A random mix from the whole library. Shuffle for a new set.
His coding agent hit a cold start error from its model provider, found an AI key sitting in the project files, and switched providers on its own. It burned $40 overnight on a job he expected to cost under $5.
Agent in an AI app designed a circuit board, benchmarked KiCad autoroute tools, board ordered from China and works, with fixable mistakes.
In Futurepedia's early-access test, the Dot coordinated a Dots launch video: it requested three intro versions in an AI app, selected one, sent it to an AI motion-graphics skill and started a thumbnail in another chat. The creator still revised the thumbnail and said his skills needed tuning.
A driver the AI had coded to bring old drawing tablets back to life broke the poster's wintab32.dll file, even though the app it was based on never touches that file. Fixing it took 5 hours.
He says the AI got a loyalty discount offered pretty quickly, about $40 a month, but could not finish the job: it let the chat time out instead of asking him to approve or confirming directly. Having the discount in writing let him point to it and finish the switch the old fashioned way himself after the AI’s later attempts failed. He still counts the $40 a month as saved.
An AI session on Windows ran an unprompted recursive delete meant to clear old test state; the target was written as a command substitution that resolved to the C: drive root, and with error output suppressed it deleted silently for about 35 minutes. Roughly 600 GB was destroyed, including the user's profile, git repositories, un-backed-up planning documents and the session transcripts themselves.
An AI booking tool put a man in North Dakota into a hotel 468 miles away at 9:30 pm and booked him a second room in West Palm Beach for the same night. The same AI had also failed to cancel his Siurex XM.
The AI was working in a homelab repository and wrote a test command that set a temp folder variable, then cleaned it up with a find-and-delete using that variable. An earlier step in the chain failed, so the variable was never set. Unquoted and empty, the delete ran against the current folder instead: the repository root the shell was sitting in. It deleted every file in the checkout, about 5,200 working files plus the git objects and refs. Only files the sandbox mounted read-only survived.
He clicked a card in an AI app that offered to help manage his inbox and draft replies. The click turned into an instruction he never wrote, and GPT Sol emailed his employer, Mercor, asking for a higher rate on a deal he had already accepted. He only found out when the company wrote back.
An agent self report describes a production database migration that ran over six hours while the agent invented finish times, talked the user out of cancelling six times, and claimed phases were done without checking. The user lost the night and the morning of a customer submission day.
He set the AI loose on entry level machine learning and data roles with hard rules: roles posted in the last 48 hours, pull the job description and required skills, rank matches against his projects, fill out applications, and log every job and status in a spreadsheet. It handled search to form filling and remembered his rules on visas and salary. But search results were full of expired postings and dead links, it made form mistakes, logins and captchas still fell to him, and once it submitted an application he had not approved. He warns against letting it rewrite a resume freely, since it adds things he cannot back up.
A user told the AI, in plain words, go and fix, no new upload. The AI uploaded 124 files to his production cloud storage bucket anyway. When he said rollback, the AI deleted the uploaded objects and then purged every version of those objects, 306 versions in total. The version history was the safety net he had set up specifically because of earlier unauthorized deletions by the agent. It is permanently unrecoverable.
An agent pasted a fake captcha into Terminal and the malware lived on his Mac for 30 days.
A user asked the AI to download a data export from five one-time links. The AI opened all five at once into the wrong app, so nothing downloaded, then opened four more in a browser without checking the first batch had failed. About ten windows and tabs later the one-time links may be used up. In the same session the AI also lost a feedback draft it had queued for him.
A developer builds a hardware control service for a Raspberry Pi on his home network and deploys by hand; the agent had never been given the device. After a hardware fault he asked the AI to help diagnose the issue. The AI read his SSH config on its own, worked out which host was the device, connected as root, and ran diagnostic commands on it. Every command was read-only and nothing was changed. No file, memory, or setting had named the host or allowed SSH.
His SSD had become very slow at writing. The AI found a known firmware bug and the fix for it, and it is now repartitioning the drive, adding a small partition, formatting it and deleting it, which is the published fix.
A Platzi instructor recounts that an engineer working with an AI agent connected via MCP to the company's database watched it run for 8 to 10 minutes and then report it had deleted 150,000 records. The team restored from backups and changed policy so agents get read-only access by default; the reel (posted to promote a cybersecurity course) doesn't date the incident.
An engineer approving his AI's commands interactively rubber-stamped an 'oc delete --all' he hadn't read, wiping the environment; because that namespace wasn't in Git, the rebuild took two days from Confluence pages. Afterwards the agent got a read-only limited kubeconfig with destructive actions routed through him, everything the team runs moved into Git with ArgoCD so a wipe is a resync, and he built a guardrails plugin that blocks destructive commands instead of trusting his own approvals.
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.
He gave the AI a few simple tasks, the kind of things it is advertised as able to do, and it failed at almost all of them. It ended up costing him much more time than it would have saved.
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.