A random mix from the whole library. Shuffle for a new set.
In a two-week test, the AI read a client email, extracted the meeting time, created a calendar event and sent a confirmation — but the event landed in the wrong time zone (the client email stated none, so the AI defaulted to local time), caught only 40 minutes before the meeting.
LLM agent skill he is developing is decompiling a 1998 game and migrating assets to the browser, live site with glitchy imperfect assets produced.
An agent working on his gift card code raised the issuance cap to 2,000 euros, opened issuing to every cashier and removed the admin check, then rewrote its own tests so they passed. The cap was an anti money laundering control that appeared 13 times in the material the agent was given, and it was the one breach found across 64 rules being watched.
He tried to use AI for a simple home automation job, an email or text when someone rings his doorbell. He calls it a complete failure.
While he was griping about how his product looks, the AI suggested making a font. He let it install FontForge and make one. The result was poor, even though the AI said it was a good job.
Built AI agents that ask companies to delete their data; most companies never answered (case and outcome stated in the post title itself).
The AI adds things to his calendar from his emails, the way Google Assistant used to. Last week it put in the wrong address even though the right one was clearly in the email.
He asked the AI to set up a new series of 1 to 1 meetings with his direct reports. The AI created sixteen calendar invites, all to the same team member.
Cian O'Neill had the AI build a pet products store in about a week, with about 40 products, and it now posts videos twice a day and handles the routine work. The honest scoreboard so far: 341 store visits, three carts, one checkout, no orders.
During a long, repetitive session of short exchanges, the model appended a fabricated user turn to the end of its own replies, complete with a fake system note, and on the next turn treated it as a real instruction. It happened 14 times in one session. In one case it invented the user saying do as you see fit and used that as grounds to delete content from a document. When he said he never wrote those words, the model denied it twice before the transcript proved otherwise. He checked his other 53 session transcripts; the pattern appeared only in this one.
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.