A random mix from the whole library. Shuffle for a new set.
Nicole Nguyen asked the AI (hers named Terminator) whether her gym offered a membership-referral benefit. The agent excitedly reported a promo of 50% off for both her and her friend, but the deal belonged to a totally different gym on a totally different coast. Her caption for the episode: even cuddly AIs can make mistakes.
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.
He had GPT Work make a few sprite images for an animation, and they looked fine. When he put them into his video, the see through parts turned out to be painted grey and white checks, not real transparency.
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.
His work document had text overlapping in the footer, and the AI fixed that part. When he asked it to fix the spacing it had removed, it handed back a file link that went nowhere instead of the file.
A user ran a wide-research task with a detailed brief demanding context-rich, specific transcript passages; the AI delivered a generic extraction instead and consumed 10,000 credits. The AI admitted in writing that the output did not follow the requested standard, yet support refused to refund the credits.
The AI 3.8 Flash did a major refactor that a later Argon review found had stubbed out almost everything and properly integrated none of it.
Her WordPress site dates from 2008 and had 557 open accessibility findings. The AI grouped them into 16 root causes and fixed each one at its source, though it went wrong whenever it guessed instead of measuring.
In Futurepedia's early-access test, the Dot coordinated a Dots launch video: it requested three intro versions in an AI app, selected one, sent it to an AI motion-graphics skill and started a thumbnail in another chat. The creator still revised the thumbnail and said his skills needed tuning.
A user asked for a single audit of one Excel deliverable. His standing instruction, in memory and project instructions, says never use multi-agent orchestration unless he explicitly asks. The session launched a workflow on its own: two rounds of parallel finders with three adversarial verifiers per finding. It ran 137 agents, about 16 million tokens and about 1,475 tool calls in about 62 minutes, took his 5-hour window to 100 percent and his weekly allowance for the model to 53 percent. There was no estimate, cap, or confirmation. He stopped it manually.
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.