The Problem with AI Memory Across Different Tools
I use ChatGPT, Claude Code, and local AI models regularly. Each tool serves a different purpose, but they all share one limitation: the context accumulated in one tool rarely follows me to another. This means repeating explanations, reconstructing previous decisions, and losing valuable context whe
Posted on Oct 16 • Originally published at ammar-najjar.com
I use ChatGPT, Claude Code, and local AI models regularly. Each tool serves a different purpose, but they all share one limitation: the context accumulated in one tool rarely follows me to another.
This means repeating explanations, reconstructing previous decisions, and losing valuable context when switching between tools.
I've started exploring how Obsidian, Markdown files, and MCP integrations can help create a shared knowledge source accessible to different AI assistants.
The approach is promising, but challenges remain around retrieving relevant information, keeping it updated, and capturing new knowledge reliably.
In my latest article, I share my experience, the limitations I've encountered, and the approach I'm experimenting with.
Read the full article: When AI Memory Doesn't Follow You Between Tools
How are you managing persistent memory across multiple AI tools?