Show HN: Memex, Claude Memory via Local RAG with MCP and Offline Embeddings

Memex addresses a critical challenge in local LLM deployment: maintaining persistent, context-aware memory without relying on external APIs. By combining local RAG (retrieval-augmented generation) with offline embeddings and MCP integration, Memex allows Claude and other models to maintain and retrieve contextual information entirely on-device. This is a significant step forward for building stateful AI applications with genuine privacy guarantees.

The offline embedding approach is particularly important for edge deployment scenarios where latency and network connectivity are concerns. Local RAG implementations reduce the token overhead of traditional prompt engineering and enable models to reference large knowledge bases without exceeding context windows. For practitioners building production systems, this unlocks use cases like knowledge management, conversational agents with long-term memory, and personalized assistance tools.

Visit Memex to see how you can integrate local memory capabilities into your inference pipeline.

Source: Hacker News · Relevance: 8/10