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Andrej Karpathy's first Neural Networks: Zero to Hero lecture, which builds micrograd, a tiny autograd engine, from scratch in Python. It explains backpropagation and neural network training step by step, assuming only basic Python and some high-school calculus.

Part 10 of Dan Gelbart's prototype-building lecture series, covering flexures: flexible joints that give precise, frictionless motion. It shows how a cheap piece of aluminum can become a nanometer-accurate mechanism.

The final part of Dan Gelbart's 18-part Building Prototypes series, focused on mechanical design. It opens with a demonstration of stiffness and covers practical design principles for prototype machines.

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.

Tom Wildenhain's guest lecture for CMU's Esoteric Programming Languages course shows that PowerPoint transitions and slides are Turing complete without macros, building up to a mind-bending fractal ending.

Stanford Health Care hosts Christopher Gardner and Robert Lustig on whether all calories are equal, with a focus on processed food and sugar. The talk questions the idea that calories in versus calories out fully explains weight and health.

The first lecture of Stanford's CS107 Programming Paradigms, taught by Jerry Cain, giving an overview of the course. The course goes from C and assembly through C++, concurrency, Scheme and Python.

Segment co-founder and CEO Peter Reinhardt recounts how his company found product-market fit, including how a product grew out of a small feature of another. It is a lecture from Stanford's Startup School course.

Lecture 1 of Harvard's Statistics 110 with Joe Blitzstein, introducing sample spaces, the naive definition of probability, counting, binomial coefficients and the sampling table.

The first lecture of Stanford's Natural Language Processing with Deep Learning course, taught by Chris Manning and Richard Socher. It introduces NLP and word vectors, including SVD, skip-gram, CBOW, negative sampling and word2vec.

Brian Will critiques object-oriented programming and rewrites a roughly 3800-line OO program in a procedural style, cutting code and complexity. It is part of a series of OOP critiques.

First lecture of Stanford's CS106A Programming Methodology by Mehran Sahami, giving a course overview and starting on programming. The course teaches software engineering principles using Java.

Floyd Toole's CIRMMT distinguished lecture on the science of sound reproduction, drawing on decades of blind listening research. It covers what listeners prefer in loudspeakers and why measured accuracy and anechoic response matter.

Erik Demaine's MIT 6.006 lecture introducing dynamic programming through Fibonacci numbers and shortest paths. It covers memoization, subproblems and optimal substructure.

Ron Vale of UCSF and HHMI gives an introductory lecture on kinesin, dynein and myosin motor proteins. He covers their roles in cells and how they turn ATP hydrolysis into directed motion and force.

Y Combinator CEO Michael Seibel lectures at Startup School on building a product people want: identifying the problem, the customer, an MVP and which customers to pursue first.

A philosophy lecture by Professor Moeller on the philosophy of identity. It builds on a discussion of Abigail Thorn's video and covers authenticity, sincerity and how online profiles reshape identity.

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.

Lecture 1B of MIT's 6.001 Structure and Interpretation of Computer Programs (1986) with Gerald Sussman, covering procedures, processes and the substitution model. It includes the line about the power of naming things.

Douglas Crockford's first Yahoo lecture puts JavaScript in historical context. He traces the language's structure and quirks back through the early decades of computer science and programming language history.

Chris Granger, creator of Light Table and the Eve language, gives a Berkeley data science lecture on what computation and programming might look like in ten years. He covers the path that led to Eve and ends with demos.

The first lecture of MIT 6.042J Mathematics for Computer Science, taught by Tom Leighton, introducing mathematical proofs. It covers propositions, implication and basic proof methods.

A long recorded seminar in which Alan Kay discusses object-oriented programming, probably from the mid-1980s. He covers the original idea of objects, messaging and the work done at Xerox.

David Shaw of D. E. Shaw Research presents Anton, a special-purpose machine that speeds up biomolecular simulations about a hundred-fold. The lecture covers molecular dynamics and how simulation helps understand how drugs bind.

An excerpt of Richard Feynman's 1985 lecture Q&A about whether machines can think. He discusses the difficulty of defining what computers can never do and compares machine intelligence to machines that don't copy animals.

Geoffrey Hinton's 2025 Royal Institution Discourse on what AI has learned from biological intelligence and how it might surpass it. He explains neural networks and argues that human language use resembles what backpropagation-trained networks do.

Peter Norvig's UBC distinguished lecture on how massive amounts of simple data, such as for language modeling, can yield understanding that hand-built models could not.

A short Harvard Online animated lesson on how the mitochondrial electron transport chain works. It shows proton pumping and the machinery that drives ATP synthesis.

A 78-minute Richard Feynman lecture of anecdotes about working at Los Alamos on the Manhattan Project, followed by about ten minutes of audience Q&A in which he tells three safe-cracking stories.

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.

Noam Chomsky's 2011 University of Oslo lecture on Newton's contributions to the study of mind. He argues Newton abandoned mechanical explanation and lowered the bar for scientific intelligibility, with consequences for the limits of human understanding. Q&A starts at 45:33.

A recording of Richard Feynman's talk on the possibilities of manipulating matter at very small scales, widely seen as an early vision of nanotechnology. This is a later version of his 1959 "There's Plenty of Room at the Bottom" lecture.

A lecture by Noam Chomsky on thought control in democratic societies, covering propaganda and manufacturing consent. The upload has no description.

Casey Muratori's 2005 video introducing immediate-mode GUIs as an alternative to retained widget toolkits. He explains the motivation and shows minimal-state button logic.

John Gustafson presents posits, a proposed drop-in replacement for IEEE 754 floats that is simpler in hardware and more flexible in precision. The seminar includes a live demo and machine learning results.

Sacha Arnoud, Waymo's Director of Engineering, gives a lecture in MIT's 2018 deep learning for self-driving cars course on the rise of machine learning in autonomous vehicles. It covers how Waymo uses deep learning and how it handles tricky driving situations.

An excerpt from Hal Abelson's opening SICP lecture introducing computer science and contrasting declarative and imperative knowledge. He argues that computer science is not really a science and is more like magic or engineering of ideas.

Professor Ruzic explains modular micro-reactors: self-contained nuclear units with passive safety whose fuel lasts about 20 years. He covers their use for process and district heat and where they can compete with diesel generation.

Rick Hartley gives a roughly two-hour live training on proper grounding in PCB design. He explains how energy actually flows in the space between conductors, and how that governs noise, EMI, return paths and signal reflections.

The first lecture of MIT's 1986 6.001 Structure and Interpretation of Computer Programs, given by Harold Abelson, introducing Lisp and the idea of computer science as the study of process.