Fascinating article – a great example of J's array-processing power for concise, performant geometric computation.
It got me thinking about how different paradigms could complement this. I've been working on a Python project[0], which is a framework for quaternion-driven traversal of tree-like structures based on orientation rather than just position or order.
Essentially, J handles the low-level "how" of vector math at scale, while SpinStep-like concepts could provide a higher-level, more semantic "what" and "why" for decisions driven by explicit orientation sets and angular relationships.
It's an interesting thought experiment on combining the raw power of array languages for geometry with more specialized frameworks for orientation-based reasoning.
It got me thinking about how different paradigms could complement this. I've been working on a Python project[0], which is a framework for quaternion-driven traversal of tree-like structures based on orientation rather than just position or order.
Essentially, J handles the low-level "how" of vector math at scale, while SpinStep-like concepts could provide a higher-level, more semantic "what" and "why" for decisions driven by explicit orientation sets and angular relationships.
It's an interesting thought experiment on combining the raw power of array languages for geometry with more specialized frameworks for orientation-based reasoning.
[0] https://github.com/VoxleOne/SpinStep