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?