Clean code and its misconceptions

• Uncle Bob’s Clean Code book from 2008 has been misunderstood; many think "clean code" means "bad code" due to overabstraction, but this is a misinterpretation.
• Proper "clean code" should be generic, simple, direct, and comprehensible, irrespective of the paradigm (OOP, functional, procedural).

Performance in optimization examples

• Casey Muratori’s comparison shows a 25x performance boost using optimized C code with AVX over an OOP approach with virtual methods.
• The dense, contiguous memory layout is crucial for CPU efficiency; the OOP example failed due to scattered heap allocations and tiny virtual function workloads.
• Performance requirements are highly context-dependent; what’s acceptable for a mobile game may fail in a high-demand 3D game.

Flexibility vs. performance

• OOP provides flexibility, enabling different shapes to contain custom data and methods. However, for high-performance needs (e.g., processing 1 million shapes per frame), dense linear arrays and optimizations like AVX are non-negotiable.
• A mixed approach: Utilize OOP for high-level organization and optimized loops for performance-critical sections.

Broader implications and future directions

• Just as in other fields, balancing overhead and workload is key. This principle applies from tiny operations (nanoseconds) to extensive processes (years).
• As AI and scripting languages become more prevalent, allowing broader participation in coding, the division of labor between high-level scripting and low-level optimization will extend further.