Author here. You are very kind, but Kotlin∇ is definitely a work-in-progress and its claimed contributions are more modest in nature. It is true that Kotlin∇ wa

Author here. You are very kind, but Kotlin∇ is definitely a work-in-progress and its claimed contributions are more modest in nature. It is true that Kotlin∇ was one of the first AD frameworks to introduce shape-safe tensor arithmetic, however we were not the only ones to realize the importance of this concept, which has since been adopted by more powerful type systems (e.g., Dex and Hasktorch). Right now, our efforts have mostly migrated from gradient-based machine learning in continuous spaces to generalized differentiation over finite fields (details forthcoming in our ARRAY'22 presentation), which allows us to take derivatives in discrete spaces such as bounded-width regular and context free languages. Our findings indicate that generalized AD has practical applications to robust-parsing, error-correcting codes, type inference and program synthesis.