Haskell Weekly Issue 493 (haskellweekly.news)

This issue highlights practical and ecosystem work that matters for ML/AI engineers using Haskell. Ajeet Grewal’s piece on state management tackles a core friction when building neural nets in a pure language: updating weights while preserving a “functional core, imperative shell.” The article underscores common patterns (pure data-transformations that return updated parameters, or narrow, controlled mutation at the edges using ST/IO or references) and the trade-offs between purity, performance, and easier reasoning — important when you need repeatable training runs, deterministic gradients, or GPU/FFI integration. Complementing that are hands-on algorithm posts: a Dynamic Programming primer comparing Haskell and Rust (memoization, strictness vs laziness, and array/vector choices) and a Reversi implementation with Minimax and Alpha‑Beta pruning demonstrating classic AI techniques implemented in idiomatic Haskell. The rest of the issue shows ecosystem maturation that helps ML workflows: cabal-matrix (a matrix builder for reproducible builds across compilers/dependency versions), initial releases of stable subsets of template-haskell (safer metaprogramming), and various educational resources — from functional optics with a visual notation to a wide-ranging Philip Wadler interview on monads, type classes, and communicating ideas. Together these items signal Haskell’s growing practicality for reliable ML systems: stronger tooling for reproducible experiments, clearer abstractions for stateful models, and community knowledge to bridge theory and production.