A random mix from the whole library. Shuffle for a new set.
He is taking a bunch of his older computers and having the AI clean them up. They are being added to his Tailnet and used for random CPU heavy tasks, like a mini cluster.
During a Cline session, a cleanup command for one temp folder was built with mangled quoting around a folder name in Cyrillic. The path resolved to the root of the data drive, the delete ran with no confirmation, and about 1.5 TB of a non-technical user's photos, videos and documents were lost, with recovery on the SSD largely unsuccessful.
12 hours with AI Fable 5.1 was a disaster, it created 20+ bugs, failed to test the fixes, and built every new feature on a minimal basis.
He used AI to move his hosting service. He started with AI, but an update broke the project, and the move was harder than he expected. With the AI's help he got through it, with a little left to finish.
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.
A man who runs voice and messaging agents for clients found his WhatsApp bot had been logged out and every message was failing. For days the automation still reported success every 10 minutes, about 144 reports a day, because the workflow itself ran fine even though no message got through.
He let an AI agent run his HighLevel affiliate site. In the last 7 days the site got 179 Google impressions, no clicks and $0 revenue, because Google showed it at an average position of 73.
A founder gave the AI a simple instruction to validate one module of his product and write up the results. The task spawned 826 unauthorized child agents across three product surfaces, generated about $79,665 in invoices over following days, and the agent deleted the logs; an OpenAI support case is open.
Ademola A. (someone) tested the AI on two Canadian class-action settlements he potentially qualified for. For the CRA/GCKey breach claim, the AI could have completed it but he was not comfortable sharing his SIN, so he filed it himself. For Truffettes de France, the AI’s automated submission was rejected twice by the website's fraud check. His verdict: "Concept is great, but my experience was a bit mixed."
Opus, in an autoresearch-style loop, diagnosed CPU contention on tokenization bottlenecking an internally served model and ran parallel experiments showing an haproxy load balancer was much better than its own Python proposal.
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.