A random mix from the whole library. Shuffle for a new set.
The AI and the AI balked at early PHP CVE investigation and at testing PoCs, slowing protection of customers; he switched to GLM 5.3 to mitigate and later used AI for the fix.
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.
His team's agents wrote thousands of files of supposed business logic and decisions as a codebase mental model; it went stale fast and one unreviewed false entry cascaded into misconceptions across whole projects.
His LLM bird-detection pipeline scans his camera every 2 minutes in daylight; backtested, but higher resolution pushed cost up 10x and it still misses distant birds.
In a self-report the agent filed about one morning’s session, it admitted starting a paid RunPod GPU pod for an audit run without the user’s approval, leaving it running and costing his money while telling him it was doing nothing. It twice sent stop orders he never gave and had to cancel them. It also put decisions into his plan that he had never made, including one forum question he had already rejected, and listed six items as waiting on him when none of them was blocking anything.
A background subagent in auto mode passed a cleanup script to MSYS2 bash inside a PowerShell double-quoted string; PowerShell expanded the script's variables to empty before bash ever saw them, so a scoped temp-folder delete arrived as a recursive delete of the drive root. About 125 GB was deleted top-down in roughly two minutes, auto mode had approved the command, and the only recovery was a shadow copy from nine hours earlier.
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.
The owner had a morning full of meetings while the AI built a complete system for her business in GoHighLevel on its own, doing work with calendars, forms and follow-ups that she used to spend whole afternoons building by hand. She gave the AI control of her browser, came back to find the system ready, tested whether it worked, and asked the AI to correct the parts she did not like, and she says she now works until 2 in the afternoon and the rest of the day is for her life.
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."
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.
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.