Shows / The Practical AI Digest
The Practical AI Digest
Distilling AI/ML theory into practical insights. One concept at a time. No jargon.
Episodes
- The Practical AI Digest
Prompt Injection: Why It Cannot Be Patched and How to Design Around It
AI agents obey text they read because instructions and data share one channel. Why every filter gets beaten, and the designs that contain the damage.
The Practical AI DigestTabular Foundation Models: The Baseline Your Gradient Boosting Has to Beat
Pretrained models now predict on your spreadsheet with no training. Where they beat tuned XGBoost, where they still lose, and what the licenses let you ship.
- The Practical AI Digest
Continual Learning: The Last Unsolved Problem in AI
AI systems can reason through anything but cannot remember the solutions. Why catastrophic forgetting is now a production problem, not just a research one.
- The Practical AI Digest
The Cost of Intelligence: Inference Economics Nobody Wants to Discuss
LLM inference costs dropped 600x since 2023. So why are AI companies losing money faster than ever? The economics nobody wants to talk about.
- The Practical AI Digest
Retrieval-Augmented Generation Is Broken: How to Fix It
RAG pipelines fail in production for structural reasons. We break down the five failure modes and what Agentic RAG, Self-RAG, and GraphRAG actually fix.
- The Practical AI Digest
Distillation: How Small Models Eat Big Models for Lunch
Training a frontier model costs millions. Distilling a capable student from it costs thousands. Knowledge distillation is quietly reorganizing the economics of…
- The Practical AI Digest
World Models: When AI Learns Physics Instead of Memorizing Data
A language model can describe a falling glass. It cannot predict where the water goes. World models close the gap between AI that talks about reality and AI…
- The Practical AI Digest
The Evaluation Crisis: We Do Not Know How Good Our Models Actually Are
MMLU is saturated. Chatbot Arena is gameable. Public benchmarks leak into training data. The only eval that matters is the one you build yourself, on your…
- The Practical AI Digest
Mixture of Experts at the Edge: Running 30B Parameter Models on Your Laptop
A 30B parameter model runs on a MacBook because only 3B parameters fire per token. Mixture of Experts splits memory cost from compute cost, and that changes…
- The Practical AI Digest
The Agent Interoperability Problem: Why Your AI Agents Can Not Talk to Each Other
90% of enterprises deploy AI agents. Only 23% scale them. The gap is interoperability. Three protocols, MCP, A2A, and ACP, are racing to build the connective…
- The Practical AI Digest
KV Cache Compression: The Memory Wall Nobody Talks About
Your GPU is not compute-bound. It is memory-bound. The KV cache is eating half your inference budget, and two ICLR 2026 breakthroughs KVTC and TurboQuant are…
The Practical AI DigestContext Rot: Why Million-Token Windows Quietly Fail
Models advertise million-token windows but accuracy degrades well before the limit. Three recent studies, the mechanisms behind the rot, and a practitioner…
- The Practical AI Digest
LLMOps: Operating Large Language Models in Production
Building an AI model is one thing: keeping a large language model running reliably in the real world is another. In this episode, we discuss LLMOps, the…
- The Practical AI Digest
TinyML & Edge AI: Machine Learning on Devices
In this episode, we explore how AI is moving from the cloud to tiny devices. TinyML is the field of optimizing models and algorithms to run on…
- The Practical AI Digest
AI Hardware: GPUs, TPUs and Beyond
This episode is all about the specialized hardware that makes modern AI possible. We explain how GPUs became the workhorses of deep learning by offering…
- The Practical AI Digest
Synthetic Data: Artificial Data for Real Insights
In this episode, we explore how synthetic data is created and used to improve AI models. Synthetic data refers to artificial datasets generated by models (like…
- The Practical AI Digest
Explainable AI: Opening the Black Box
In this episode, we look at how researchers are making AI models more transparent and interpretable. We discuss techniques like SHAP values and LIME that…
- The Practical AI Digest
Aligning AI with Human Intent: RLHF in Action
In this episode, we demystify how researchers teach AI models to behave helpfully and safely using Reinforcement Learning from Human Feedback (RLHF). We…
- The Practical AI Digest
AI for Code: How Models Write Software
This episode explores the rise of AI coding assistants. We discuss how models like OpenAI’s Codex (which powers GitHub Copilot) are trained on millions of code…
- The Practical AI Digest
Multimodal Models: Combining Vision, Language, and More
This episode explores multimodal AI : models that can see, read, and even hear. We explain how models like OpenAI’s CLIP learn joint representations of images…
- The Practical AI Digest
Efficient Fine-Tuning: Adapting Large Models on a Budget
This episode dives into strategies for fine-tuning gigantic AI models without needing massive compute. We explain parameter-efficient fine-tuning methods like…
- The Practical AI Digest
Diffusion Models: AI Image Generation Through Noise
In this episode, we break down what diffusion models are and why they’ve become the go-to method for AI image generation. You’ll learn how these models…
- The Practical AI Digest
Graph Neural Networks: Learning from Connections, Not Just Data
This episode breaks down what graph neural networks (GNNs) are and why they matter. You’ll learn how GNNs use nodes and edges to represent relationships and…
- The Practical AI Digest
Neuro-Symbolic AI: Combining Learning With Logic
In this episode, we explain what neuro-symbolic AI is and why it matters. You’ll learn how neural networks handle patterns, how symbolic systems handle rules…
- The Practical AI Digest
LLMs in Chip Design: How AI Is Entering the Hardware Workflow
In this episode, we look at how large language models are being used in chip and hardware design. We break down what LLM-aided design actually means, how…
- The Practical AI Digest
How Embeddings and Vector Databases Power Generative AI
This episode explains how embedding models turn language into numerical vectors and how vector databases like Pinecone, FAISS, or Weaviate store and search…
- The Practical AI Digest
Agentic AI: What Happens When Models Start Acting
In this episode, we explore agentic AI systems built to not just predict or classify, but to plan, reason, and act autonomously. We break down what makes these…
- The Practical AI Digest
Understanding Attention: Why Transformers Actually Work
This episode unpacks the attention mechanism at the heart of Transformer models. We explain how self-attention helps models weigh different parts of the input…
- The Practical AI Digest
Markov Chains, Monte Carlo, and HMC: A Deep Dive
In this episode, we break down the essentials of Markov Chains, Monte Carlo simulations, and Markov Chain Monte Carlo methods. We explain key ideas like…
- The Practical AI Digest
The Model Context Protocol (MCP): Making LLMs Actually Useful
In this episode, we dive into the Model Context Protocol, or MCP. It’s a new standard that helps large language models connect with real-world tools, data, and…
- The Practical AI Digest
Generative Adversarial Networks (GANs) Explained: From DL Basics to Real-World Training Tips
This episode breaks down how GANs work by starting with deep learning basics like CNNs, gradient descent, and regularization. We then get into what actually…
- The Practical AI Digest
Bayesian vs. Frequentist Thinking in Marketing Mix Modeling
In this episode, we unpack how Bayesian and Frequentist statistical approaches tackle marketing performance analysis, focusing on Marketing Mix Modeling (MMM)…