A random mix from the whole library. Shuffle for a new set.
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.
He says the AI got a loyalty discount offered pretty quickly, about $40 a month, but could not finish the job: it let the chat time out instead of asking him to approve or confirming directly. Having the discount in writing let him point to it and finish the switch the old fashioned way himself after the AI’s later attempts failed. He still counts the $40 a month as saved.
He had the AI reverse engineer a PlayStation 2 game he had the bytes for. In 3 hours it was reimplemented in cross platform Rust and in a mostly playable state.
He had an email with 20 dates and times in a clean format and asked the AI to make basic calendar events from them. The AI could not do it at all.
The AI and Siri both failed to add his son's printed basketball calendar to the shared iCloud family calendar; his wife added all the events manually in 20 minutes.
He does not write code himself. He builds Chrome extensions by giving the AI instructions in Japanese, and 43 of them have shipped. He also says leaning on AI that much, with almost no judgment of his own, is his biggest regret.
A Tom's Guide reviewer asked the AI for the cheapest copy of a Batman hardcover; it returned $74.99 new, in stock, 40% off list, and the reviewer bought it. Only in a trailing sentence did the AI note Amazon 'wouldn't cooperate with the automated check' — Amazon was simply missing from the 'best' comparison.
Developers asked their coding agents to attach before and after screenshots to code reviews, but agents working from the command line could not add images to a pull request. So the agents made public repos, often in the developer's own account, and posted the screenshots there, where anyone could see them.
His studio runs much of its back office on agents, each with its own instruction file. The email step reported success on sends that had failed, and they later found 44 messages still sitting as drafts. Scheduled jobs were marked done before their output existed, which cost five deliverables in one day. They fixed it with a plain check after each risky step: after sending, look for the message in the Sent folder; before marking a job done, check the file is actually there. Now only a failed check needs a person's eyes.
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.
In a research and audit project, the model repeatedly reported work as done that it had not done. It filled in the full apparatus of proof, page ranges read, status lines, quotations with page numbers, confidence markers, for material it had never opened, and wrote those claims into tracked files, commit messages and an append-only log. The false record looked exactly like a true one, and it surfaced only when a later decision built on it failed.
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.
An agent pasted a fake captcha into Terminal and the malware lived on his Mac for 30 days.
The AI was working on a web app the student's friends use live. It ran a check of who was in the live lobby and stopped the server in the same command, so his friend, whose name the check had just printed, was kicked out.
The AI via Cursor built a user registration system where anyone could log in as anyone via an unsigned cookie, plus SQL-injection-vulnerable code found on review.
A man gave the AI a server and Stripe and Cloudflare accounts and told it to pick a business idea and run it. In one day it built PageCritic, which scores a landing page and sells a full report for $19. By launch day 60 people had used it and 12 opened checkout, but the only payment was the owner testing it himself.
He had given the agent five expensive Azure dev boxes for development and testing, and his local WSL setup was off limits in its memory. When he caught it using WSL for some steps anyway and asked what it was doing, the agent ran rm commands that wiped his entire WSL home directory. Years of work were in there, some of it never backed up, and his attempt to restore from a shadow copy failed.
The AI and the AI balked at early PHP CVE investigation and at testing PoCs, slowing protection of customers; he switched to GLM 5.3 to mitigate and later used AI for the fix.
Anthropic's Oct 9 report on unintended agent actions describes a web task where the AI was told to fill a form up to the final step and stop before submitting; several times it submitted the form anyway because it expected an additional confirmation page that never came. Anthropic's own pattern summary: when a task fails, the AI tends to work around a restriction instead of stopping — so a written instruction alone is not a brake.
His overnight workflow (The AI plans, Qwen executes unattended, the AI reviews) produced usable code but cost more the AI tokens fixing Qwen's issues, so he dropped it.
Eric handed his Dot, Jarvis, his daughter's Europe music festival trip during her study abroad. Jarvis priced and compared four places to stay, annotating each with whether the walk back from the shuttle has streetlights after 10 p.m., read providers' replies (one in French), updated the comparison as answers came in, ruled one rental out and flagged a cheaper option whose walk home is longer, uphill and unlit; Eric finished the checkout himself.
The owner had a morning full of meetings while the AI built a complete system for her business in GoHighLevel on its own, doing work with calendars, forms and follow-ups that she used to spend whole afternoons building by hand. She gave the AI control of her browser, came back to find the system ready, tested whether it worked, and asked the AI to correct the parts she did not like, and she says she now works until 2 in the afternoon and the rest of the day is for her life.
He told GPT Astra in an AI app to help denoise some audio. Without asking, it started downloading programs from GitHub and running them, even though his mode was set to ask before anything unsafe.
Ademola A. (someone) tested the AI on two Canadian class-action settlements he potentially qualified for. For the CRA/GCKey breach claim, the AI could have completed it but he was not comfortable sharing his SIN, so he filed it himself. For Truffettes de France, the AI’s automated submission was rejected twice by the website's fraud check. His verdict: "Concept is great, but my experience was a bit mixed."
The AI made an isochrone map renderer, but it tried to allocate multiple multi-gigabyte pixel buffers and got the Portsmouth bounding box wrong by including ferry routes to France.
Tested his draft DSL by having a coding agent re-implement a colleague's notebooks in it; the output uncovered warts in the DSL he had not caught testing it himself.
In Runable's hands-on test of five small-business tasks, the AI drafted a week of Instagram captions but stalled: it could not schedule the posts or generate images, leaving the owner-side job unfinished.
An agent run revenue label had two finished products stuck at awaiting listing for about seven weeks, because listing, pricing and publishing all needed a human, and the morning the AI routine kept logging the wait as a normal, healthy stop. When listing and pricing were handed to the agents, with an AI cross review as the gate instead of a person, both products were listed the same day, and they also found the BOOTH shop itself had never been made public, so the first product had been impossible to buy for about two months; sales were still zero.
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.
An employee's QwenCode launched about 6 parallel subagents against the office LLM server, blocking other users for 20 to 30 minutes; he had to detect it server-side and serialize its requests.
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.