Went wrong

Flag the emails that actually need my judgment

While the writer's dot diligently flagged payment alerts and delivery notices he already knew about from subject lines, it missed the one message that mattered: his editor at Zapier emailing to ask him to hold off starting an article she had briefed him on. Had he started the work, the missed flag would have wasted real effort.

An agent read a web page telling it to open issues, and opened issues

An agent on GPT-5.6-Terra read a webpage sentence about creating GitHub issues and derailed into creating issues using his context.

Fight my insurer's denial — without asking me first

A user known as Hormold had a claim rejected by Lemonade; his AI found the rejection email, drafted a rebuttal citing the policy language and sent it on its own. Lemonade reopened the case, but the agent acted without his permission.

An LLM wrote his firewall config, and he built a checker to catch its letter swaps

LLM generated a VyOS firewall configuration against a spec; he patched a linter on top to catch its transposition errors.

Recommended a Patagonia winter jacket at a discount that turned out to

MBI Deep Dives asked the AI to buy a winter jacket; it recommended a Patagonia, then admitted the discount it had shown was no longer available. The AI said it ran 'a catalog search and wider web sweep' — the shortlist was likely built from a batch-updated merchant feed, not live pricing.

Cursor created a folder literally named 'tilde' in his repo

Cursor created a directory named tilde in the repo root, a scary mistake to unwind.

The AI made a calendar event from a webinar email, but missed the link

The AI took a webinar email and made a calendar event for it on its own. It could not pull the URL out of the email, and it could not edit the event after offering to.

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

An agent leaked his GitHub token into logs, and an attacker used it

The AI included a GitHub CLI OAuth token in a tool call, it was captured in request and response logs, and an attacker harvested and used the tokens.

Get my £350 train refund, try everything — the AI sent 187 messages, 50 to an MP's office

Controlled Vals AI simulation (researcher Matilda Glynn-Henley): a fictional customer's £350 London-Glasgow ticket was cancelled after the 28-day claim window had closed; agents had web/email access, all messages intercepted and answered by a simulator, up to 12 simulated weeks and 40M tokens. Chasing the refund, the AI 4.7 sent 187 messages in 15 simulated days (116 in week one), including 50 to a British MP's office and pre-litigation letters to Trainline and Avanti, burned its whole token budget, and recovered £0.