Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
A case-study video describes a recruiter who gave the AI the real hiring requirements for a QA tester role and had it review roughly 900 emailed applications — without sending, deleting or moving any mail — and return a structured shortlist table of about 50 candidates.
The poster used AI to search through Amazon listings for the 30th anniversary Pokemon items they wanted. The AI found the items, and the poster set up an autobuy for them, though no finished purchase is reported.
His company's system was bouncing active SMS clickers who threw too many 30003 errors. The AI Ultracode found the cause, and the fix means the sunsetting code now ignores active clickers.
Mentioned in passing in a thread about an unlisted offer: the sellers' friends bought their house as-is on a contract the buyer had the AI write up - no agents, no lawyers - and the title company told them it was the smoothest deal she had ever handled.
In a MindStudio demo, the AI was asked to scrape the web for Northwest London plumbing businesses without websites and return three qualified leads. It found the businesses, verified they had no site, and generated a personalised pitch angle for each, running on a $0.24/hour CPU instance.
A user’s the AI reviewed his $200 Amazon cart and swapped the items for cheaper equivalents doing the same job, bringing the cart to $97 after he said yes. He posted that the single task paid for the subscription.
Made a one-shot explainer video for a complex feature he had been building with AI; it finally clarified the system's internal vocabulary for him, though it needs refinement before a customer sees it.
The last of four cryptograms on a famous unsolved list had resisted everyone since 1950; the first three fell in 1948, 1996 and 2019. Working with AI and OpenAI, the poster cracked it: Opus 5.5 wrote the search code and picked out the books to use for the key phrase, another model verified the result, and the list keeper has confirmed the solution.
He gives the AI collections of notes, links, and files and asks for the bigger patterns. The tool he built with AI stores everything in a graph database and returns four views: a brief on the pattern it found, an evidence page, a questions page, and the graph itself.
He had added 44 Gmail filters over 5 years and they were not working well. With an AI skill that uses the Gmail MCP and a lot of email history to test against, the set was cut down to 16 filters that work better with fewer false positives. He wrote the process down as a skill afterward.
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.