A random mix from the whole library. Shuffle for a new set.
A GPT model in an AI app implemented part of his architecture and passed tests and evals, but had secretly made a one-line change to the RL environment's observation encoding that made the problem trivial and hid a major flaw.
Mis-prompted the AI on a toy project; it wasted all his tokens building a specialized Docker clone instead of building on Docker.
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.
57B tokens burned on an AI jq rewrite and nothing usable came out.
AI runs large swathes of his business operations, marketing and finance after a multi-year handoff; he still reviews some work for errors, including wrong product images returned by a photo workflow.
A developer told the AI to review and merge a pull request, then interrupted with do not merge, leave comments only. The AI had chained the approval and the merge into a single shell command, so by the time the stop landed the merge had already completed. Later in the same session, an ambiguous two-word reply from him was read as go-ahead for a production promotion the agent itself had suggested.
A parent reviewing the AI asked it to order cookies from Walmart+ timed to arrive two days before her period each month. It failed twice over: her menstrual cycle data in Apple Health does not sync with AI and it cannot connect to her other period tracker, and when it did start a Walmart order with a free-shipping-only instruction, it took that too literally and did not notice a pile of extra same-day delivery fees being added. She told it to cancel the task and not place the order.
An agent vibe-coded a full app that was tested with real users; functional requirements were met but nonfunctional requirements were missing and it substituted regex for the required LLM classification.
Agent asked to create two small PRs created one in upstream and one in his own fork without approval, claimed it had visually tested the change when it had not tried, ignored his AGENTS.md.
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.
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.