d515ff7598
- Wiki Map.canvas: 13 nodes at golden angle 137.5deg (Fibonacci/sunflower pattern) - index at center, 3 domain _indexes in ring 1, content in outer rings - Color coded: orange=concepts, purple=entities, green=sources, blue=navigation - 17 semantic edges with labels - graph.json: search:path:wiki — eliminates all plugin file noise - hideUnresolved:true — removes phantom Subject A/B nodes - nodeSizeMultiplier 1.8 — hub nodes visually dominant - repelStrength 30 — clean radial spreading - Created wiki/concepts/_index.md, wiki/entities/_index.md, wiki/sources/_index.md - These are domain hub nodes that create the 3-ring hierarchy in graph view - All wiki pages now link to their domain _index - Creates proper hub-and-spoke topology in force-directed graph
2.9 KiB
type, title, complexity, domain, aliases, created, updated, tags, status, related, sources
| type | title | complexity | domain | aliases | created | updated | tags | status | related | sources | ||||||||||
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| concept | Compounding Knowledge | basic | knowledge-management |
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2026-04-07 | 2026-04-07 |
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mature |
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Compounding Knowledge
The central insight behind the LLM Wiki Pattern: knowledge in a wiki compounds like interest in a bank. Every source added, every question answered, every analysis filed makes the wiki more valuable — not just by adding pages, but by enriching the connections between existing pages.
Why Normal AI Chats Don't Compound
In a standard chat, knowledge is ephemeral. Each session starts fresh. Even if you upload the same documents repeatedly, the LLM re-derives the same insights from scratch. Nothing accumulates.
The same is true of most RAG systems: they index raw documents and retrieve chunks at query time. The retrieval gets the right fragments, but no synthesis is built up. Nothing is compiled. Ask the same complex question twice and you get the same assembly process twice.
How Wiki Knowledge Compounds
When a new source arrives, the LLM doesn't just index it. It integrates it:
- Updates entity pages with new information
- Flags contradictions with existing claims
- Strengthens or challenges the evolving synthesis
- Adds cross-references from the new source to existing pages and back
The cross-references are already there next time. The contradictions have already been flagged. The synthesis already reflects everything that was read.
The wiki is pre-compiled knowledge. RAG re-compiles on every query.
The Maintenance Problem
Wikis maintained by humans decay. The maintenance burden grows faster than the value — updating cross-references, keeping summaries current, noting when new data contradicts old claims. Humans abandon wikis because no one wants to do the bookkeeping.
LLMs don't get bored. They don't forget to update a cross-reference. The cost of maintenance is near zero. This is the practical reason the wiki pattern works: the entity that's best at the tedious maintenance work is the same entity that reads and writes the wiki.
In Practice
One X user turned 383 scattered files and over 100 meeting transcripts into a compact wiki and dropped token usage by 95% when querying with Claude. The drop came from two sources: better navigation (index + hot cache vs. full document search) and pre-compiled synthesis (no re-deriving the same insights from scratch).
Connections
See LLM Wiki Pattern for the full architecture. See Hot Cache for the session context mechanism. See Andrej Karpathy for the origin of this framing.