The Practical AI Digest
A generative AI-powered podcast by M Mostagir Bhuiyan. Each episode distills advanced AI/ML topics into real-world insights for practitioners.
About the Podcast
The Practical AI Digest is built using NotebookLM to synthesize research papers, technical reports, and industry developments into conversational episodes. The format is designed for engineers and technical leaders who want to stay current on AI/ML without reading every paper. Episodes cover topics from model architecture and training techniques to deployment patterns and infrastructure decisions.
Hosted by M Mostagir Bhuiyan, the podcast draws on hands-on experience building AI infrastructure, writing research on retrieval-augmented generation and micro-containerization, and leading cloud platform teams.
Listen On
Episodes
Continual Learning: The Last Unsolved Problem in AI
October 8, 2026
AI systems can reason through anything but cannot remember the solutions. Why catastrophic forgetting is now a productio...
The Cost of Intelligence: Inference Economics Nobody Wants to Discuss
September 24, 2026
LLM inference costs dropped 600x since 2023. So why are AI companies losing money faster than ever? The economics nobody...
Retrieval-Augmented Generation Is Broken: How to Fix It
September 10, 2026
RAG pipelines fail in production for structural reasons. We break down the five failure modes and what Agentic RAG, Self...
Distillation: How Small Models Eat Big Models for Lunch
August 27, 2026
Training a frontier model costs millions. Distilling a capable student from it costs thousands. Knowledge distillation i...
World Models: When AI Learns Physics Instead of Memorizing Data
August 13, 2026
A language model can describe a falling glass. It cannot predict where the water goes. World models close the gap betwee...
The Evaluation Crisis: We Do Not Know How Good Our Models Actually Are
July 30, 2026
MMLU is saturated. Chatbot Arena is gameable. Public benchmarks leak into training data. The only eval that matters is t...