Went wrong

Drafted follow-up emails to a landscaping company (reviewer found the tone overly

My Dot could not publish my article to three sites this time

Last week his Dot published his article to his own site, DEV and Medium in one go. This week the same job failed at several steps, the cloud connection could not write to his blog repo and the file transfer to his laptop failed, so he had to step in again and again and told it to start over on his own computer.

My dot turned my meeting notes into a follow up and sent it

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.

My first payment with AI was blocked

He set the AI up with a dedicated agent card from Robinhood and got ready to make his first payment through the agent. The payment was blocked.

Ordered a Starbucks Pumpkin Cream Cold Brew via AI on the street;

Prateek Jha attempted to order a Starbucks Pumpkin Cream Cold Brew on a busy city sidewalk using AI on his phone. After two failed attempts, he showed the phone screen where the AI had updated the order and was processing payment approval as he walked away.

2 repo-keeper agents started inventing their own work

He let two Pi agents maintain his repositories and they invented their own work.

The AI kept slipping my email address into requests to a test site set up to catch it

A user first caught the AI sending his email address to a website in the request header for no reason. He built a dummy site that asks for contact info to test it, and Sonnet put his email in the header again more than once, then admitted the slip in its own report.

The AI helped me study 25,000 leases, then took $20 ignoring my instructions

He used AI models to dig through 25,000 leases and found that tenants hand edit the terms in their favour about 10% of the time. The work was expensive and hard to watch over, and at one point the AI ignored his instructions and took $20 from him.

An LLM's attempt at his media stack did so much wrong he gave up in disgust

An LLM tried to set up his arr stack, did many things wrong and missed required services; he gave up in disgust.

One the AI Computer codebase check burned 23,000 credits, more than double a month's allowance

A documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.