Designing scalable AI systems, including AI model infrastructure, cloud deployments, microservices, and efficient model-serving architectures. Covers AI orchestration frameworks and best practices for integrating AI into production environments.
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My #1 rule:
Deploy the boring loop first. Add intelligence later.
Because if the simple loop doesn't work in production, the fancy version won't save you.
Your vibe-coded AI app does not need a complicated backend on day one.
🙅♀️ No RAG
🙅♀️ No tools
🙅♀️ No streaming
🙅♀️ No multi-agent orchestration
It needs one boring backend loop:
MCP (Model Context Protocol) is a new open protocol designed to standardize how applications provide context to Large Language Models (LLMs).
Think of MCP like a USB-C port but for AI agents: it offers a uniform method for connecting AI systems to various tools and data sources.
This post breaks down MCP, clearly explaining its value, architecture, and how it differs from traditional APIs.