How It Works

You open your terminal. You describe what you want. Claude Code reads your project, understands the architecture, and makes changes across multiple files.

It is not a chat window that suggests snippets. It is an agent that operates on your actual codebase. It reads files, creates new ones, runs commands, and verifies results. You stay in your terminal the entire time.

Native integrations exist for VS Code, Cursor, Windsurf, and JetBrains. But the core experience is the CLI — you talk to it like a very capable junior developer who has read your entire codebase.

What Makes It Different

Most AI coding tools work at the file level. You open a file, the tool suggests edits for that file.

Claude Code works at the project level. It understands how your files relate to each other. When you say "add a subscription feature," it knows which files to create, which existing files to modify, and how the new code connects to your auth system, database, and API layer.

The difference matters when your change touches 5+ files. A file-level tool makes you orchestrate every edit. A project-level agent handles the orchestration.

Anthropic's data shows the biggest productivity gains come from what they call "Feedback Loop" patterns — the agent implements, you review, you give feedback, the agent adjusts. The human stays in the loop, but the typing is delegated.

How I Use It

I built 4 native apps in a month using Claude Code as my primary development tool. Not as a helper on the side — as the main way code gets written.

My workflow:

1. Define what needs to happen in plain language
2. Claude Code implements it across the codebase
3. I review the changes — does this match my intent?
4. I give feedback on what to adjust
5. Repeat until correct

The key insight I learned: Claude Code is only as good as the project it operates in. In a well-structured codebase with clear patterns, typed schemas, and consistent conventions, it produces reliable code. In a messy project, it produces messy code. The quality of your architecture determines the quality of the agent's output.

This is why I pair Claude Code with a boilerplate. The boilerplate provides the structure. The agent provides the speed.

What It Cannot Do

Honesty matters here.

Claude Code cannot decide what to build. It cannot tell you whether your product idea is good. It cannot replace the thinking that comes before implementation.

It also struggles with truly novel problems — things with no clear precedent in its training data. Edge cases in obscure libraries. Platform-specific bugs that require reading Apple's documentation carefully.

The rule I follow: delegate implementation to the agent, keep architecture and product decisions with the human. AI made execution cheaper. It did not make judgment cheaper.