최신 글
4,485개정렬
16:01EN
3Blue1Brown· 2016년 4월 1일The Brachistochrone: Why the Fastest Descent Is a CycloidGrant Sanderson, Steven StrogatzGrant Sanderson and Steven Strogatz explore Bernoulli’s light analogy and a geometric proof.
1:06:39EN
Jane Street· 2016년 3월 31일Why 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.
23:58ENJane Street· 2016년 3월 31일Effective 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.
1:04:56ENJane Street· 2016년 3월 31일Yaron 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.
1:12:41ENJane Street· 2016년 3월 31일Ron 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.
51:35ENJane Street· 2016년 3월 31일Yaron 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.
52:20EN
GOTO Conferences· 2015년 11월 1일Toyota Kata for Continuous ImprovementHåkan ForssHåkan Forss explains target conditions, coaching and small experiments.
7:27EN3Blue1Brown· 2015년 6월 21일A 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.
1:36:30EN
Google DeepMind· 2015년 5월 13일Reinforcement Learning Lecture 5: Model-Free ControlDavid SilverDavid Silver explains Monte Carlo control, SARSA, eligibility traces and Q-learning.
6:13EN3Blue1Brown· 2015년 3월 5일Euler’s Identity as a Rotation: An Early 3Blue1Brown ExplanationA geometric introduction to how the exponential connects addition, multiplication and rotations in the complex plane.
9:47EN
Lex Fridman· 2014년 6월 16일팟캐스트 · 2명Ryan Hall on Principles and Priorities in Jiu-Jitsu게스트 Ryan Hall · 진행자 Lex FridmanA grappling instructor discusses fundamentals, details and learning.
9:16ENLex Fridman· 2014년 6월 5일팟캐스트 · 2명Ryan Hall on the Value of Competition게스트 Ryan Hall · 진행자 Lex FridmanA martial artist discusses preparation, discipline and honest feedback.
1:10:27EN
Lenny's Podcast팟캐스트 · 2명Hard Skills, Soft Skills and Mentors: Jules Walter's Playbook for Growing as a PM게스트 Jules Walter · 진행자 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.
1:00:51ENLex Fridman팟캐스트Deep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network Foundations진행자 Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.
1:56:35EN
Cognitive Revolution "How AI Changes Everything"팟캐스트From Autocomplete to Alien Optimizer: Riley Goodside on Three Eras of Prompting진행자 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.
1:30:46ENLenny's Podcast팟캐스트Skip the Funnel of Doom: Cursor's Playbook for Hiring the Top 50진행자 Lenny RachitskyAdam Ward, Cursor's head of talent, explains how to scope, map and relentlessly pursue the best people instead of hiring whoever survives a recruiting funnel.
1:45:49ENCognitive Revolution "How AI Changes Everything"팟캐스트Trust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the Company진행자 Nathan LabenzAndreas Stuhlmüller and Jungwon Byun explain how Elicit keeps frontier models checkable for high-stakes science, and where they think AI reasoning is heading.
1:07:29ENLex Fridman팟캐스트Deep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZero진행자 Lex FridmanLex Fridman's MIT 6.S091 opening lecture walks from the basic RL framework through DQN, actor-critic methods and AlphaZero to the unsolved problem of moving from simulation to the real world.
1:42:04ENLenny's Podcast팟캐스트 · 2명Paid Growth, Step by Step: Shopify's Performance Marketing Lead on Tests, Signal and Team Design게스트 Timothy Davis · 진행자 Lenny RachitskyTimothy Davis walks through when to start paid, how to run signs-of-life tests, which platforms to try, what to measure and who to hire first.
1:21:44ENLenny's Podcast팟캐스트From Rowing to Steering: Inside OpenAI's Push Toward Persistent AI Coworkers진행자 Lenny RachitskyTara Seshan, who leads product for Codex and ChatGPT Work at OpenAI, explains how PM work changes when agents do the rowing and people do the steering.
1:01:00ENLenny's Podcast팟캐스트 · 2명Control Over Attention: How Substack Builds Product for Writers and Readers게스트 Sachin Monga · 진행자 Lenny RachitskySubstack's head of product, Sachin Monga, explains the principle behind the company's roadmap, the story of the Recommendations feature, and what he thinks the next era of writing on the internet looks like.
1:26:22ENLenny's Podcast팟캐스트Start Small, Earn Autonomy: What 50+ AI Product Deployments Taught Two Builders진행자 Lenny RachitskyAishwarya Naresh Reganti and Kiriti Badam explain why AI products need a different build process, and how to grow agent autonomy step by step without losing customer trust.
1:25:13ENLenny's Podcast팟캐스트Codex Is Not Just a Coding Tool: OpenAI's Plan for an Agent That Behaves Like a Teammate진행자 Lenny RachitskyCodex product lead Alexander Embiricos explains the 20x growth, why every useful agent may need to write code, and why human review speed is the real bottleneck.
1:23:56EN
Unsupervised Learning: With Jacob Effron팟캐스트A Three-Month Window: The AI 2027 Authors on Superhuman Coders, Faked Alignment and Who Ends Up in Control진행자 Jacob EffronDaniel Kokotajlo and Thomas Larsen walk through their AI 2027 scenario, where they disagree on timelines and alignment, and what they think governments and the public should do.더 보기4,485개 중 24개 표시