DefaultConfig now describes a more realistic world: a moderately novel fake
(novelty 0.3), some ambient harm awareness (0.2), and a strong but imperfect
education program (programEffect 0.8) so educated students mostly, not always,
refuse. The headline shifts from 99/70/7 (a perfect program) to 100/83/21 out
of 120 (83/69/18 percent): no program >> random >> most-connected still holds,
targeting still wins by ~4x, but the program is no longer a perfect wall.
Golden values re-pinned in the engine and API tests; the preset base matches.
The forward-chance formula test now neutralises its baseline so it pins the
formula, not the tuned defaults.
Novelty, HarmAwareness and ProgramEffect each get a 0..1 realism bound,
served in ConfigBounds and enforced in ValidateConfig with field-named
errors (the frontend matches on the JSON name to flag the right control).
Reject/boundary test cases added.
Forwarding was a single global ForwardProb; make it a per-student
composite (Config.ForwardChance): baseline propensity raised by the
fake's Novelty, lowered by ambient HarmAwareness, and scaled down for an
educated student by ProgramEffect (1 = today's hard block). RunCascade
now takes a precomputed per-student []float64 chance and has no education
special case. Defaults are behaviour-neutral, so the 82/58/6 golden
tests are unchanged; the model is tuned in a later slice.
ConfigBounds (students 10-500, edgesPerNode 1-8, forwardProb 0-0.9,
triangleProb 0-1) caps what RunScenario accepts: a student does not
keep 150 close friendships and a guaranteed forward is not a real
base rate. numEducated gains its missing upper limit (numStudents).
Bounds-side validation also protects the public server from absurd
numStudents values. GET /api/config/default now returns
{config, bounds} so the frontend can drive its controls from the
same numbers; the frontend does not call it yet, so this commit
changes no UI behaviour.
One panel's whole world in one call: effective config in, echoed
config + cascade result + undirected edge list out. Edges are
[from, to] pairs with from < to in deterministic node order;
Graph.Edges() walks the adjacency once, GraphEdges(config) rebuilds
the seeded world (~25us) so Result stays lean and /api/comparison
stays untouched. This closes the topology gap the design brief
flagged; the frontend's seeded d3-force layout consumes these pairs.
Go bits: [][2]int is a slice of fixed-size arrays; [2]int is a value
type, comparable, and JSON-marshals to [a, b], exactly the wire
shape the spec asks for.
tygo regen includes a fix: engine.Strategy now maps to the generated
Strategy type instead of decaying to 'any' in ScenarioRequest.
Verified live through the dev stack: 7/120 reached, 351 edges.
App.vue fetches the default config, posts it to /api/comparison, and
renders ComparisonTable; the Vite dev server proxies /api to the Go
server so the browser sees one origin. src/lib/api.ts is the only
fetch code and uses exclusively generated types: no shape is defined
on the frontend. Scaffold example components removed.
Engine fix surfaced by the smoke test: a nil Go slice marshals to
JSON null, violating the generated 'educated: number[]' contract;
RunScenario now returns an empty slice instead.
Verified end to end: curl through the Vite proxy returns the golden
99/70/7. Vitest covers the table rendering; type-check, oxlint,
eslint clean. This completes the milestone 2 parity check.
rng.go is a construction point, not an abstraction wall; components
take *rand.Rand directly (idiomatic Go dependency injection) and only
RunScenario creates streams, from config seeds. Decided 2026-06-10
over the alternatives (interface wall, folding newRand into
scenario.go).
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.
The strategy list moves into the engine (AllStrategies), where the API
and frontend will read it too: one source of truth, per the handoff.
main shrinks to the standard Go shell pattern: all work happens in
run(out io.Writer) error; main only maps the error to stderr and the
exit code. Writing to an interface instead of stdout is what lets
main_test.go capture output in a bytes.Buffer.
errcheck flagged every unchecked Fprintf, so formatting became pure
Sprintf string building with one checked write at the end: nicer than
discarding four errors with '_, _ ='.
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.
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).
Two ways to spend the same education budget: a uniform random sample
(spray and pray) vs the highest-degree hubs, ties broken stably towards
lower node numbers so the pick is deterministic. The origin is never
educated; they post the fake. rng.Shuffle does a seeded Fisher-Yates,
so the random pick is reproducible too. min() is a builtin since
Go 1.21.
One random threshold per DIRECTED edge, drawn once per world: scenarios
sharing thresholds differ only in who is educated, the prototype's
'same world, different lever' trick. The cascade is plain BFS in rounds;
an educated student receives the fake but never forwards it, which is
the entire effect of the lever. The result stores the first-reached
round per node (exactly what a frontend animation needs).
Tests hand-craft a 4-node line graph with exact thresholds, so every
expectation is exact: spread, directional blocking, educated cutoff.
Preferential attachment via an attachment pool that holds one entry per
edge endpoint, so uniform draws are degree-proportional: that is the
whole hub-forming mechanism. A triangle step closes friend-of-a-friend
links with probability triangleProb, giving friend-group clustering.
Semantics ported from networkx, NOT its RNG stream (per the handoff,
no cross-language number matching).
Tests are property-based: size, edge bounds, connectivity, hub
formation across seeds, plus exact determinism for a fixed seed.
'go test ./...', gofmt and golangci-lint all clean.
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.
math/rand/v2 (Go 1.22+) replaces the old math/rand: PCG generator,
no global Seed(), and rand.New(rand.NewPCG(seed, 0)) gives an isolated
deterministic stream. Each randomness consumer (graph, thresholds,
education sampling) will get its own stream so levers vary independently.
Tests live next to the code as *_test.go; 'go test ./...' runs them all.
'for i := range 100' is Go 1.22 range-over-int.
