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

@techreport{cnl2026semantic, author = {}, title = {The Semantic Glyph Engine: A Methodology for Visual Compression of Textual Topology}, institution = {Canemah Nature Laboratory}, year = {2026}, number = {CNL-FN-2026-001}, month = {january}, url = {https://canemah.org/archive/document.php?id=CNL-FN-2026-001}, 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.} }