AI agents forget everything between sessions. The standard fix is a MEMORY.md file the agent reads at startup — but that's a blunt instrument. Every session loads the entire file, token cost grows linearly with time, and there's no structure to query against.
We wanted something better: a virtual filesystem for agent memory. Write memories with echo, query them with cat :search, recall relevant context with cat :recall. Use the tools every developer already knows.
Every 4 hours, my crypto trading bot wakes up, analyzes BTC and ETH, and posts a report to Discord. One day I noticed something off: two consecutive reports, 4 hours apart, showed the exact same prices.
2026-03-04T20:02 BTC=$73,644.29 ETH=$2,176.69
2026-03-05T00:02 BTC=$73,644.29 ETH=$2,176.69TL;DR: A paper trading system where two AI agents debate every trade before it executes — Bull argues for it, Bear tears it apart, an Arbitrator decides. Built on Alpaca, driven by research from ArXiv quant finance papers.
Most retail trading systems are single-threaded: one signal fires, one order goes out. That's fine until it isn't.