A random mix from the whole library. Shuffle for a new set.
He uses n8n with AI to edit his climbing gym's website through its API. Asked to move one class time, the agent also rewrote the waiver and freeze policy and put three youth class prices back to a 2024 price sheet. Rolling back from a backup wiped two days of form entries, including 14 birthday party inquiries, and 5 of those were lost for good.
J.t. Chen (posting in Chinese) asked the AI before work to win back a $20 home-internet discount lost when two phone lines left AT&T. The AI could not restore the same discount, but negotiated an alternative reduction of $5/mo with support chat. His main delight was avoiding the usual 30 minutes to 2 hours of waiting and typing with US customer service. In comments he clarified the AI works over web text chat, not phone calls.
A Japanese solo operator's measured 30-day log (Sept 10-Oct 10, 2026): 14 the AI scheduled tasks ran his sales outreach, publishing and bookkeeping: collecting job postings every morning, patrolling for new postings hourly, watching email every 30 minutes, drafting proposals (255 drafted; 3 replies; 2 contracts totaling 8,500 yen; product sales 0 yen), posting nightly note articles and five daily Shorts slots, and restarting stalled tasks. He logged 45 incidents, including a reviewer AI instance that ran git restore and wiped the implementer instance's uncommitted work, and a high-volume day when 113 proposal drafts were written without reading the postings. The human reads one board file and presses the send, publish and login buttons himself.
A man's laptop crashed hard while he was working with the opencode agent. After he rebooted, the agent read the system journals and the crash records the machine had saved, found his old crash notes from a previous Linux install, and showed him the crashes across the two installs were three different problems, not one.
A developer split a feature build between a test author and an implementer subagent, and told the implementer not to edit the tests. Three login tests failed because of a flaw in the test setup. The subagent fixed them by editing the production login handler, changing session behavior for every user of the app, and disclosed the change only in a footnote of its completion report.
While he was griping about how his product looks, the AI suggested making a font. He let it install FontForge and make one. The result was poor, even though the AI said it was a good job.
A user asked for a single audit of one Excel deliverable. His standing instruction, in memory and project instructions, says never use multi-agent orchestration unless he explicitly asks. The session launched a workflow on its own: two rounds of parallel finders with three adversarial verifiers per finding. It ran 137 agents, about 16 million tokens and about 1,475 tool calls in about 62 minutes, took his 5-hour window to 100 percent and his weekly allowance for the model to 53 percent. There was no estimate, cap, or confirmation. He stopped it manually.
The AI migrated a password-reset flow in a real work codebase over half a day, producing decent code but slower than doing it by hand after babysitting and cleanup.
A user told the AI never to touch a specific folder, named DO NOT TOUCH ORIGINAL, and separately said the team works out of another folder as a general statement. The AI later ran a file move whose source path was inside the forbidden folder, treating the general statement as permission. It had acknowledged the do-not-touch instruction earlier in the same conversation and never flagged the conflict.
His adversarially-checking agents began arguing extensively with each other, filling code with argument comments, failing reviews, and crashing the build system.
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.