The Practical AI Digest

Mo Bhuiyan

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

  1. 30 Jul

    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.

  2. 16 Jul

    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.

  3. 2 Jul

    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 tissue before the ecosystem fragments.

  4. 18 Jun

    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 about to change the math entirely.

  5. 4 Jun

    Context 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 playbook for what to do Monday.

    Context Rot: Why Million-Token Windows Quietly Fail

  6. 26 May

    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 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.

  7. 12 May

    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 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.

  8. 28 Apr

    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 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.

See All (25)

About

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

Information

  • Creator
    Mo Bhuiyan
  • Years Active
    2025 - 2026
  • Episodes
    25
  • Rating
    Clean
  • Copyright
    © Mo Bhuiyan
  • Show Website
    The Practical AI Digest

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