How are you giving your AI tools memory? We compared 5 methods
Most of us now use 2–4 AI tools, and every one of them starts from zero. We put together an honest comparison of the 5 common ways to build an “AI second brain”:
Markdown files: free and portable, but lots of copy-paste
Local files (CLAUDE.md, rules files): best for coding agents
ChatGPT / Claude / Gemini Projects: easiest if you use one assistant
Drive / GitHub connectors: good for large archives
A portable memory layer over MCP, which is what we build with AI Context Flow
Projects is enough if you only use one assistant. Whereas a memory layer helps iwhen you switch between models, share context with a team, or run workflows (n8n, agent teams) that need to read and write the same memory.
We have put together a guide to help you decide which one is the best for you (and no, we don't say AICF is the best for everything :D )
Guide: https://plurality.network/blogs/ai-second-brain/
Did we miss anything? Where is your AI second brain built?
Replies
I'd compare how each option handles a changed decision. If a launch date moves, can it replace the old date across tools and show the source, or does the assistant now retrieve both?
@al_kub in AI Context Flow, if a date is changed, all tools connected via MCP will now get the new date. And if version history is needed to be seen, that's available too but searches will include only the most recent update (unless otherwise asked).
How have you built your second brain? curious to know
Local files are still my default for coding — a CLAUDE.md lives with the repo, so the agent gets the same context I do and there's nothing extra to sync. Where your comparison rings true is switching tools: the moment I move between 2-4 assistants, that file stops travelling with me, and a portable layer over MCP starts to make sense. The changed-decision point above is the test I'd use too — stale memory is worse than no memory.