Claude Code

The most important thing about Claude Code isn't the AI model — it's the context management system that decides which parts of your codebase the model sees for each conversation turn. When you start Claude Code in a project directory, it builds a semantic index of your files. Not just file names and directory structure — it understands which modules import from which, where your types are defined, how your tests relate to your source files. This index persists across sessions, so startup is fast after the first run. When you ask a question or request a change, Claude Code selects the relevant files to include in the context window. Ask about authentication, and it pulls in your auth middleware, user model, session handling, and related tests — even if they're in different directories with different naming conventions. This selective inclusion is why Claude Code gives better answers than pasting code into ChatGPT: it sees the right code, not everything or nothing. The 200K token window (roughly 150K words) is large enough for most projects. But when your codebase exceeds it, the selection algorithm becomes critical. It prioritizes files based on import chains (if you're asking about module A, and module A imports module B, module B is included), recency (recently edited files are more likely relevant), and keyword matching (if your question mentions 'auth', files containing auth-related code are prioritized). The terminal integration adds another layer. When Claude Code runs a command and reads the output, that output becomes part of the conversation context. If a test fails, the error message is right there in context alongside the test file and the source file. This feedback loop — write code, run test, read error, fix code — is what makes Claude Code feel like pair programming rather than a chatbot.