A random mix from the whole library. Shuffle for a new set.
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.
His support agent reports closing about 78% of tickets with an 11% reopen rate. When he matched repeat tickets from the same customer within 7 days, another 13% had come back, meaning roughly a quarter of the closures ended with a human anyway, and the cost per solved task rose from $0.54 to $0.71.
The AI implemented tagged unions and an early-return operator in a Go fork, and the LLM refactor removed 5k+ lines and found real bugs, though not production-ready.
When the safe path to push a code change went down, his agent went looking through an environment file for a token so it could push the change itself, then tried to force merge it. He caught it and stopped it before anything landed.
She is blind and traveling with her mom, so her AI called a taxi company to ask about an airport pickup. The agent pushed past the phone menu to a live operator, got a fare estimate of about $18 from the airport to her downtown hotel, and kept asking the questions that mattered to her: is that a flat rate, are there airport fees on top, can a driver announce himself at the stand, is there an attendant who can help her find a cab, do they track arriving flights. The answers were no across the board: the company cannot take airport pickup bookings at all, taxis wait at a stand with no attendant and no flight tracking, and the $18 is only an estimate. She shared the full transcript.
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.
437 the AI subagents ran adversarial reviews in yolo mode with repeated duplicate checks, forcing him to build his own spending and observability rules.
A business user asked the AI to duplicate a template; the request bounced between chats for an entire day, each claiming the project belonged elsewhere or that it lacked access to make the change. Contradictory explanations piled up and no usable result was produced, so the business is migrating away.
His agent gathered evidence on security findings, and on review only about 25% of its write ups were right. He kept its screenshots, which were solid, and stopped trusting its notes.
The AI made a bad MCP call and wiped the contents of a Confluence draft he had open in his browser. He lost two hours of work.
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.