Episodes
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1 Agents You Can t Answer For w_learnbydoingwithsteven.github.io_linktr.ee_learnbydoingwithsteven
1 Agents You Can t Answer For w_learnbydoingwithsteven.github.io_linktr.ee_learnbydoingwithsteven
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AI Review Debt
1 AI Review Debt w_learnbydoingwithsteven.github.io_linktr.ee_learnbydoingwithsteven
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Architecting the Agentic Conveyor Belt
1 Architecting the Agentic Conveyor Belt w_learnbydoingwithsteven.github.io_linktr.ee_learnbydoingwithsteven
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A Song of Types and Agents
1 A Song of Types and Agents w_learnbydoingwithsteven.github.io_linktr.ee_learnbydoingwithsteven
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2 Teaching Coding Agents to Master Spreadsheets (EN) Shorts_learnbydoingwithsteven.github.io_linktr.ee_learnbydoingwithsteven
2 Teaching Coding Agents to Master Spreadsheets (EN) Shorts_learnbydoingwithsteven.github.io_linktr.ee_learnbydoingwithsteven
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1 AI Psychosis Shift w
1 AI Psychosis Shift w
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1 AI Developer Paradox w
1 AI Developer Paradox w
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1 ACP Agent Blueprint w
1 ACP Agent Blueprint w
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AI_Trust_Design_Patterns
AI_Trust_Design_Patterns
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Why_Better_NLP_Won_t_Fix_Your_Compliance_False_Positives
AI-Driven Multi-Document Correlation for Financial Compliance Transition from reactive validation to proactive, cross-document intelligence. Entity Correlation Engine built on graph database to reveal hidden relationships. Adaptive Probabilistic Risk Model combining multiple signals to compute...
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AI-Driven Multi-Document Correlation for Financial Compliance
✅ Transition from reactive validation to proactive, cross-document intelligence. ✅ Entity Correlation Engine built on graph database to reveal hidden relationships. ✅ Adaptive Probabilistic Risk Model combining multiple signals to compute confidence-based risk scores. ✅ Cross-Jurisdictional...
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**AI-Driven Multi-Document Correlation for Financial Compliance**
Transition from reactive validation to proactive, cross-document intelligence. Entity Correlation Engine built on graph database to reveal hidden relationships. Adaptive Probabilistic Risk Model combining multiple signals to compute confidence-based risk scores. Cross-Jurisdictional Normalization...
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From Model-Centric to System-Centric AI Engineering: Keynotes from AI Engineer Miami Day 2
The AI engineering landscape is transitioning from model-centric prompting to system-centric execution. Day 2 of the AI Engineer Miami conference detailed critical advancements across fast inference hardware, structured context databases, agent-to-agent architectures, and behavior runtimes. Key...
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Abundance of Intelligence and the Shift in Software Architecture: Keynotes from AI Engineer Miami
Abundant, near-zero-cost intelligence is fundamentally reshaping the software engineering paradigm. At the AI Engineer Miami event, leading architects and researchers detailed the shifts occurring across multi-agent orchestration, hardware-level model quantization, and developer identity. Key...
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Selecting the Optimal Balance for On-Device AI: The "SAGE" Model Strategy
Cloud-based foundation models offer immense capabilities but introduce systemic issues for production environments: high latency, security concerns, internet dependence, and escalating API costs. Research indicates that 4 seconds is the upper boundary for human-believed latency in user…
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Core Insights from Stanford CS336 Lecture 15
🚀 Core Insights from Stanford CS336 Lecture 15: Large Language Model Alignment and Post-Training Processes Based on the content of the fifteenth lecture of the Stanford University CS336 course in Spring 2025, this article comprehensively and objectively reviews the key technical pipelines…
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🚀 Stanford University CS336 Lecture 14 Language Model Data Filtering and Deduplication Algorithms [notebooklm summary]
🚀 Stanford University CS336 Lecture 14 Language Model Data Filtering and Deduplication Algorithms [notebooklm summary] This lecture explores the data processing mechanics used for training language models, focusing specifically on quality filtering and data deduplication algorithms. Training…
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The Agentic Architecture: Five Essential AI Terms Explained
✅ Recently, the evolution of Artificial Intelligence from conversational models to autonomous agents is driven by an instruction layer wrapped around Large Language Models (LLMs). ✅ The internal behavioral framework of an agent is defined by project-specific rules in the agents. ✅ While project...
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The Agentic Architecture: Five Essential AI Terms Explained
✅ Recently, the evolution of Artificial Intelligence from conversational models to autonomous agents is driven by an instruction layer wrapped around Large Language Models (LLMs). ✅ The internal behavioral framework of an agent is defined by project-specific rules in the agents. ✅ While project...
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Data Science Periodic Table Explained: A Strategic Map for Analytical Maturity and Workflow
✅ Recently, the landscape of data science is often perceived as a confusing collection of disparate terms and techniques, ranging from ETL to cross-validation. ✅ The horizontal structure of the table tracks the data data maturity lifecycle, moving from unrefined data to actionable insights. ✅ The...
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The Production AI Playbook: Five Pillars for Enterprise Scaling
✅ Transitioning AI from prototype to production requires closing three critical gaps: observability, evaluation, and governance. ✅ The "Week 7 Rule" advises building the evaluation layer and data foundation before choosing a specific model. ✅ Enterprise evaluation requires a three-layered…
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Bridging the LLM Data Gap with Web Access Platforms
✅ LLMs often prioritize answering over admitting failure, leading to up to 60% of web citations resulting in 404 errors. ✅ When blocked by CAPTCHAs or IP blocks, agents enter the "invisible failure group" and fail silently. ✅ Websites employ "AI Labyrinths" to trap crawling bots and feed them…
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🚀 Stanford CS336 Lecture 13: The Evolution of Language Model Data. -Notebooklm Summary
Stanford CS336 Lecture 13 focuses on the critical evolution of language model training data. While model architectures are widely disclosed, dataset details remain highly proprietary due to commercial competition and copyright considerations. The lifecycle of language model training spans...
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Stanford CS336 Language Modeling from Scratch Lecture 12 highlights - Evaluation Overview
Stanford CS336 Language Modeling from Scratch Lecture 12 Evaluation Overview Evaluating language models may seem as simple as measuring a specific model's performance, but it is actually fraught with challenges. The industry currently evaluates models through various metrics, such as benchmark...
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Stanford University CS336 Lecture 11 highlights Application of Scaling Laws in Large Language Models and Maximal Update Parameterization
Stanford University CS336 Lecture 11 Application of Scaling Laws in Large Language Models and Maximal Update Parameterization This lecture explores how modern large language model builders use scaling laws as part of their model design process, and details case studies from relevant papers...
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Stanford CS336 2025 l10 highlights : In-Depth Analysis of Language Model Inference Efficiency and Generation Mechanics
Stanford CS336 2025 l10: In-Depth Analysis of Language Model Inference Efficiency and Generation Mechanics Inference is the most costly and frequently invoked computational phase in the lifecycle of a language model, supporting a wide range of application scenarios from interactive chatbots and...
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Stanford CS336 Lec 9 highlights 📈 The Science of Scale: Why Bigger Isn't Always Better in LLMs.
Stanford CS336 Lecture 9 dives into the laws that govern AI performance. We're moving from the "bigger is better" Kaplan era into the "data-rich" Chinchilla era. Key Takeaways: 🔹 Chinchilla Laws: Compute-optimal training requires ~20 tokens per parameter. 🔹 Inference-Optimal Scaling: Why models...
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🚀 We are hitting the "language-only ceiling" in AI
🚀 We are hitting the "language-only ceiling" in AI. To build true physical agents, models must transition from text translation to sensory fluency. The era of Native Multimodal Intelligence is here: Universal Tokens, Transfusion, and Mixture of Transformers! 👇 All my links:...
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Are we hitting the "language-only ceiling" in AI? 🌐
Are we hitting the "language-only ceiling" in AI? 🌐 In a fascinating Stanford CS25 lecture, Victoria Lynn of Thinking Machines Lab highlighted that our world isn't just text—it's a dense tapestry of visual, auditory, and spatial information. To evolve into real-world physical agents, AI must...
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🚀 The AI Agent "evaluation gap" is real. To deploy agents in high-stakes environments, our benchmarks must evolve beyond static datasets.
🚀 The AI Agent "evaluation gap" is real. To deploy agents in high-stakes environments, our benchmarks must evolve beyond static datasets. We need to measure 3 things: 1️⃣ Environment Complexity 2️⃣ Autonomy Horizon 3️⃣ Output Complexity Are your agents ready? 👇 All my links:...
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The AI agent era is here, but our benchmarks are lagging behind. We are facing a critical "evaluation gap." 📊
The AI agent era is here, but our benchmarks are lagging behind. We are facing a critical "evaluation gap." 📊 While coding agents are advancing rapidly, deploying them in high-stakes environments (healthcare, finance) requires rigorous measurement. We need to evolve from static datasets to…
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Don't Build Slop: The 4 Levels of AI Agent Maturity
EN IT PDF https://www.patreon.com/posts/en-it-pdf-dont-4-158887432?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link
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Don't Build Cascaded Pipelines: The Rise of Native "Any-to-Any" Multimodal Agents
EN IT PDF https://www.patreon.com/posts/en-it-pdf-dont-158887968?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link
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Don't Build Cascaded Pipelines: Skilling Up Coding Agents for System Observability
[EN IT PDF] https://www.patreon.com/posts/en-it-pdf-dont-158888273?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link
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Google I/O 2026 Comprehensive Review: Entering the Agentic Gemini Era
EN IT PDFhttps://www.patreon.com/posts/en-it-pdf-google-158887215?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link
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Mapping the Humanoid Robotics Value Chain: The "ChatGPT Moment" for Physical AI
Mapping the Humanoid Robotics Value Chain: The "ChatGPT Moment" for Physical AI The 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...
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The One-Person Company (OPC) Paradigm: AI-Driven Industrial Re-Architecture
The One-Person Company (OPC) Paradigm: AI-Driven Industrial Re-Architecture The 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...
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🚀 Scaling LLMs is no longer just about more GPUs—it's about the geometry of the cluster-Stanford's CS336 Lecture 8
🚀 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...
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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-All Hardware Topology: NVLink & RDMA Overcoming the communication bottleneck…
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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...
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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...
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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...
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Stanford CS336 L3: Language Model Architectures Evolution, Standardizations, and Optimization Strategies
Stanford CS336: Language Model Architectures Evolution, Standardizations, and Optimization Strategies In 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...
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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....
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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...
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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...
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The state of global AI governance in 2026: Ambition meets Reality.
1️⃣ The EU AI Act faces structural friction with GDPR & the Data Act. Overlapping compliance mandates are creating bottlenecks for European innovators. 2️⃣ MIT's latest mapping: Out of 1000+ governance docs, 43% of "hard law" is already defunct. The rate of legislative churn is unprecedented. 3️⃣...
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The LLM Lifecycle: From Distributed Pre-training to High-Efficiency Inference
The LLM Lifecycle: From Distributed Pre-training to High-Efficiency Inference The evolution of Large Language Models (LLMs) has shifted from a mere parameter race to a sophisticated systems engineering challenge. A new comprehensive review analyzes the complete LLM lifecycle. The report…
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Global AI Governance 2026: EU Regulatory Friction and Technical Gaps
Global AI Governance 2026: EU Regulatory Friction and Technical Gaps As the 2024 EU AI Act transitions into full implementation, global AI regulation has entered a critical phase. However, recent research highlights significant challenges in maintaining institutional coherence. A new study...
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Comprehensive Report on Frontier Dynamics and Multi-domain Applications in the Artificial Intelligence Industry
The provided sources describe the 2026 emergence of OpenClaw, an influential open-source AI agent framework nicknamed "小龙虾" (Little Lobster) that automates complex tasks through local execution. This technological shift enables autonomous product knowledge graph construction and the rise of...
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