I design and ship open-source developer tools where strong types, fast runtimes, and clear product thinking meet. My work spans Python and Rust data systems, embedded databases, semantic-web tooling, server-rendered UI, and learning infrastructure.
The through-line is simple: take a complicated system, give it an API that feels inevitable, document the tradeoffs honestly, and ship the whole product—not just the interesting algorithm.
🧩 HedronTyped Python components for FastAPI and HTMX. Build dashboards, admin tools, and CRUD apps without adopting a JavaScript application stack. PYTHON · FASTAPI · HTMX · PYDANTIC |
A VS Code environment for browsing, editing, querying, reasoning over, and validating OWL, RDF, and OBO ontologies. RUST · TYPESCRIPT · LSP · SEMANTIC WEB |
🗄️ ModelVaultA schema-first embedded database for Pydantic models and dataclasses, with validation, indexes, migrations, and single-file deployment. RUST · PYTHON · PYDANTIC · PyO3 |
A React-first framework for accessible, trackable learning experiences with telemetry, xAPI, SCORM, cmi5, and LMS packaging. TYPESCRIPT · REACT · XAPI · SCORM |
🌊 ETLanticOne typed pipeline model with deterministic plans and pluggable execution across Python, Polars, Pandas, SQL, and PySpark. PYTHON · DATA CONTRACTS · PIPELINES · PLUGINS |
A PySpark-compatible DataFrame engine for fast local pipelines and JVM-free unit tests, powered by Rust and Polars. RUST · PYTHON · POLARS · PYSPARK API |
| Project | Role in the portfolio |
|---|---|
| PydanTable | Strongly typed DataFrames combining Pydantic schemas with a Rust execution engine |
| Moltres | DataFrame-style SQL with pushdown execution, CRUD operations, and async support |
| Oxiland | Embedded RDF datasets, SPARQL, persistence, and streaming I/O for Rust and Python |
| PolarPandas | A pandas-compatible interface backed by Polars for faster local analytics |
- Types are product design. Good schemas and interfaces make the correct path the easy path.
- Documentation is part of the runtime. Quickstarts, decision guides, migration notes, and explicit limitations all ship with the code.
- Interoperability beats lock-in. I build bridges across Python, Rust, SQL, Spark, Polars, Pydantic, web frameworks, and open standards.
- A release is the unit of progress. Packages, wheels, crates, extensions, examples, CI, and upgrade paths turn experiments into usable tools.
- Building Python dashboards or internal tools? Start with Hedron.
- Testing PySpark logic without waiting on the JVM? Try Sparkless.
- Storing typed application models locally? Explore ModelVault.
- Authoring learning experiences in React? Use LessonKit.
- Working with ontologies in VS Code? Install Strixonomy.
Build the abstractions you wish existed.
For project-specific questions or collaboration, open an issue or discussion in the relevant repository.