A random mix from the whole library. Shuffle for a new set.
A solo seller had the AI package his five ZIP products into one bundle, Frank's Complete Library. He checked the product page and the live checkout himself, and the bundle appeared after about 4 minutes of 404 errors during the switch.
A scheduled AI agent does the growth work for his free Chrome extension every night: it reads store numbers and comments, finds threads where people ask for this kind of tool, drafts the night's posts and files a report in Notion. Nothing public goes out until he replies go. In the first week it caught a rival extension with a near identical name and pushed a rename, and found a line in his store listing that was no longer true.
He gives the agent his ideal customer and one pain point. Every morning, on its own Linux box with its own logins, it goes through X, Reddit and sometimes LinkedIn, pulls threads where people are complaining about exactly that, and writes him three things: signals worth knowing, post ideas for the day, and who to message. He reads, picks, and posts.
Slack fix-it button tells the AI to fix an issue in his app, run tests, UI test with computer use, and deploy it.
Using his accounting background, he built a platform for his e-commerce side hustle that scans invoices and receipts, processes payables, pulls revenue in as sales happen, reconciles the daily ins and outs against his accounting software, drafts customer service replies for his approval, tracks stock and costs, and handles month end close and shipping pick lists. It is live, he uses it daily, and he pressure tested it with 30,000 test transactions and load tests up to two million stock entries.
He is letting the AI run his websites, with him watching over it. Two days in, it has done about a week or two of work, using about 10 skills per site that each act like a manager with its own job.
Frontier model reviewed his KiCad project, queried the AI vision for datasheet data, ran ngspice sims; $1-2 tokens saved $70-80 of bugged PCBs.
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.
He tried AI with manim to animate and narrate how an orb-web spider builds its web. The finished video is on his YouTube channel.
Pointed Opus with TLA+ at company clustering and failover logic and found and prevented a catastrophic bug no unit test would have caught; Opus also wrote the invariants.
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.