Facts
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Top percentile OS contributor on GitHub.†
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Most popular authored OS tool has 688 stars.‡
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My most technically challenging work was a relatively recent project — an experimental (proof of
concept) markdown renderer implemented in terms of Apple's new low-level TextKit2 layout and rendering
framework.§
About Me
I fell in love with purely functional programming in my late teens and went deep. While
not a professional compiler engineer, I’ve spent years building parsers, modeling data structures, and
transforming ASTs — an experience that shaped my approach to solving abstract, technically difficult problems
from first principles.
I’ve taken a nontraditional path — spending most of my twenties pursuing independent technical work. It
taught me how to solve hard problems (though not always how to make money from them). Today, I’m especially
strong in systems involving Rust and Swift, particularly in domains that demand deep control, like custom
TextKit2 rendering or workflows where off-the-shelf tools — including AI — fall short.
Secret Startup Experiment
Video DRM
GitHub:
https://github.com/SubSys/Compiler
A cross-language compiler pipeline that translated Elm to Rust,
implemented in Haskell with full constraint based type inference and checking.
Key Features
- Elm → Rust transpilation with semantic fidelity.
- Typed IR modeled and verified in Haskell.
- Explores correctness as a first-class design constraint.
Compiler Engineering Elm Rust
Haskell Type Systems Experimental
GitHub:
https://github.com/colbyn/commands
My least loved project but IMO a greatly improved Bash dialect via an indentation sensitive parser. The
syntax mimics the clarity of Python, but compiles to raw shell for automation heavy workflows like
AWS deployments.
Key Features
- Indentation-sensitive syntax for nested blocks.
- Function embedding, aliasing, and multiline strings.
- Supports structured parsing, silent execution, and CLI grouping.
CLI Tools Automation DSL
Bash-Inspired
imager.io
| github.com/imager-io
One of the best image optimization tools on the market.
A modular, open-source image optimization platform designed to outperform commercial tools — with no SaaS
dependencies wrapped in a clean CLI and consumable through Node.js bindings.
The toolchain included native bindings (webp-dev-rs, ffmpeg-dev-rs,
x264-dev, vmaf-sys) and a Rust-to-JS bridge via imager-io-js.
Benchmarks showed over 90% file size reduction compared to popular SaaS optimizers — without
perceptual quality loss.
Image Processing Machine Learning Rust Node.js WebP FFmpeg x264 VMAF Performance Optimization
GitHub:
https://github.com/colbyn/web-images-js
A zero-dependency image pipeline for Node.js — built entirely in Rust and embedded directly in JS
workflows. Offers full control over memory, binary size, and output quality without external tools or native
runtime bindings.
Key Features
- High-performance image loading and transformation in native Rust.
- Statically linked builds for reproducibility and safety.
Rust Node.js Web Development
Performance Optimization Image Processing Dependency-Free
GitHub:
https://github.com/colbyn/subscript-old
I renamed this project to subscript-old after I decide to call my note taking tools
‘subscript’ which seemed more fitting because it dealt with typesetting. There’s actually some pretty cool
ideas in here. Abandoning this project is a major regret of my life.
A data-driven, Rust-native frontend library designed for backend developers — Subscript rethinks web UI as
infrastructure. Built for those who want to author views, state, and styles entirely in Rust, with zero XML,
full CSSOM control, and expressive compile-time macros.
Ideal for backend developers treating the frontend as just another client — including full inline CSS
support (media queries, keyframes, pseudo elements) via a "selector-less functionalized CSS" model. Built-in
routing, component messaging, and versioned view syntax enable deeply structured applications without
frontend boilerplate.
Feature Highlights
- Inline Rust macros for media queries, keyframes, and pseudo
classes.
- Pattern-matching-like URL parsing using
parse_url!, with totality checks
and type-safe bindings.
- Component messaging and subscriptions, including typed broadcasting and routing by
component type.
- Versioned view macros like
v1! for ergonomic and incremental UI design.
- Zero-runtime-diffing model closer to Incremental DOM than virtual DOMs.
Example Syntax
parse_url! {
[] => {
Page::Homepage
},
["account", user_id: Uuid] => {
Page::AccountUser {
id: user_id
}
},
_ => Page::NotFound
};
v1! {
display: "flex";
button !{
event.click[] => {
move || Msg::Increment
};
"Increment";
}
}
Rust Frontend WebAssembly
Macro Systems Routing UI
Framework CSSOM
Impact: Inventive take on frontend architecture through Rust. Encourages architectural
unification between client and server codebases, with precise control over styling and rendering.
Began College
Notable Comments
You are such a profound writer and thinker. It has been my privilege to be your instructor of record.
One day I will say, I had him in my English class. I have such high hopes for you! Go conquer your world!
You're awesome.
—Dr. Jim Birrell
LaTeX
UVU CS Grader - Computer Science
In my first semester at UVU I took CS1400 by professor Bianca Ruiz, who then offered me a grading position
at the end of the semester. I enjoyed this job and wish I stayed (I took trigonometry and calculus
concurrently and was expecting subsequent semesters to be just as difficult).
The instructor said my code looked “beautiful” before I signed up as a grader.
Python Grading Teaching
Communication
Subscript (iPad Edition + Authoring Tools)
GitHub:
https://github.com/subscript-publishing/subscript
Content publishing VIA Web-Technologies!
Supports freeform and typed markup content in one medium.
Key Features
- Seamlessly intermix markup with hand drawn content VIA the Subscript Freeform Tools (iPad only).
- Redesigned macOS and iOS editor interfaces from the ground up.
- Native rendering pipeline tuned for complex typeset math, and freeform sketch inputs.
Academic Publishing LaTeX-Inspired Markup Language
Drawing Tools JavaScript HTML
iPad UI/UX Design Swift
macOS iOS
GitHub:
https://github.com/colbyn/ami-uploader
A lightweight Rust CLI designed to automate the upload of LinuxKit-generated AMIs to AWS
via S3. It streamlines the gap between image generation and cloud deployment — replacing manual S3 uploads
and AMI registration with a single, scriptable command.
Built for reliability in CI pipelines, the tool supports alternate credential injection, customizable AMI
naming, and metadata tagging. Designed for infrastructure engineers managing reproducible builds.
Key Features
- One-command upload from local disk to registered AMI.
- Supports name overrides, alternate AWS keys, and region targeting.
- Integrates cleanly into LinuxKit or DevOps build pipelines.
CLI Tools Rust AWS S3 AMI LinuxKit DevOps
GitHub: https://github.com/colbyn/punk-lang
A deliberately minimal markup language with LaTeX-inspired syntax and depth-sensitive highlighting —
designed to test how little structure is needed to express complex educational content clearly.
Language Design Educational Content LaTeX-Inspired Syntax Highlighting Markup
Language Experimental
Subscript Freeform Note-Taking App (iOS/macOS)
A handwritten, vector-based note-taking app built from scratch to prioritize human expression,
long-term readability, and semantic structure. Originally released on the App Store, I later
withdrew it to re-architect the data model and rethink some things.
The app uses a custom model space for device-independent rendering, handwritten stroke capture with
velocity-aware smoothing (via a Swift port of perfect-freehand), and a semantically driven
outline system (H1–H6) for navigation and TOC generation.
Subscript reflects a broader philosophy: separation of content and presentation, inspired
by LaTeX, combined with the authenticity of freeform input—a medium resistant to AI mimicry.
Intro. My Note-Taking App & Why It Matters in the Age of Bots (YouTube): https://youtu.be/PEC5PyNhIds?si=W6w6zOrrK29rD37C
Swift Native iOS/macOS Unsafe Rust
(FFI) Vector Graphics Digital Ink Document Compiler
Parser & Tree Visualization Toolkit
A cohesive suite of language tooling libraries for Swift and Rust,
focused on parser combinator frameworks and human-friendly visualization of abstract syntax trees (ASTs) and
nested structures. Designed to support debugging, inspection, and language toolchain development in
functional and systems programming contexts.
Core components include MonadoParser†, a monadic parser combinator framework for
Swift; pretty-tree-rs‡, a minimal Rust library for rendering cleanly formatted
hierarchical data; and SwiftPrettyTree§, a Swift-native port for readable tree
inspection in IDEs or CLI workflows.
Key Features
- Composable, lossless parsers with precise position tracking (
MonadoParser).
- Compact, dependency-free tree renderers for ASTs and nested structures.
- Interoperable debugging tools for language tooling, REPLs, and test harnesses.
Links:
Parser Combinators Swift Rust
Functional Programming AST Debugging
Tools Compiler Engineering Open Source
3in1Spanish Your go-to Spanish Dictionary, Phrasebook
& Flash Cards
A Bilingual Spanish Dictionary, Phrasebook & Flash Card App.
Required a very sophisticated dataset generator that I called the compiler generator. See my YouTube Video
for details with commentary.
‘How I autogenerate massive (dictionary) datasets with ChatGPT/LLMs and
why this matters’:
https://youtu.be/nofJLw51xSk?si=WrOwCT7WA6_VTBrO
Prompt Engineering Data Pipelines AI
Centric Pipelines Dataset Generation
SuperSwiftMarkdownPrototype
GitHub:
github.com/SuperSwiftMarkup/SuperSwiftMarkdownPrototype
While markdown UIs can be trivial to implement the goal here was actually very challenging to do well:
provide a rich and intuitive text selection experience (across all GitHub flavored markdown block types) that
meets the expectations of iOS and macOS users (an aspect that is very lackluster in the ChatGPT iOS app and
especially prior to my work). My proof of concept rendering engine handles text selection, including
multi-cursor text selection across and within tables in such a manner that I’d love to one day build a
markdown based spreadsheet app upon this technology.
Why did I endeavor to tackle this challenge? (Hint: I didn’t do this so that the iOS devs at OpenAI and
other companies can steal some of the techniques I developed.) I’m working on a chatbot client based on a
branching data model and there are several aspects that while possible is very lackluster as embedded web
views. I needed more control that is only possible with native UI toolkits but when it came to advanced text
functionality I needed even more control that motivated my early explorations in text rendering.
Swift Markdown Rendering iOS
macOS TextKit2 Prototype