I'm Arjuna, a Gonzaga Alum and Software Developer from Roseville, Minnesota.
I’m interested in how technology and people intersect—whether
that’s developing reliable software, improving processes, or
helping teams and clients achieve their goals.
I take pride in learning quickly, solving problems with care, and
bringing a thoughtful approach to everything I work on.
My full name is Arjuna Herbst and I'm a big Manchester City
& Gonzaga Basketball fan. I love getting outside and taking pictures of nature.
Currently, I'm a backend dev at Hatchproof, a behavioral-science backed, AI-powered
platform built to help companies hire and retain people who thrive in their roles.
Using Docker, Node.js, and Ollama, I created a self-hosted AI chat interface with streaming responses and markdown rendering. Serves from my domain which is hosted behind an nginx proxy. Try it above, it's fully functional right here on the page.
Built a web page with 3D animations and models using Vite.js with Three.js. May not load properly on smaller devices.
Using Jupyter Notebook, I implemented KNN clustering to analyze NBA 2022-23 player data and find trends and patterns within the data.
Using Node.js, Express, React.js and PostgreSQL, I created a gear store web application for backpackers. Users can build custom gear bags, and track pack weight.
Using React Native and Supabase, I developed a weather app that provides real-time weather updates and forecasts. Map view is available and you can tap anywhere to get the conditions.
This course covered the basics of computer organization and architecture. This course was taught in C and assembly language. Topics covered included: CPU articheture, ALU's, logic gates, registers, calling functions in assembly, and more. Course Github repo below.
This course served as an introduction to the implementation of machine learning algorithms in Python using Google Colab. Topics covered incduded: linear regression, logistic regression, A* search, model fitting, LLM's, and more. Course Github repo below.
CPSC321 was a course that covered the basics of relational dat abase management. Languages used included SQL, Javascript (Node.js), and Python. Topics covered included: ER diagrams, advanced and basic SQL queries, querying a database from a frontend web application (see project link), and more. See YouTube video on this course's final project below
This course covered principles and techniques of computational complexity. Topics could include dynamic programming, B-trees, minimum spanning trees, Floyd and Warshall algorithms, various string matching algorithms, computational geometry, exponential growth of round-off errors, NP-completeness and reducibility.
In this course, I learned the main components of programming languages and the architecutre of intepreters and compilers. We used Java as a base language to implement our own language called "MyPL". This involved building a lexer, parser, type checker, VM, and code generator to compile MyPL code to bytecode. Also covered was OCaml and functional programming.
This course provided a detailed overview of the processes and techniques used in creating data science applications. Topics included data preparation and cleaning, summary statistics, basic data visualization techniques, feature selection, discretization, k nearest neighbors, naive bayes, decision trees, ensemble methods, apriori rule mining, and k-means clustering.
Send me an email and I'll get back to you as soon as possible.
Please provide your name and a brief description of what you're looking for.
Glacier NP, MT
Spokane, WA
Roseville, MN
Colville, WA
Marina Bay, Singapore
Grand Tetons NP, WY
Glacier NP, MT
The Enchantments, WA
