How I Built a Persistent Memory Layer for AI Coding Tools
📰 Dev.to · Sri
Learn how to build a persistent memory layer for AI coding tools to improve their performance and retention
Action Steps
- Build a persistent memory layer using MCP
- Implement Ebbinghaus decay scoring to prioritize memories
- Integrate semantic search to enable efficient information retrieval
- Test and refine the memory layer using real-world scenarios
- Configure the memory layer to optimize performance and retention
Who Needs to Know This
AI engineers and developers can benefit from this knowledge to create more efficient and effective AI coding assistants
Key Insight
💡 A persistent memory layer can significantly enhance the performance and retention of AI coding assistants
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🤖 Improve AI coding tools with a persistent memory layer! 🚀
Full Article
AI coding assistants forget everything between sessions. I built Smara — a persistent memory layer using MCP, Ebbinghaus decay scoring, and semantic search — to fix it.
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