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.

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.

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.

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.

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.

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.

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.

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.