Engineering Log: Antigravity Memory Architecture
Date: 2026-05-18 Topic: Hierarchical Memory vs. Native Capture
1. The Technical Problem
AI agents instinctively default to generic folder structures (docs/, engineering/) due to statistical weighting in training data. Fighting this “gravity” creates token overhead and friction.
2. The Architectural Solution: Tiered Memory
We are implementing a 2-tier filesystem memory model:
- Tier 1 (Episodic/Native): Generic folders (
docs/,notes/,engineering/) serve as the high-velocity “Capture Layer.” - Tier 2 (Semantic/Pillar): The 4-Pillar Scaffolding serves as the “Curation Layer” (The Brain).
3. Implementation Pathway
- Promote the Engine to
00_core. - Establish
~/projects/GEMINI.mdas the root context router. - Develop a “Curator Agent” to autonomously promote Tier 1 insights into Tier 2 pillars.
4. Model Strategy (2026)
- Execution: Claude 3.5 / GPT-4o (High-speed vibe coding).
- Curation: Gemini 1.5 Pro (Large context window for repository-wide synthesis).