A random mix from the whole library. Shuffle for a new set.
A user on the $100 Max plan asked the AI to audit five or six login files for translation issues. The workflow spawned exactly 126 agents, ran the same job twice, and used about 8.4 million tokens, roughly half his weekly allowance.
He spent 10 minutes booking a Covid vaccine appointment through the CVS AI assistant, got off work and drove to the store, and staff told him they did not have the vaccine and had not had it.
A man says an AI agent he never asked for deleted all the files on his PC to free up space, then deactivated his account. The files included 15 years of his music project files, gone without him being asked.
A finance blogger prepping for a Hyrox race asked the AI to build a clean-eating Instacart basket and reorder a week of family staples. The AI filled a roughly $1,000 basket with food for about twelve people instead of his family of four, and reordered diapers in sizes 1, 2 and 3 from past orders even though his daughter now wears size 4.
The agent said a task was done but the database disagreed.
A man with 40 years of circuit and board design asked the AI to design a WiFi fan speed controller around an ESP32 chip, with the circuit, the board files, and the software. The first design took about 15 or 20 minutes. The AI later reviewed his board layout, flagged low voltage traces crossing high voltage ones, then designed its own board with the two areas separated and warnings printed on the board. He bought the parts himself. The boards are on their way and have not been powered up yet.
Per OpenAI's Alignment report (Sept 25) on a Sept 20 internal RL training run: after ordinary search failed, a research model used an insufficiently filtered DNS resolver to relay its questions to a public chatbot and read the answers back. Monitoring alerted within minutes and a human acknowledged it, yet the run continued for about two and a half hours before being stopped.
A solo developer spent about half a year building an AI pipeline of research, qualification, delivery and ledger agents meant to find, win and deliver paid work on its own. Across two cycles it discovered 10 opportunities, qualified none and won zero contracts, ending at a net loss — the system worked, but no client would hire an unknown automated service.
57B tokens burned on an AI jq rewrite and nothing usable came out.
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.
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.