2 "Geometry" Posts

How Large Language Models (LLMs) Think: Turning Meaning into Math

Exploring how large language models use linear algebra to create geometric meaning

Oct 7, 2025 • Learning Paths: How Large Language Models Work · Inside the Transformer • Category: AI and the Mathematics of Language • Tags: #AI · #embeddings · #geometry · #linear algebra · #LLM

What You'll Learn

How an LLM turns words and tokens into numerical vectors it can process
How distance and direction in embedding space can represent relationships in meaning
Why linear algebra and geometry are two ways of describing the same internal structure
How matrix operations transform information as it moves through a model
Why high-dimensional spaces can represent many subtle features of language at once
How probability guides a model from its current context toward the next token

From Ice Shows to Algorithms: Cracking the Truck-Packing Problem

Using Python, heuristics, and 3D visualization to tackle a real-world optimization problem

Aug 20, 2025 • Category: Applied Modeling and Simulation • Tags: #algorithms · #geometry · #optimization · #Python

What You'll Learn

Why loading a truck efficiently is a difficult 3D optimization problem
Why NP-hard problems usually require practical heuristics instead of perfect solutions
How box dimensions, orientation, weight, and truck boundaries become constraints in a packing model
How a greedy Python algorithm can build a workable packing plan
How 3D visualization helps reveal overlaps, wasted space, and placement mistakes
How more advanced techniques can improve packing efficiency when simple heuristics are not enough

Behind the Code

My focus is the engineering beneath modern AI systems. After several years of building applications with AI through APIs, my interests have moved below that abstraction layer: understanding how models are architected, trained, evaluated, and served, why they’re designed the way they are, and the engineering tradeoffs behind those decisions.

That direction follows a pattern across my forty-year career as a software developer. I’ve primarily built applications, but I’ve repeatedly found myself pushing beyond the application itself and building the systems, abstractions, and tooling behind it: metaprogramming layers, dynamic runtimes, configuration-driven engines, and automation platforms designed to outlive the original feature request.

The pattern shows across my career: Forms Express at AT&T, a four-layer dynamic runtime shipped for under $500K against a $2M consulting bid; VeriSign’s first-ever customer self-service mobile activation system, which won the 2003 SUPERQuest Award; and TAG at Microsoft, a production LLM orchestration platform that shipped nearly 100 Terraform articles by parsing XML templates, routing prompts to Azure OpenAI, and composing results with browser-driven Azure portal capture. Along the way: C++ MVP, nine technology books, 100+ articles for .NET Programming Tips & Techniques, and helping run CodeGuru, the largest Windows developer community of its era.

After 21 years at Microsoft, I’m now full-time on what’s next: completing my Master’s in Computer Science and publishing at Signal & Syntax, where I research and write about the mathematics and engineering underneath modern AI. I use mathematical models, experiments, and small implementations to explore everything from gradient descent and attention to inference-time behavior and training infrastructure; not just how to use these systems through an API, but how the machinery beneath that API actually works.

Connect with me on LinkedIn .