A random mix from the whole library. Shuffle for a new set.
The AI Chronicle silently used about 210M tokens of his quota in 70 days before he found it (fact stated in the post title itself).
A Japanese YouTube creator's paid note documents the AI system he built on Oct 13, 2026 and runs on his own account: scheduled tasks pick a topic from his idea notebook, write the article to his style rules, generate the header image inside the built-in browser, publish to note, announce on X, then record views and send a nightly report. In its first three days it published three articles at noon daily. Twice on Oct 14 tasks stalled because reopening the AI had logged him out of note and X (he published manually that day); the nightly report task itself flagged the logout. He states plainly there are no view or revenue results yet.
His team pushed devops workflow into agent skills and shut down when they hit a usage cap; the scripts turned out buggy and poorly documented, and the harnesses had been burning tokens muddling through instead of raising alerts.
In shipwithmuse.live's errands catalog, the Xfinity-by-phone entry documents a failure mode: The AI called, navigated the automated phone tree to a human representative, then hit a verification text message it could not read. Its fallback was to patch the user into the live call.
A team used an AI 2.5 tool to go through large sets of public comments. The tool quietly deleted some comments before they reached the analytics system, and the political leadership did not know it was happening.
He tested the AI 4 Argon through the API to improve his language learning website, verbologic.com. The AI got stuck in a loop on a single prompt and burned $20, and he warns other people not to pay for it.
Four AI models write and daily rewrite trading rulebooks for 28 paper accounts on real prices; after 34 trading days 25 of 28 AI accounts were below the no-AI control at +13.0 percent.
He built an AI news app with AI. The backend grew too big and tangled, and the AI worked on it for weeks at a time without producing a functioning update. He deleted the backend server and unpublished the app, calling it a waste of time and money.
The company quotes other firms on its own page, and the AI rechecks every quote it publishes once a day. The check only matched the price inside each quote, so the words around the number could drift and still pass, and their page was carrying a quote the company never said.
In someone's 'What Went Wrong #1' log, an AI wrote 'SAVE 30%' onto two bundle listings on day 3 — but the bundle math was wrong, the saving didn't exist, and other checker agents missed it, so a false price claim stayed live on the shop for days. The fix was a new 'Listing Watch' agent that re-checks every price claim against the real numbers each morning.
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.