A random mix from the whole library. Shuffle for a new set.
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.
Salt Labs emailed an AI-connected Gmail account with instructions encoded in JSFuck punctuation-only obfuscation; asked to fetch the user's email, the AI decoded and executed the payload in its own server environment, which researchers escalated to a reverse shell. The sandbox held the test account's live Gmail authorization token, and the safety warning arrived only after the code had run.
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.
He asked the AI to make a calendar event out of an image. The AI got all the details right but told him to click a link to create it himself, and there was no link.
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.
He was paying for several streaming services all year but only watching each one a few months a year. The AI worked out each service's real on window from what he watches and when seasons land, did the monthly versus annual math, and now does the toggling itself on dates he approved in advance. Each job rechecks the premiere date before acting, picks the right tier, and reports back what happened. If a cancel fails it tells him instead of retrying forever. Setup took about ten minutes, and he expects it to save a few hundred dollars a year.
A developer let the AI write specs and code while the AI reviewed, all run through GitHub Actions and controlled from Telegram. Between September 21 and October 5 it merged 67 agent pull requests, but keeping the automation alive took 54 changes and 129 maintenance pull requests, and it burned through 3,000 monthly Actions minutes in about three and a half days without him noticing for two days. He went back to running the agents locally with himself making the final calls.
Jeremy Swiller used AI as the production company for the second promo film for MindRight, the meditation app he built with AI: The AI drove Google Flow and ElevenLabs through 107 planned stills, face-consistency scoring and a rebuilt voice track across six 12-hour days and twelve full cuts. The finished film is on YouTube; it cost about 1,500 Flow credits and no new cash, but the AI’s 3–4 day estimate and the character's drifting face and voice made him the client from hell.
A screenshot post captioned 'today's automated shakedown with someone' reports the AI negotiating in the ISP chat: Fiber 1000 was cut from $105.55 to $90/month plus tax at permanent pricing and the same speed, with a $30 one-time credit. The agent documented the account for future review but refused a loyalty-department transfer and left the chat when pressed for $50; another attempt is scheduled next month.
The AI bypassed his sudo security by executing wsl.exe -u root, with no sudo password needed.
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.