I built a persistent memory MCP with Hebbian learning and GraphRAG

The Problem AI coding assistants forget everything between sessions. Every conversation starts from zero. You explain your architecture, your patterns, your preferences — and next time, it's gone. I built Cuba-Memorys to fix this. What is it? An MCP server that gives AI agents persistent long-term memory using a knowledge graph backed by PostgreSQL. 12 tools with Cuban-themed names. Key Features 🧠 Knowledge Graph Entities, observations, and typed relations that persist across sessions. Your AI remembers projects, decisions, patterns, and connections between them. ⚡ Hebbian Learning "Neurons that fire together wire together" — memories strengthen with use (Oja's rule) and fade adaptively using FSRS spaced repetition (the algorithm behind Anki). 🔍 4-Signal RRF Fusion Search Not just keyword matching. Cuba-Memorys fuses 4 search signals: TF-IDF similarity PostgreSQL full-text search Trigram matching Optional pgvector HNSW embeddings Results are ranked using Reciprocal Rank Fusion for maxi