Add README and documentation

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

docs/how-to-play.md

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+# How to Play
+
+## Getting Started
+
+When you start a new game, you name your AI company and begin in the **Startup era** with $50,000 in seed money. Your goal: build the world's leading AI company and eventually achieve AGI.
+
+The game runs in real-time at 1 tick per second. Use the speed controls in the top bar to play at 1x, 2x, or 5x speed, or pause to plan your next moves.
+
+## The Core Loop
+
+The fundamental cycle of the game:
+
+1. **Buy GPUs** — Purchase compute hardware in the Infrastructure page
+2. **Allocate compute** — Split between training new models and serving inference
+3. **Train a model** — Start a training run in the Models page
+4. **Deploy** — Put your trained model into production
+5. **Earn revenue** — Users pay for API access and subscriptions
+6. **Reinvest** — Buy more GPUs, hire talent, fund research
+
+Everything else in the game builds on or modifies this loop.
+
+## Key Resources
+
+### Money
+Your primary resource. Earned from API revenue and consumer subscriptions. Spent on GPUs, talent, data, and energy. Shown in the top bar with income trend.
+
+### Compute (FLOPS)
+Total processing power from your GPU fleet. The main bottleneck — you never have enough. Split between training (building new models) and inference (serving users).
+
+### Reputation (0-100)
+Public trust in your company. Affects talent acquisition, user growth, investor confidence, and regulatory treatment. Composed of safety record, public perception, employee satisfaction, and regulatory standing.
+
+### Talent
+Headcount across four departments: Research, Engineering, Ops, and Sales. Each department has effectiveness and morale scores that affect their output.
+
+### Data
+Training data quality and quantity. Better data produces better models. Acquired through the data marketplace or generated passively from your user base.
+
+### Research
+Progress through the tech tree. Unlocks better GPU tiers, model architectures, efficiency improvements, and safety techniques.
+
+## Game Systems
+
+### Infrastructure
+
+Your datacenters house GPU clusters. Seven global regions are available, each with tradeoffs:
+
+- **US-West / US-East** — Balanced costs, good latency to North American users
+- **EU-West / EU-North** — Higher energy costs, strict regulation, access to EU market
+- **Asia-East / Asia-South** — Lower costs, emerging markets
+- **Middle-East** — Cheap energy, political risk
+
+Each datacenter has a size (GPU slots), cooling level, and redundancy level. Higher redundancy reduces GPU failure rates but costs more. GPUs can fail randomly — lost hardware means lost capacity until you replace them.
+
+GPU types unlock through research. Early on you have basic GPUs; research unlocks progressively more powerful (and expensive) tiers.
+
+### Research
+
+The tech tree has two dimensions:
+
+- **Generations**: Small → Medium → Large → Frontier → AGI-scale models
+- **Specializations**: Reasoning, coding, creative, multimodal, agents
+
+Research projects require researchers and time. Completing projects unlocks new capabilities:
+- Better GPU tiers
+- Training efficiency improvements (quantization, distillation)
+- Safety techniques (alignment research, interpretability)
+- New product capabilities
+
+### Models
+
+Training a model requires:
+- **Compute**: How many FLOPS to dedicate to training
+- **Data**: Training data tokens to use
+- **Time**: Training runs take real time (boosted by researcher and engineer quality)
+
+When training completes, your model gets capability scores across five dimensions: reasoning, coding, creative, multimodal, and agents. A composite benchmark score determines its market competitiveness.
+
+#### Safety vs Capability
+
+This is the game's central tension. Safety research improves your model's safety score but penalizes benchmark performance. Low safety scores risk:
+- **Safety incidents**: PR disasters that damage reputation
+- **Regulatory backlash**: Higher compliance costs
+
+High safety scores mean:
+- Lower benchmarks (competitors may outperform you)
+- Better regulatory standing
+- Protection from reputation-damaging incidents
+
+There's no single right answer — it depends on your strategy.
+
+#### Model Tuning
+
+After deploying a model, you can tune its behavior:
+
+- **Presets**: Quick settings (Helpful-Safe, Performance, Creative, Balanced)
+- **Sliders**: Fine-grained control over safety, creativity, verbosity, and speed/quality tradeoff (unlocked after completing alignment research)
+
+### Market
+
+Revenue comes from two sources:
+
+**Consumer Subscriptions**: Users pay a monthly fee for your chat product. Subscriber count grows based on model quality and shrinks from churn. Higher quality models and competitive pricing accelerate growth.
+
+**B2B API**: Enterprise customers pay per token. Set your input/output token pricing to balance revenue against demand.
+
+#### Overload Policy
+
+When demand exceeds your inference capacity, you choose how to handle it:
+- **Queue depth**: How many requests to buffer
+- **Rate limits**: Max requests per user
+- **Degrade quality**: Serve faster but lower-quality responses
+- **Prioritize enterprise**: Give B2B customers priority over consumers
+
+Each choice has tradeoffs. Degrading quality hurts satisfaction. Enterprise prioritization frustrates consumer users.
+
+#### Open Source
+
+You can open-source deployed models. This:
+- Boosts reputation significantly
+- Attracts more talent
+- Reduces direct revenue from that model
+- Increases subscriber growth (community effect)
+
+### Talent
+
+Four departments, each critical:
+
+| Department | Effect |
+|-----------|--------|
+| Research | Speeds up R&D projects and improves model training quality |
+| Engineering | Speeds up model training and infrastructure reliability |
+| Ops | Reduces infrastructure costs and failure rates |
+| Sales | Increases enterprise API demand |
+
+Hire to increase headcount. Morale affects effectiveness — keep your teams happy by managing workload and company reputation.
+
+### Competitors
+
+Three rival AI labs compete with you. Each has a personality:
+
+- Some prioritize safety, others move fast
+- Some are big-tech giants with deep pockets, others are scrappy startups
+- They release models, gain users, and react to your moves
+
+In later eras (Big Tech and AGI), you can **acquire** competitors, absorbing their talent and technology.
+
+### Events
+
+Random and conditional events keep the game dynamic. Categories include:
+
+- **Industry**: Breakthroughs, open-source releases, benchmarks
+- **Regulatory**: Hearings, compliance requirements, AI bills
+- **PR/Cultural**: Media coverage, safety debates, public opinion shifts
+- **Internal**: Employee issues, technical problems
+- **Market**: Demand spikes, pricing pressure
+- **Geopolitical**: Export controls, energy crises, natural disasters
+
+Most events present 2-3 choices with meaningful tradeoffs. Some trigger chain events with delayed consequences.
+
+### Funding
+
+Raise capital through VC rounds as you grow:
+
+| Round | Amount | Dilution | Key Requirement |
+|-------|--------|----------|----------------|
+| Seed | $100K | 10% | $100/s revenue |
+| Series A | $500K | 15% | 100 users, 20 reputation |
+| Series B | $2M | 12% | 1,000 users, 30 reputation |
+| Series C | $10M | 10% | 10,000 users, 40 reputation |
+| Series D | $50M | 8% | 50,000 users, 50 reputation |
+| IPO | $200M | 20% | 100,000 users, 60 reputation |
+
+Each round permanently dilutes your founder equity. Time your raises carefully — you want enough runway to grow but minimum dilution.
+
+## Era Progression
+
+The game has four eras that unlock progressively. Transitions happen automatically when you meet thresholds:
+
+### Startup → Scale-up
+- Revenue: $10,000/s
+- Best model capability: 15+
+- Reputation: 30+
+
+### Scale-up → Big Tech
+- Revenue: $1,000,000/s
+- Best model capability: 50+
+- Reputation: 60+
+
+### Big Tech → AGI
+- Revenue: $100,000,000/s
+- Best model capability: 90+
+- Reputation: 70+
+
+Each era unlocks new game systems and sidebar pages. Watch for the "NEW" badge on newly available pages.
+
+## Strategies
+
+### The Safety-First Path
+Invest heavily in alignment and interpretability research. Your benchmarks will lag competitors initially, but you avoid safety incidents and build strong regulatory standing. Good for steady, sustainable growth.
+
+### The Move-Fast Path
+Minimize safety investment, maximize raw capability. You'll lead benchmarks and attract users quickly, but safety incidents can crater your reputation. High risk, high reward.
+
+### The Open-Source Play
+Open-source your models to build massive community goodwill and attract top talent. Revenue per model drops, but subscriber growth accelerates and reputation soars. Strong mid-game strategy.
+
+### The Vertical Integrator
+Invest in multiple specializations and diverse products. Spread your compute across reasoning, coding, creative, and multimodal capabilities. More resilient but slower to dominate any single benchmark.
+
+## Tips
+
+- **Don't neglect infrastructure redundancy.** GPU failures at scale can cripple your capacity.
+- **Watch your burn rate.** It's easy to over-hire and run out of money before your models generate revenue.
+- **Timing funding rounds matters.** Raise too early and you give up equity cheaply. Raise too late and you run out of runway.
+- **Safety research compounds.** Each safety project improves all future models.
+- **Check competitor activity.** If a rival just released a strong model, expect to lose some subscribers unless you respond.
+- **Events have lasting consequences.** Read the options carefully — some choices trigger follow-up events.
+- **The data flywheel is real.** More users generate more data, which trains better models, which attract more users.
+- **Deploy your models.** A trained model sitting idle generates zero revenue.
+- **Use speed controls.** Pause when making big decisions. Speed up during waiting periods.
+
+## Saving
+
+The game auto-saves to your browser's localStorage every 60 ticks. You can also:
+- **Export** your save as a JSON file from the Settings page
+- **Import** a previously exported save
+- **Cloud save** by creating an account (requires the backend server)
+
+Closing the browser tab is safe — when you return, an offline catch-up system simulates what happened while you were away (up to 24 hours).
+
+## Achievements
+
+15 achievements track your progression milestones, from training your first model to reaching AGI. Check the Achievements page to see what you've unlocked and what's still ahead.