In a prior post, we broke down how large language models actually work — tokenization, embeddings, transformers, context windows, and hallucinations. If you haven't read that one yet, go do that first. This builds directly on it. Here's the key takeaway from that post: LLMs are incredibly
Beyond the Backlog, AI Deep Dive, Part 3 In the first post, we cracked open deep learning: nodes, layers, weights, backpropagation, and the fundamental machinery that lets neural networks learn from data. In the second, we zoomed into large language models: transformers, attention, context windows, and the mechanisms that make
In the last post, we cracked open the black box of neural networks. Nodes, layers, weights, backpropagation — the fundamental machinery that powers deep learning. If you missed it, go read that first. Everything in this post builds on that foundation. An LLM is a neural network — a very specific, very
Every product leader I know is making decisions about AI features right now: which capabilities to build, where to invest, and how to evaluate what's real versus what's hype. And yet, I don't think the vast majority of product people know what's
A few weeks ago, a PM friend of mine was prepping for an interview at a well-known growth-stage company. We were chatting about the interview process and how it's changed since they were last looking for a new role, and one set of questions in particular was different.
There's enormous pressure right now to go agentic. Every product leader I talk to is feeling it — from their boards, their customers, their competitors, and honestly, from themselves. The promise is real. Agentic AI can automate complex workflows, reduce toil, and unlock value that wasn't possible
I just finished Julie Zhuo's "The Making of a Manager," and while it's technically a book about general management, I found myself dog-earing pages that speak directly to the challenges I face as a product leader early in my management journey. Zhuo's
Most product leaders I talk to are obsessed with how to build AI into their products. What features to ship. What models to use. How to differentiate. Those are the right questions — but they're incomplete. The question that gets far less attention: how is your organization actually using
I just finished "The Dream Machine" by M. Mitchell Waldrop, and I can't stop thinking about J.C.R. Licklider. The book is a biography of Licklider and the history of the early computing revolution. "Lick" (as everyone called him) was a psychologist who
This is the third post in my series recapping takeaways from Reforge's AI Strategy course. Part 1 covered how AI accelerates PMF collapse; Part 2 introduced the AI Maker Matrix for user segmentation. This week, I'm diving into something that's been gnawing at me
I'm taking a break this week from my takeaways from the Reforge AI Strategy course to do my EOY breakdown of products I've loved. Every year I find myself quietly building a "favorites list" in my head—the products that actually changed how I
Last time, I kicked off this series on my takeaways from Reforge's AI Strategy course by exploring how AI accelerates PMF collapse. This week, I'm going deeper: who exactly are you building for, and why does getting that wrong doom so many AI products? The framework