Agent-based dashboard: how a deepfake spreads through a school network, and where a limited education budget actually helps. Illustrative, not validated.
Justin Visser
e73db0bdd4
The generator's working state (graph, attachment pool, rng) moves into a holmeKimBuilder struct so each algorithm step is a small named method: attachNewNode, link, pickMutualFriend, degreeProportionalSample. Max nesting drops from five levels to two. The RNG call order is untouched, and the golden test (99/70/7 reached) plus the fixed-seed determinism test prove behaviour is bit-for-bit identical. Go bits: pointer receivers ((b *holmeKimBuilder)) let methods mutate the builder; (value, ok) multiple returns are the idiom for 'may not exist', as in pickMutualFriend. |
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| cmd/spreadlab | ||
| internal/engine | ||
| .gitignore | ||
| go.mod | ||
| LICENSE | ||
| README.md | ||
spreadlab
Self-hosted dashboard that runs an agent-based deepfake-spread model live: change the levers, watch the spread, and (later) search for the best intervention under a budget. Output is illustrative, not validated.
Status: milestone 1, porting the simulation engine.
Layout:
internal/engine/- the pure simulation engine (no web dependencies)cmd/spreadlab/- the server binary (milestone 2)web/- Vue 3 + TypeScript frontend (milestone 2+)
MIT licensed.