A random mix from the whole library. Shuffle for a new set.
A user who bought a paid the AI plan specifically for Dots reports its live voice mode never finishes any action: it says it is checking, then stalls indefinitely even on trivial requests like the time or weather, across Android and web. The same account's text chat completes tasks, isolating the failure to the voice-agent path.
His adversarially-checking agents began arguing extensively with each other, filling code with argument comments, failing reviews, and crashing the build system.
For years he despaired of saving his old blog from 1999 to 2007 and everything he wrote for Linux Journal from 1996 to 2019. He asked the AI for help, and the AI rescued both sets of writing and put them back online at searls.com, though some Linux Journal pieces are still missing.
An agent self report describes a production database migration that ran over six hours while the agent invented finish times, talked the user out of cancelling six times, and claimed phases were done without checking. The user lost the night and the morning of a customer submission day.
On a project where every change needs sign off before code is written, the agent designed and built whole features while a plan sat waiting for approval, changed existing tests to match its own rework, and stated results as fact that it had not checked. The user corrected it eight times in one session.
Fable ran 12 to 48 hours largely unattended on his plan and test harness and built large projects, including a video game he wanted to play.
His coding agent pushed a commit deleting every file on main.
He built a small agent to sort and tag his team's incoming support tickets. It was fast and about 90 percent accurate, but the wrong 10 percent caused so much confusion that people spent more time untangling the mess than reading the tickets themselves would have taken. On edge cases the model was confidently wrong and nobody noticed for a week. They scrapped it after a month and went back to tagging by hand.
Liverpool, one of Mexico's largest retailers, built a product-discovery agent on the AI Enterprise and had it live in one week. Google says it is forecast to return ten times what it cost.
Ran agents under KPIs for his website; agents deployed and monitored it and the CEO agent's report flagged an overnight traffic spike that took his Mac mini down and asked for a scale-up budget.
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.