I'm a CS student at Lawrence Tech and I run a small web dev shop called Gamache Technology Solutions. Most of my paid work is WordPress and WooCommerce, and outside of that I spend a lot of time on ML research and keeping my homelab alive.

This is where I spend most of my hours. I build and maintain WooCommerce stores for real businesses, which means the work is less "pretty landing page" and more "why is the cart total wrong when someone adds a bundle with a variable product on sale." A lot of it is debugging — tracking down a dead PayPal script that's killing bundle JS, figuring out why UPS is returning weird shipping rates, fixing template overrides that haven't been updated in four major WooCommerce versions.

The stuff I build from scratch tends to be:

• Custom plugins for specific client workflows (HubSpot sync, dynamic product filters, kit/bundle logic)
• ACF-powered Gutenberg blocks that editors can actually use without breaking the layout
• Performance work — caching, deferred CSS, font loading, cleaning up JS that's blocking render
• Integrations with third-party services (shipping, inventory, CRM, payment)

I care a lot about leaving sites in better shape than I found them. Most WordPress codebases I inherit have accumulated years of "temporary" fixes, and part of the job is quietly undoing that without breaking anything in production.

My senior project is on violence anticipation in streaming video — predicting risk a few seconds before something happens rather than classifying it after the fact. I'm working with the XD-Violence dataset, transformer-based architectures, and custom streaming evaluation metrics (since most benchmarks assume you have the whole clip, which defeats the point). Anticipation is a harder framing than detection, and the evaluation side has been surprisingly interesting.

I also co-authored a paper on process and outcome reward modeling for reasoning in small language models — using a larger teacher model to train 1.5B-parameter reward models. Mostly taught me how finicky reward modeling is and how much variance there is between runs.

What I actually enjoy: the full pipeline. Dataset handling, training loops, evaluation design, and thinking about what it takes to run any of this in production with real latency constraints.

I run a few servers for backups, monitoring, and game servers for friends. It's a low-stakes way to practice the infra stuff I don't get to do much in WordPress land — Linux administration, networking, automation, keeping services up without babysitting them.

Web: PHP, JavaScript/TypeScript, WordPress, WooCommerce, ACF, Gutenberg, REST APIs, SQL, WP Rocket, Cloudflare
ML: Python, PyTorch, NumPy, Pandas, transformers, standard video/data pipeline stuff
Infra: Linux, Nginx/Apache, Cloudflare, cron, rsync, Docker when it earns its place

• Writing cleaner architectures without overengineering — it's easy to reach for patterns that don't pay off at the scale I actually work at
• Getting ML models out of notebooks and into things that run fast and reliably in production
• Systems fundamentals: networking, observability, deployments that aren't scary

• Portfolio: https://www.nathangamache.com/
• LinkedIn: https://www.linkedin.com/in/nathan-p-gamache/
• Email: nathanpaulgamache@gmail.com