A random mix from the whole library. Shuffle for a new set.
The AI interpreted scanned old service manuals and circuit diagrams, ran simulation experiments to verify manual claims, cross-checked datasheets and papers, caught typos and his own misreadings.
His second attempt at building software by voice with an agent produced Feather, a working Nostr client. He had to talk it out of adding an app store, a login page, and a settings menu it kept trying to build, and he paid about 3,000 sats in lightning tips to people who reviewed the code.
He built Barrelbound with GPT-6 Astra and generated art. The game has two heroes, three jungle courses, barrels and double jumps, and anyone can play it free in the browser.
Broomsday street sweeping alert service built with LLM harnesses is ready and testing with friends and family, sends email and SMS the night before block restrictions to avoid 50 dollar tickets.
A large part of his ripped CD collection had broken gapless playback. The AI looked through the mp3 files, found that a library program had added four frames of silence to each track, and removed exactly those four frames from every track, and playback is seamless again.
A writer had the AI read more than 1,000 of his Apple Notes from 2016 to today — letters, journals and dream logs — and build a private local site mapping the ideas he keeps returning to. It also produced a model of his writing voice and a query tool to ask new questions at lower token cost.
Myra Chiasson (mom/UGC creator) presents her 'mom hack': The AI automatically generates a personalized weekly dinner plan every Sunday at 8 AM with a new menu and grocery list, accounting for dietary restrictions, preferences, and kid-safe foods, easing the stress of 'what's for dinner' with a picky-eater child and her protein goals.
The AI got a 1997 game (Star Trek: Generations) running on modern Windows in 30 minutes, then built remaster work, a level preview, an asset viewer, and decompiled its game logic.
Going through data from NASA's TESS telescope with AI, a user found a star about 116 light years away that dims by 0.05 percent every 3.18 days for about two hours, the same dip in 2018, 2020 and 2025 data. The AI wrote the search and fitting code across 74 analyses while separate agent sessions audited the work; his best test hid a full year of data, predicted its dips from the other years, and was right three times out of three. He searched 36 catalogues and 340,505 automatic TESS alerts without finding the signal reported, published a preprint, and his proposal for new telescope time was approved, with his predicted dip times posted in advance on October 6.
He needed an app on his QNAP NAS that only existed as open source Linux code, with no NAS package. He gave the AI the model, the AI searched the web and gave him a downloadable package in about ten and a half minutes, and he installed it and it works.
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.