machine learning

Introduction customer annoyance from wait times. Calls arrive randomly, so wait time X follows an Exponential distribution—most waits are short, just a few are painfully long. Now I’d argue that annoyance isn’t linear: a 10-minute...

: The Midnight Paradox Imagine this. You’re constructing a model to predict electricity demand or taxi pickups. So, you feed it time (corresponding to minutes) starting at midnight. Clean and easy. Right? Now your model sees...

were first introduced for images, and for images they are sometimes easy to know. A filter slides over pixels and detects edges, shapes, or textures. You possibly can read this text I wrote earlier...

technique that may turn LLMs into actual agents. It's because MCP provides tools to your LLM which it will possibly use to retrieve live information or perform actions in your behalf. Like all other...

! Welcome back to the “EDA in Public” series! That is Part 2 of the series; when you haven’t seen Part 1 yet, read it here. Here’s a recap of what we conquered. In Part...

in some interesting conversations recently about designing LLM-based tools for end users, and one in every of the vital product design questions that this brings up is “what do people find out about...

first stable v1.0 release in late October 2025. After spending the past two months working with their latest APIs, I genuinely feel that is essentially the most coherent and thoughtfully designed version of...

a decade old now. Back then, OpenAI felt like one (well-baked) startup amongst others. DeepMind was already around, but not yet fully integrated into Google. And, back then, the “triad of deep learning” —...