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.
He filmed his kid birthday on a camera and forgot about the indoor fluorescent light, so every video had horrible horizontal flickering. He tried ffmpeg fixes himself and they did a poor job. The good result was in the $295 studio version of DaVinci Resolve. Instead he asked the AI to write a small terminal app that does the fix, and it did it in a single prompt.
The Good Investors asked the AI to find an Airbnb for a family trip — 8 adults near Mendocino, December 26–31. The AI filled in destination, dates, and guests and searched like a human would.
He ran AI on the task with 6 agents at a time, about 75 agents in total over about 6 days. They produced transcriptions of tens of millions of handwritten images and indexed them all.
A developer had the AI build mazkir, a shared-memory service: tell the AI something once and it appears in his next chat, in an AI app and even in an AI app, stored as plain Markdown notes. Launch week brought about 160 visitors and exactly one sign-up — his friend.
He has worked in SEO since 2009 and has no coding background. He shipped a SaaS with paying users anyway. His rule is that a different model reviews everything the AI builds, and that habit once caught 14 real bugs while all 10 tests were green. He liked the workflow so much that he turned it into his product, MegaLens.
Michelle Solomon (recipes/homemaking, 9.5K) typed her weekly meal plan for four into AI, which returned a consolidated grocery list organized by produce, meat/seafood, etc., keeping the total under $200 using store-brand picks. She approved the AI logging into her grocery account; the groceries arrived and she cooked glazed chicken breasts.
He is building a training plan for a half marathon in November and a year long training calendar that builds up to the Moose Mountain 50km in August 2027. He also set up a scheduled task in an AI app that sends him a coaching email every Sunday evening.
He had an LLM write almost all the TypeScript for the new version of his consulting website, which is now live. Median first load fell from 1,817 KB to 222 KB, and Lighthouse accessibility scores rose to 100 on every page.
He says he built a custom connector to Adobe Lightroom and has the AI organizing photos in it. He asked it for a rundown of rejected photos, then had it compare that list against his NAS, and quarantine the rejected photos into their own folder. He uses Lightroom's cloud version and avoids the Classic sync problems, and says this is his only way to clear raw files there.
Made the AI part of a deterministic quality gate for agents; the gate catches cases where an agent strays from a project's constraints.
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.