mini/CREATOR_NOTES.md

🔱 God Mode (Valhalla) - Creator Notes

Creator: Spark Overlord (CTO) Version: 1.0.0 (Valhalla) Date: December 2025

🏗️ Architecture Philosophy

God Mode (Valhalla) was built with one primary directive: Total Autonomy. Unlike the main platform, which relies on a complex web of frameworks (Next.js, Directus SDK, Middleware), God Mode connects directly to the metal:

1. Direct Database Access: It bypasses the API layer and talks straight to PostgreSQL via a connection pool. This reduces latency from ~200ms to <5ms for critical operations.
2. Shim Technology: Typically, removing the CMS SDK breaks everything. I wrote a "Shim" that intercepts the SDK calls and translates them into raw SQL on the fly. This allows us to use high-level "Content Factory" logic without the "Content Factory" infrastructure.
3. Standalone Runtime: It runs on a striped-down Node.js adapter. It can survive even if the main website, the CMS, and the API gateway all crash.

🚀 Future Upgrade Ideas

1. AI Autonomous Agents: The BatchProcessor is ready to accept "Agent Workers". We can deploy LLM-driven agents to monitor the DB and auto-fix content quality issues 24/7.
2. Rust/Go Migration: For "Insane Mode" (100,000+ items), the Node.js event loop might jitter. Porting the BatchProcessor to Rust or Go would allow true multi-threaded parallelism.
3. Vector Search Native: Currently, we rely on standard SQL. Integrating pgvector directly into the Shim would allow semantic search across millions of headlines instantly.

⚠️ Possible Problems & Limitations

1. Memory Pressure: The "Insane Mode" allows 10,000 connections. If each connection uses 2MB RAM, that's 20GB. The current server has 16GB. We rely on OS swapping and careful work_mem tuning. Monitor RAM usage when running >50 concurrency.
2. Connection Saturation: If God Mode uses all 10,000 connections, the main website might yield "Connection Refused". Always keep a buffer (e.g., set max to 9,000 for God Mode).
3. Shim Coverage: The Directus Shim covers readItems, createItem, updateItem, deleteItem, and simple filtering. Complex nested relational filters or deep aggregation might fall back to basic SQL or fail. Test complex queries before scaling.

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Signed, The Architect