Most people are still treating LLMs like goldfish with infinite context windows. But the real power comes when you give your AI systems persistent, structured, and reliable memory. I’ve been diving deep into three distinct approaches: - Open Brain (OB1) — the personal exocortex - Poor Man’s Memory (PMM) — the ultra-lightweight, git-native path @Millenial Cat - Cognee — the structured graph + vector layer for serious agents Each represents a completely different philosophy for how we should capture, store, and retrieve context. Full breakdown dropping soon: architecture comparisons, strengths & tradeoffs, how they actually fit together in a real stack, and when I’d choose one over the others. If you’re building any kind of long-term AI workflow, personal knowledge system, or agent setup — this one’s for you.What’s your current memory strategy? Drop it below