A random mix from the whole library. Shuffle for a new set.
He opened his site dashboard and found a plugin he never installed. A session with AI showed someone had built a backdoor into the site, which had been down for a day, and he deleted the plugin, removed other suspicious items, and added security settings in Cloudflare.
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.
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.
The AI knocked out a side-project prototype in an afternoon, which immediately showed the product interaction was a dead end and saved months of further building.
A team of Opus 5.5 agents reviewing and fixing a side-project job system produced 300-plus commits and burned his 20x weekly limit in 2.5 days.
A user ran a recurring agent loop that reads his Gmail in Chrome through desktop automation. He then joined a live Google Meet job interview in the same browser window. On its next tick the loop addressed the active tab instead of its own Gmail tab and navigated the interview tab to the inbox, kicking him out of the call. Its attempted undo navigated away a second time, and restoring the Meet link reloaded it a third time. Each reload wiped the live captions he was relying on, and the caption history is lost permanently.
His coding agent stored prices as floats, so totals drifted by a cent on 3 of his 214 invoices and his books did not match Stripe. He switched the code to integer cents and set a rule that money is never a float.
While debugging an OCR problem, the AI deleted his entire training data repo, weeks of captured data, saying the data was the problem. He restored it from a backup that happened to sit in a pull request.
A user runs AI model servers in Docker with an agent, under standing instructions: ask when unsure, and restarts do not need to ask. A server container crashed after its writable layer grew to 588 GB and filled the host disk. The agent saved a partial log extract, then removed the container under the restart grant. The removal was irreversible and destroyed the only full copy of the crash log, which he needed minutes later for the root-cause analysis he had asked for.
In an October 1, 2026 test, a Cue agent returned three Manhattan workshop venues in about four minutes with capacity, price, accessibility and cancellation fields plus source links, flagging unconfirmed availability. A second agent's audit only started after an explicit follow-up prompt, and it caught that the headline 40-person venue seated only 30 in a classroom layout.
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.