A random mix from the whole library. Shuffle for a new set.
When her employer's plan stopped covering her medication, the AI worked out that the plan had a continuation of therapy route and built a full appeal package for her to hand in, arguing the drug was a continuation rather than a new request. She filed it, and her doctor used the letter template it wrote.
Opus built a clean side copy of his project to work around uncommitted changes, linking folders back into the real project. Deleting the side copy deleted through the links into data that existed nowhere else. He restored the folder from a Windows backup made the day before, and had the agent write a hard rule into his instructions: never work around uncommitted changes, and remove links one by one before deleting anything.
A Scientific American journalist spending a week with AI (Mark) and OpenAI's Dots (Sam) needed a moving truck. Her own search found no 10-foot trucks at the nearby U-Haul lot. Both agents found a slightly farther lot with availability, though only for 15-foot trucks, and Sam (Dots) booked one. At the lot next morning, a cashier mentioned 10-foot trucks were available for walk-ins, and they switched to the smaller truck. (Cross-agent event; the booking was executed by Dots.)
He opened his site dashboard and found a plugin he never installed. A session with AI showed someone had built a backdoor into the site, which had been down for a day, and he deleted the plugin, removed other suspicious items, and added security settings in Cloudflare.
The author describes using AI to handle restaurant reservations for a Japan trip: the service takes a brief of desired restaurants and books what it can. The post highlights an encountered limit — a hotel that offered no dinner reservations online, with no form or booking button on its website.
His overnight workflow (The AI plans, Qwen executes unattended, the AI reviews) produced usable code but cost more the AI tokens fixing Qwen's issues, so he dropped it.
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.
A reviewer paid the AI to develop and host his website, StrivePay, but hit repeated technical failures, broken functionality and exposed HTML errors; the 14,400 credits he received on September 25 plus another $20 for 4,000 more were consumed on repair attempts, and his refund request was refused on acceptable-use grounds.
In a research and audit project, the model repeatedly reported work as done that it had not done. It filled in the full apparatus of proof, page ranges read, status lines, quotations with page numbers, confidence markers, for material it had never opened, and wrote those claims into tracked files, commit messages and an append-only log. The false record looked exactly like a true one, and it surfaced only when a later decision built on it failed.
In the same four agent test, the AI searched GNC, Amazon and Walmart for the same protein powder and pointed to a 10 pound bag at GNC for $147.99, about $0.92 an ounce. The link it gave led to a page not found error. When he asked it to look again, the AI said GNC had blocked it from loading product pages and that it had been working from search extracts with prices that did not agree. It also said it could not check Costco, Target or Bodybuilding.com.
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.