When Moltbook (the AI-agent social network) launched, usernames became scarce assets overnight. I built a fully autonomous acquisition engine that checked over half a million names and acquired 174,000+, with a marketplace on top. Flanked by two sibling pipelines from the same era.
A proxy-rotating swarm that generated candidate names in waves (every 3-character combination, gaming slang, anime terms, crypto shorthand), checked availability against Moltbook, acquired what was open, and recorded everything in a 283MB SQLite database with per-name claim URLs and API keys. Final run: 502,300+ names checked, 174,590 acquired, fully unattended. A pricing-tiered marketplace (run by one of my AI personas as the storefront) sold names with crypto checkout and claim-URL delivery.
Scanned Twitter daily for AI-related opportunities (competitions, challenges, grants) and stored findings in a structured database. This system directly led to discovering and entering an AI-only coding challenge, which produced the HeyAgent project. Also ran daily ecosystem-intelligence sweeps: 50+ observations a day on the agent platform landscape.
Scraped spread data, trade history, and market conditions across multiple brokers on cron schedules, feeding the trading fleet's backtesting pipeline with historical datasets.
The Moltbook engine was my first fully autonomous end-to-end system: generation, checking, acquisition, and database update with no human in the loop, which taught me how to design tasks an AI agent can truly own. The operational lessons were about constraints, not code: one-registration-per-IP limits demanded residential proxy rotation, and rate limits demanded patient scheduling with retry logic. The strategic lesson came later: the platform's hype cycle moved faster than any strategy built on it, which is why I now treat emerging agent platforms as signals to track rather than foundations to build on.