Used nit-picking history-teacher agents to evaluate a few hundred generated stories for accuracy, and pointed the AI at HN comments to aggregate issues and fix them with his OK; reported issues mostly fixed.
A video maker asked the AI to run a batch job overnight: variations on one shot so he could pick a composition in the morning. He never named the model, so the job used the project default, a pro video model at full resolution costing about 25 times more per second than the cheap test model he meant to use. Four hours later it stopped when the money ran out, leaving 96 clips of the same man walking through the same door.
$2,500at stake
This one went wrong. See what happened before you try it.
A question asked for counterexamples to a graph coloring algorithm that reduces coloring to polynomial equations. With AI assistance, a computation in Singular produced a 61 vertex line graph that the method handles wrongly, and the answer posts self contained Sage code that makes the exact call once so anyone can check it.
A developer asked his dot to download all the English learning materials he had purchased from a page and gather them in a folder. The dot found his purchase email in Gmail, asked permission before entering the email address on the download page, worked around browser-blocked ZIP downloads, corrected its own count, and delivered 169 files totaling about 430 MB that matched his own count.
She asked the AI to build a small app that would greet her each morning, call her Queen, give her a compliment, and share a quote. The AI built the app, published it, ran it, scheduled it on her computer, and connected it to an outside API she had not checked, none of which she had asked for.
This only partly worked. Worth knowing before you try it.