All Episodes
Steven AI Talk — 712 episodes · page 1 of 8
1 Agents You Can t Answer For w_learnbydoingwithsteven.github.io_linktr.ee_learnbydoingwithsteven
AI Review Debt
Architecting the Agentic Conveyor Belt
A Song of Types and Agents
2 Teaching Coding Agents to Master Spreadsheets (EN) Shorts_learnbydoingwithsteven.github.io_linktr.ee_learnbydoingwithsteven
1 AI Psychosis Shift w
1 AI Developer Paradox w
1 ACP Agent Blueprint w
AI_Trust_Design_Patterns
Why_Better_NLP_Won_t_Fix_Your_Compliance_False_Positives
AI-Driven Multi-Document Correlation for Financial Compliance
**AI-Driven Multi-Document Correlation for Financial Compliance**
From Model-Centric to System-Centric AI Engineering: Keynotes from AI Engineer Miami Day 2
Abundance of Intelligence and the Shift in Software Architecture: Keynotes from AI Engineer Miami
Selecting the Optimal Balance for On-Device AI: The "SAGE" Model Strategy
Core Insights from Stanford CS336 Lecture 15
🚀 Stanford University CS336 Lecture 14 Language Model Data Filtering and Deduplication Algorithms [notebooklm summary]
The Agentic Architecture: Five Essential AI Terms Explained
The Agentic Architecture: Five Essential AI Terms Explained
Data Science Periodic Table Explained: A Strategic Map for Analytical Maturity and Workflow
The Production AI Playbook: Five Pillars for Enterprise Scaling
Bridging the LLM Data Gap with Web Access Platforms
🚀 Stanford CS336 Lecture 13: The Evolution of Language Model Data. -Notebooklm Summary
Stanford CS336 Language Modeling from Scratch Lecture 12 highlights - Evaluation Overview
Stanford University CS336 Lecture 11 highlights Application of Scaling Laws in Large Language Models and Maximal Update Parameterization
Stanford CS336 2025 l10 highlights : In-Depth Analysis of Language Model Inference Efficiency and Generation Mechanics
Stanford CS336 Lec 9 highlights 📈 The Science of Scale: Why Bigger Isn't Always Better in LLMs.
🚀 We are hitting the "language-only ceiling" in AI
Are we hitting the "language-only ceiling" in AI? 🌐
🚀 The AI Agent "evaluation gap" is real. To deploy agents in high-stakes environments, our benchmarks must evolve beyond static datasets.
The AI agent era is here, but our benchmarks are lagging behind. We are facing a critical "evaluation gap." 📊
Don't Build Slop: The 4 Levels of AI Agent Maturity
Don't Build Cascaded Pipelines: The Rise of Native "Any-to-Any" Multimodal Agents
Don't Build Cascaded Pipelines: Skilling Up Coding Agents for System Observability
Google I/O 2026 Comprehensive Review: Entering the Agentic Gemini Era
Mapping the Humanoid Robotics Value Chain: The "ChatGPT Moment" for Physical AI
The One-Person Company (OPC) Paradigm: AI-Driven Industrial Re-Architecture
🚀 Scaling LLMs is no longer just about more GPUs—it's about the geometry of the cluster-Stanford's CS336 Lecture 8
Scale or Fail! 🌐 Just summarized Stanford CS336 Lecture 7: Distributed Computing, GPU Parallelism, and Collective Operations.
Stanford CS336 Lecture 6: Mastering GPU Programming Models, Performance, and Triton Kernels
How GPUs Actually Drive LLM Scaling: Insights from Stanford CS336 L5 2026
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.
Generative AI is the next paradigm
Foundations of Convolutional Neural Networks
Sequence Modeling Recurrent Neural Networks and Transformers
Demystifying the Foundations of Neural Networks with MIT
Bridging the AI Security Gap: From Vulnerabilities to Principaled Defenses
Summary of public notes regarding Claude Code source code
Building a Personal Knowledge Management (PKM) system
LLM Teams as Distributed Systems
Continual Learning in Large Language Models: A Comprehensive Survey
Large Language Model-Enhanced Relational Operators Taxonomy and Benchmark Analysis
How LLMs Distort Our Written Language
Dynamic World Modeling Dataset and Benchmark for Generative Action Role-Playing Games
Accelerating LLM Reasoning
Beyond Tokens: The Rise of Context Engineering in Agentic AI
The Sovereign Standard: Scaling Private Models as a Service
Securing the Autonomous Frontier: Agentic Security Runtime
Defending the Loop: Securing Autonomous AI Agents
🚀 Transforming Wealth Management: Inside Morgan Stanley’s AI Efficiency Engine
Inside the Softmax: A New Frontier in LLM Hallucination Detection
The Architecture of Mind and Machine: Navigating Neuro-Linguistic Integration
Benchmarking Open Source VLM and OCR Model Performance
🚀 Automating the Future: Inside Goldman Sachs’ AI-Driven Transformation
Optimizing Open-Source LLMs: RAG, Quantization, and Mistral Small 4
Generative AI in Banking: Transformation and Operational Efficiency
Digital Transformation Trends in Equipment Leasing and B2B Finance
Agentic Storage: Solving AI State and Persistence with MCP
Agentic Storage: Solving AI State and Persistence with MCP
Connecting Agents and Data: A Guide to A2A and MCP
Connecting Agents and Data: A Guide to A2A and MCP
Connecting Agents and Data: A Guide to A2A and MCP
OWASP Top 10 Vulnerabilities for Large Language Models
OWASP Top 10 Vulnerabilities for Large Language Models
OWASP Top 10 Vulnerabilities for Large Language Models
Agentic Storage: Solving AI State and Persistence with MCP
Beyond the Super Agent: Designing Collaborative Agentic Systems
Beyond the Super Agent: Designing Collaborative Agentic Systems
Beyond the Super Agent: Designing Collaborative Agentic Systems
RAG vs. Long Context: The Future of Model Architecture
RAG vs. Long Context: The Future of Model Architecture
The Human Control Plane: Architecting Reliable AI Agents