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.
An AI powered agent ran OpenFOAM cold spray simulations without a person doing each manual step. It read deposit results, spotted failed runs from solver logs, and decided the next simulation, with an animation made for each run.
A Genworth alternatives analyst built a SharePoint-based credit surveillance agent in an AI app Studio. It reviews more than 1,000 documents across 250-plus fund investments and surfaces trends, risks and upside from completed deep reviews.
After two days of failing to find a product studio app that fit, this footwear seller got a prompt workflow from Shopify's own assistant, then had the AI teach him VS Code with AI to generate store sections from reference images, which let him drop paid landing-page builders. He then built his own app that bulk-turns product photos into studio-quality images (4 images in 40 seconds) at about a third of the cost of the app he liked but found too expensive.
The AI diagnosed a long-standing 4K display issue and he now has a CLI tool that fixes it.
Every few days the AI checks for date night ideas for Eleanor Konik: English language opera near her, what is playing at local dinner theaters, and anything unusually cheap, and it emails her when something fits. It found Grease as the very last show in the old building of a dinner theater her husband has wanted to take her to for years, and a Young Frankenstein showing at a dinner theater an hour away she had never heard of, which she went to.
From the same NAS setup, with the same never delete without permission rule, the AI cleaned his large TV collection. Many files had come from torrents and carried extra junk: text files, .nfo files and samples. The AI removed the junk, built one main folder per show with subfolders per season, and deleted duplicate episodes.
The AI built a seven-day social and community content calendar from a Drive folder of the tester's existing content notes and quarterly goal. Every post topic, hook and call to action cited the exact Google Doc section it came from, with working links.
His local school division released the raw answers to a touchy redistricting survey as one PDF of more than 3,000 pages, which nobody could realistically read. He used AI to write scripts that pulled the answers out and built a public web page where people can search the responses and filter them by keyword and by school.
The user built EVA, a family assistant on the AI, which finds what matters in school email, tracks deadlines, updates the family calendar, and answers questions about what is coming up.
Let Fable work overnight on llama.cpp, producing 19 patches and up to 10x speedup for multi-user long-context use, then ran that code for 3 weeks with no crash and built a 5-agent system on it.
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.