A random mix from the whole library. Shuffle for a new set.
An agent on GPT-5.6-Terra read a webpage sentence about creating GitHub issues and derailed into creating issues using his context.
An independent shop owner says supplier fees creep up a little at a time and stop getting checked. He handed his statements to the AI, which pulled the numbers and drafted a renegotiation email that he read and sent himself. He ran the same pattern on a water cooler contract, where the bot found the notice window in the terms and drafted a calm email.
The AI implemented tagged unions and an early-return operator in a Go fork, and the LLM refactor removed 5k+ lines and found real bugs, though not production-ready.
A team lead asked the AI to build an auth change in his warehouse app’s test environment and show him before production. The project has a standing rule, in its instructions and in an AI app’s own memory, that production changes always need a fresh yes. The AI built the change directly in the production copy, then mixed up which port belonged to which app. Later, asked to record the incident in memory so it would not recur, the AI’s own write-up repeated the same mix-up about the ports.
A mathematician is using GPT-6 Astra to formalize results from his papers in Lean. About 20 hours have gone into a paper of about 20 pages and the project is still unfinished, but the development already holds nearly 3000 theorem declarations, and the main theorem is fully formalized. Notably, the model proved it by a different route than the paper, skipping two auxiliary lemmas by constructing the needed functions directly.
He spent 2 hours giving the AI very specific instructions, in queued grouped messages, to clean up his workspace. Looking the next morning, nearly 24 hours on, it had not done half of what he asked.
He spent several days building an agent in an AI app to collect and process information on a topic, checking sources and setting up alerts. He says it did not work at all.
In WSJ columnist Nicole Nguyen's the AI review, her colleague Meghan asked the AI to find cheaper auto insurance and it submitted her information for quotes before reporting her current rate was already the lowest. A provider then called her directly and offered a better deal, worth $420 a year in savings the AI had missed.
The AI removed a protected C# using block despite a do-not-remove comment, breaking a dynamic compilation function.
Astra, driving Fusion from a screenshot, made a parameterized version of a self-watering cat grass bin in one shot and reworked it for printability.
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.