The AI Coding Manifesto: Building Scalable Codebases

This manifesto outlines a strategy for maintaining codebase integrity when working with AI coding agents. It advocates for a strict separation between minimal, pure 'semantic' functions and complex 'pragmatic' wrappers. By also enforcing data models that make invalid states impossible, developers can ensure their code remains scalable and resistant to decay.

Key Points

• Semantic functions should be minimal, pure, and self-describing building blocks that require no comments and are easily unit-testable.
• Pragmatic functions serve as wrappers for complex, changing processes and should be documented with context rather than just restating the function name.
• Data models must be designed to make invalid states impossible, utilizing precise naming and brand types to prevent silent logic errors.
• Codebase degradation occurs when the boundaries between semantic and pragmatic logic blur or when models are bloated with optional fields for convenience.
• Intentional architectural patterns are essential to prevent AI coding agents from rapidly creating a 'sloppy' and unscalable codebase.

Sentiment

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In Agreement

• The author argues that AI is not inherently making codebases worse — developers who fail to be intentional about directing AI code generation are the actual root cause of declining quality