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.
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 '_, _ ='.
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).
