The Practical AI Digest

Mo Bhuiyan

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

  1. Oct 22

    Tabular 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.

    Tabular Foundation Models: The Baseline Your Gradient Boosting Has to Beat

  2. Oct 8

    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.

  3. Sep 24

    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.

  4. Sep 10

    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.

  5. Aug 27

    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 entire AI industry.

  6. Aug 13

    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 that can act in it.

  7. Jul 30

    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 data, for your task.

  8. Jul 16

    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 everything about where AI can run.

See All (31)

Ratings & Reviews

4
out of 5
3 Ratings

About

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

Information

  • Creator
    Mo Bhuiyan
  • Years Active
    2025 - 2026
  • Episodes
    31
  • Rating
    Clean
  • Copyright
    © Mo Bhuiyan
  • Show Website
    The Practical AI Digest

You Might Also Like