Another interesting pattern is the ability to generate structs at compile time.

Ive ran experiments where a neural net is implemented by creating a json file from pytorch, reading it in using @embedFile, and generating the subsequent a struct with a specific “run” method.

This in theory allows the compiler to optimize the neural network directly (I havent proven a great benefit from this though). Also the whole network lived on the stack, which is means not having any dynamic allocation (not sure if this is good?).