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Episodes


Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 7 - Evaluation

Stanford CS153 Frontier Systems | The Road Ahead: Resilience Required

Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 16: Post-Training - RLVR

Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 15: Mid/Post-Training

Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 14: Data

Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Infrastructure, Capstone Case
The AI supercycle centers on the massive scaling of compute infrastructure required to support frontier models and agentic workflows. Revenue for frontier labs remains a lagging indicator directly correlated with compute capacity, which must triple annually to meet demand. As workloads shift from simple inference to co...

Stanford CS25: Transformers United V6 I Advancing Science and Medicine with Collaborative AI Agents

Stanford CS153 Frontier Systems | The Discipline of Delivering Value per Gigawatt
Scaling AI infrastructure requires moving beyond raw compute capacity toward maximizing "good put" and value per dollar. As training frontier models necessitates massive, synchronous clusters, system balance—optimizing the ratio of HBM bandwidth, network throughput, and compute—becomes the primary technical challenge. ...

Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Enterprise Internal Knowledge
Specializing AI models for enterprise needs requires moving beyond general-purpose architectures toward systems optimized for proprietary data and specific business outcomes. While pre-training establishes foundational intelligence, post-training—specifically through reinforcement learning with verifiable rewards—enabl...

Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Economics of Generative AI

Stanford Robotics Seminar ENGR319 | Spring 2026 | Integrated Learning and Planning

Stanford Robotics Seminar ENGR319 | Spring 2026 | Interactive Autonomy

Stanford CS25: Transformers United V6 I Distinct Modes of Generalization from Parameters and Context

Stanford CS153 Frontier Systems | The AI Native Company: How One Founder Becomes a 1000x Engineer
AI-native agentic systems are fundamentally redefining startup productivity, allowing small teams to achieve eight-figure revenues by automating complex, non-technical workflows. By moving from open-loop decision-making to closed-loop systems, founders can integrate agents like Hermes and OpenClaw directly into their o...

Stanford CS547 HCI Seminar | Spring 2026 | HCI and Human-Centered AI for Digital Health

Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 13: Data (Sources, Datasets)

Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 12: Evaluation

Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 11: Scaling Laws

Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 6 - Model Training
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