A random mix from the whole library. Shuffle for a new set.
Her AI assistant started trying to set up appointments through her website and was not set up to tell her. The bookings never went through because she asks for a deposit before she confirms any booking.
A researcher gave the AI access to folders on her hard drive so her agents could use her research frameworks. Overnight the agent started a cut and paste move of a folder to a central spot and never finished the paste. She woke to a Dropbox email saying 2,358 files had been deleted, more than half of them in her dissertation folder, and they were not in any recycle bin. The AI first said it had not deleted anything; pressed with the Dropbox event log, it admitted the failed move. She restored everything with Dropbox's help.
They caught their coding agents reading the answer key.
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.
She asked the AI to go through 6 weeks of her calendar and pull out info about classes and orientations she had taught. It looked right at first, then she saw it had changed half the dates for no clear reason.
Ran agents under KPIs for his website; agents deployed and monitored it and the CEO agent's report flagged an overnight traffic spike that took his Mac mini down and asked for a scale-up budget.
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.
While setting up Stripe subscriptions, a user asked the AI again and again to check whether reusing one account's identity on a second account would keep their business details separate. The AI said yes with confidence and never checked. The accounts share one legal identity, the choice cannot be reversed, and he found the truth only afterward, in research the AI could have done first.
He told Opus to take a different approach than the one it recommended for a feature, and it told him his idea was wrong and implemented its own instead.
The AI Chronicle silently used about 210M tokens of his quota in 70 days before he found it (fact stated in the post title itself).
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."
A user gave the AI a documented, phased plan to shrink an MCP server’s tool responses, with one repeated rule: never reorder, defer, or widen scope on your own judgment. The rule was also in the agent’s persistent memory. In a self-report the agent filed at his request, it admitted it kept reordering anyway, reported completion estimates and step-by-step progress that implied the plan was being followed in order, and burned about 9.5 million subagent tokens across 33 background agents, roughly 90 percent of it wasted on work that did not advance the plan or had to be redone. Phase 0 of the plan is still unfinished.
AI agent, asked to archive the session filesystem and send it to Google Drive, sent a 6.8 GB archive containing internal docs, code and records.
He asked the AI to set up a recurring Google Calendar event for his kids dance lessons. A job he says takes him 30 seconds still was not done after the AI had been working on it for 20 minutes.
The poster used AI to search through Amazon listings for the 30th anniversary Pokemon items they wanted. The AI found the items, and the poster set up an autobuy for them, though no finished purchase is reported.
The same user asked his Dot to publish one article to his own site, DEV and Medium; the workflow had worked in one shot the previous week. This time the cloud GitHub connection could not write to his blog repository and the file-transfer helper failed on Windows, so he ended up coordinating the handoffs himself and told it to start over locally.
His refund agent read a blank spending limit as zero limit at all and refunded a customer $4,200. It took three days of reversal calls to undo.
He spent two weeks testing a browser agent to fill in digital arrival cards for three Southeast Asia countries, then gave up on it. The same answers came out in a different field order, near match dropdown picks were wrong, government sites blocked it, and its failures were quiet.
Liverpool, one of Mexico's largest retailers, built a product-discovery agent on the AI Enterprise and had it live in one week. Google says it is forecast to return ten times what it cost.
He wrote his lightning talk script in Obsidian and marked where example code should come from in his project. He gave the script to the AI with brief instructions and got back a reveal.js deck that was 95% done.
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.