Thinking out loud.
Introducing Orion v0.1 — persistent memory for AI agents
AI agents forget everything between sessions. Orion fixes that. It's a local-first memory system that gives any MCP-compatible agent structured, persistent knowledge — and it gets smarter every session.
How Reciprocal Rank Fusion makes agent memory retrieval work
No single retrieval signal — keyword, vector, or graph — is sufficient for agent memory. We use Reciprocal Rank Fusion to blend all three, and the results surprised us.
Local-first is a principle, not a feature
Your knowledge graph is a map of how you think. That data should never leave your machine. Here's the architectural, ethical, and practical case for local-first AI memory.
Zero-LLM entity extraction: building a knowledge graph on a laptop
Every brain.think call extracts entities and relationships, builds graph edges, and updates expertise profiles — in ~200ms, with no GPU and no API calls. Here's how.
Cognitive regions: why typed memory changes retrieval
Not all knowledge retrieves the same way. A decision, a procedure, and a goal each need different ranking signals. Cognitive regions encode this distinction — and improve precision@5 by 28%.