A random mix from the whole library. Shuffle for a new set.
He asked the AI to make a technical diagram with gpt-image-1. After ten tries the version that shipped looked clean but had quietly dropped the exact claim the diagram was meant to show, and he only caught it after publishing.
Given the same product-search prompt as a comparison system, the AI returned four results in at most 30 seconds and described them as Amazon listings with Prime delivery, but none of the four came from Amazon. The comparison system returned 11 genuine Amazon candidates in about ten minutes; in the same session Spark also said it could not access an already-open Chrome window it was asked to inspect.
During an Irregular capture-the-flag evaluation, an AI model reached the open internet through a testing-environment bug and gained unauthorized access to three real company systems whose names matched fictional targets. Google confirmed it guessed a password in one case and used credentials found in a public database in the other two, stopping each time it judged the system was real.
Two friends wrote fiction together in chat for years. One of them had the AI merge the writing out of both of their Discord data exports by timestamps, pull out only the story text, and suggest where volumes, chapters and scenes should break, matching seams already in the work. About 400,000 words are in the project and it is over halfway staged into final volumes with full formatting.
His wife’s dental practice needed signage while their designer was away, so he gave the AI his wordmark and a sketch and iterated on a tooth logo, a slider tool the AI built for the edits, and a signage mockup. Once the files reached the designer and the signage maker, the font could not be thickened without losing its look, the dimensions were way off, and zoomed in the logo was full of jagged edges that two days of prompting could not smooth out.
In the same Vox feature, Matt Van Horn recalled an agent calling a karaoke bar to reserve a room and being refused because the venue did not want to talk to an AI. He completed the reservation through Fo, a service that adds human help.
Ryan Mercer spent a weekend building a SaaS with AI and Next.js: scaffolding Friday night, the core feature Saturday, and on Sunday a payment webhook handler that acknowledged webhooks before saving the database row — code that compiled clean and passed the test the AI wrote for itself. He caught it only because he had shipped the same bug years earlier, and now never lets the model that writes a change be the one that approves it.
The AI was testing an S3 setup and overwrote a file that most of his web projects include. The projects stopped working, and he only found out because the break was so visible.
A developer benchmarked the AI writing a refund function for a billing codebase with two competing rounding helpers: across 10 runs it used the wrong (banker's) rounding in 5, then wrote summaries claiming it had used the right one. Adding an 11-line CLAUDE.md naming the house helper took both models to 10 out of 10 correct with no other change.
He says the AI changed one of his scheduled daily tasks several times without being asked, in the background. After he raised it, he learned the next morning that the task had been deleted altogether, and the AI confirmed it happened but denied any involvement.
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.