Replaces the simplified single-subscriber market with a full competitive simulation:
shared TAM with softmax market shares across 4 segments, multi-tier consumer
subscriptions (Free/Plus/Pro/Team) and API tiers (Free/PAYG/Scale/Enterprise),
enterprise sales pipeline (Lead→Qualification→POC→Negotiation→Active→Renewal)
with SLA tracking, developer ecosystem flywheel, technology obsolescence pressure,
seasonal demand cycles, and two new product lines (Code Assistant, AI Agents Platform).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace the single-stage training + flat capability score with a realistic AI
development pipeline: pre-training with Chinchilla scaling laws, SFT with
specializations, alignment with safety/capability tradeoffs (RLHF/DPO/Constitutional),
model families with distillation/fine-tuning/quantization variants, named benchmark
suite with compute-costing eval jobs, and segment-specific market quality.
Phases 1-6 of the model rework plan: new types, engine rewrite, save migration,
training events/risk system, concurrent training, variant creation, benchmark
evaluation with leaderboard, and market integration.
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>
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>
Four-tab panel with resource manipulation, time controls, state inspection,
and event triggers to accelerate testing across all game systems.
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>
Replace basic admin panel with cloud console-style interactions:
- Fleet summary bar with aggregate stats (DCs, racks, FLOPS, uptime, cost)
- Launch Racks provisioning panel with SKU table, quantity stepper, live
cost sidebar, and review-before-launch flow
- Rack inventory table with sortable columns, checkbox multi-select,
and bulk decommission with confirmation modal
- Pipeline kanban grouping (same SKU/stage collapsed) with ETA display
- Tabbed DC cards (Inventory | Launch | Upgrades) to reduce scroll
- Build DC panel with cost breakdown and capacity preview
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The deployedModels selector used .filter() which created a new array
reference on every store change, triggering cascading re-renders.
Changed to compute bestQuality as a primitive directly in the selector.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three intertwined fixes:
1. Zero-capacity utilization: when inference allocation was 0%, the
guard clause returned 0% utilization instead of 100%, so the market
system never penalized satisfaction and subscribers never churned.
2. Stale compute in market: restructured tick order so capacity is
computed before market runs, giving satisfaction calculations
current-tick demand/capacity ratio instead of previous tick's.
3. Subscriber growth: replaced pure compound growth (reached billions
in minutes) with logistic saturation curve. Era-based market caps:
startup 10K, scaleup 1M, bigtech 20M, agi 100M. Quality and
reputation expand the effective cap.
Also tuned FLOPS-to-tokens multiplier (10 → 26) for balanced
demand/capacity feel across all eras, and added market saturation
indicator to the Market page.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ToastContainer's useEffect had `seen` (a Set state) in its dependency
array — each setSeen created a new Set, re-fired the effect, and under
rapid notifications cascaded past React's 50-update limit. Replaced
with a ref since seen doesn't need to trigger re-renders.
Also added useShallow to DataCenterCard's pipeline selector to prevent
.filter() from causing spurious re-renders on unrelated store changes.
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>
Rapid clicks on order rack bypassed capacity checks because usedSlots
and usedPowerKW were only updated during the tick. Now both the store
action and UI compute slot/power usage live from racks + pipeline,
so each order immediately reflects in the next check.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The random events (GPU shortages, regulatory hearings, PR crises, etc.)
added interruption without enough gameplay value. Removed all event
types, definitions (~1800 lines of event data), the event processor,
EventModal UI, store actions, and tick integration. Updated docs to
reflect the removal. Bundle size drops ~47kB.
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>
Remove chart Tooltip components and wrap fundingStatus.reason in
String() to eliminate possible object-as-React-child errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Recharts Tooltip formatter should return a string, not a
single-element array which React can't render as a child.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace all crypto.randomUUID() calls with a uuid() utility that
falls back to Math.random-based generation when the Web Crypto API
is unavailable (plain HTTP contexts).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Leaderboard page with category tabs and score submission, shareable
company stats card with clipboard copy, dynamic regulation system
(compliance costs scale with capability and era, regulatory standing
tracks safety research), 6 geopolitical events (export controls, energy
crisis, natural disaster, AI safety summit, immigration policy, data
sovereignty), safety-capability tradeoff (safety score affects benchmark,
low safety triggers incidents with reputation damage), and enhanced
event consequence handling for regulation and talent types.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
VC funding system (seed through IPO with requirements gating), 15
achievements with engine checker, model tuning presets and unlockable
sliders, overload policy controls, open-source mechanic with reputation
boost, enhanced Recharts analytics (subscriber/reputation/revenue vs
expenses charts), M&A acquisition system, sidebar NEW badges on era
transitions, tutorial hints, and wired-up settings toggles.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Server app (apps/server) with Hono framework and Drizzle ORM:
- PostgreSQL schema: users, saves, leaderboard, achievements tables
- Anonymous auth with UUID tokens, optional email/password linking
- Cloud save API: list, get, upsert, delete with auto-save hook
- Leaderboard API: per-category rankings with score submission
- CORS configured for dev server ports
- Typed middleware with Hono env variables
Frontend cloud save integration:
- API client with auth token management in localStorage
- useCloudSave hook auto-saves every 300 ticks when authenticated
- Vite env type declarations for VITE_API_URL
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tech tree with 21 research nodes across 5 categories (infrastructure,
efficiency, generation, specialization, safety). Research page with
category-grouped cards, progress tracking, prerequisite gating.
Event engine with 34 events across industry/regulatory/PR/internal/market
categories, weighted random firing, cooldowns, expiry, and choice modal
with consequence preview. Events auto-expire with default choice.
Competitor system with 3 rival AI labs (Prometheus AI, Nexus Labs, Titan
Computing), personality-driven milestone progression, and comparison UI.
Talent page with department hiring, headcount management, and key hire
recruitment from a pool of 10 named characters with special abilities.
Data marketplace with 8 purchasable datasets, user data flywheel from
subscribers, and data system processing in tick loop.
Era transition system checks revenue/capability/reputation thresholds.
All new systems integrated into tick processor with notifications.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add reusable Tooltip component and rich tooltips on all TopBar KPIs
(cash breakdown, compute utilization, reputation context). Add save
import button to Settings page. Fix game balance: reduce GPU maintenance
100x, increase organic API demand 200x, accelerate subscription revenue
timescale, boost early subscriber seeding, use sqrt scaling for model
compute factor, simplify deploy to activate all product lines at once.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Model training system: training jobs produce TrainedModels with
calculated capabilities based on compute, data, and research
- Market system: organic API demand and consumer subscriptions now
generate real revenue from deployed models
- Talent system: salary costs calculated from department headcount
- Toast notification system for game events (training complete, etc.)
- Offline catch-up: progress bar + summary screen when returning
- Market page: pricing controls for API and subscription products
- Finance page: income statement, cash charts, funding history
- Tick processor now runs all 7 systems in correct dependency order
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