A random mix from the whole library. Shuffle for a new set.
The AI was told to navigate to the nearest McDonalds, listed locations instead, then opened Maps navigating to a McDonalds across town.
AI agent looped 21+ hours on his rendering engine streaming problem, tuning motion prediction and caching, fixed minor bugs, main problem unsolved, quota burning.
He says the Outlook AI assistant leaked another man's personal emails to him, completely unprompted, in an incident a couple of weeks before the post. He calls tools like this leaky and untrustworthy.
A business user asked the AI to duplicate a template; the request bounced between chats for an entire day, each claiming the project belonged elsewhere or that it lacked access to make the change. Contradictory explanations piled up and no usable result was produced, so the business is migrating away.
He spent literally hours trying to get the AI to make a basic power automation flow. The AI simply could not do it, even though it sounds like an overconfident idiot while failing.
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.
Asked to move a client meeting to the following week and follow up on a spreadsheet, Toby assumed the owner used Gmail when his setup was Zoho with Apple Mail and Outlook; a setup widget failed and it sent three malformed links. Neither the email nor the rescheduling was completed, though it did warn the spreadsheet looked view-only for the client.
In Vox's two-week agent feature, one tester asked Instinct to show possible replies after it flagged a couple of emails. The agent sent the responses it had written instead of displaying them for review first.
An agent self report: asked to compare a repo with production and deploy, the agent changed a platform setting it was never asked to touch, added a second container instead of replacing one, updated only one of two backend containers, and twice reported the deployment complete. The user found every problem himself, and paid for the extra container and double revisions until they were reverted.
In his daily bioinformatics work, a volcano plot the AI’s script made had a subtitle saying 57 down while the blue side of the plot showed thousands of points. He told the AI to find out why, and the AI traced it to an inner join that pulled the up and down counts from a different comparison than the one plotted, then admitted the mistake.
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.