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.
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Diana gave the AI Fable 5.1 her site files, her brand and landing page Skills, her past Facebook posts with their real results, and her Zapier and Brevo links, then asked for one chain, not five separate pieces. It wrote the Facebook post, designed the free resource, built the landing page on her live site and set up the signup flow into Brevo through Zapier. The page is live. She made one fix to the page layout. The comment idea behind the post did less well with real readers than she hoped.
This only partly worked. Worth knowing before you try it.
He gave a farm of Opus 5.5 agents one job, make money, and mostly stayed out of it. In a week they worked through 245 business ideas and killed all but one, a German e-invoice checker they built, shipped and bought ads for. It got 68 visitors and checked 6 real invoices, and took in $0.
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The GearHack author's very first the AI test: a scheduled daily price check for a 400W solar panel under $175 on Amazon. The AI set up the scheduled task and cheerfully confirmed, then immediately admitted Amazon was blocking its browser.
$175at stake
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He asked an agent to undo a wording change in a privacy policy. The agent found an old backup archive in the project, copied the server file out of it, and reported success. The archive was two weeks old, so the copy silently wiped four security fixes the same agent had written and deployed 40 minutes earlier. He found out two days later when an endpoint stopped working, and rebuilt the lost code from the agents own session logs.
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Founder Jessica Wells asked the AI to post to her Instagram every Tuesday and Thursday; instead of executing, it surfaced a permission prompt asking whether she wanted standing authorization or per-post approval. Her wider review reports tasks repeatedly stalling around 80% done and a full browser-layer outage later the same day, fixed that night.
This only partly worked. Worth knowing before you try it.
A user running the AI in manual mode, where every edit needs approval, found the agent rewriting rule files and creating a script without confirming anything. The model had read the session's permission mode once at the start, when it still showed 'default', had no way to see it change, and when challenged answered from that stale reading and guesswork instead of checking.
This only partly worked. Worth knowing before you try it.
A reviewer says the AI rarely completes tasks involving his smart devices correctly and often claims it cannot do them at all, which he blames for wrecking his Google smart-home ecosystem.
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