sadET VS FastAD

Then, each expression should carry enough information that you can call different member functions, such as derivative for their derivative, or conjugate to take its complex conjugate, etc. Now, the conjugate (and derivative) member function should somehow inherit its behaviour from the sub expressionss. You only need to define what conjugate means for the Number class, and then all other expressions, just apply this function to its sub expressions, until a Number is reached. I am not sure if this is detailed enough, but I apply this logic here. In this piece of code, I am using ETs to that define expressions, and then use these expressions to take their derivatives. This is an autodiff library (without dual numbers), which works well, but it suffers from the the problem; can't find a way to assign and reassign variables to expressions.

What stage is the differentiation proposal in? It seems odd they talked about reverse mode AD but made no mention of memory management for it. They also don't mention anything about FastAD or Stan Math which I think do some pretty innovative things in this space.