Karthikeya Vaibhav

AI has become an everyday tool, and the gap between technical and non-technical builders has never been slimmer. Anyone can now describe an idea and have a working prototype in an afternoon.

But a prototype is not a product. Building something that serves millions of requests, survives traffic spikes and outages, keeps data safe and stays within latency and cost budgets still takes real engineering depth. Turning a prototype into an enterprise-scale solution requires system design, and learning it has always been painful, scattered across blogs, papers and hard-won production incidents, with no clear structure.

System Design Studio gives that structure:

• A structured path: 37 chapters from first principles like caching, sharding and consensus to modern AI systems like RAG, agents and LLM serving.
• Hands-on Labs: design an architecture on a visual canvas with generic and AWS, GCP and Azure components, push traffic through it and watch where it breaks.
• Clear explanations: every bottleneck and failure is explained, with links back to the chapters that cover it.
• An AI tutor: ask questions about any chapter while you learn.