Steven AI Talk

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Steven AI Talk(English)

  1. -7 H

    Scale or Fail! 🌐 Just summarized Stanford CS336 Lecture 7: Distributed Computing, GPU Parallelism, and Collective Operations.

    Scale or Fail! 🌐 Just summarized Stanford CS336 Lecture 7: Distributed Computing, GPU Parallelism, and Collective Operations. Deep dive into: 3D Parallelism (Data, Tensor, Pipeline)Collective Ops: All-Reduce, All-to-AllHardware Topology: NVLink & RDMAOvercoming the communication bottleneckAll my links: https://linktr.ee/learnbydoingwithsteven #CS336 #DistributedComputing #GPUParallelism #DeepLearning #LearnByDoingWithSteven #AIInfrastructure #LLM #AITraining #NVLink #RDMA

    6 min

  2. -3 J

    Stanford CS336 Lecture 6: Mastering GPU Programming Models, Performance, and Triton Kernels

    🚀 Deep dive into GPU architecture! Just summarized Stanford CS336 Lecture 6: Mastering GPU Programming Models, Performance, and Triton Kernels. ⚡️ Key takeaways: Memory hierarchy: Registers > Shared Memory > HBM Kernel Fusion to beat the Memory Wall Tiling strategies for MatMul Why Triton is a game-changer for custom kernels Full video in my channels. linktr.ee/learnbydoingwithsteven #GPUProgramming #TritonKernels #StanfordCS336 #DeepLearning #CUDA #PerformanceOptimization #LanguageModeling #LearnByDoingWithSteven #StevenDataTalk #AIInfrastructure #LLM

    10 min

  3. -5 J

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

    How GPUs Actually Drive LLM Scaling: Insights from Stanford CS336 Ever wondered why the "Memory Wall" is the biggest hurdle in AI training? Stanford's CS336 (Lecture 5) dives deep into the hardware foundations that make today’s large language models possible. Key takeaways on system-level optimization: Compute vs. Memory: GPU throughput is outpacing HBM bandwidth. Modern AI engineering is more about managing memory movement than raw calculation.The Power of Low-Precision: Moving to FP8 and FP4 isn't just about saving space; it's about maximizing hardware utilization through specialized matrix units.FlashAttention's Secret: It’s not just a faster algorithm; it’s a masterclass in tiling and operator fusion that avoids the quadratic memory bottleneck.Understanding the underlying hardware—from SMs to warps to shared memory—is essential for anyone building or scaling next-gen AI systems. All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #AI #GPU #Hardware #DeepLearning #FlashAttention #Stanford #CS336 #LLM #SystemOptimization #ComputerArchitecture #AIInfrastructure

    6 min

  4. -6 J

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

    How do we scale LLMs beyond current limits? This lecture explores the transition from quadratic attention to linear alternatives and the rise of sparse Mixture of Experts (MoE). Topics Covered: The fundamental bottleneck of Transformers.RNN-like inference speed with linear attention.How MoE partitions parameters for efficiency.Optimizing for hardware with shared experts and MLA.Full videos in youtube, tiktok, substack, etc Subscribe for more SOTA AI research summaries! All my links: https://linktr.ee/learnbydoingwithsteven #CS336 #StanfordAI #MoE #Architecture #LearnByDoingWithSteven #StevenDataTalk #数能生智

    9 min

  5. 1 MAI

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

    Stanford CS336: Language Model Architectures Evolution, Standardizations, and Optimization StrategiesIn this session, we analyze the architectural blueprints of state-of-the-art language models. From the shift to Pre-Normalization to the rise of Rotary Positional Embeddings (RoPE), we cover why certain design choices have become industry constants.Topics Covered:- The importance of residual stream purity.- Why SwiGLU is favored over traditional activations.- Optimization strategies for long-context windows.- Memory handling with Grouped Query Attention (GQA).Full video on youtube, substack!Check out my Linktree for all social media and podcast links!All my links: https://linktr.ee/learnbydoingwithsteven#LLMArchitecture #StanfordCS336 #LearnByDoingWithSteven #StevenDataTalk #数能生智

    12 min

  6. 29 AVR.

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

    Stanford University’s CS336 course, "Language Modeling from Scratch," offers a deep technical dive into building LLMs from the ground up. In an era of increasingly closed frontier models, mastering the entire stack—from hardware efficiency to scaling laws—is crucial for fundamental AI research. This session focuses on the five pillars of development and the critical role of tokenization (BPE) in bridging raw text with machine intelligence. Key Takeaways: Beyond prompting: The importance of bottom-up engineering mastery.Scaling Laws: Predictability in high-stakes training.Tokenization: Balancing compression efficiency with vocabulary diversity.All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #StanfordCS336 #LLM #DeepLearning #AIResearch #Tokenization #ScalingLaws #NLP #MachineLearning #AIEngineering

    8 min

  7. 27 AVR.

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

    How to build "Professional Grade" Agent Skills? 8 Essential Tips. 🛠️ 1️⃣ Precision Triggers: Description determines success. Too broad leads to misfires; too vague leads to failure. 2️⃣ Constraints vs. Paths: Define the goal and give the agent space to iterate, avoid rigid SOPs. 3️⃣ Layered Loading: Keep the core instructions lean. Load auxiliary files on demand to save context. 4️⃣ Negative Cases: Defining what NOT to do is as critical as defining what to do. 5️⃣ Retirement Plan: If the base model absorbs the capability, delete the skill. A great skill’s ultimate goal is to become obsolete. All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #AgentSkills #AI #SoftwareEngineer #PromptDesign #LLM #Automation #TechTips #FutureOfTech #DeveloperGuide

    7 min

  8. 25 AVR.

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

    The future of engineering is no longer about writing code, but building the "harness" for AI. 🚀 1️⃣ Harness Engineering: Redesigning the entire organization around the assumption that AI is the primary builder. 2️⃣ Monorepo Revival: Giving agents 100% context to eliminate architectural blind spots. 3️⃣ Self-healing: An automated closed loop from error triage to fix validation. 4️⃣ Efficiency Jump: Enabling 10-person teams to deliver at the scale of 100+. The edge in the AI era belongs to those who design the most efficient "control plane." full video in all my channels, youtube, substack, etc All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #AIEngineering #SystemDesign #HarnessEngineering #FutureOfWork #DevOps #AI #TechInnovation #SoftwareArchitecture #Productivity

    6 min
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