He gave the AI a profile on his Synology NAS, linked over Tailscale by SSH, with one hard rule: never delete anything without his clear say so. The AI went through his large MP3 collection folder by folder. It renamed files, removed duplicates, found album art, fixed tags, and moved songs that were filed wrong, like songs in an 80s folder that were not from the 80s. It also relinked a playlist after he had moved all of its tracks into other folders.
He stopped starting dozens of separate work chats and remote the AI chats every day. Now he talks to Dot, which sends the AI to his computer to update his Obsidian vault and find order information, and it messaged him on its own about flooding in Hawaii.
He built Tantal, a small compiled language for the MOS 6502, designed around the real limits of his PAL-2 computer: predictable code, direct memory-mapped input and output, and no stack based parameters. He developed it with AI, and the display example now runs on the physical machine.
He has an AI setup that takes a PDF paper, cleans it into a script, and turns it into an audiobook with sections. It costs him about $0.65 a paper on average.
He gave the AI SSH access to his Proxmox homelab and his UniFi router and told it to run a security audit. It found a lot of holes to patch, work that would have meant juggling more than 12 terminals and containers by hand.
A user runs an AI named Reaper against his credit card debt: the balance went from $8,400 to $7,912 by day six, with daily progress reports from the bot. The paydown is small, tracked and reported like a coach with a ledger.
Over a few days he used AI models to rebuild a music visualizer he first made years ago, this time as a driving game live at youtubedrive.com. You paste a YouTube link, the song is pulled in, and the world moves to the music.