Steven AI Talk

How GPUs Actually Drive LLM Scaling: Insights from Stanford CS336 L5 2026

Modern artificial intelligence has shifted from a reliance on increasing CPU clock speeds to a horizontal scaling model utilizing GPUs and TPUs. This transition addresses the "memory wall," where computational power outpaces data transfer speeds, transforming software development into a hardware logistics challenge. Op...

Stanford CS336 2026 L4: Linear Time Attention and Sparse Architectural Alternatives

Stanford CS336 L3: Language Model Architectures Evolution, Standardizations, and Optimization Strategies

Stanford University’s CS336 course, "Language Modeling from Scratch," offers a deep technical dive into building LLMs from the ground up

How to build "Professional Grade" Agent Skills? 8 Essential Tips. 🛠️

The future of engineering is no longer about writing code, but building the "harness" for AI.

The state of global AI governance in 2026: Ambition meets Reality.

The LLM Lifecycle: From Distributed Pre-training to High-Efficiency Inference

Global AI Governance 2026: EU Regulatory Friction and Technical Gaps

Comprehensive Report on Frontier Dynamics and Multi-domain Applications in the Artificial Intelligence Industry

The Global Embodied AI Market Outlook 2033

Programming Life with Generative AI

Do No Harm: Is it Time for a Hippocratic Oath in AI?

The Architecture of AI Assistants: A Guide to LLM Post-Training

Understanding Large Language Models: A Technical Deep Dive

Frontiers of Deep Learning: Limits, Failures, and New Horizons

Claude Code is most powerful when treated as a configurable teammate

Google Antigravity is not just an assistant

Fundamental principles of Deep Reinforcement Learning from MIT 6.S191. 🚀🚀🚀

Codex is strongest when you stop using it like one generic chatbot.