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Planet Haskell

A follow-up to an earlier post on RPG die roll probability distributions conditioned on survival scenarios. The author built a performant Python DSL using numpy for probabilistic computations, addressing combinatorial explosion through a backward liveness pass to delete unneeded state and lazy generator support. The DSL vectorizes numpy operations across many states, similar to GPU computing patterns. Notably, probability computations occur in a semiring, enabling exact rational arithmetic via the Chinese Remainder Theorem without bignum libraries. Performance comparisons show numpy competitive with PyTorch and faster than TensorFlow for this ad hoc workload. The code is a deeply embedded DSL using Python as a host for AST interpretation, not mere operator overloading.

A Neighborhood of Infinity: "What does it take to be a hero?" revisited