gigaslop/src/sim/balance.ts
type-two 32a2d3a248 M2: platform-decay treadmill, infra tier, Cash Furnace
Platforms adapt to your slop (ALGO HEAT): each publish raises spam-filter
wariness which throttles all view rates up to 85%; training a new model
tier slips past the filters. Verified loop: publish -> squeeze -> train ->
relief. New hardware: 20A sub-panel (+1500W), fiber uplink (1Gbps, 8
ports), Cash Furnace (burns $1.50/s for +40% views, breaker-immune).
Fix: New Game wipe no longer clobbered by the beforeunload autosave.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-01 10:05:30 +10:00

49 lines
1.8 KiB
TypeScript

// All gameplay tunables in one place. Sim-day pacing target: first upgrade ~2 min in.
export const BAL = {
SIM_DT: 0.1, // s per tick (10 Hz)
START_CASH: 25,
ROOM_W: 10,
ROOM_H: 10,
// Power
BREAKER_WATTS: 1400, // bedroom circuit
KWH_PRICE: 0.35, // $/kWh — punchy so burn rate is visible
CHECKPOINT_LOSS: 0.25, // training progress lost on breaker trip
// Heat (per-tile scalar, °C above ambient)
AMBIENT_C: 24,
DIFFUSE: 0.06, // neighbor averaging factor per tick
DECAY: 0.004, // passive loss toward ambient per tick
FAN_DIFFUSE_BONUS: 0.2, // fans spread heat (3x3)
THROTTLE_C: 85,
THROTTLE_FLOOR: 0.2, // perf multiplier at max overheat
// Data
MBPS_PER_TOK: 0.5, // bandwidth cost of shipping slop per token/s
// Economy
TOK_PRICE: 0.001, // baseline API $/token (inference share)
VIDEO_COST_TOK: 200, // tokens to render one slop video
VIDEO_LIFETIME_S: 90,
VIDEO_BASE_VIEWS_S: 10, // views/s at birth, tier 1, non-viral
VIRAL_CHANCE: 0.12,
VIRAL_MULT_MAX: 25,
CPM: 2.4, // $ per 1000 views
// Platform adaptation treadmill: every published video teaches the platforms'
// spam filters; wariness throttles ALL view rates until you train a fresh model.
ADAPT_PER_VIDEO: 0.03, // wariness gained per publish (soft-capped toward 1)
ADAPT_DECAY_S: 0.0005, // passive wariness loss per second (platforms drift)
ADAPT_MAX_PENALTY: 0.85, // at 100% wariness, views are cut by this fraction
ADAPT_TIER_RELIEF: 0.35, // wariness is multiplied by this on tier-up
// Training / model tiers: tokens required to reach tier i+1, revenue multiplier per tier
TIERS: [
{ name: "SlopLM-1B", mult: 1 },
{ name: "SlopLM-7B", mult: 2.2, tokens: 4_000 },
{ name: "SlopLM-70B", mult: 5, tokens: 40_000 },
{ name: "SlopMoE-8x70B", mult: 12, tokens: 250_000 },
],
};