How Docker containers work using Linux namespaces, cgroups, container runtime, and the shared host kernel

Should your startup choose a monolith or microservices? Compare both architectures, understand their trade-offs, and learn why starting with a modular monolith is often the practical choice for growing startups.

Technical debt can slow you down later, while overengineering can slow you down today. Learn how startups can balance speed, simplicity, scalability, and maintainability when making architecture decisions.

How do you design a SaaS application for 1 million users without overengineering it? Learn a practical architecture for scaling application servers, databases, caching, background jobs, storage, security, and observability.

Do you really need a dedicated vector database for your AI application? Learn how PostgreSQL with pgvector handles embeddings, similarity search, RAG, metadata filtering, and when specialized vector infrastructure actually makes sense.

Learn how vector databases work from a backend developer’s perspective, including embeddings, similarity search, ANN indexes, RAG, pgvector, and when you actually need a dedicated vector database.

Learn how to build an MCP server with TypeScript, create tools, validate inputs, connect backend services, test with MCP Inspector, and prepare your server for production.

Building an AI agent demo is easy. Making it reliable, secure, observable, and safe for real users is much harder. Learn the architecture and engineering practices required to build production-ready AI agents.

What actually happens when an AI application connects to an MCP server? Learn how Model Context Protocol works, including MCP hosts, clients, servers, tools, resources, prompts, transports, security, and production architecture.