functional core

Gabriel Lebec's Fullstack Academy talk introduces lambda calculus syntax, the history of mathematical logic, and the essence of functional programming using JavaScript. It also covers combinators, currying and Church encodings.

Douglas Hofstadter's Stanford Presidential Lecture argues that analogy is the core of cognition, illustrating how concepts, categorization and thought arise from analogy-making.

Microsoft's Brian Beckman gives an hour-long, whiteboard-style walk through monads, building from function composition and monoids using school-level math. It is aimed at practicing programmers rather than category theorists.

The first lecture in Bartosz Milewski's category theory series for programmers. It covers the motivation and philosophy behind category theory and why composition matters for handling complexity.

Simon Peyton Jones gives Churchill College's annual computer science lecture on Haskell's birth and evolution. He covers the most influential ideas, from laziness to monads and type system evolution, plus current developments and guesses about the future.

Yaron Minsky of Jane Street gave this talk to Harvard's OCaml-based CS51 class in 2010. It collects advice on being an effective ML programmer in an industrial setting.

Larry McEnerney of the University of Chicago Writing Program gives a graduate-student session on writing effectively. His core idea is that readers care about value to them, not the writer's thinking process.

The opening lecture of MIT 6.824 Distributed Systems (Spring 2020), introducing the course and core challenges of distributed systems. It begins a playlist that goes on to cover Raft and other consensus papers in Go.

Ian Piumarta's 2007 Stanford EE380 lecture on building a dynamic programming environment from the ground up, from the Viewpoints Research Institute's work. It covers the design of a language and system with a recursive design, parsing through code generation, and the COLA architecture.

The first lecture of David MacKay's Cambridge course on information theory, pattern recognition and neural networks. It introduces the core ideas of his book Information Theory, Inference, and Learning Algorithms.