Projects

Built it, shipped it, learned something.

Production systems, open source, and the experiments that taught me the most. Over a hundred side projects — these are the ones worth reading about.

100+ Side projects

4 Written up

2 Open source

the abandoned ones taught me plenty too.

Open source Live

Simple RAG

built to be read end to end

Ruby Faiss RAG GitHub ↗

The problem: Every RAG tutorial started with a vector database, a framework, and eleven dependencies — so Rubyists reading them concluded this wasn't for them.
What I built: The whole RAG architecture in one small gem: no vector database, a few light dependencies, and an API key. Indexing, embedding, retrieval, generation — readable in one sitting.
What changed: Rubyists stopped asking whether it was possible and started asking about chunk sizes. That's the shift I wanted.

Open source Live

reasoning and acting, no framework required

Ruby Agents Tool use GitHub ↗

The problem: Agent frameworks hide the loop, which is exactly the part you need to understand before you trust one in production.
What I built: A direct implementation of the ReAct pattern in Ruby — reason, act, observe — with real access to external tools and nothing clever in the way.
What changed: A reference people can read to see where autonomy actually needs brakes.

Experiment Archived

Rubyists deserve nice things

Ruby Data apps Read the write-up GitHub ↗

The problem: Python had Streamlit. Ruby had nothing for standing up a quick data app around a model.
What I built: A Streamlit-like framework for Rubyists — declarative UI over a script, so an experiment could become something you show someone.
What I learned: Tooling gaps are community problems, not language problems. Also: shipping a framework is a lifestyle, not a weekend.

Experiment Live

it genuinely works

Ruby Notebooks IPython Read the write-up

The problem: AI coursework lives in notebooks, and notebooks meant Python — so Rubyists were locked out of the standard way to learn this material.
What I built: IPython magic that runs Ruby inside Google Colab, plus the writeup that shows how to get there in a few cells.
What changed: Ruby developers can follow along with an ML curriculum in the language they think in.

older, smaller, still mine

Forecasting the Future Materials from the RailsConf talk: a full machine-learning project in native Ruby, using linear regression because it explains itself. GitHub ↗
BannerCreator.pro A micro-SaaS for making banners and social headers — templates, text overlays, image enhancement.
Expand.ly A stealth project on AI and writing: adaptive, personalised content to help people learn a topic faster.
Foodie Log A mobile app for documenting and rating meals — photos, ingredients, notes on the dishes worth remembering.

RAG, once, clearly

from the Madison Ruby talk — the whole idea on one page.

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I take on a small number of advisory and build engagements. Tell me what's misbehaving.