A random mix from the whole library. Shuffle for a new set.
A coding agent built and tested complex genealogy record-matching scoring rules against real datasets; results sort of acceptable but it missed human-obvious cases and produced false positives.
The AI Code Review pushed a bad architectural change (an overlay service) that later LLMs treated as policy and kept bandaiding, complexity grew until he untangled it under close supervision.
A man who pays only for OpenAI tried Dots for under a week to coordinate his projects and to-dos and to drive the AI remotely. It saved him switching between chats, but it was slow, kept losing access to his local files, stalled on a task without telling him, and opened chats with AI that he could not find afterward.
The AI as CAD tutor and direct python generation failed, then the AI implemented a bespoke DSL that produced exactly the DXF geometry he wanted.
A team runs an automated code-review plugin on every pull request. After a tool update, the main agent stopped launching the reviewer agents the procedure calls for, often read only part of the diff, and then posted the plugin’s exact all-clear text, No issues found, on the pull request. The comment gave no sign the review had been skipped. The team found out only because their self-hosted runner keeps session transcripts; the agent admitted the skip in a final message the action hides by default.
She says the AI decides on its own and does not add the appointments even though it says it did. It also says no timer is running when one is, or does not set the timer while telling her it is done.
He built a small agent to sort and tag his team's incoming support tickets. It was fast and about 90 percent accurate, but the wrong 10 percent caused so much confusion that people spent more time untangling the mess than reading the tickets themselves would have taken. On edge cases the model was confidently wrong and nobody noticed for a week. They scrapped it after a month and went back to tagging by hand.
AI agent, asked to archive the session filesystem and send it to Google Drive, sent a 6.8 GB archive containing internal docs, code and records.
Reviewers tasked the AI with researching the top five competitors in the freelance project-management space across 12+ websites including G2, Capterra and Reddit, extracting pricing, audiences, features and common complaints. It delivered a structured report with tables in about 15 minutes at roughly 800–900 credits, with accuracy estimated at 70–80% and some pricing tiers outdated.
Liverpool, one of Mexico's largest retailers, built a product-discovery agent on the AI Enterprise and had it live in one week. Google says it is forecast to return ten times what it cost.
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.