The value of software lies not in the code itself but in the skills, time, and processes that surround its creation. While coding can be done quickly, the real effort involves team dynamics, business logic, and design, often making the actual code less significant. The author argues that starting from scratch can sometimes yield better results than refactoring existing code, as much of the value resides in knowledge rather than the codebase.

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The article discusses the design space of AI coding tools, summarizing a paper that analyzes 90 AI coding assistants and identifies 10 design dimensions across four categories: user interface, system inputs, capabilities, and outputs. It contrasts the converging trends in industry products with the more experimental approaches in academia, highlighting the varying needs of different user personas.