Composition Intuition II
summary
Sequel masterclass demonstrates concrete function composition patterns across C++, Python, Haskell, APL, J, BQN, Jelly and Weva, showing exactly which combinators and library calls solve each problem: warm‑up “sort letters” is done via C++11 transform+sort+transform then upgraded to ranges+zip+values and range‑v3 actions and reimplemented as Haskell point‑free and BQN grade+select+before; parity adjacency uses zip/adjacent_transform/pairwise across languages; min‑average is solved in C++ with zip_transform + Blackbird partials and three BQN variants (reshape+under trick). Live demos and browser editors run, slides and libraries (Blackbird, Dovekey, Jello) are on GitHub, and the concrete claim is that recognizing B1 combinator and delta (before) combinator patterns leads to simpler, more parallelizable code and easier GPU acceleration.
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chapters (9)
- 0:00Introduction
- 3:19Warm-up problem
- 14:44Combinator recap
- 28:56Live coding warm-up
- 30:21Special array problem
- 45:52Live coding array languages
- 1:07:10Solution comparisons
- 1:09:55Minimum average example
- 1:28:30Conclusion and Q&A
