The Semantic Glyph Engine: A Methodology for Visual Compression of Textual Topology

Document ID: CNL-FN-2026-001
Version: 1.0
Date: January 1, 2026
Author: Michael P. Hamilton, Ph.D.

AI Assistance Disclosure: This field note was developed through morning dialogue with Claude (Anthropic, claude-opus-4-5-20250514). The AI contributed to conceptual synthesis, feature extraction methodology, prompt grammar development, and manuscript drafting. The author takes full responsibility for the content and conclusions.

Abstract

This note captures an emerging methodology for rendering essays as visual glyphs—translating textual topology into image form. Prompted by Karpathy’s observation that LLMs need their own GUI (visual output native to the medium), we explored whether the Coffee with Claude essay corpus could be compressed into both textual synopses and generated images that preserve semantic architecture. Using “The Cognitive Prosthesis” as test case, we developed a four-stage pipeline: feature extraction, symbol mapping grammar, arrangement rules, and prompt assembly. Two rendering modes were tested: rich atmospheric (nano-banana style) and constrained symbolic (Pictish). Results suggest the method captures recognizable topology across modalities. This note documents the conceptual framework and proposes implementation architecture for future development.

1. Origin

Three sources converged on the morning of January 1, 2026:

1. Karpathy’s “2025 LLM Year in Review” — observing that LLMs need a GUI equivalent; text is the model’s native format, not the human’s. The “nano banana” concept: joint text-image-knowledge generation, not illustration but native multimodal expression.
2. Karpathy’s “Power to the People” — the inversion of technology diffusion; LLMs benefiting individuals more than institutions.
3. Herman’s “Aggressive Bots” — the shadow side: vibe coding democratizes the capacity to accidentally DDoS websites.

The question emerged: could the CWC essay corpus serve as test data for both the synopsis engine (textual compression) and the image prompt engine (visual compression)?

2. Conceptual Lineage

This methodology extends four prior CNL documents:

The Pictish analysis moved: visual form → Hu moments → taxonomy. We propose the inverse: semantic content → feature extraction → visual form.

3. Pipeline Architecture

3.1 Four-Stage Process

[Essay Text] 
    → Stage 1: Feature Extraction (structured JSON)
    → Stage 2: Symbol Mapping Grammar (lookup table)
    → Stage 3: Arrangement Rules (composition logic)
    → Stage 4: Prompt Assembly (template population)
    → [Generated Image]

3.2 Stage 1: Feature Extraction

Input: Essay text + assigned tags

Output: Structured JSON

{
  "tags": ["cognitive prosthesis", "dialogic production", "..."],
  "domain_weights": {
    "SELF": 0.6,
    "LIFE": 0.2,
    "EARTH": 0.15,
    "HOME": 0.05
  },
  "temporal_reach": "decades",
  "temporal_layers": 3,
  "voice": "memoir-theoretical",
  "structure": "dialectical-recursive",
  "motifs": ["boundary_dissolution", "threshold", "recursion", "extension"],
  "emotional_register": ["contemplative", "wonder", "continuity"],
  "citation_density": "moderate",
  "first_person_intensity": "high"
}

3.3 Stage 2: Symbol Mapping Grammar

Two vocabularies developed:

Rich (atmospheric) parameters:

Constrained (Pictish) vocabulary:

3.4 Stage 3: Arrangement Rules

Rich mode: Compositional grammar derived from feature weights.

Pictish mode:

• Primary symbol (highest weight) → upper register
• Secondary → lower register
• Tertiary → flanking position
• Fill type selected by structural quality

3.5 Stage 4: Prompt Assembly

Templates populated from mapped parameters. Two templates developed:

• Rich atmospheric template (~150 words)
• Pictish constrained template (~100 words)

4. Test Case: “The Cognitive Prosthesis”

4.1 Feature Extraction Results

Tags (7): cognitive prosthesis, dialogic production, ecological intelligence, electronic museum institute, human-ai collaboration, macroscope, writing and thinking

Domain weights: SELF (primary), LIFE (secondary), EARTH (tertiary), HOME (background)

Temporal architecture: Immediate (single morning), career span (41 years), cultural (millennia), deep time (Cambrian analogy)

Structure: Dialectical-recursive (two articles in tension → third-way synthesis → recursive turn)

Motifs: boundary dissolution, threshold/dawn, recursion, extension not replacement

4.2 Rich Rendering

Prompt grammar emphasized: dawn light transition, archaeological strata, connective tissue (neural/mycelial), glass sphere with spiral (observer inside observation), organic-digital threshold texture, 35°F/93% humidity atmosphere.

Generated image captured: layered platforms with root-structures and circuit-traces, transparent recursive sphere, dawn color transition, sensor-like vertical forms, atmospheric weight of waiting.

4.3 Synopsis Output

~150 words preserving: dialectical origin, 1984 artifact, prosthesis metaphor, recursive turn, open ending. The textual and visual compressions recognized each other—same topology, different modality.

4.4 Pictish Mapping

Symbols selected:

• Double Disc and Z-Rod (upper) — dialogic production
• Crescent and V-Rod with spiral fill (lower) — career arc, recursive
• Mirror (flanking) — observer inside observation

4.5 Pictish Prompt Grammar

A Class I Pictish symbol stone, tall irregular slab of grey-green 
sandstone with weathered surface and lichen traces.

Incised lines only, no relief carving. The ancient style—clean 
geometry cut into stone with iron tools, filled with time.

Upper register: Double Disc and Z-Rod symbol. Two circles connected 
by flowing diagonal rod, suggesting exchange, dialogue, two-becoming-one.

Lower register: Crescent and V-Rod with Type A spiral fill. The crescent 
opens upward/rightward toward dawn. Tight spiral ornament within—
continuous, recursive, the same pattern turning inward. V-rod crosses 
at precise angle, extending reach beyond the arc.

Left flank: Mirror symbol with handle. Simple, small, the sign of 
seeing-oneself-seeing.

Stone surface shows natural fractures, weathering, forty centuries 
of rain and wind. Morning light rakes across at low angle, catching 
the incised lines, casting subtle shadows that reveal the cuts.

Background: Scottish hillside, grey sky, wet grass. The stone stands 
where it was placed, still marking something we half-remember.

No text. No modern elements. Archaeological silence.

5. Open Questions

1. Vocabulary completeness: Does the Pictish symbol set (~40 symbols) provide sufficient coverage for CWC semantic space?
2. Validation criteria: How do we assess whether a glyph “captures” an essay’s topology? Reader recognition? Author recognition? Clustering consistency?
3. Scalability: Can Stage 1 extraction be fully automated, or does it require human-in-loop for motif identification?
4. Cross-essay consistency: Will the same motif (e.g., “recursion”) map to the same symbol across different essays?
5. Generalization: Does the method hold for essays with different structural patterns (narrative vs. analytical vs. dialectical)?

6. Next Steps

1. Test generalization: Apply pipeline to 2-3 additional CWC essays with different structural profiles
2. Formalize extraction schema: Develop complete JSON schema for Stage 1 output
3. Build symbol vocabulary: Complete mapping table for both rich and Pictish modes
4. Prototype implementation: PHP/Python script implementing Stages 1-4
5. Integration: Consider as optional CWC export format alongside existing markdown/docx

Document History

End of Field Note