Infrastructure

Table of Contents Introduction What is Amazon EC2? Key Components You Need to Know Prerequisites and Setup Step-by-Step: Launching Your First EC2 Instance Connecting to Your Instance Understanding Instance Types and Configurations Security Groups and Network Settings User Data Scripts and Automation Managing Your Instances: Stopping vs. Terminating Cost Optimization and Free Tier Benefits Advanced Topics: IAM Roles and Elastic IPs Troubleshooting Common Issues Next Steps and Resources Conclusion Introduction Amazon EC2 (Elastic Compute Cloud) stands as one of the most powerful and widely-used services in the AWS ecosystem[1]. Whether you’re a developer looking to host applications, a data scientist setting up computing environments, or an infrastructure engineer managing cloud resources, understanding EC2 is essential in today’s cloud-first world. ...

Introduction Docker has revolutionized application deployment by enabling developers to package applications and their dependencies into lightweight, portable containers. However, containers don’t exist in isolation—they need to communicate with each other, with the host system, and with external networks. This is where Docker networking comes in. Understanding how Docker networking works is essential for building scalable, secure, and efficient containerized applications. Docker networking is the system that enables containers to communicate with each other, the host, and external networks[3]. It defines how data flows between containers and provides the infrastructure necessary for multi-container applications to function seamlessly. Whether you’re running a simple web application or a complex microservices architecture, mastering Docker networking is crucial for success. ...

As large language models become more capable, the challenge shifts from “can they reason?” to “can they act?” The Model Context Protocol (MCP) is Anthropic’s answer to this question—a standardized way for LLMs to discover, understand, and safely use tools, data, and actions in the real world. Before MCP, every AI application required custom integrations, hard-coded tool definitions, and fragile glue code. MCP changes this by providing a universal protocol that any LLM can use to interact with external systems. ...