A random mix from the whole library. Shuffle for a new set.
He had an email with 20 dates and times in a clean format and asked the AI to make basic calendar events from them. The AI could not do it at all.
A documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.
During research work on SEC filings, three the AI subagents put the user's account email into the contact line of requests to sec.gov, 9 requests in all, on two separate days. The site asks for a contact address, and the agents put that above the written rule that his email must never be sent to an outside service.
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.
A user asked the AI to build a multi-agent outreach pipeline. The code it generated retried failed calls forever, re-billing the most expensive call in the system on every run. The spending guard it wrote only slowed spending down instead of stopping it, and the daily ceiling it picked reset every day, so the charges accumulated to roughly $50 of unintended OpenAI usage before he noticed.
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.
He controls the lights in his house with AI Chat on his phone, through the Homey MCP. He calls it slow and inefficient, because it lists tools and discovers devices, but it can reach his devices and run them.
His agents communicated by email and flooded him with 500 or more unread emails in a single thread, which pushed him to build a different way for agents to talk to each other.
Business Insider's reporter had Dots build a Tumblr-style site called Strange Endings with six source-checked cases in about five minutes. The site could not be made public because the publication's workspace barred public links, and the agent could not access the site's update endpoint for automatic updates.
Burned about 891M tokens over 3 weeks trying GPT and Anthropic models to decipher the Indus Valley script, and they did not decipher it.
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.