In this episode, @EngramLab co-founder and CEO @dan_biderman joins @allenpark to cook Mediterranean meatballs with yellow rice and talk about building AI that actually learns from you: why long context, RAG, and compaction eventually break down, how Engram compresses knowledge into cartridges and model weights, what continual learning could unlock for long-horizon agents, why token efficiency is inseparable from intelligence, how personal models could improve like Tamagotchis, and what it takes to build the research and infrastructure for millions of continuously updated AI memories. Timestamps: 0:00 Intro 0:26 Engram’s $98M Launch and Meatballs 1:45 From Naval Special Operations to AI Research 4:32 Israeli Military Culture and Founder Maturity 7:12 Why Engram Is Betting on Context and Continual Learning 9:14 Knowledge Cartridges, Compression, and Model Intuition 14:10 Trillion-Token Company Knowledge and Context Rot 18:05 Long-Context Limits, Compaction, and Neural Memory 22:20 Test-Time Training and “Destroying Prefill” 24:31 Harvey and Holistic Enterprise Queries Beyond RAG 27:02 Personal AI Models and Tamagotchi Weights 30:00 What Belongs in Weights vs. Text 32:25 Autonomous Memory and User-Specific Feedback Loops 34:20 Token Efficiency, Model Routing, and Harder Tasks 38:03 Engram’s Research Team and Product Culture 43:02 Hiring Researchers and Infrastructure Engineers 45:25 Doing More With Less 47:41 Where to Find Engram 48:19 Final Taste Test