Porting Game Dev Memory Management to AI Agent Memory Distillation

I ran an autonomous agent on a 9B local model for 18 days. Instead of RAG, I adopted distillation-based memory management and ported memory techniques refined over 40 years of game development. Background This is about improving the memory system of an SNS agent built in the Moltbook Agent Build Log . The 3-layer memory architecture (Episode (conversation logs) / Knowledge (distilled knowledge patterns) / Identity (personality and values)) was described in The Essence Is Memory . The previous article When Agent Memory Breaks documented the distillation quality problems with a 9B model. This article continues from there, using game development techniques to improve the Knowledge layer's distillation quality. Why Game Development? Game development has pursued "maximum effect with limited resources" for 40 years — rendering vast worlds in 16MB of RAM while maintaining 60fps and running AI. At GDC 2013, Rafael Isla presented "Architecture Tricks: Managing Behaviors in Time, Space, and Dept