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Mapping the Humanoid Robotics Value Chain: The "ChatGPT Moment" for Physical AI
3d ago00:04:27Tap to summarizeMapping the Humanoid Robotics Value Chain: The "ChatGPT Moment" for Physical AIThe convergence of large foundation models and physical automation is driving a major transition in industrial robotics. According to Morgan Stanley's newly launched "Humanoid 100" index, the embodied AI sector is reaching a scientific inflection point comparable to the historical integration of electricity and magnetism.This system-level mapping segments the global value chain into three critical layers: 1️⃣ The "Brain" (Software and Semiconductors): Dominated by Western software infrastructure (Alphabet, Meta, Palantir) and semiconductor giants (NVIDIA, TSMC, Samsung Electronics), this layer defines the foundational autonomy models and spatial compute. 2️⃣ The "Body" (Industrial Components): Actuators, thermal management systems, high-precision rollers, and specialized gears form the core hardware. While traditional European, American, and Japanese suppliers dominate high-end precision components, Chinese suppliers (Top集团, 三花智控, 双环传动) are closing the efficiency and precision gap rapidly. 3️⃣ The "Integrators" (Full-Machine Assembly): The consolidation point for diverse manufacturing giants across automotive (Tesla, Toyota, BYD), consumer electronics (Xiaomi), and e-commerce (Amazon) sectors.Long-term macro forecasts project massive addressable markets by 2050: 📈 United States: Over 62 million humanoid units adopted, impacting roughly 3 trillion USD in cumulative labor wages (primarily in production, maintenance, and food preparation). 📈 China: Over 59 million new units adopted, representing an equipment market exceeding 6 trillion RMB.Understanding this three-part value chain is key for strategic capital allocation and supply chain planning in the era of embodied intelligence.pdf: https://www.patreon.com/posts/mapping-humanoid-158618477?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link All my links: https://linktr.ee/learnbydoingwithsteven#HumanoidRobots #EmbodiedAI #MorganStanley #ValueChain #RoboticsSupplyChain #IndustrialAutomation #Semiconductors #FutureOfWork #HardwareEngineering #learnbydoingwithsteven
Transcribe →The One-Person Company (OPC) Paradigm: AI-Driven Industrial Re-Architecture
3d ago00:05:10Tap to summarizeThe One-Person Company (OPC) Paradigm: AI-Driven Industrial Re-ArchitectureThe definition of entrepreneurship is undergoing a fundamental structural transition. Driven by advanced generative AI frameworks and automated operational pipelines, the "One-Person Company" (OPC) is no longer a simple legal designation, but the core building block of the modern digital economy.An objective analysis of the 2026 China OPC landscape reveals critical structural trends: 1️⃣ Scale and Velocity: By mid-2025, China's active one-person limited liability companies exceeded 16 million, representing over 25% of all national business entities. This supply-side explosion is highly concentrated in digital-native sectors like autonomous agent development and specialized digital media. 2️⃣ The Institutional Sandbox: Over 20 major municipalities have established targeted support frameworks, shifting from basic rent-free physical spaces to deep infrastructure provisioning, including computing power vouchers, desk registration protocols, and dedicated micro-seed funding. 3️⃣ Severe Revenue Polarization: Despite ultra-low startup barriers, commercialization remains highly competitive. The revenue profile is sharply pyramidal, with roughly half of exploration-phase founders earning under 7,000 RMB monthly, while a tiny elite of domain-specific, asset-reusable builders achieve multi-million RMB annual run rates. 4️⃣ Organizational Inertia: Large technology corporations are struggling to adapt their enterprise service pipelines to cater to this highly decentralized micro-client market, leaving a critical gap in lightweight API access and distribution support.As the industry enters its secondary phase of development, the priority for solo builders must shift from superficial tool experimentation to the validation of concrete commercial orders, converting individual labor into reusable, long-term digital assets.Source Report: https://www.opcquan.com All my links: https://linktr.ee/learnbydoingwithsteven#OnePersonCompany #ArtificialIntelligence #StartupEcosystem #DigitalEconomy #BusinessInsights #Automation #TechPolicy #AIAgents #Entrepreneurs #learnbydoingwithsteven
Transcribe →🚀 Scaling LLMs is no longer just about more GPUs—it's about the geometry of the cluster-Stanford's CS336 Lecture 8
2w ago00:07:08Tap to summarize🚀 Scaling LLMs is no longer just about more GPUs—it's about the geometry of the cluster.In Stanford's CS336 Lecture 8, we dive deep into the parallelization strategies that make training trillion-parameter models possible. From Zero Redundancy Optimizers (ZeRO) to 4D parallelism, the complexity is staggering.Key Takeaways: 🔹 ZeRO-3 (FSDP) allows sharding parameters "almost for free" on high-speed networks. 🔹 Tensor Parallelism is mandatory for intra-node scaling but relies on massive bandwidth. 🔹 Pipeline Parallelism is the bridge for cross-node training, now improved with "Zero-Bubble" techniques. 🔹 Expert Parallelism (MoE) decouples MLP layers for sparse routing efficiency.The golden rule? Use all sharding methods until the model fits in memory, then scale with Data Parallelism.Check out the full technical summary and transcripts in our repo! All my links: https://linktr.ee/learnbydoingwithsteven#learnbydoingwithsteven #LLM #AIResearch #DistributedComputing #MachineLearning #DeepLearning #StanfordCS336 #GPU #TPU #ModelParallelism #DataParallelism
Transcribe →Scale or Fail! 🌐 Just summarized Stanford CS336 Lecture 7: Distributed Computing, GPU Parallelism, and Collective Operations.
2w ago00:06:10Tap to summarizeScale 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
Transcribe →Stanford CS336 Lecture 6: Mastering GPU Programming Models, Performance, and Triton Kernels
2w ago00:10:21Tap to summarize🚀 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 > HBMKernel Fusion to beat the Memory WallTiling strategies for MatMulWhy Triton is a game-changer for custom kernelsFull video in my channels.linktr.ee/learnbydoingwithsteven#GPUProgramming #TritonKernels #StanfordCS336 #DeepLearning #CUDA #PerformanceOptimization #LanguageModeling #LearnByDoingWithSteven #StevenDataTalk #AIInfrastructure #LLM
Transcribe →How GPUs Actually Drive LLM Scaling: Insights from Stanford CS336 L5 2026
3w ago00:06:05Tap to summarizeHow GPUs Actually Drive LLM Scaling: Insights from Stanford CS336Ever 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
Transcribe →Stanford CS336 2026 L4: Linear Time Attention and Sparse Architectural Alternatives
3w ago00:08:32Tap to summarizeHow 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, etcSubscribe for more SOTA AI research summaries! All my links: https://linktr.ee/learnbydoingwithsteven#CS336 #StanfordAI #MoE #Architecture #LearnByDoingWithSteven #StevenDataTalk #数能生智
Transcribe →Stanford CS336 L3: Language Model Architectures Evolution, Standardizations, and Optimization Strategies
3w ago00:11:40Tap to summarizeStanford 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
Transcribe →Stanford University’s CS336 course, "Language Modeling from Scratch," offers a deep technical dive into building LLMs from the ground up
3w ago00:07:51Tap to summarizeStanford 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
Transcribe →How to build "Professional Grade" Agent Skills? 8 Essential Tips. 🛠️
4w ago00:07:10Tap to summarizeHow 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
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