Oct 22, 202621 minS4
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.
About this Podcast:
Distilling AI/ML theory into practical insights. One concept at a time. No jargon.
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31%
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Who hosts The Practical AI Digest?
The Practical AI Digest is hosted by Mo Bhuiyan. The show has published 31 episodes to date.
What has The Practical AI Digest covered recently?
Recent episodes include "Tabular Foundation Models: The Baseline Your Gradient Boosting Has to Beat" (22 October 2026, 21 minutes), "The Cost of Intelligence: Inference Economics Nobody Wants to Discuss" (24 September 2026, 21 minutes), and "Retrieval-Augmented Generation Is Broken: How to Fix It" (10 September 2026, 20 minutes).
How often does The Practical AI Digest publish?
The Practical AI Digest publishes roughly every two weeks, with episodes averaging 24 minutes across 31 episodes.
How hard is it to get booked on The Practical AI Digest?
Booking probability for The Practical AI Digest is 31%, and the host is rated Selective on guests. The Practical AI Digest requires a Pod Score of 80 or higher. The Practical AI Digest's contact route on record: verified email.
Who should pitch The Practical AI Digest?
The strongest fits for The Practical AI Digest are IT Services & Software Development. The Practical AI Digest has a technology audience.
About the host
Mo Bhuiyan hosts The Practical AI Digest, a technology podcast with 31 episodes published.
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Recent episodes
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
Sep 24, 202621 minS3LLM inference costs dropped 600x since 2023. So why are AI companies losing money faster than ever? The economics nobody wants to talk about.
Sep 10, 202620 minS3RAG pipelines fail in production for structural reasons. We break down the five failure modes and what Agentic RAG, Self-RAG, and GraphRAG actually fix.
- Aug 13, 202620 minS3
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 that can act in it.
- Jul 30, 202621 minS3
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 data, for your task.
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- Jul 16, 202621 minS3
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.
- Jul 16, 202621 minS3
<p>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.</p>
- Jul 2, 202622 minS3
<p>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.</p>
Jul 2, 202622 minS390% 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.
- Jun 18, 202622 minS3
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.
- Jun 18, 202622 minS3
<p>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.</p>
Jun 4, 202621 minS3<p>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.</p>
- May 26, 202628 minS2
<p>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...
- May 12, 202626 minS2
<p>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...
Episode #8
AI Hardware: GPUs, TPUs and Beyond
Apr 28, 202626 minS2<p>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...
- Apr 14, 202631 minS2
<p>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...
Episode #6
Explainable AI: Opening the Black Box
Mar 31, 202625 minS2<p>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...
- Mar 17, 202625 minS2
<p> 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...
Episode #4
AI for Code: How Models Write Software
Mar 3, 202631 minS2<p>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 repositories to generate software from natural language...
- Feb 17, 202629 minS2
<p>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 and text (by matching pictures with their captions)...
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