GoingDeep: C9 Lectures: Greg Meredith - Monadic Design Patterns for the Web 4 of n

GoingDeep

Greg Meredith, a mathematician and computer scientist, has graciously agreed to do a C9 lecture series covering monadic design principles applied to web development. You've met Greg before in a Whiteboard jam session with Brian Beckman.The fundamental concept here is the monad, and Greg has a.

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0h40m
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Episode synopsis

Greg Meredith, a mathematician and computer scientist, has graciously agreed to do a C9 lecture series covering monadic design principles applied to web development. You've met Greg before in a Whiteboard jam session with Brian Beckman.

The fundamental concept here is the monad, and Greg has a novel and conceptually simplified explanation of what a monad is and why it matters. This is a very important and required first step in the series since the whole of it is about the application of monadic composition to real world web development.

In part 4, Greg primarily focuses on the idea that a monad is really an API—a view into the organization of data and control structures, not those structures themselves. In OO terms, it's an interface. To make this point concrete, Greg explores one of the simplest possible data structures supporting at least two different, though consistent, interpretations of the same API. The structure used, Conway's partisan games, turns out to be tailor-made for this investigation. Not only does this data structure have the requisite container-like shape, it provides opportunities to see just what's necessary in a container to implement the monadic interface. 

Running throughout the presentation is a more general comparison of reuse between an OO approach and a more functional one. When the monadic API is "mixed into" the implementing structure, we get less reuse than when the implementing structure is passed as a type parameter. Finally, doing the work puts us in a unique position to see not just how to generalize Conway's construction monadically, but also the underlying pattern that allows the generalization to suggest itself.

See part 1
See part 2
See part 3

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