3D Knowledge Terrain

Most knowledge maps (Obsidian graphs, concept maps, mind maps) are flat networks that show connections but nothing about depth, density, or downstream importance. “Linear Algebra” looks no different from “Arithmetic” in a 2D graph, even though one is a mountain summit and the other a foothill. Terrain as a metaphor for knowledge structure doesn’t really exist as a tool.

The idea maps a domain’s prerequisite structure onto actual 3D terrain: elevation for how many prerequisite concepts stand between you and a topic, ridge complexity for how much branches off it, and radiance for how much downstream work depends on it existing, so Linear Algebra glows, because it unlocks machine learning, graphics, physics, and optimization all at once. A first version would hand-curate one domain (mathematics, arithmetic through linear algebra) at a small scale, just to answer one question cheaply: does seeing dependency depth rendered as literal elevation actually produce a moment of real spatial insight, or does the metaphor not hold up in practice?