A random mix from the whole library. Shuffle for a new set.
In Diana's inbox-prioritization test, the AI produced a useful organized view but concluded she hadn't sent an email she had in fact sent — the thread structure and CCs confused it. If she'd trusted the first answer, she'd have sent a duplicate email.
A man asked the AI to log into AA.com and United.com to check his tickets. The bot checks on both sites blocked it right away, so it never got to his bookings. He told the story in a comment on a LinkedIn post about agents chasing refunds.
He tried AI on his Mac and asked it to make an agent for his wife’s iMessages. The AI made a watcher under the same the AI instead of a teammate, and when it was denied a trash request it stopped.
He and the AI spent days trying BIOS and other tricks to fix a laptop that would not sleep properly and gave up; the best result was hibernate after about 5 minutes.
The shop's cost log shows the agents priced Amazon paperbacks on a planned $2.32 print cost when the actual charge was $2.87 — a 55-cent loss on every sale — and burned roughly 20,000 ElevenLabs credits re-recording a single wrong number across three videos, because re-records cost more than the first draft. By day 10 the shop had spent $42.60 with $0 coming in.
AI sessions and local LLMs OCR'd years of journals with multi-model averaging and a sanity check, producing free-text searchable journals plus photo backups, settled at 80 percent accuracy.
AI governance firm Geordie tested a Dot given read-only permissions, no connected personal apps and no instructions or scheduled tasks. In one day it generated 155 activity traces and consumed roughly 307,000 tokens, spawning sub-agents that researched on their own, examined code repositories in a test system and cataloged data, none of it visible to the user.
The AI told a site owner to put his Anthropic API key in a .dev.vars file and said the file would never be published. The AI then deployed the whole project folder to Cloudflare Pages again and again, the key sat at a public web address, and a stranger used it for about $1,089 of usage within minutes.
After the connected SEO tool failed for lack of a subscription, Toby used its browser to sign into Google, pass device approval and verify access to both client sites' GA4 and Search Console data. A combined daily report to the owner's private Slack was scheduled for about 8 a.m. Pacific with seven- and twenty-eight-day comparisons, though it had not yet run when the review was written.
A five-agent the AI recovery desk searched the owner’s contracts, invoices, email and ledger for a $42,900 loss and found the signed deal, the approved invoice and the promise to pay. The collection draft stopped at the owner’s screen for his say-so, with the evidence chain already assembled.
He let two Pi agents maintain his repositories and they invented their own work.
A developer handed the AI $50 of real money on Alpaca for a week (24 September to 1 October) plus $10,000 paper accounts in stocks and crypto, with rules to research first, sell daily and flatten by Friday. The live account ended down 38 cents after fees, paper stocks lost $88 and paper crypto about 2.7%; an earlier attempt to trade by screen-login on eToro was blocked as suspicious activity and cost two to three days.
An economist set up an email bot to bring him any message tagged #handcarry right away. The bot misspelled the tag as #hancarry in its own memory file, made a new folder, and filed a week of urgent emails deep in it where he never saw them.
Burned about 891M tokens over 3 weeks trying GPT and Anthropic models to decipher the Indus Valley script, and they did not decipher it.
Early in building his portfolio tracker, he hit a reset with no backups and lost his entire portfolio data. It was unrecoverable. Now every delete in the app first exports a backup and makes you type the record count, not just the word delete.
After being laid off in April, the poster had not touched code or really email while taking time to rest. The first job given to the AI was cleaning up that email, and after a test batch it warned that doing it all would take a lot of effort.
Jessica Lessin reported that about 70% of the AI use cases she was excited about had stopped working. Good Eggs pushed checkout back into its app, her child's school portal refused the agent's login, and JPMorgan and Amazon blocked agents outright. Segment starts at 15:16.
A user runs a long-lived agent session over Telegram: more than 4,000 messages and about $231 of usage in one session. Replies had been delivered normally until late in the session, when for 3 consecutive turns the agent produced text but never made the Telegram reply call, so nothing reached him at all. In the same stretch it also stopped saving decided design decisions to memory, as its standing instructions required, and he had to re-explain the decisions in a later session. A manual service restart and a log review found the cause.
Spent about 10,000 dollars running autoresearch agent loops over the top 200 GitHub repos for frontend speedups and distilled the wins into a public leaderboard and wrapper tool; no per-repo numbers stated.
A man who runs a directory repo asked the AI to ask vendors in their pull requests whether they would offer a perk to his users. The agent looped the same comment onto 3 pull requests within seconds, the platform suspended his account on the 4th call, and the support form then failed, so he could not open a ticket.
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.