Research & Innovation/75% confidence

Analysis Version

Summary

Hayden Donnelly is a technically versatile developer with a strong focus on fundamental computer science problems, exploring low-level systems programming, machine learning, and game engine architecture. Their portfolio demonstrates deep technical curiosity, ranging from zero-dependency Rust applications that interface directly with the Linux kernel to educational Neural Radiance Field (NeRF) models built with JAX and Numba.

Score Context

The score reflects GitHub profile completeness and project packaging rather than raw technical capability. Strong foundational engineering, advanced math, and low-level kernel knowledge are highly evident despite a lack of production-grade CI/CD and testing infrastructure.

Tech Stack

PrimaryC#5Unity5Python3C3·ProficientRust1JAX1Nix1

Repositories

rusty-dungeon

This is a small game I made for fun. It was programmed for Linux with Rust and inline x86-64 Assembly. It doesn't use the C Runtime or the Rust Standard Library.

“Demonstrates exceptional understanding of operating systems, kernel ABIs, and zero-dependency programming constraints.”

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no-cuda-ngp-nerf

An educational implementation of Instant NGP NeRF using JAX and Numba instead of custom CUDA kernels.

“Showcases advanced mathematical implementations and deep learning architecture construction from scratch.”

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vehicle-physics

This is a Unity project implementing a custom vehicle controller with a raycast suspension.

“The most starred repository, representing strong domain knowledge in game physics and custom controller design.”

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pose-estimation

Demo of movenet pose estimation. Can be used to create videos with overlayed pose estimations for up to six people.

“Highlights computer vision capabilities and ML integration prototyping using TensorFlow Hub and OpenCV.”

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dotfiles

My NixOS configuration.

“Shows proficiency with Nix and declarative environment configuration, validating Linux systems operations skills.”

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Score History

Persona

Low-level Prototyping9/10

Consistently builds foundational systems from scratch, such as writing custom memory allocations and bypassing standard libraries to understand core mechanics.

Mathematical Implementation8/10

Highly capable of translating complex math (spherical harmonics, custom Vector-Jacobian Products, raycasting) into functional code.

Production Readiness & CI/CD3/10

Projects prioritize educational value and prototyping over polish; they generally lack automated testing suites, continuous integration pipelines, and dependency management.

Skills

Rust (Systems Programming)8/10

Successfully bypassed the Rust standard library and C runtime to build an application directly interfacing with the Linux kernel via x86-64 assembly in rusty-dungeon.

Machine Learning & Python7/10

Implemented advanced mathematical concepts like Instant NGP NeRFs with JAX/Numba and utilized TensorFlow Hub for pose estimation, though projects exhibit performance bottlenecks in CPU/GPU synchronization.

C# & Unity (Game Development)7/10

Developed multiple game jam entries and a custom raycast-based vehicle physics controller, demonstrating strong 3D math and engine familiarity, albeit on older Unity LTS versions.

C/C++6/10

Created foundational systems like a lightweight 2D game framework (Feather) using an Entity-Component-System (ECS) architecture.

Systems Configuration (Nix)6/10

Utilizes NixOS for declarative dotfile and environment configuration, showing a solid understanding of modern, reproducible Linux system management.

Growth

1.Introduce automated testing and CI/CD pipelines to validate complex math logic and custom gradient calculations, preventing regressions.

2.Refactor monolithic files (such as the 600-line entry point in rusty-dungeon) into modular, encapsulated components to improve readability and maintainability.

3.Modernize older Unity projects by upgrading to a current LTS release and adopting the Unity Package Manager (UPM) standard for distribution.

4.Optimize Machine Learning architectures by keeping training loops natively on the GPU (e.g., using jax.lax.while_loop instead of Numba) to resolve severe host-to-device memory transfer bottlenecks.

5.Ensure critical source files are consistently committed across repositories to allow for thorough code-level collaboration and review.