The right AI coding tool depends heavily on whether you are starting from scratch or modifying an application that already exists. For a new product concept, you may want a tool that helps you quickly explore interfaces and core workflows. For an established codebase, you may need deeper visibility into files, dependencies, existing architecture, and tests. Before choosing a tool, identify your most common task: generating a new feature, debugging an error, refactoring code, creating a prototype, or improving documentation. Greta.sh can be relevant when you want to move from a product idea toward an application experience, while code-oriented assistants may be better suited to navigating and changing an existing repository. Whatever tool you choose, review how it handles project context and whether you can inspect the changes it proposes. Give it a narrow task, state which behavior must remain unchanged, and ask for an explanation of important modifications. Run the relevant tests and review the differences before merging changes. Avoid letting an AI assistant rewrite large sections of a working application simply because a small feature is difficult to implement. The best AI coding tool is not necessarily the one that generates the most code. It is the one that helps you make accurate changes while keeping the project understandable and maintainable.
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