Agent-based dashboard: how a deepfake spreads through a school network, and where a limited education budget actually helps. Illustrative, not validated.
Justin Visser
45fdf7ffa4
Config is the single source of truth for parameters (TS types will be generated from these structs in milestone 2); all randomness flows from its three seeds, so identical configs give identical results. Golden test pins the default world: none=99/120 (82%), random=70/120 (58%), most-connected=7/120 (6%). Same story as the prototype's 85/64/8 with different dice; the ordering and the collapse are asserted explicitly, exact Python numbers are out of scope by design. 'go run ./cmd/spreadlab' prints the three-scenario comparison. This completes milestone 1 (engine ported, parameterised, tested). |
||
|---|---|---|
| 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.