Document limits, load control and performance
- New docs/09-limits-and-performance.md: principles, all protection layers, what players see, configuration reference, measurements, tuning guide, monitoring and how to test - Server chapter summarises the limits and links to chapter 9; circuit breaker description and AI_MAX_INFLIGHT default corrected - Architecture: new files in the layout, design decisions for load handling; docs index lists chapter 9 - Protocol, AI and operations chapters link to chapter 9; test counts - Root and component READMEs describe the limits and settings Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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| 1 | [Architecture overview](01-architecture.md) | The four components, how a move travels through the system, repository layout, key design decisions |
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| 2 | [Game rules as implemented](02-game-rules.md) | Every rule of Wizard as the code implements it, with the exact function that enforces it |
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| 3 | [Game engine (`wizard_game`)](03-game-engine.md) | The rules package: cards, the `WizardGame` state machine, observations, actions, events |
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| 4 | [Game server (`wizard_server/app`)](04-server.md) | Sessions, reconnects, lobbies, running games, AI turns, broadcasting, configuration |
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| 4 | [Game server (`wizard_server/app`)](04-server.md) | Sessions, reconnects, lobbies, running games, AI turns, broadcasting, limits (summary), configuration |
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| 5 | [Protocol reference](05-protocol.md) | Every websocket message between browser ↔ server and server ↔ AI, with examples |
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| 6 | [Frontend (`wizard_website`)](06-frontend.md) | The React app: connection hook, screens, components, styling, build |
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| 7 | [AI player (`wizard_ai`)](07-ai.md) | Agents, feature encoding, policy interface, training, evaluation, production service — and how to plug in a real model |
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| 8 | [Deployment, development & operations](08-operations.md) | Docker, networks, Caddy, environment variables, local setup, tests, troubleshooting, how-to recipes |
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| 9 | [Limits, load control and performance](09-limits-and-performance.md) | Resource caps, abuse limits, overload detection, what happens when compute runs out, AI cost bounds, measurements, tuning and monitoring |
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## The system in one paragraph
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