What Karpathy's LLM Wiki Is Missing (And How to Fix It)

Andrej Karpathy's

That's not enough.

When Karpathy talks about the LLM "noting where new data contradicts old claims" and "flagging contradictions," he's describing semantic relationships. But the underlying link format can't express any of them. [[Note A]] doesn't tell you whether Note A supports, contradicts, supersedes, or was caused by the current note. The meaning lives in the prose around the link, invisible to every tool in the Obsidian ecosystem.

This matters because the whole point of a compiled wiki is that the structure does work for you. If your graph can't distinguish "this supersedes that" from "this contradicts that," you're leaving some of the most valuable information trapped in unstructured text, which is exactly the problem you were trying to solve.

The fix: typed relationships inside wikilinks

On save, the plugin syncs matched types to YAML frontmatter automatically:

---

supersedes:

  - "[[Previous Analysis]]"

supports:

  - "[[Redis Paper]]"

references:

  - "[[Redis Paper]]"

---

That's it. Standard YAML frontmatter. with penfieldlabs/obsidian-wikilink-types.

What this changes

With typed links, your vault goes from a tangle of identical connections to a queryable knowledge graph. You can write Dataview queries like "show me everything that contradicts my current hypothesis." You can trace causation chains. You can see at a glance which notes have been superseded and which are current.

This is what Karpathy's pattern needs but doesn't have: links that carry meaning.

Gap 2: You shouldn't have to type every relationship yourself

A wiki with typed links is more useful than one without. But manually typing @supersedes and @contradicts on every note is tedious, and you'll miss connections that aren't obvious.

The whole premise of the LLM Wiki is that the LLM does the bookkeeping. So let it discover the relationships too.

The fix: AI-discovered typed relationships

The is the build phase. You give it to your agent with a vault path and walk away. The agent creates a git branch, surveys the vault, classifies notes as hubs or spokes, then works through them in priority order: hub-to-hub relationships first (the highest-value connections), then spoke-to-hub (the bulk of the work), then lateral spoke-to-spoke connections. It commits every 20-50 notes, writes a linking log with stats and confidence levels, and never touches your main branch. If you're running multiple agents in parallel (one per folder, say), the prompt includes coordination rules: each agent only writes to its assigned notes, verifies target files exist before linking, and logs anything it had to skip.

is a persistent memory and knowledge graph system for AI agents. It stores memories, artifacts, and typed relationships in a backend accessible via MCP (Model Context Protocol) from any compatible client.