Agent-based dashboard: how a deepfake spreads through a school network, and where a limited education budget actually helps. Illustrative, not validated.
Justin Visser
e9f36f509e
internal/api stays a translation layer: decode (with
DisallowUnknownFields so typos 400 instead of silently defaulting),
run the engine, encode; all validation stays in the engine. Routes use
Go 1.22 mux patterns ('POST /api/comparison'), so wrong methods get
405 from the stdlib for free. httptest runs handlers fully in memory;
the API test re-pins the 99/70/7 golden values end to end.
ComparisonResponse is added to tygo.yaml with a type mapping so the
generated web/src/types/api.ts reuses the engine's TS types.
cmd/spreadlab now serves on -addr (default localhost:8080); -table
keeps the CLI comparison as a sanity check.
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|---|---|---|
| cmd/spreadlab | ||
| internal | ||
| web | ||
| .gitignore | ||
| generate.go | ||
| go.mod | ||
| go.sum | ||
| LICENSE | ||
| README.md | ||
| tygo.yaml | ||
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.