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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.
Stanford professor released a free 1-hour lecture covering the fundamentals of AI agents, tool calling, multi-step workflows, planning and reflection.
CMU Advanced NLP lecture clarifies how reinforcement learning optimizes whole-output rewards (correctness, helpfulness, safety) rather than next-token prediction used in pretraining/fine-tuning.
Andrew Ng and Laurence Moroney delivered a Stanford lecture described as the most honest AI career playbook, covering why now is the best time to build in AI and what actually gets candidates hired in 2026.
Stanford University offers a 1.5-hour lecture on LLM architecture covering fundamental concepts and design principles of large language models.