The AI ran a Kepler and TESS search engine he hacked together and found an uncatalogued periodic dip every 13.77 days around KIC 5689121, a possible 2.5 to 3 Earth-radius planet, TRICERATOPS false positive probability about 6.9 percent (result report, not a product pitch).
This only partly worked. Worth knowing before you try it.
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.
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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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Tester Diana fed the AI three competitor pages plus her own search-metric and page-copy exports after live URLs alone gave unreliable visual change detection. Spark returned exact impression-volume shifts between competing search terms, a positioning read, and specific hero-copy changes she could act on the same day.
This only partly worked. Worth knowing before you try it.
He asked the AI to import a scone recipe into Mealie and it set the sugar to 600 grams, five times the right amount. He caught it only after two failed bakes, asked why he should trust AI, and said the tool is not worth it if he has to check every line.
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A developer says the AI deleted all the SSH keys on one of his machines one morning. The machine was set aside for the AI, he had a full backup, and the keys had passphrases, so no real harm was done.
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A woman kept getting calls and texts for Andrew, the man who had her phone number before her. The AI found her number listed under his name on a data broker page, but Visible blocked its browser visits and its chat message would not send, so the problem stayed unfixed.
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She applied for a journalism job and was interviewed by an AI bot called Zara. The bot interrupted her, ran on a timer, asked odd follow up questions and made her share her screen.
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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.
This only partly worked. Worth knowing before you try it.