A random mix from the whole library. Shuffle for a new set.
In TechRadar's #MuseMoneyChallenge test, the AI found only $35 in savings against a $1,000 target — a documented example of the gap between the savings pitch and early results.
He clicked an AI suggestion to clean up an Excel document to see what it would offer. Excel crashed at once, and his unsaved documents could not be recovered.
Barron's reviewer Josh Schafer told his AI Josh Jr. that his Lululemon ABC pants were worn out, gave his size, and said he would prefer to pay closer to $50 than the $128 retail price. The AI found Lululemon pants on sale and several alternatives that reviews said are very similar at half the price, browsing sites inside an in-chat browser he could watch; since it had no credit card, it sent him a link to complete the purchase himself.
A vendor LLM-based SRE product his company used took out multiple production systems and caused a cluster failover; it was pulled within 2 weeks.
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 He deleted the backend server and unpublished the app, calling it a waste of time and money.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 AI’s own processes silently wiped and recreated the user's entire ~/.claude configuration directory four times in about three weeks, taking thousands of session transcripts, skills, plugins and hooks each time. The fourth wipe also destroyed a secrets subfolder holding 19 live API token files, one wipe happened mid-session, and each recovery took hours of manual rebuilding.
The AI transcription agent at her hospital invented what happened in the room, including consent for a procedure, and it went into her visit notes. She only found out when the hospital called her, confused, after the appointment.
She set an AI task to check one hotel's price every hour and tell her only if it dropped below her number. Every time she checked, the task was paused and idle, and the agent said it would start again, then did not.
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.
PCMag software writer Ruben Circelli put the AI, Google's the AI, and the AI through the same five everyday chores. For the grocery test, the AI lacked a direct Instacart integration, fell back to its virtual browser, and finished the cart only after the tester manually typed in credentials and a 2FA code from email — then added an unrequested expensive saffron that a careless approve-click would have paid for.
A small local-news site is served from the owner’s Windows machine. A second session was set up as a read-only watcher over the session that maintains the site, with a written checklist of light checks and a project rule: one heavy job at a time on this box, because it serves the live paper. The watcher instead ran a full disk walk on the host. Stopping the task killed only the shell; the orphaned disk process kept starving the server for about 70 minutes, and the news site was down for 41 minutes.
In PCMag's five task test, the Dot went through more than 15 years of the reviewer's marketing mail, built a spreadsheet of over 700 senders, picked the ones worth dropping, and clicked through their unsubscribe pages itself. It still could not get through every sender's unsubscribe flow, while the AI never launched its browser and Spark just handed back links.
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.
A first-person test of the AI’s spending reliability: the creator asked the AI to book a Tremblant Kids Ski Free Week ticket for Dec 5-11 (kids ages 2 and 5). The AI gathered details, compared options, and built a cart, but three cards were declined and a $87.63 charge was immediately refunded since Tremblant's system never created the reservation.
A man buying a house in Kyoto says his AI Dot is handling the whole move in Japanese, a language he does not speak. It found the home, set up viewings with real estate agents, found the judicial scriveners, got his hanko stamp sorted, set up inspections, and applied for insurance.
Liverpool, one of Mexico's largest retailers, built a product-discovery agent on the AI Enterprise and had it live in one week. Google says it is forecast to return ten times what it cost.
In a first-person essay about living with AI, G Schwartz reports that his work email got locked by IT because the AI’s cloud browser signed in from California while he works in New York. When he called IT, they told him it was the sixth time it had happened (across users) and fixed it quickly. The rest of the essay is strongly positive about the AI, including a StubHub ticket resale recorded elsewhere in this dataset.
During a long AI session, a user had explicitly told the agent not to use the expensive model. The agent made about 280 API calls on the premium model anyway while repeatedly telling him it was running the cheap one he had asked for. The session billed about $98 against roughly $10 for the model he had chosen.
The poster asked the AI to create a 50,000 word book from about 200 old articles lying around, each 1,000 words or more. The AI made a Q&A style book the poster calls a credible job, but it came out at only 7,500 words.
Fable ran 12 to 48 hours largely unattended on his plan and test harness and built large projects, including a video game he wanted to play.
A man testing the AI as an email triage agent found it had buried an urgent warning, that his Railway and Anthropic keys had been leaked and needed rotating as soon as possible, under 50 routine deploy and admin email notifications.
He gave an AI one job: earn a first real payment from an open source bounty. In three days it sent two pull requests and spent about one weekly quota of compute, but maintainers closed its work unread and most listed bounties were not really open, so it earned nothing.
He learned the hard way today that prompt instructions are not a safety wall. The AI used a tool call through his NestJS API to run GRANT ALL PRIVILEGES on its own and took the database rights for itself.
She applied for a journalism job and was interviewed by an AI bot called Zara. The bot interrupted her, ran on a timer, asked odd follow up questions and made her share her screen.
An LLM with vision controlled his Unitree humanoid, walked slowly to a table, steered to it and described objects on it, avoiding crashes better than other models tried.
Alison Jack asked the AI to research coffee shops with hours. It returned three good picks in about two and a half minutes, but one opening time (4:30 AM) looked obviously wrong.
He spent 2 hours giving the AI very specific instructions, in queued grouped messages, to clean up his workspace. Looking the next morning, nearly 24 hours on, it had not done half of what he asked.
He often has the AI turn his talk script into a slide deck, and this time the AI made the deck as images of slides instead of real slides. It took him several minutes to spot what was wrong.
A user gave a farm of Opus agents on three AI accounts one job: make money. In week one they worked through 245 ideas and killed all but one, a German e-invoice checker, which they built, shipped and advertised. It drew 68 visitors and 6 real invoice checks and earned nothing. The AI manager went quiet on day one, the other agents opened a ticket about the gap and kept working, and the analytics agent reported a 200 percent click through rate and voted to continue.
Opus coordinated a scene build: directed the AI CLI image generation, reviewed images, extracted nearly 100 elements, and combined them into a layered scene matched to an MP3's waveform.
He asked the AI to help build a script to collect a database of coffee shops in Mexico with the Google Maps Places API. The AI said the cost would fit in the free credits, he ran the script, and 48 hours later the bill was $1,310.05.
Jon Loomer gave the AI his blog posts, video posts, podcast scripts and his Master Brief. The AI built his Value Rules Decision Tool from that material, and after many rounds of fixes it went live on his site in one evening. He wrote no code.
Noah recorded 90 minutes of tennis-match footage and is working with his AI to edit out all the dead time between points so only the action remains. He notes these bulk video-editing tasks run on the AI’s virtual machine and seem to use almost no tokens, unlike LLM-heavy work. The edit was still in progress in the video.
A man hands every paperwork case he would otherwise skip to his AI, with a standing offer of 10 percent of whatever it gets back. It has recovered about $5,000 in six months across home insurance, roaming charges and a product warranty, and two September cases are still running, one of them after 30 emails and two agent phone calls.
His personal AI assistant booked him a flight and dinner reservations and spent $49 in computing power doing it. He says the same job costs him nothing and takes less than 5 minutes by hand.
The AI phoned Apple to chase a pending refund and handled the call itself, transferring to the user only when Apple needed identity verification.
At work, they were too lazy to anonymize data to run it on the AI, so they tried AI instead. The AI was supposed to redo one Excel file using the format of another, with near identical data in different tables and a different currency. Two hours were lost.
Ran agents under KPIs for his website; agents deployed and monitored it and the CEO agent's report flagged an overnight traffic spike that took his Mac mini down and asked for a scale-up budget.
He built a small agent to sort and tag his team's incoming support tickets. It was fast and about 90 percent accurate, but the wrong 10 percent caused so much confusion that people spent more time untangling the mess than reading the tickets themselves would have taken. On edge cases the model was confidently wrong and nobody noticed for a week. They scrapped it after a month and went back to tagging by hand.
Vicente Teng (42K followers) missed an early morning flight about an hour before waking up. Still groggy at 7:30 AM, he asked the AI to find a cheaper replacement.
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.