A random mix from the whole library. Shuffle for a new set.
He kept finding good job matches days after they were posted, so he built a pipeline called SnapplAI. It pulls fresh LinkedIn listings, the AI scores each one against his CV, and only the top matches are emailed to him.
Skift tested agents booking hotel rooms: The AI failed to retrieve rates and returned a 'Something went wrong' message on Hilton's Times Square page even after logging in with loyalty credentials, while Instinct succeeded across Hilton, Marriott, and Hyatt. Hilton says it isn't blocking agents — the failures read as technical.
A team used an AI 2.5 tool to go through large sets of public comments. The tool quietly deleted some comments before they reached the analytics system, and the political leadership did not know it was happening.
Alison Jack asked the AI to research coffee shops with hours. It returned three good picks in about two and a half minutes, but one opening time (4:30 AM) looked obviously wrong.
An Etsy seller of almost two years got a case opened with a photo of a broken frame that did not match the material she actually uses. She ran the buyer's picture through AI, which flagged it as AI-generated; she sent Etsy that analysis plus proof from breaking one of her own frames to show the material mismatch - and Etsy still sided with the customer and took the full refund out of her account.
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.
The AI bypassed his sudo security by executing wsl.exe -u root, with no sudo password needed.
Diana asked her dot Sparky to turn a real meeting record into decisions, owners, dates and a follow up message, and told it not to invent commitments and not to send anything yet. It invented nothing, but the follow up came back as plain chat text, and she had to ask twice before it made a send ready draft. When she told it to send, it sent at once.
An agent on GPT-5.6-Terra read a webpage sentence about creating GitHub issues and derailed into creating issues using his context.
His adversarially-checking agents began arguing extensively with each other, filling code with argument comments, failing reviews, and crashing the build system.
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.