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25:44ENWhy Functional Programming Matters at Jane StreetSebastian Funk · Jane StreetA Jane Street developer revisits John Hughes’s classic argument and explains why expressive static types rank highest for trading systems.
28:31ENIncremental: Self-Adjusting Computations in OCamlSebastian Funk · Jane StreetSebastian explains Jane Street’s dynamic dependency graph library and the algorithms behind efficient updates.
13:58ENSolving the Towers of Hanoi by Counting in BinaryKeith SchwarzBinary carries select the disk to move, and a recursive argument explains why the solution uses the fewest possible moves.
16:29ENWhy Topology Matters: An Inscribed Rectangle ArgumentGrant SandersonGrant Sanderson turns pairs of points on a curve into a Möbius strip.
1:29:03ENFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPPresenta: Lex FridmanRichard Socher explains how word embeddings, recurrent models, GRUs, pointer mixtures, and dynamic memory networks turn language tasks into trainable end-to-end systems.
13:10ENWhy Cross-Product Coordinates Match Their GeometryGrant SandersonA linear transformation and duality connect the determinant formula to area and direction.
43:16ENHow Platforms Work: From Deployment to RecoveryCasey WestCasey West outlines six platform capabilities and demonstrates them with Cloud Foundry in 2016.
30:03ENMustafa Suleyman’s 2016 Vision for Technology in Patient CareMustafa Suleyman · DeepMindA historical talk on clinician-led design, kidney-injury alerts, eye-scan research and the need for scrutiny.
1:09:24ENYaron Minsky on Effective ML: Writing OCaml for Code ReadersYaron Minsky · Jane StreetPractical lessons from Jane Street on using types, interfaces and abstraction to keep a large OCaml codebase understandable and correct.
16:01ENThe Brachistochrone: Why the Fastest Descent Is a CycloidGrant Sanderson, Steven StrogatzGrant Sanderson and Steven Strogatz explore Bernoulli’s light analogy and a geometric proof.
1:06:39ENWhy Jane Street Uses OCamlYaron Minsky · Jane StreetA Jane Street talk explains how functional programming and static types support reliable trading software, and examines the costs of choosing a less common language.
23:58ENEffective ML: Errors, Abstraction and Managing ComplexityYaron Minsky · Jane StreetA Jane Street engineer discusses explicit failure, boilerplate, type-system restraint and the practical role of side effects.
1:04:56ENYaron Minsky on Incremental: From Research Idea to Production LibraryYaron Minsky · Jane StreetA history of the algorithms, memory problems and performance tradeoffs behind Jane Street’s OCaml library for self-adjusting computations.
1:12:41ENRon Minsky on Trading, Correctness and Jane Street’s Shift to OCamlYaron Minsky · Jane StreetA Jane Street technologist explains the market-making business and why readability, types and predictable performance shaped the firm’s choice of OCaml.
51:35ENYaron Minsky on Effective ML: Interfaces, Invariants and Code ReadersYaron Minsky · Jane StreetIn the first part of a 2011 Harvard lecture, Jane Street’s Yaron Minsky shows how OCaml interfaces and types can make long-lived code easier to understand and change.
52:20ENToyota Kata for Continuous ImprovementHåkan ForssHåkan Forss explains target conditions, coaching and small experiments.
7:27ENA Proof of Euler’s Formula Using Graph DualityA planar graph and its dual have complementary spanning trees. Counting their edges gives a visual proof that V − E + F = 2.
1:36:30ENReinforcement Learning Lecture 5: Model-Free ControlDavid SilverDavid Silver explains Monte Carlo control, SARSA, eligibility traces and Q-learning.
6:13ENEuler’s Identity as a Rotation: An Early 3Blue1Brown ExplanationA geometric introduction to how the exponential connects addition, multiplication and rotations in the complex plane.
9:47ENRyan Hall on Principles and Priorities in Jiu-JitsuInvitado: Ryan Hall · Presenta: Lex FridmanA grappling instructor discusses fundamentals, details and learning.
9:16ENRyan Hall on the Value of CompetitionInvitado: Ryan Hall · Presenta: Lex FridmanA martial artist discusses preparation, discipline and honest feedback.
1:10:27ENHard Skills, Soft Skills and Mentors: Jules Walter's Playbook for Growing as a PMInvitado: Jules Walter · Presenta: Lenny RachitskyYouTube product lead Jules Walter explains how he builds IQ and EQ skills, asks for honest feedback, and recruits mentors with the smallest possible ask.
1:00:51ENDeep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network FoundationsPresenta: Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.
1:56:35ENFrom Autocomplete to Alien Optimizer: Riley Goodside on Three Eras of PromptingPresenta: Nathan LabenzScale AI's first staff prompt engineer traces how language models went from pure text prediction to instruction following to RLHF, and what that means for building with them.
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