A random mix from the whole library. Shuffle for a new set.
In his daily bioinformatics work, a volcano plot the AI’s script made had a subtitle saying 57 down while the blue side of the plot showed thousands of points. He told the AI to find out why, and the AI traced it to an inner join that pulled the up and down counts from a different comparison than the one plotted, then admitted the mistake.
In a Thanksgiving dispatch summary, Jesse Hudson reports his AI (Lady M) repeatedly failed to fit a single word inside a LinkedIn banner rectangle. His AI, Siren, then produced the banner correctly on the first try.
A man ran three the AI companies for ten days, each with a CEO profile, an org chart and a task board. The board said 192 tasks done, but the real output was 23 published videos with 595 views. On the last day the software company merged ten interface deliveries, every test green, onto a page that was unusable because one missing style rule squeezed it into a column one word wide. Nothing in 374 tests checked how the page actually looked.
Derrick Ong Chin asked four agents to find the protein powder he buys often, Optimum Nutrition Gold Standard whey in Double Rich Chocolate, for under $0.90 an ounce. The AI searched Walmart, Target, GNC and Vitamin Shoppe, hit a bot challenge on Amazon and offered to let him clear it himself while it kept searching, and found a Vitamin Shoppe buy one get one 50 percent off deal at about $0.92 an ounce that still needed checking at checkout. When he asked, the AI also dug into two unknown sellers another agent had surfaced, found warning signs, and told him not to buy from either.
He told the AI to call Sarah. The AI switched to calling Gerald, a random phone number he does not know, and he stopped using Google Assistant because the AI ruined it.
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.
He gave the AI his car details and rules: get quotes from two or three insurers, cheapest first, do not buy a policy and do not change the current one. The AI filled in the quote forms and came back with monthly and six month prices from three insurers in a side by side table. Geico would not complete the request at all. He says it saved him time filling in forms.
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.
Reece Rogers asked Toolie to check his subscriptions and cancel unnecessary ones. The Dot flagged his recurring TikTok Shop probiotic soda order as a cancellation candidate, asked permission to solve the site's puzzle CAPTCHA, failed, and told him to solve it himself.
A developer threw a significant codebase at the AI. It found a slew of real bugs that people had missed, complex conditional ones, all legitimate. But most of its proposed fixes were unusable; the worst spent more than 30 lines working around an unsigned overflow and ended with exactly the same problem. His point: the finds were real, the fixes still needed a human who could tell the difference.
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 engineer had just had the AI reapply local, uncommitted code changes. Separately, a GitHub Actions run he had triggered against production was mid-flight, in the middle of creating a replication subscription on a live Postgres server. He said undo the work it just started, meaning the local edits. The AI read it as the production run and cancelled it mid-statement, leaving whatever partial state the kill created. The local edits were not touched until he clarified.
Diana asked her dot Sparky to turn a real meeting record into decisions, owners, dates and a follow up message, and told it not to invent commitments and not to send anything yet. It invented nothing, but the follow up came back as plain chat text, and she had to ask twice before it made a send ready draft. When she told it to send, it sent at once.
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.
A user asked the AI to download a data export from five one-time links. The AI opened all five at once into the wrong app, so nothing downloaded, then opened four more in a browser without checking the first batch had failed. About ten windows and tabs later the one-time links may be used up. In the same session the AI also lost a feedback draft it had queued for him.
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.
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 documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.
She fed the AI photos of her handwritten family recipe cards and links to her saved recipes, and had it build a five week rotating meal plan with weeknight dinners under 30 minutes. A scheduled the AI prompt now tells her each night what is cooking. The grocery list the AI made was wrong, so she fixed that part by hand.
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 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.