Modeling the Physical World
Andrew Diaz
Computer Vision + Prediction Infrastructure for the physical world.
"Trying to listen."
Computer Vision + Prediction Infrastructure for the physical world.
Andrew Diaz
"Do the work."
The Thesis
The physical world generates data that nobody is reading. A tennis player's split-step latency. A customer's stall time before ordering at a brick-and-mortar counter. A retail food operation's demand rhythm. A warehouse worker's route deviation. These signals are ambient, continuous, and invisible to most models trained on public datasets.
I studied electrical engineering — signal processing, the math of systems that sense and respond. A graduate course in computer system design taught me that every complex system is just a file system — structured layers of abstraction all the way down. That principle scales: from operating systems to prediction pipelines to the physical world itself.
The same architecture extends everywhere. Sports is the proof of concept — a fast, measurable odds market with real capital at stake. If the pipeline works there, the machine learning is sound, and it works anywhere physical-world behavior generates signal. Restaurants. Retail. Logistics. Every venue is an unmonetized sensor network.
A deployed sports prediction model put $20K of my own capital at risk and is up $4,304 (+21%) — purely statistical, CatBoost on historical match data, no computer vision. Autonomous agent systems I've built — multi-agent workflows orchestrating parallel models, content engines, and decision pipelines — prove that one person with agent orchestration can now operate at the pace of a full engineering team. Different domains, same discipline: capture the signal nobody else is reading, automate the response, ship it.
The edge isn't better models. It's better data. Build the sensor.
B.S. Electrical Engineering, University of Florida
Analytics · Applied AI · Computer Systems · Applied Probability
7 years professional poker · Applied probability
Backstory
I find predicting the future to be incredibly satisfying. Predictions are good indicators of how much you know about the world, but more importantly of how much you know about yourself.
I sold sodas out of my backpack in middle school and washed dishes professionally in high school. I studied electrical engineering at the University of Florida — not because I wanted to be an engineer, but because I wanted to be literate enough to build what I saw in my head.
After college I played professional poker for seven years. In late 2019, while backed by ImAWhale, then a poker-staking group, I generated more than $25K for the stake over roughly three months. That's where I learned to separate edge, backing, and risk of ruin.
Get in touch with Andrew →The Bets
Part of that poker bankroll later became an early Solana position in 2020 — turned a shoestring into a meaningful return, then watched most of it evaporate. That experience reinforced what the poker table had already taught me: finding an edge and keeping the money are two very different skills.
As CTO of DMG Decisions, I'm building prediction models with machine learning for businesses. Separately, I'm running an independent R&D project on computer vision for sports — the proving ground for physical-world sensing.
See the CPR prediction model →Sports is the proof of concept. Physical-world sensing is the market.
Where This Goes
The same discipline extends to every domain where humans generate unpriced signal.
// selected_systems
LIVE + R&D, BUILT AND TESTED
CV Pipeline — Proprietary Data Manufacturing
Computer vision for extracting signals that don't exist in any dataset. CUDA-accelerated inference on local GPU hardware — extracting Table Proximity Index, Serve Toss Variance, and Split-Step Latency from free sports broadcasts. Proprietary data manufactured from public video for the cost of electricity.
The moat isn't the model. It's data that doesn't exist until you build the sensor.
Recursive Agentic Systems
Cross-project orchestration infrastructure that improves itself over time. Autonomous research loops that discover, synthesize, and distribute insights across every active project — sports prediction, demand forecasting, computer vision — without manual coordination. Inspired by Karpathy's recursive research methodology. The system that builds the builder.
The Infrastructure
Five-stage pipeline from raw sensor data to sized position. The same DAG processes sports telemetry today and retail biometrics tomorrow.
What I'm Exploring
"The first principle is that you must not fool yourself — and you are the easiest person to fool." — Richard Feynman
Let's build something at the frontier.
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