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.
30 Jul 20min
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.
16 Jul 20min
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.
18 Jun 21min
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.
4 Jun 21min
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 deployi...
26 Mai 28min
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 wit...
12 Mai 25min
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 co...
28 Apr 25min
