Abstract: Automatic differentiation (AD) is an important family of algorithms which
enables derivative based optimization. We show that AD can be simply
implemented with effects and handlers by doing so in the Frank language. By
considering how our implementation behaves in Frank's operational semantics, we
show how our code performs the dynamic creation of programs during evaluation.