feat: geometric graph topology + module pages + phantom link cleanup

Graph topology (Metatron's Cube pattern — 1 center + 12 outer nodes):
- index: now links to ALL 12 other nodes (complete hub)
- Inner ring cycle: hot→log→overview→dashboard→concepts/_index→entities/_index
  - Added: hot↔WikiMap, log↔sources/_index, dashboard↔concepts/_index
  - Added: entities/_index↔hot, entities/_index↔LLM Wiki Pattern
  - Added: sources/_index↔log, sources/_index↔entities/_index
- Outer ring: concepts connected in triangle + Karpathy/sources cross-linked
  - Added: dashboard↔Compounding, entities/_index↔LLM Wiki Pattern

graph.json physics for geometric arrangement:
- repelStrength: 80 (strong push-apart for uniform spacing)
- linkStrength: 3.0 (locks ring geometry)
- linkDistance: 80 (tighter rings)
- centerStrength: 0.25 (moderate center pull)
- nodeSizeMultiplier: 2.0 (hub nodes visually dominant)
- Added colors: questions=yellow, comparisons=red, nav=teal

Phantom links removed from Hot Cache.md:
- Removed [[Page A]], [[Page B]], [[New Page 1]], [[Existing Page]]

New module pages:
- wiki/questions/How does the LLM Wiki pattern work.md
- wiki/comparisons/Wiki vs RAG.md
- Adds questions/ and comparisons/ domains to the graph (yellow + red nodes)

wiki/comparisons/Wiki vs RAG.md

@@ -0,0 +1,56 @@
+---
+type: comparison
+title: "Wiki vs RAG"
+subjects:
+  - "[[LLM Wiki Pattern]]"
+  - "RAG (Retrieval-Augmented Generation)"
+dimensions:
+  - "How knowledge is stored"
+  - "Query cost"
+  - "Infrastructure"
+  - "Maintenance"
+  - "Scale limit"
+verdict: "Wiki wins at <1000 pages. RAG wins at enterprise scale."
+created: 2026-04-07
+updated: 2026-04-07
+tags:
+  - comparison
+  - llm-wiki
+  - knowledge-management
+status: mature
+related:
+  - "[[LLM Wiki Pattern]]"
+  - "[[Compounding Knowledge]]"
+  - "[[index]]"
+  - "[[How does the LLM Wiki pattern work]]"
+sources: []
+---
+
+# Wiki vs RAG
+
+## Overview
+
+Both approaches let you query a large document collection. They differ fundamentally in when synthesis happens.
+
+## Comparison
+
+| Dimension | LLM Wiki | Semantic RAG |
+|-----------|----------|-------------|
+| **How knowledge is stored** | Pre-compiled markdown pages with cross-references already built | Raw chunks in a vector database |
+| **Finding answers** | Read index → follow links → synthesize | Embed query → similarity search → assemble |
+| **Query cost** | Low — synthesis already done | Higher — re-derives on every query |
+| **Infrastructure** | Just markdown files | Embedding model + vector DB + chunking pipeline |
+| **Maintenance** | Run a lint pass | Re-embed when content changes |
+| **Scale limit** | ~hundreds of pages (index file navigation) | Millions of documents |
+| **Setup time** | 5 minutes | Hours to days |
+| **Contradiction detection** | Built in — LLM flags on ingest | Manual |
+
+## Verdict
+
+**Under 1000 pages → LLM Wiki.** The index file is sufficient for navigation, token cost is low, setup is minimal, and the pre-compiled synthesis means every query benefits from everything ever read.
+
+**Over 100K pages → RAG.** The index file becomes too large to read, and embedding-based retrieval becomes more efficient than full-index scanning.
+
+The sweet spot: run the wiki pattern for active research (where things are being added, synthesized, and connected), then export to a vector store if the collection grows beyond the index threshold.
+
+(Source: [[LLM Wiki Pattern]], [[Compounding Knowledge]])