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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 91ce0c1ca5 engine: benchmark baseline (HolmeKim, RunScenario) 
Manual baseline, not a CI gate: go test -bench=. -benchmem.
First numbers on a Ryzen 7 5800X: HolmeKim ~25us/op (48KB, 671
allocs), full RunScenario ~45us/op (78KB, 681 allocs). Plenty fast
until milestone 5 runs thousands of cascades per request; this is the
'before' picture for that work.
b.Loop() (Go 1.24+) replaces the old 'for i := 0; i < b.N; i++'
pattern and prevents the compiler optimising the loop body away.
2026-06-10 12:50:53 +02:00
cmd/spreadlab refactor cmd: thin main, testable run(io.Writer), AllStrategies in engine 2026-06-10 12:39:03 +02:00
internal/engine engine: benchmark baseline (HolmeKim, RunScenario) 2026-06-10 12:50:53 +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.