Codex Unit (CU)
A self-contained, single-topic knowledge object designed for independent consumption and AI retrieval.
Module / Chapter-Unit
A larger CU variant optimized for “single-session comprehension,” typically bounded by context-window constraints.
Magazine / Codex Collection
A curated set of Codex Units sharing a domain framing.
Knowledge Graph
A multi-layer structure composed of:
- nodes (concepts, messages, triplets)
- edges (SIMILAR_TO, DERIVED_FROM, PREREQUISITE_OF, PART_OF)
- embeddings for soft semantic alignment
Triplet
Atomic extracted relation: (subject, predicate, object) with provenance metadata.
Dual Graph System
- Raw Graph: immutable extraction layer from conversation
- Refined Graph: AI-processed abstraction layer for retrieval and synthesis
Abstraction Pyramid
Recursive compression structure:
- messages → triplets → nodes → clusters → meta-concepts → summaries
Divergence / Convergence Streams
- Divergence: generative expansion of ideas
- Convergence: structuring, pruning, synthesis into usable artifacts
Context Window Boundary
A hard structural constraint used as a design unit for content architecture, not a limitation.
AI Mediator / Compiler
System layer that:
- extracts structure from conversation
- builds and refines graphs
- compiles books/wikis/presentations from graph traversal
Circulation Unit
A knowledge object designed for reuse, sharing, recomposition, and network propagation.
HOW THE CONCEPT WORKS
1. Conversation → Knowledge Extraction
All interaction (especially user-generated content) is treated as raw epistemic material.
Pipeline:
- conversation → message segmentation
- message → triplet extraction
- triplets → nodes + edges
- nodes → embeddings + similarity graph
Key property: provenance is preserved at every stage.
2. Graph Construction (Graph-First Principle)
Knowledge is stored as a non-linear multigraph, not documents.
- Similarity is modeled via edges, not merges
- Nodes remain distinct to preserve semantic reversibility
- Centrality identifies conceptual anchors
- Community detection identifies concept regions
3. Recursive Abstraction (Living Compression Loop)
The graph is continuously compressed upward:
- cluster nodes by similarity
- summarize clusters into concept nodes
- re-embed summaries into higher layers
- repeat recursively
This produces a pyramid of meaning, where each layer is both:
- a compression of lower layers
- a new input for further abstraction
4. Dual-Layer Knowledge System
Two synchronized but distinct representations:
- Raw Layer: faithful record of conversational reality
- Refined Layer: AI-optimized conceptual structure
Similarity links (SIMILAR_TO) are preferred over destructive merges to preserve reversibility.
5. AI as Compiler and Orchestrator
AI performs three roles:
- Compressor: extracts structure from dialogue
- Amplifier: generates new synthesis and narratives
- Assembler: builds outputs (books, wikis, presentations)
Narrative is not authored linearly; it is traversed from the graph.
6. Dynamic Content Assembly (Living Books)
Books are not stored objects but temporary graph traversals:
- user intent → graph query
- graph traversal → subgraph selection
- AI compilation → narrative construction
- output → personalized artifact
Books are effectively ephemeral interfaces into a persistent graph.
7. Feedback Loop System
User interaction feeds back into system evolution:
- reading behavior
- selection patterns
- discussion depth
- swap/return behavior (subscription models)
These signals reshape:
- node centrality
- recommendation weights
- future assembly paths