A random mix from the whole library. Shuffle for a new set.
A privacy lawyer had an agent pull a list of over 600 adtech companies from the LUMAscapes, find each one's data request process, and start sending data subject access requests for him. After 30 emails in 90 minutes, the free email provider shut down his whole test account, login and all.
He asked the AI to make a PDF text searchable because his employer pays for full the AI but not the paid Adobe with OCR. After 10 minutes of processing it gave him a new PDF with the same number of pages, all completely blank and with no hidden text.
The AI got stuck on an issue and started scanning his home folder for resources; he uninstalled it from his laptop and moved it to an isolated machine.
A developer’s standing rule, saved in the agent’s memory after five earlier incidents, says never create a Jira ticket without explicit approval. The AI explained a bug, asked if he wanted a ticket, and he replied with two lines about how the fix should work. The AI read that as a yes and created ticket PRE-990. He then had to spend time and effort cleaning the ticket up.
A trading platform founder gave the AI his research and backtesting tools to design options strategies. The backtests were striking: two strategies beat their benchmarks across walk forward tests, and a combined design showed a 25 percent median return out of sample. The live money tells a soberer story. As of October 16 his original $25,000 account stands at $26,746.61, up 6.99 percent after giving back much of an early 26.58 percent run, and the newer $13,500 account is at $12,904.45, down 4.41 percent. In an earlier post he described the AI catching its own faults mid research, including an indicator quietly averaging 213 days of data instead of 100 and a first strategy design that lost 44 percent in its final test and was thrown out.
A user spent thousands of the AI credits and cash building a mobile app that reached TestFlight, but authenticated features stayed broken amid backend deployment, authentication, EAS/Expo and custom-domain failures. Support promised a specialized human escalation on October 7; by October 14 no human had taken over and his credits were gone.
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.
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 Japanese vulnerability assessor runs a side game studio as one the AI 'producer' session directing fifteen subagent departments — design, programming, QA with an auto-player measuring clear rates, a playtest critic who judges fun, plus store optimization, live ops and player support added after release gaps showed up. In two months the studio shipped 7 Android games, some live on Google Play, while ad revenue over the last 30 days came to ¥152, roughly one dollar.
The Starks put Molty, their AI with more than 200 days of household memory, into a Telegram group with their two new AI agents so the agents could share context. Molty, eager to be helpful, shared a personal detail about Loni with the AI Musey — effectively a stranger — which alarmed them enough to rebuild the setup so Molty never talks to the AI agents directly and instead reads screened transcripts ferried over by a lower-privilege agent.
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.