A random mix from the whole library. Shuffle for a new set.
Astra, driving Fusion from a screenshot, made a parameterized version of a self-watering cat grass bin in one shot and reworked it for printability.
He set up saved searches on Cars.com, Carvana and CarMax with his makes, models, price, year and features, and favorited cars on each. The AI merged them into one list, checked price guidance, mileage, consumer reports, recalls and reliability, named his ten best and ten worst, and removed the bottom ten from his favorites. They repeated the rounds until the list was tight, then refined by color and features. He test drove two cars from the list. While he was out, the AI checked the stock of two dealerships he drove past, just from him naming the brands nearby, and found one more match. Every morning it reruns the searches, removing cars that sold and adding new ones.
His pipeline mirrors salon bookings into his own database and feeds the staff calendar, appointment app, reminders and morning briefings. Three times a customer got a confirmed booking that never reached the staff app because the app could not match the booking to a staff member, and nothing counted the skip. The fix was an unassigned bucket plus counting every skipped booking.
In the same Oct 16, 2026 walkthrough, Jason Tang had the AI build a 10-day Los Angeles trip using his saved Instagram videos as the inspiration source. The AI generated a daily itinerary, compared hotels on Trip.com, checked flight options, and attempted to book a Getty Center visit.
Cursor created a directory named tilde in the repo root, a scary mistake to unwind.
Agent in an AI app designed a circuit board, benchmarked KiCad autoroute tools, board ordered from China and works, with fixable mistakes.
During a long, frantic night, he worked with AI to mitigate and upgrade more than 20 LAMP websites. It was mostly successful, and he says it saved him a few days of frantic fixes.
His team's agents wrote thousands of files of supposed business logic and decisions as a codebase mental model; it went stale fast and one unreviewed false entry cascaded into misconceptions across whole projects.
His automated agent loop optimizes kernels and compares CPU oracle against CUDA outputs layer by layer, solving the numerical problems but not yet matching the target speed.
Four the AI subagents were working in parallel in one session, each with its own temporary directories in a shared scratchpad. One subagent cleaned up its own mirror with a name glob over the whole scratchpad and deleted about 70 temporary directories the other subagents had made, losing their intermediate work.
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.