See the tries for the trees
summary
Covers concrete algorithm choices and tradeoffs: uses Big O to compare lookup speeds, shows hash tables for O(1) property access, binary trees for O(log n) search and a trie for autocomplete where lookup scales with key length not collection size. Explains map‑reduce (Google 2004) to farm maps across many servers, describes promise vs maybe monads and how a monadic promise would let tests swap implementations cleanly, and highlights asm.js AOT that runs native‑like code (demo ~1.8% of native) in browsers.
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chapters (21)
- 0:00System one and two thinking
- 2:14Community biases and expert trust
- 5:06Ignoring history and new ideas
- 7:55Constraints drive creativity
- 9:25System two to Big O basics
- 11:52Binary trees and performance
- 13:17Tries optimize autocomplete
- 14:19Functional programming in JS
- 17:10Referential transparency benefits
- 20:40Higher order functions use
- 23:07MapReduce and data processing
- 25:54Syntax vs concept in learning
- 27:51Null checks with higher order functions
- 31:54Monads explained simply
- 33:20Academic concepts dismissed
- 35:51Monads in promises and async
- 38:20Anti-intellectualism in tech
- 39:46Why types matter in JS
- 43:21Type inference in JS
- 46:53Asm.js potential
- 48:17Call to curiosity
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