The concept of an LLM Wiki, popularized by Andrej Karpathy’s viral pattern, has revolutionized the way we organize and access knowledge. However, as we scale our wikis to hundreds or thousands of notes, we begin to encounter structural gaps that break down the system. In this article, we’ll explore the two main gaps in Karpathy’s LLM Wiki pattern and propose practical solutions to address them.
Semantic Linking: The Missing Piece
The first gap in Karpathy’s LLM Wiki pattern is the lack of semantic linking. In the current implementation, every link is identical, carrying only one bit of information: “these two notes are connected.” However, when we want to express more complex relationships between notes, such as “this note contradicts that” or “this note supersedes that,” we’re forced to rely on the prose surrounding the link, which is invisible to most tools in the Obsidian ecosystem.
Imagine a graph view of your Obsidian vault, where every connection looks the same. You can’t tell whether a note supports, contradicts, or supersedes another note. This limitation makes it difficult to extract valuable information from your compiled wiki, defeating the purpose of the LLM Wiki pattern.
Typed Relationships Inside Wikilinks
To address this gap, we can use typed relationships inside wikilinks. The obsidian-wikilink-types plugin adds semantic relationship types to standard Obsidian wikilinks using the @ syntax. For example, [[Previous Analysis|The new research @supersedes the previous analysis]] or [[Redis Paper|This @supports the caching architecture in @references the Redis paper]].
When you type @ inside a wikilink alias, you get an autocomplete dropdown of 24 relationship types, including supersedes, contradicts, causes, supports, evolution_of, prerequisite_for, and more. On save, the plugin syncs matched types to YAML frontmatter automatically.
Here’s an example of what the YAML frontmatter might look like:
supersedes: - " [[Previous Analysis]]" supports: - " [[Redis Paper]]" references: - " [[Redis Paper]]"
This YAML frontmatter can be queried by Dataview, allowing you to write queries like “show me everything that contradicts my current hypothesis.” You can also trace causation chains and see at a glance which notes have been superseded and which are current.
Benefits of Typed Relationships
Typed relationships inside wikilinks offer several benefits:
- Improved queryability: With typed links, your vault becomes a queryable knowledge graph, making it easier to extract valuable information.
- Increased accuracy: Typed relationships reduce the likelihood of misinterpretation, ensuring that you understand the relationships between notes accurately.
- Enhanced collaboration: Typed relationships facilitate collaboration by providing a standardized way of expressing relationships between notes.
Discovering Typed Relationships with AI
The second gap in Karpathy’s LLM Wiki pattern is the need for manual typing of relationships. While typed links are more useful than untyped links, manually typing @supersedes and @contradicts on every note is tedious and prone to errors.
One solution is to use AI to discover typed relationships. AI can analyze the content of notes and automatically suggest relationships between them. This approach has several benefits:
- Reduced manual effort: AI-discovered relationships save time and effort, making it easier to maintain a large wiki.
- Improved accuracy: AI can analyze complex relationships between notes, reducing the likelihood of misinterpretation.
- Enhanced scalability: AI-discovered relationships can handle large wikis with ease, making it an ideal solution for scaling Karpathy’s LLM Wiki pattern.
Benefits of AI-Discovered Typed Relationships
AI-discovered typed relationships offer several benefits:
- Improved scalability: AI can handle large wikis with ease, making it an ideal solution for scaling Karpathy’s LLM Wiki pattern.
- Reduced manual effort: AI-discovered relationships save time and effort, making it easier to maintain a large wiki.
- Increased accuracy: AI can analyze complex relationships between notes, reducing the likelihood of misinterpretation.
Real-World Examples
To illustrate the benefits of typed relationships and AI-discovered relationships, let’s consider a few real-world examples:
Example 1: Scientific Research
Imagine a researcher working on a project to develop a new cancer treatment. They have a large wiki with notes on various research papers, experiments, and results. With typed relationships, they can express complex relationships between notes, such as “this paper contradicts that” or “this experiment supersedes that.” AI-discovered relationships can also help them identify patterns and connections between notes, making it easier to extract valuable insights.
Example 2: Knowledge Graphs
Knowledge graphs are a type of semantic network that represents relationships between entities. With typed relationships, knowledge graphs can be more accurate and scalable. AI-discovered relationships can help identify complex relationships between entities, making it easier to extract valuable insights.
Example 3: Personal Knowledge Management
Personal knowledge management is a critical aspect of productivity. With typed relationships, individuals can express complex relationships between notes, making it easier to extract valuable insights. AI-discovered relationships can also help identify patterns and connections between notes, making it easier to prioritize tasks and make informed decisions.
Conclusion
In conclusion, Karpathy’s LLM Wiki pattern has revolutionized the way we organize and access knowledge. However, as we scale our wikis to hundreds or thousands of notes, we encounter structural gaps that break down the system. By using typed relationships inside wikilinks and AI-discovered relationships, we can address these gaps and create a more scalable, accurate, and collaborative knowledge management system.
The benefits of typed relationships and AI-discovered relationships are numerous, including improved queryability, increased accuracy, enhanced collaboration, and reduced manual effort. By adopting these solutions, we can create a more efficient and effective knowledge management system that scales with our needs.
Recommendations
Based on our analysis, consider the following:
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Recommendation 1: Implement Typed Relationships
Implement typed relationships inside wikilinks using the obsidian-wikilink-types plugin. This will improve queryability, increase accuracy, and enhance collaboration.
Recommendation 2: Use AI-Discovered Relationships
Use AI-discovered relationships to reduce manual effort, improve accuracy, and enhance scalability. This will make it easier to maintain a large wiki and extract valuable insights.
Recommendation 3: Experiment with Different Relationship Types
Experiment with different relationship types, such as supersedes, contradicts, causes, supports, and more. This will help you understand the benefits and limitations of each relationship type and improve the accuracy of your knowledge graph.
Future Directions
The future of knowledge management is exciting, with many opportunities for innovation and improvement. Some potential areas for future research include:
1. Improved AI-Discovered Relationships
Develop more accurate and efficient AI-discovered relationships that can handle complex relationships between notes.
2. Scalability
Develop solutions that can handle large wikis with ease, making it easier to maintain and scale Karpathy’s LLM Wiki pattern.
3. Integration with Other Tools
Integrate typed relationships and AI-discovered relationships with other tools, such as note-taking apps and project management software.
Conclusion
In conclusion, typed relationships and AI-discovered relationships offer a powerful solution for improving the scalability, accuracy, and collaboration of knowledge management systems. By adopting these solutions, we can create a more efficient and effective knowledge management system that scales with our needs.
Recommendations for Future Research
Based on our analysis, consider the following areas for future research:
Recommendation 1: Improved AI-Discovered Relationships
Develop more accurate and efficient AI-discovered relationships that can handle complex relationships between notes.
Recommendation 2: Scalability
Develop solutions that can handle large wikis with ease, making it easier to maintain and scale Karpathy’s LLM Wiki pattern.
Recommendation 3: Integration with Other Tools
Integrate typed relationships and AI-discovered relationships with other tools, such as note-taking apps and project management software.
