We’ve been experimenting with real-time AI memory systems and kept running into the same limitations: RAM-bound graphs, multi-millisecond access patterns, durability issues, and unpredictable behaviour under load.
We tried approaching the problem from a different angle and ended up with a small engine that does:
• sub-microsecond hot-path lookups
• 50M persistent nodes on an 8GB Jetson
• ACID durability (survives hard power cuts)
• mmap-streamed cold storage
• a Redis-compatible proxy
This isn’t an LLM or vector DB; it’s a lower-level substrate for structured + semantic memory in real-time environments.
Still early. Posting this mainly to understand whether others here have tried similar approaches, or see obvious architectural issues we should be thinking about.
Very open to critique, contact through ryjoxdemo .com!
JosephjackJR•44m ago
We tried approaching the problem from a different angle and ended up with a small engine that does:
• sub-microsecond hot-path lookups • 50M persistent nodes on an 8GB Jetson • ACID durability (survives hard power cuts) • mmap-streamed cold storage • a Redis-compatible proxy
This isn’t an LLM or vector DB; it’s a lower-level substrate for structured + semantic memory in real-time environments.
Still early. Posting this mainly to understand whether others here have tried similar approaches, or see obvious architectural issues we should be thinking about.
Very open to critique, contact through ryjoxdemo .com!