Building systems on top of messy real-world data. I focus on:

• Data pipelines with observability to detect poorly processed data.
• Local-first analytical UX for interactive data exploration.
• Developer tools to catch issues during development.

I write about things that broke, tradeoffs, and what didn’t work:

• Iterating on a data pipeline from concept to production.
• Serverless GPU vs CPU inference for ML workloads.

• Won a 1000 USD open source bounty (SolidHack 2024) for a UI component contribution.
• Currently contributing external Docker image support to SST.
• Added dependency highlighting to the Skott dependency visualiser.

Analytics platform for finding a place to live in Australia:

• Transforming messy scraped property data to help users compare properties across different buying strategies.
• Observable pipeline with fault tolerance on AWS (OpenTelemetry + LGTM).
• Local-first dashboard as code (DuckDB WASM).
• In production since October 2025.

Serverless ML piano audio to MIDI converter:

• Highly parallel AWS Lambda architecture
• Explores tradeoffs between CPU/GPU inference and parallel processing strategies.
• In production since April 2024.

Visualize how a fall happened to classify and train fall detection algorithms:

ESLint plugin for detecting runtime compatibility issues:

• Catches runtime incompatibilities statically before they reach production.
• Built for server-side libraries that need compatibility across runtimes.

Most projects are iterative and evolve through real-world usage and constraints. I optimise for learning through real-world projects.