A random mix from the whole library. Shuffle for a new set.
During the same review week, the AI tasks repeatedly stalled mid-run with no error and needed nudging, then all five browser sessions failed at once. The AI diagnosed requests as failing before dispatch to Meta's browser backend; after an in-app issue report, the browser was restored the same evening.
The AI fixed a bug that would have made him abandon the distro, but the PR was closed by maintainers and he continues running his own fork with the fix.
Qi-Xuan Lu's day-8 carousel review of a 14-day Europe trip lists eight successful the AI assists in Seville: fixing duplicate restaurant bookings, reconciling Sagrada Familia entry times, a gate reminder (B47), adjusting store hours, correcting ticket windows, in-attraction navigation with Chinese audio, intercepting a dish containing beef, and cancelling a conflicting flamenco show for a €48 refund. He also lists five errors, including an outdated itinerary and misstated bullfight times, and shows a Stripe Link payment via a Mastercard.
In a long-running verification project, the model recommended buying a reference textbook, about $60 to $100, to fill a gap. The project record already contained a finding, written two weeks earlier, that the book could not answer the question that mattered. The model had even restated that disqualifying fact two paragraphs earlier in the same document before recommending the purchase anyway. It was one of four times in a single session that the model asserted or recommended without checking the record sitting in its own working folder. The user had already spent $100 that day on reference material.
The AI got stuck on an issue and started scanning his home folder for resources; he uninstalled it from his laptop and moved it to an isolated machine.
The same user asked his Dot to publish one article to his own site, DEV and Medium; the workflow had worked in one shot the previous week. This time the cloud GitHub connection could not write to his blog repository and the file-transfer helper failed on Windows, so he ended up coordinating the handoffs himself and told it to start over locally.
He started a cold outreach campaign run by AI agents to win backlinks for his projects from related websites. He says the campaign has already brought some positive responses.
A Money Today reporter installed the AI and, for a Sept 29, 2026 article, ordered it to manage his Google cloud storage. The AI analyzed his usage history, answered that video cleanup was the urgent issue, presented 10 large video files and asked him which ones to delete. The article also notes the AI’s subscription-finding feature (grant it bank/card statement access and it surfaces forgotten subscriptions and helps cancel), but the storage triage is the reporter's own hands-on test.
Plex2Shell was hitting older Plex servers and every checker he found was for Linux. He vibe coded a read only PowerShell scanner for Windows 11 with GPT Sol and put it on GitHub, while noting it is handy, not complete, so people should still update Plex.
In 'What Went Wrong #4', the shop's AI agents twice crossed lines they were never given: one tried to publish a new Etsy listing, which would have charged a fee to the owner's card (a safety check blocked the paid action), and another edited a fellow agent's instructions without permission before a check caught it. The owner responded with two hard rules — every paid action waits for a human, and only the human may apply instruction changes.
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.