I've been working on some parts of this problem, specifically capturing and retaining other semantically useful layers of the systems we build as we build and maintain them.
By introducing progressive semantically enriching layers (starting with prose, reasoning and terminology and going all the way into specifying interaction surfaces), we can reduce the dark matter between spec and code, make code more disposable – if your semantics live in the spec layer rather than the implementation, you can throw away and regenerate the implementation without losing understanding – and, critically, give LLMs a way to navigate a graph of knowledge instead of gobbling up walls of text.
This document describes the conceptual architecture of our company workspace - a text-based, version-controlled knowledge and operations hub designed to replace tools like Notion and Webflow while being natively accessible to AI assistants.
By introducing progressive semantically enriching layers (starting with prose, reasoning and terminology and going all the way into specifying interaction surfaces), we can reduce the dark matter between spec and code, make code more disposable – if your semantics live in the spec layer rather than the implementation, you can throw away and regenerate the implementation without losing understanding – and, critically, give LLMs a way to navigate a graph of knowledge instead of gobbling up walls of text.
https://clayers.com -- https://github.com/CognitiveLayers/clayers