94 lines
3.6 KiB
Markdown
94 lines
3.6 KiB
Markdown
---
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type: source
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title: "Nate Herk LLM Wiki Transcript"
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source_type: transcript
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author: "Nate Herk"
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date_published: 2026-04-07
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url: "https://youtube.com/@nateherk"
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confidence: high
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key_claims:
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- "LLM wiki makes knowledge compound like interest — nothing is re-derived on every query"
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- "Hot cache (~500 words) enables cross-project context without crawling the full wiki"
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- "One article can generate 15-25 wiki pages with full cross-references"
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- "One user dropped token usage by 95% switching from inline context files to wiki"
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- "Obsidian is the IDE, Claude is the programmer, the wiki is the codebase"
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- "Index file is enough at small scale (~100 sources) — no RAG infrastructure needed"
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created: 2026-04-07
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updated: 2026-04-07
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tags:
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- source
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- llm-wiki
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- obsidian
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- karpathy
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status: mature
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related:
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- "[[LLM Wiki Pattern]]"
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- "[[Hot Cache]]"
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- "[[Compounding Knowledge]]"
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- "[[Andrej Karpathy]]"
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- "[[index]]"
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sources:
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- "[[.raw/nate-herk-llm-wiki-transcript.md]]"
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---
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# Nate Herk LLM Wiki Transcript
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Raw source: [[.raw/nate-herk-llm-wiki-transcript.md]]
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Nate Herk demonstrates the [[LLM Wiki Pattern]] in practice. He shows two live vaults: one for his YouTube transcript archive (36 videos) and one personal second brain. He breaks down Andrej Karpathy's original post and shows a 5-minute setup workflow.
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---
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## Key Takeaways
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**The core insight**: normal AI chats are ephemeral. The wiki makes knowledge compound. Every source ingested, every question answered, every analysis filed — all of it stays and grows richer over time.
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**The stack is simple**: Claude Code + Obsidian + a folder of markdown files. No vector databases, no embeddings, no infrastructure. Just files and Claude.
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**The hot cache**: a ~500-word file (`wiki/hot.md`) that captures recent context. In an executive assistant setup, this prevented having to crawl dozens of wiki pages at the start of each session. See [[Hot Cache]].
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**Cross-project referencing**: other Claude Code projects can read this vault by pointing at it in their CLAUDE.md. Nate's executive assistant reads from his herk-brain vault. Token usage dropped significantly compared to inline context files.
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**At scale**: the index file alone is sufficient for hundreds of pages. Vector RAG only becomes necessary at millions of documents.
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---
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## Obsidian as IDE
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Obsidian is just a markdown viewer with graph visualization. The graph view shows which pages are hubs (many connections) and which are orphans (none). Real-time — you can watch the wiki grow as Claude creates pages.
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The key Obsidian features used:
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- Graph view — visualize the knowledge structure
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- Backlinks — follow connections between pages
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- Dataview — query pages by frontmatter
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- Web Clipper — send articles directly to `.raw/` from any browser
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---
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## Workflow Demonstrated
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1. Install Obsidian, create a vault
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2. Paste Karpathy's LLM wiki idea into Claude Code
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3. Claude scaffolds the structure (raw/, wiki/, CLAUDE.md, index, log)
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4. Drop a source into `.raw/` using Web Clipper
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5. Tell Claude: "ingest this"
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6. Claude reads, creates 15-25 wiki pages, cross-references everything
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7. Query the wiki for insights
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The ingest for one article (AI 2027) took 10 minutes and created 23 pages: 1 source, 6 people, 5 organizations, 1 AI systems page, multiple concepts, plus an analysis.
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---
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## Entities Mentioned
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- [[Andrej Karpathy]] — originated the LLM wiki pattern
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- Nate Herk — demonstrated the pattern in this video
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---
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## Connections
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See [[LLM Wiki Pattern]] for the full architecture.
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See [[Compounding Knowledge]] for the core insight on why this works.
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See [[Hot Cache]] for the session context mechanism.
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