How Coding
Systems Work

Code is a set of precise instructions that tells a computer what to do. Every app you use, every website you visit, every AI you talk to — it's all code running on hardware somewhere.

At its core, code is just text. Files full of structured text that follow rules strict enough for a machine to interpret. Programming languages like Python, JavaScript, TypeScript, Rust, and Go give humans a way to write those instructions in something readable.

The computer itself only understands binary — electrical signals that are on or off, 1 or 0. Your human-readable code gets translated (compiled or interpreted) down to machine instructions. The programming language is the bridge between your intent and the machine's execution.

Think of code like a recipe. The programming language is the language the recipe is written in. The computer is the kitchen. The runtime is the cook.

You don't need to understand binary to write code, just like you don't need to understand combustion to drive a car. Each layer of abstraction lets you work at a higher level without worrying about what's underneath.

Modern software is built in layers. Developers call this “the stack” — each layer handles a different level of abstraction.

Each layer abstracts away the complexity below it. A web developer using Next.js doesn't think about CPU registers. A DevOps engineer managing cloud infrastructure doesn't think about React components. Specialization happens at each layer.

When someone says “full-stack developer,” they mean someone who works across multiple layers — typically the framework, application, and some infrastructure. Nobody works all layers. The stack is too deep.

An AI agent is a program that uses a large language model (LLM) to reason, plan, and take actions. It's the difference between asking someone a question and hiring someone to do a job.

A chatbot responds to prompts. An agent interprets your intent, breaks it into steps, uses tools, handles errors, and delivers results. It operates in a loop: observe the situation, decide what to do, take an action, evaluate the result, repeat.

What makes agents powerful:

Read and write files on your machine
Execute shell commands and scripts
Search and navigate codebases
Make API calls to external services
Plan multi-step tasks and track progress
Self-correct when something goes wrong

The leading AI coding agents today are Claude Code (Anthropic), Codex (OpenAI), Goose (Block), and Aider. They sit in your terminal, understand your project's context, and write real code that ships.

An agent without tools is just a chatbot with ambition. The tools are what make it useful.

Skills are packaged capabilities that extend what an agent can do. Instead of one agent knowing everything about everything, skills give it specialized knowledge and workflows on demand.

A skill might teach an agent how to deploy to Vercel, write Playwright tests, optimize images for SEO, generate PDF reports, or set up a CI pipeline. When you need that capability, the skill loads. When you don't, it stays out of the way.

Most skills are structured text files — markdown with frontmatter that defines when the skill should activate, what it does, and how it should behave. They're lightweight, composable, and shareable.

The skill ecosystem is growing fast. There are skills for SEO auditing, database migrations, code review, documentation generation, and hundreds more. You can install community skills or write your own.

MCP is a standard protocol that connects AI agents to external tools and data sources. Think of it as USB for AI — a universal interface that lets any compatible agent talk to any compatible tool.

Before MCP, every integration was custom. Want your agent to access your database? Write a custom integration. GitHub? Another one. Notion? Another. MCP standardizes all of this into one protocol.

The architecture is simple:

With MCP, an agent can connect to your Supabase database, your GitHub repositories, your Notion workspace, your email, your Figma files — all through standardized, interchangeable connectors. Add a server, restart the agent, and the new tools are available immediately.

MCP is open source and maintained by Anthropic. The ecosystem includes official reference servers and hundreds of community-built servers covering everything from Postgres to Slack to Kubernetes.