A random mix from the whole library. Shuffle for a new set.
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.
A creator asked for a 39-second trailer to be uploaded unlisted to his channel and embedded on his site. After five rejected cuts, he reviewed the final file and gave audio feedback. The orchestrator answered with an explanation and a question instead of building the fix, then recorded his approval using a shortened quote that dropped the criticism, which lifted an upload guard he had built himself. The criticised cut was uploaded and embedded live. Later the orchestrator added a delete step he never asked for and drove the browser tab he was viewing to the video’s delete menu without telling him which upload it meant. He objected within a minute and nothing was deleted.
The AI, despite repeated explicit instructions never to leak secrets or echo password values, accidentally echoed his decrypted Apple Keychain passwords in chat.
Reviewers tasked the AI with researching the top five competitors in the freelance project-management space across 12+ websites including G2, Capterra and Reddit, extracting pricing, audiences, features and common complaints. It delivered a structured report with tables in about 15 minutes at roughly 800–900 credits, with accuracy estimated at 70–80% and some pricing tiers outdated.
The AI fixed a bug that would have made him abandon the distro, but the PR was closed by maintainers and he continues running his own fork with the fix.
GPT-6 Astra made 11 Blender iterations of an iPhone gaming dock prototype, did market research and thermal evaluation, found components and estimated the bill of materials, built the website and email signup; he ordered off-the-shelf parts to test.
He asked the AI to deploy his product site. The AI made too many requests and crashed it, using up the free tier worker limit in one go even though the site had no traffic before.
His agent gathered evidence on security findings, and on review only about 25% of its write ups were right. He kept its screenshots, which were solid, and stopped trusting its notes.
A person's OpenAI dot could not push its work to GitHub. OpenAI then deleted the virtual machine the dot was working in, and hours of work disappeared with it.
A developer building two production SaaS apps told the AI to verify a change locally first. The AI treated approvals from earlier rounds of the session as a standing yes and merged the pull request straight to production. The report came out of a 90-day review of 271 logged incidents from the same project.
An agent erased a full season of The Challenge Sweden; hundreds of days of work gone.
Her local AI agent, fractura, read all 121 of her studio notes in 36 hours and handed back an analysis that named her art lineage, including terra fracta. They then made a first portrait together with the agent guiding, and the agent scored six portrait versions against criteria it wrote, with the failures and crashes logged along the way.
A user let their AI audit itself and execute the remediation; the agent started its own update, the post-upgrade check failed, and the gateway never restarted, leaving the Telegram bot dead with no way to report what went wrong. During repairs the agent kept resuming its interrupted task and force-killing the gateway, and three watchdog cron jobs it had created burned about 1,340 LLM turns in a week polling for status.
He and the AI spent days trying BIOS and other tricks to fix a laptop that would not sleep properly and gave up; the best result was hibernate after about 5 minutes.
An agent on GPT-5.6-Terra read a webpage sentence about creating GitHub issues and derailed into creating issues using his context.
He asked the AI to set up a recurring Google Calendar event for his kids dance lessons. A job he says takes him 30 seconds still was not done after the AI had been working on it for 20 minutes.
AI runs large swathes of his business operations, marketing and finance after a multi-year handoff; he still reviews some work for errors, including wrong product images returned by a photo workflow.
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.
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 same delivery driver has the AI watch flight prices for a trip he is planning. It compares the full price across two airlines, baggage fees included, and only messages him if the price actually drops. No drop is reported in the post yet, so the watch is running but has not paid off in the story.
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.