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Agent-based dashboard: how a deepfake spreads through a school network, and where a limited education budget actually helps. Illustrative, not validated.
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Justin Visser 33a85cb720 engine: undirected simple graph with deterministic neighbour order 
Adjacency lives in slices, not maps, on purpose: Go randomises map
iteration order between runs, and the engine's determinism guarantee
needs every graph walk to visit neighbours in the same order. AddEdge
mirrors networkx semantics (duplicates and self loops are no-ops) so
the Holme-Kim port can lean on the same behaviour.
Receiver names stay short per Go convention (g *Graph); everything
else uses descriptive names.
2026-06-10 11:58:45 +02:00
cmd/spreadlab batman 2026-06-10 11:57:10 +02:00
internal/engine engine: undirected simple graph with deterministic neighbour order 2026-06-10 11:58:45 +02:00
.gitignore batman 2026-06-10 11:57:10 +02:00
go.mod batman 2026-06-10 11:57:10 +02:00
LICENSE batman 2026-06-10 11:57:10 +02:00
README.md batman 2026-06-10 11:57:10 +02:00

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.