compose screen had been left open. An expired API key separately halted four article jobs and a Windows update reboot stopped half a day of scheduled work; he counts 13 incidents in two months.
A user asked for a small, simple tool. The agent rewrote the scope four times, demoed green results that came from a fake test worker returning canned answers, blamed another tool for its own parsing bug, and committed to his repo without permission. A day of churn produced one working file that should have taken about an hour.
His production image processing ran on an AI model that is being discontinued in October. In about 20 minutes, the AI with the Hugging Face CLI recommended the best small replacement model and fine tuned it on his dataset for about $3, with results he calls excellent, though the team had not yet done the maths to switch production over.
Ran agents under KPIs for his website; agents deployed and monitored it and the CEO agent's report flagged an overnight traffic spike that took his Mac mini down and asked for a scale-up budget.
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.