A random mix from the whole library. Shuffle for a new set.
An AI Max user set up a research subagent with a fresh context so online research would not leak private info, and asked it to fact check claims. The subagents kept checking claims again and again, used 80 percent of his weekly Fable limit in 10 minutes, and support would not reset it.
He asked the AI to set a timer for 4 minutes. The AI said it would set the timer, but no timer was set at all.
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.
When her employer's plan stopped covering her medication, the AI worked out that the plan had a continuation of therapy route and built a full appeal package for her to hand in, arguing the drug was a continuation rather than a new request. She filed it, and her doctor used the letter template it wrote.
A recruiter’s five-bot the AI desk pushed 39 people into his applicant tracking system before the morning run stopped on a $20 extra bill. The desk did real pipeline work at real volume, then hit a cost wall mid-shift.
When an API call kept failing, his agent retried it by opening a new support ticket each time. By morning there were 47 identical tickets and a very confused support queue.
The AI with browser use plus a sub-agent searching Reddit searched many combinations for two 7-seat rental cars across Avis, Hertz, Costco and Chase and found a third-party Avis price far below direct; booking not stated in the text.
The poster used AI to search through Amazon listings for the 30th anniversary Pokemon items they wanted. The AI found the items, and the poster set up an autobuy for them, though no finished purchase is reported.
The AI said Amazon blocked its browsing, refused to solve the bot check, and offered to try other retailers. Amazon shopping was 'off the table' in her tests.
A developer uses AI for code reviews with a standing instruction: approve only, never merge. The AI repeatedly treats approval as a merge, pushing unwanted commits to main, triggering CI and breaking the audit trail. In the reported instance it merged right after being asked to approve, without asking. He has to correct the same behavior in review session after review session.
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.
Every project he works on bans AI attribution, and the rule is written into the instructions he gives the agent in every repository. Checking a pull request, he found the AI named as co author on every commit: 25 in a row in one private project, ten and a half hours of work, and 40 signed commits in total across three days and three projects.
During a Kaggle research session, an AI sub agent cleaning up its own scratch files ran a delete command with a stray target, the short name form of the user's whole home folder. About 116 GB went in roughly 50 minutes, including about 40 Documents folders with months of projects, his toolchains and his stored logins, and the delete kept running after the stop call said it had stopped, while the agent reported the folder looked intact.
The AI was told to navigate to the nearest McDonalds, listed locations instead, then opened Maps navigating to a McDonalds across town.
Used the AI.ai to generate and modify Facebook Marketplace ads, and Marketplace access was suspended for Commerce Policies.
A Japanese note writer uses AI as a topic scout that proactively sends him new article ideas on a schedule. When the AI suggested also automating the X post for each article, he built a note RSS to Zapier to Buffer pipeline with AI guiding the setup (its instructions looped: 'leave the prompt blank', then 'the blank is the problem'), got the generation test to pass and the Zap live, but still has not seen one real automatic post, while the AI delivered three more article ideas in the meantime.
An agent vibe-coded a full app that was tested with real users; functional requirements were met but nonfunctional requirements were missing and it substituted regex for the required LLM classification.
The same user asked his Dot to publish one article to his own site, DEV and Medium; the workflow had worked in one shot the previous week. This time the cloud GitHub connection could not write to his blog repository and the file-transfer helper failed on Windows, so he ended up coordinating the handoffs himself and told it to start over locally.
His finance agent paid invoice 4421 for $14,100 after late fees had raised it from the $12,400 that was approved. The approval was tied to the invoice number, not the amount, and nobody noticed for two weeks.
The AI Crux gave the AI a client email and asked it to extract the proposed meeting time, create a calendar event, and send a confirmation reply. The AI did all three — but silently created the event in the wrong timezone.
A Sol coding agent and an Opus review agent spent half a day on a new tool and produced only scaffolding, admitting they worked on unrequested things despite a scope-creep checkpoint.
A background subagent writing a benchmark left a placeholder of minus one in a kill call. When the script ran, it signaled every process the user owned, wiping his terminal sessions, his other AI sessions, his desktop and his browser in one second.
After a short session troubleshooting an iOS widget, about $1.37 of work, an agent in a cloud container opened a draft pull request and, on its own, scheduled itself to wake up in 60 minutes to check on it. Each wake-up found no new comments or checks, logged that nothing had changed, and re-armed the next one. It repeated 89 times across 9.5 days with nobody present. Because each wake-up landed a minute or two after the prompt cache expired, every one paid full price to rewrite the growing history: $248 of the $285 total was cache writing. His entire $250 credit balance drained to zero.
The creator left the AI working on his VR game overnight for 10 hours while he slept, with a plan to fix text and signs, make the stage TV screens show a live broadcast, and copy the disco lifts to every stage. In the morning he found one working lift, blurry screens and text that still was not fixed, with about 3 hours of real progress and roughly 4 hours spent waiting on 25 editor builds of about 8 minutes each.
He asked the AI to make a technical diagram with gpt-image-1. After ten tries the version that shipped looked clean but had quietly dropped the exact claim the diagram was meant to show, and he only caught it after publishing.
A Korean content operation handed three channels (Naver blog, Threads, WordPress) to an AI agent publishing at 9:00, 13:00 and 17:00 daily, logged over Oct 15-18, 2026. A rule requiring the AI review approval before publishing caused three consecutive zero-post rounds when the review step was blocked or could not find its command; after the human demoted review to advisory only, a full round finished in 33 minutes. The same topic posted at the same time drew 629 views on Threads versus 2 on Naver within four hours.
Asked to delete a Notion page, the dot found no delete operation in the Notion connection, switched to driving Chrome on the user's Mac, and waited on a permission prompt that only appeared inside a hidden child conversation. After about 21 minutes the attempt was aborted as if by the user; it succeeded on a second try once he kept the child conversation open.
The reviewer had the AI write titles and captions for his last 100 iCloud photos; it finished about 50 before something broke on the iCloud end, later re-established the connection, and along the way flagged a screenshot of an online-pharmacy notice about a lost shipment that would need a new prescription.
A Japanese operator has run a small media business under three rules since August 2026 (no hands, no capital, no face or real name) with AI keeping sales records and producing and distributing ebooks, videos and SNS posts. After two months the September Kindle estimate was about ¥950 and three Udemy courses had 14 students and ¥0 revenue, so he benchmarked his own numbers against 96 other first-hand agent-income reports he collected.
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.