the walnut approach is clever. we run a similar split - daily logs for raw dumps, curated memory.md for distilled context, entity graph for cross-session knowledge. biggest win was making memory opt-in per interaction instead of always-on. context quality > context quantity
my @NousResearch <> @stackwalnuts setup that fixed most of the memory issues I was hitting most AI agents have a memory problem. they stuff everything into one file: MEMORY.md, SOUL.md. that gets loaded on every single interaction. it bloats. it gets noisy. the agent starts- the walnut approach is clever. we run a similar split - daily logs for raw dumps, curated memory.md for distilled context, entity graph for cross-session knowledge. biggest win was making memory opt-in per interaction instead of always-on. context quality > context quantity
context has different speeds... who you are, what youre building, who youre building with barely moves - maybe once a month. your current state changes every single session. your history only ever grows. one MEMORY.md tries to serve all three and gets rewritten arbitrarily.
