1 "Kinematics" Post

Rethinking the Three-Second Traffic Rule: When Physics Says It’s Not Enough

Using kinematics and Python to test when a familiar following-distance rule stops being safe

Oct 23, 2025 • Category: Applied Modeling and Simulation • Tags: #kinematics · #physics · #Python

What You'll Learn

Why a three-second following gap can be adequate in some situations but dangerously short in others
How reaction time and braking distance combine to determine the minimum safe following gap
Why higher speeds require more than a fixed three-second buffer when the lead car stops instantly
How dry, wet, and icy roads change the amount of time a following driver needs to stop
How kinematics can turn a familiar driving rule into a mathematical model
How Python can simulate and visualize safe following times across speeds and road conditions

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 .