The Practical AI Digest – Details, episodes & analysis

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The Practical AI Digest

Mo Bhuiyan via NotebookLM

Technology

Frequency: 1 episode/18d. Total Eps: 24

Distilling AI/ML theory into practical insights. One concept at a time. No jargon.

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Data updated on 21/07/2026

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Score global : 48%

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Mixture of Experts at the Edge: Running 30B Parameter Models on Your Laptop

Season 3 · Episode 4

Thursday, July 16, 2026 • Duration 20:50

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 everything about where AI can run.

The Agent Interoperability Problem: Why Your AI Agents Can Not Talk to Each Other

Season 3 · Episode 3

Thursday, July 2, 2026 • Duration 21:52

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 tissue before the ecosystem fragments.

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KV Cache Compression: The Memory Wall Nobody Talks About

Season 3 · Episode 2

Thursday, June 18, 2026 • Duration 21:57

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 about to change the math entirely.

Context Rot: Why Million-Token Windows Quietly Fail

Season 3 · Episode 1

Thursday, June 4, 2026 • Duration 21:08

Models advertise million-token windows but accuracy degrades well before the limit. Three recent studies, the mechanisms behind the rot, and a practitioner playbook for what to do Monday.

LLMOps: Operating Large Language Models in Production

Season 2 · Episode 10

Tuesday, May 26, 2026 • Duration 28:18

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 emerging set of practices and tools for deploying, monitoring, and maintaining large language models (LLMs) in production. We cover challenges unique to LLMs (like handling the huge model sizes, long context lengths, unpredictable outputs, and continuous updates with new data). You’ll learn about techniques for versioning and evaluating LLMs, setting up feedback loops (human or automated) to catch issues like drift or toxicity, and infrastructure like model hubs and the new Model Context Protocol (MCP) that connects LLMs with external tools and data. We tie it together with examples of how companies manage AI like GPT-4 as a service, ensuring it stays efficient, safe, and up-to-date post-deployment.

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TinyML & Edge AI: Machine Learning on Devices

Season 2 · Episode 9

Tuesday, May 12, 2026 • Duration 25:51

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 microcontrollers, smartphones, and other edge devices with very limited compute and power. We discuss techniques like model compression, quantization, and architecture search that make models small and efficient enough to fit on a $5 microcontroller, bringing capabilities like wake-word detection, sensor analytics, or even vision tasks directly onto devices. You’ll hear about examples like MCUNet, an MIT system that achieved ImageNet-level vision recognition on a microcontroller, and why on-device AI can be beneficial (low latency, no internet needed, data privacy). We also cover real-world applications already using TinyML, from smart appliances to wearable health monitors.

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AI Hardware: GPUs, TPUs and Beyond

Season 2 · Episode 8

Tuesday, April 28, 2026 • Duration 25:37

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 massive parallelism for matrix math, and how companies like Google went further to build TPUs (Tensor Processing Units) optimized for neural network workloads. You’ll hear about the latest AI chips, from NVIDIA’s powerful GPUs driving large model training, to emerging AI accelerators like Graphcore’s IPU, Cerebras’s wafer-scale engine, and even AI on the edge (Apple’s neural engines, etc.). We discuss what each brings in terms of speed, memory, efficiency, and how they’re deployed, giving a peek into the data centers (and devices) where AI calculations run.

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Synthetic Data: Artificial Data for Real Insights

Season 2 · Episode 7

Tuesday, April 14, 2026 • Duration 30:59

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 GANs or language models) that mimic real data. We discuss how this can help in situations with little real data or strict privacy requirements for example, generating realistic medical records to train an AI without exposing any patient’s information. You’ll learn about techniques for producing synthetic images, text, and tabular data, and how they are validated to ensure they reflect real-world patterns. We also cover the benefits and challenges of synthetic data, from reducing bias and augmenting rare cases, to ensuring the synthetic data doesn’t inadvertently leak sensitive info.

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Explainable AI: Opening the Black Box

Season 2 · Episode 6

Tuesday, March 31, 2026 • Duration 24:54

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 explain model predictions by attributing importance to features! So an AI system isn’t just a black box, you can understand why it made a decision. You’ll hear about example use cases (like explaining a medical AI’s diagnosis to a doctor or a loan model’s decision to a loan officer) and recent research into interpreting the internals of neural networks (from visualizing what vision models detect to “probing” language models’ knowledge). By the end, you’ll appreciate the growing toolkit for Explainable AI (XAI) and why it’s crucial for building trust in AI systems.

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Aligning AI with Human Intent: RLHF in Action

Season 2 · Episode 5

Tuesday, March 17, 2026 • Duration 24:31

In this episode, we demystify how researchers teach AI models to behave helpfully and safely using Reinforcement Learning from Human Feedback (RLHF). We discuss why even very large models can generate undesired outputs and how RLHF addresses this by incorporating human preferences. You’ll learn how methods like InstructGPT were trained: first by gathering human-written demonstration responses, then by having humans rank model outputs to train a reward model, and finally using reinforcement learning (e.g. with PPO) to fine-tune the model so that it better aligns with what users want. We also talk about improvements like Constitutional AI and why aligning AI with human values is an ongoing challenge.

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The Practical AI Digest – Details, episodes & analysis

Podcast details

Recent rankings

Apple Podcasts

Spotify

Shared links between episodes and podcasts

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Apple Podcasts

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The Practical AI Digest – Details, episodes & analysis

Podcast details

Recent rankings

Apple Podcasts

Spotify

Shared links between episodes and podcasts

RSS feed quality and score

Publication history

Latest published episodes

Similar podcasts and content

Related Shows Based on Content Similarities

Apple Podcasts

Spotify

Related Shows Based on Content Similarities