Gravity systems

Semantic Gravity Map

let a document or app reveal which concepts carry the most weight

frame — bodies 0 relationships 0

Running the actual recipe live via applyRecipe() — not a mock.

"Semantic Gravity Map" — let a document or app reveal which concepts carry the most weight. It has a gravity layer (universal attraction toward mass), a link layer (ropes / cloth between neighbours), a cohesion layer (flocking — pull toward neighbours), and a memory layer (a decaying trail / hysteresis). The active render stack is heatmap, metaballs, links, particles.

Gravity pulls heavy concepts to the center while link and cohesion bind related ones; importance is mass, memory keeps recurrence felt over time.

Runtime tokens: gravity link cohesion memory
Concepts: semantic mass
Metrics: mass attention relation-strength density mass, relation-strength, density are host-supplied lanes — drive them with data-field-<metric> (or a domain model); without that, their --field-* stays inert.
Diagnostics: potential topology heatmap
Render: heatmap · metaballs · links · particles
Reduced motion: a weighted concept index with an importance rail and a section density map
Without motion: mass becomes a sorted importance ranking with explicit weight values per concept

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<field-root></field-root>
  <div data-body="gravity" data-strength="1.1" data-range="400" data-feedback></div>
  <div data-body="link" data-strength="0.7" data-range="320"></div>
  <div data-body="cohesion" data-strength="0.6" data-range="280"></div>
  <div data-body="memory" data-strength="0.5" data-range="260"></div>

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See also