Research now costs money (drained per-tick) with ~2.5-3.5x longer durations by category.
Early-game talent budget costs reduced via era multiplier (startup 0.2x → bigtech 1.0x).
New seed-driven PersonaStrategy with 8 axes of variation for meaningful multi-run testing.
CI multi-run switched from greedy to persona strategy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The old overload policy had dead controls (maxQueueDepth, rateLimitPerCustomer never read)
and trivial flat penalties. This replaces it with a full serving pipeline where deployed
models form a fleet, requests route through priority/degradation logic, and policy choices
create meaningful strategic tradeoffs.
New serving pipeline: fleet building from deployed models (size/quant/MoE multipliers),
demand categorization by 5 priority tiers, enterprise capacity reservation, priority-ordered
serving with overflow behaviors (queue/reject/degrade), auto-degradation to faster models
under load, and Batch API to fill idle capacity at discounted rates.
4 new research nodes gate features progressively: Intelligent Request Routing, Priority
Queue System, Request Batching, and Auto-Scaling. New dedicated Serving page with pipeline
metrics, model fleet utilization, and research-gated policy controls.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
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>
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 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>
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>
josh