Eliminates the 22K-object switchRegistry that caused O(n×m) scans 4x per tick.
Network health is now tracked as aggregate counts per tier (totalByTier/healthyByTier)
with RepairBatch timers, cutting late-game tick cost from ~50ms to ~0.3ms.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a full simulation harness (game-simulation package) with greedy/random strategies,
36-metric diagnostics, multi-run orchestration via child processes, and a statistical
interpreter. Includes 2.3x engine performance optimizations (research bonus caching,
per-DC dirty tracking, reduced allocations in tick pipeline, single-pass loops).
Fixes a critical balance bug where training pipelines stalled on insufficient VRAM would
permanently block training slots — the engine never re-checked stalled pipelines, and the
greedy strategy didn't pre-check VRAM requirements. This caused 20-25% of seeds to get
stuck in Scale-up era. All three fixes (engine un-stalling, strategy VRAM pre-check,
stalled pipeline cancellation) bring pass rate from 75% to 100% across 20 random seeds.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Create researchBonuses utility to aggregate tech tree effects into all game systems
(infrastructure energy costs, compute efficiency, training speed, model capability, reputation)
- Rework model capability from sqrt(compute) to 4-pillar formula (params + compute + data + research)
- Make context window affect benchmarks and inference speed
- Add MoE tradeoffs: 1.5x VRAM, 0.8x training speed
- Enforce research point costs as a gate for unlocking research
- Add real consequences to data contamination events (reputation hit, legal costs)
- Scale talent costs from $0.03 to $5/tick per headcount
- Scale compliance costs 100x to be meaningful
- Rework competitor acquisition: cheaper but grants headcount, RP, and reputation
- Remove dead code: sfxVolume, autoSaveInterval, notificationsEnabled,
FAST_FORWARD_BATCH_SIZE, CHINCHILLA_OPTIMAL_RATIO
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Expand from 10 to 18 rack SKUs across NVIDIA, AMD, and custom ASIC vendors, each with
distinct training vs inference FLOPS, VRAM capacity, cooling requirements, and interconnect
technology. Adds cooling hierarchy (air/liquid/immersion) that gates rack deployment, VRAM
requirements that gate model training by generation, interconnect multipliers for distributed
training scaling, and PUE-based energy cost reduction for advanced cooling. Includes save
migration from v4 to v5, 6 new research nodes, and UI updates showing split compute stats.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace aggregate network health stats with a full 6-tier Clos topology
(ToR → T1 → T2 → T3 → T4 → T5) where every switch is an individually
tracked entity with uplinks, repair pipelines, and failure cascades.
Key mechanics:
- Bottleneck bandwidth model (min along path) affects FLOPS and satisfaction
- Rackdown on full disconnect → racks re-enter testing pipeline on recovery
- Binomial failure sampling per tier, dirty-flag cascade optimization
- Flat switch registry for performance at scale
- Three new research nodes: network-redundancy, fast-repair, hot-standby
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
These fire constantly at scale with thousands of racks, flooding the
notification panel with noise.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Campus level: "Fill All DCs" instantly fills all operational DCs with
selected SKU in one click. "Retrofit Campus" queues a staggered retrofit
with configurable concurrency (1/10%/25%/custom) so only a fraction of
DCs go offline at a time, preserving capacity during the upgrade.
Cluster level: "Fill All DCs" fills across all campuses in one action.
The game engine automatically advances the retrofit queue each tick,
promoting pending DCs as active ones complete.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
computeRacksFailed was incremented on production failure and never decremented
when repaired racks came back online, while repair cohorts also tracked the
same racks. This caused usedSlots to inflate past the DC capacity over time.
Fix: derive computeRacksFailed from repair cohorts each tick instead of
maintaining it as a running counter. Include repair cohorts in pipeline slot
accounting so all racks are counted exactly once. Also fixes power limit in
fillDCToCapacity to only count online racks (pipeline racks don't draw power).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace flat DataCenter/Rack model with Cluster > Campus > Data Center > Racks
hierarchy. Individual rack entities eliminated in favor of statistical batch
simulation using deployment cohorts. Adds tiered network topology (ToR/agg/core)
with proportional outage model, DC retrofitting, bulk operations, and drill-down
UI navigation with breadcrumbs. First cluster and campus are free to preserve
early game flow. Rebalances starting economy ($600K), funding rounds, and
cohort scaling for hypercluster-scale gameplay.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Failed racks were removed from dc.racks in Phase 3 before uptime was
calculated in Phase 4, so healthyCount always equaled totalInDc. Now
counts racks in the repair pipeline as down capacity.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Racks can now be marked for decommission from the DC view. The rack
leaves production immediately (freeing slot and power), enters the
pipeline as a timed decommission order, and is removed when complete.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace flat GPU buying with a realistic data center + rack pipeline:
- 4 DC tiers (small/medium/large/mega) with construction time, dual
capacity constraints (rack slots + power budget kW), and era/research
gating
- 10 predefined rack SKUs from consumer GPUs through custom ASICs, each
with unique FLOPS, power draw, cost, and pipeline timings
- 6-stage procurement pipeline (order → mfg → receive → install → test
→ production) with Kanban UI, talent-influenced speed bonuses
- Test failures (5-25% base rate) reduced by cooling, ops talent, and QA
research; auto-repair with cost and re-test cycle
- Production failures at low per-tick rate, racks sent to repair pipeline
- Cooling and redundancy upgrades per DC (reduce failure rates)
- 4 new tech tree nodes (DC Engineering II/III/IV, Quality Assurance)
- Save version bump (1→2) with migration that resets old saves
- Updated economy system to account for rack repair costs
- Redesigned Infrastructure page with pipeline Kanban, capacity bars,
rack ordering, and DC upgrade panels
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Turborepo monorepo with three packages:
- packages/shared: TypeScript types for all 14 game systems + balance constants + formatting utils
- packages/game-engine: Pure TS simulation engine with tick processor, economy, infrastructure, compute, research, market, and reputation systems
- apps/web: React + Vite + Tailwind + Zustand frontend with sidebar dashboard layout, new game screen, dashboard with charts, infrastructure management, and model training pages
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
josh