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Nathan Lambert shares a video lecture covering prerequisites for his book, including language model basics, probabilities, and training pipelines, using GLM 5.2.
Natolambert announces a new lecture covering synthetic data and the history of distillation, from Hinton 2015 to modern on-policy distillation, with over 7 hours of video content.
A tweet recommends a lecture by an OpenAI researcher on how LLMs are built, claiming it taught an MIT CS grad more than his entire degree.
A Stanford professor delivered a public lecture providing a comprehensive breakdown of how modern LLMs like GPT, Claude, and LLaMA are built under the hood, making advanced architecture accessible to the public.
A tweet highlights a 47-minute Stanford lecture that allegedly provides the value of a four-year engineering degree, and promotes 25 AI prompts as a key productivity tool.
A tweet shares a lecture at MIT by David Shirokoff covering the fundamentals of Markov Chains, including transition probabilities, Markov matrices, eigenvalues, and long-term steady state.
This Twitter thread highlights Stanford CS221 lecture 6 on heuristics, explaining how A* search improves agent efficiency by using heuristics to guide decision-making. Key takeaways include building heuristics by relaxing problems, the danger of bad heuristics, and the optimality of A* with the right estimate.
A free Stanford lecture by Percy Liang on AI generalization explains why models excel on benchmarks but fail on real codebases, covering benchmark memorization, bias-variance tradeoff, and hallucination.
An X thread highlights that Anthropic pays engineers $500K+ to deploy what Percy Liang teaches for free in Stanford CS221 Lecture 3, explaining how watching it changes understanding of Claude's behavior.
A tweet shares a Stanford lecture on AI image generation, encouraging people to learn it for career advancement.
Guest lecture at MIT 6.566 on AI agent security covering system-level threats, prompt injection, tool-use vulnerabilities, and demonstrations with LLMs like GPT-5.4 and Qwen 3.5.
A tweet shares the story of an MIT professor who gave his last lecture before dying, distilling his life's knowledge into one hour.
A Stanford lecture explaining how ChatGPT and Claude are built is available for free, as shared on Twitter.
Snapchat paid $150 million for Looksery, a deep learning computer vision startup. A free MIT lecture teaches building neural networks from scratch.
Promotes a one-hour Cambridge lecture by Demis Hassabis that provides deep insights into the future of AI, claiming it will teach more than most learn in five years.
A thread sharing a video of self-play RL training with lidar and PPO in Unity, followed by a lecture on building AlphaGo from scratch.
Stanford CS153Systems lecture featuring Jensen Huang of Nvidia discussing the compute behind intelligence.
A blackboard lecture by Eric Jang walks through building AlphaGo from scratch with modern AI tools, covering RL, MCTS, self-play, and connecting to LLM training, along with a discussion on automated AI research.
A Cornell lecture by Marcos Lopez de Prado shares the quant trading framework using neural networks that Jane Street quants use, with potential earnings of $750k/year.
Geoffrey Hinton warns that AI is developing unintended capabilities and surpassing humans in cognitive tasks, and the post provides a practical guide for using Claude effectively.