Machine Learning Engineer · Systems Builder · Reality Modeling

I build real systems under constraint.

I've shipped forecasting models for Peru's Central Bank, built and deployed full-stack commerce platforms, and created developer tooling in Rust to support real research and production work. I work across the stack — modeling, infrastructure, and deployment.

Why I Built These Tools

Working seriously with AI — on real research, real forecasting, real codebases — exposed a gap nobody was filling. Context gets lost between sessions. Projects fragment. The model forgets what you built last week, and so, eventually, do you.

I didn't want wrappers around ChatGPT. I wanted tools that treated the problem structurally: flatten a codebase into something injectable, control what a conversation remembers, give unstructured data a shape that survives without a database schema.

These aren't side projects. They're the infrastructure I actually use — for my research, my consulting work, and the book I'm currently writing.

Interactive Systems

Production-grade tooling built to support real research, forecasting, and engineering workflows. Designed from friction, not theory.

Kodomata World

kodomata.vercel.app

An emergent automata environment built from minimal local rules. Designed to study how simple constraint systems produce large-scale collective structure.

Emergence · Automata · Collective Dynamics

Wormhole Chess

wormholechess.vercel.app

A non-Euclidean chess engine with paradoxical geometry and explicit spatial mapping. Built to explore constraint modeling and engineered rule systems beyond standard board abstractions.

Non-Euclidean Geometry · Constraint Systems · Engine Architecture
Rendering studywormhole-render.vercel.app

The Fractal Prince

runawayfromthepain.vercel.app

A recursive platformer built and shipped end-to-end. Designed as an experiment in constrained traversal and self-referential level structure.

Recursive Spatial Logic · Game Systems · Shipped

Developer Infrastructure

Tools I made because the existing options were frustrating, fragile, or just not built for serious use. All of them I still use.

How I Actually Work

Not principles in the abstract. Things I've learned the hard way.

The model is the bottleneck

I spent months forecasting GDP with state-of-the-art ML tools. The math was rarely the problem. How you frame the question — what variables you include, how you define the search space — determines everything downstream. My research came directly out of that frustration.

Tools should survive real pressure

I built Yggdrasil because I needed it mid-project, not because it was a clean idea. The same goes for FUR and Membrane. Friction is a better design brief than a feature list.

Rust is a choice, not a flex

I write tools in Rust because I want them to be fast, stable, and honest about what they do. Python is where I explore. Rust is where I commit.

Clarity compounds

Teaching computer science to undergraduates taught me more about my own understanding than most research did. If you can't explain a system simply, you don't control it yet.

What I Work With

Technologies and domains I've used on real projects, not just read about.

Machine Learning

  • Forecasting and nowcasting
  • High-dimensional variable selection
  • PyTorch, Keras, scikit-learn
  • Time series and panel data

Systems & Tooling

  • CLI development in Rust
  • LLM context and workflow management
  • YAML-based knowledge systems
  • Codebase flattening and AI artifact generation

Web Engineering

  • Next.js, React, TypeScript
  • Full-stack deployment
  • Payment integration
  • Simulation and interactive environments

Get in Touch

For organizations and teams looking for an ML engineer who can work across the stack — research, tooling, deployment.

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Designed and built with clarity in mind.