A random mix from the whole library. Shuffle for a new set.
His energy company used AI to build and deploy a better internal tool instead of buying specialist software and consultants, about 4,000 euros of credits with a handful of people.
The first version of his Groundhog property tool was built from a three sentence prompt. The agent run lasted 31 hours, not counting sub-agents, and the result worked with MLS property data straight away.
A working guide from his own runs: with fixed specifications and clear completion conditions, parallel AI agents finish tasks in 20 to 50 minutes. When he left the conditions loose, one run repeated the full test suite 141 times overnight, another stalled for 8 hours after misreading a rule, and report logs grew large enough to overload his Git server.
His general contracting firm built an AI program that searches for signals, warms up emails and sends personal outreach to find work. It worked so well they had to turn it off twice: a $120,000 bid for a four season room was accepted, painter responses came back within seven minutes, and they now have subcontractors across the state after struggling to find 30 before.
An AI finance desk handled a business’s finance admin around an $87,055 receipts figure, run with read-only account access plus a dedicated receipts@ inbox so the week never has to be rebuilt from browser tabs. The desk’s design keeps money movement out of the bot’s hands.
Agency owner Nick Saraev documented handing his business's work to the AI for a week: auditing every task, having the AI plan the automation systems, building evals, and turning the winners into reusable skills. The tasks that scored well were scheduled to run without him; he kept judgement calls for himself.
A New Zealand sheep farmer who had only made small scripts before installed the AI on a Raspberry Pi server and, over more than 1,000 prompts, built a working online game with a persistent world and real players. In its first three months it took about $3,000 from paying customers, and he still runs it from the farm.
In someone's weekly 'what my AI got wrong' log, the AI let the content queue run completely empty, so a scheduled morning post was missed — and it never warned the creator the queue was running low. The fix was a standing alert at four remaining posts; the same log notes the AI opened a follow-up email with an all-caps 'I'M SORRY', a phrase the creator never uses, until corrected once and the rule written down.
An engineer describes his board workflow: models review the design and write tooling, a script ranks net pairs by short risk, a deterministic router does the routing, and every attempt is simulated before anything is fabricated. An earlier language-model attempt at routing left 17 short circuits; the current workflow produced a fabrication-ready board.
The AI files his detailed bug reports and his agent pulls the project's new changes daily to check whether his issue is fixed.
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.