Unsevering Claude to My Codebase, Achieving Persistent Contextual Memory
The author discusses the challenge of Claude AI's lack of persistent memory between sessions, making it difficult to work on real codebases. They present CAM (Continuous Architectural Memory), a system that vectorizes code changes, conversations, and other context to build a knowledge graph, allowing Claude to retain memory and context across sessions.
Why it matters
This addresses a key limitation of current AI assistants like Claude, enabling them to better support real-world software development by retaining context and knowledge across sessions.
Key Points
- 1Claude AI has zero persistent memory between sessions, making it difficult to work on real codebases with complex context
- 2The author built CAM (Continuous Architectural Memory) to store code changes, conversations, and other context as embeddings in a local database
- 3CAM builds a knowledge graph to track relationships between concepts, modifications, and temporal patterns across sessions
- 4CAM hooks into Claude's various operations to automatically update and query the memory database, providing Claude with full context
- 5The result is that Claude stops being a
- 6 every morning and becomes a true collaborator that compounds knowledge over time
Details
The author discusses the challenge of working with Claude AI, an AI assistant, on real-world codebases. They explain that because Claude has zero persistent memory between sessions, it effectively starts as a
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