Technical writing, data analysis notebooks, and research on data science, machine learning, and system optimization.
What I learned when my live trading system's ML ensemble silently degraded in production, and the disciplined reintroduction of machine learning that came after.
How the Atlas forecasting system handles 542,000 rows/second of market data with sub-second regime detection — async service architecture, dependency-ordered startup, and 10Hz health monitoring.
Atlas couldn't start. The trading system's database initialization was taking 6.6 seconds, blocking 37 features from loading. The fix was small.
Serving architectures, containerization, lifecycle management, performance optimization, drift detection, and monitoring — with benchmarks and code from production systems.
A working reference for production database tuning — SQLite PRAGMAs, schema, indexing, transactions, batching, pooling, and the monitoring that proves it's working.
Six database optimization techniques — predicate pushdown, row group pruning, result caching, async I/O, indexes, and where SIMD goes wrong — with impact numbers and code.
I replaced DuckDB with a custom Rust query engine for a trading-system time-series workload. Five iterations, 10.4× speedup, one optimization that backfired.
A small Python library of matplotlib themes — Film Noir, Ghibli, Wes Anderson, Blade Runner, Star Wars — applied to 50,000 IMDB reviews.
A field-tested reference for taking ML models from prototype to production — serving patterns, containerization, monitoring, drift detection, and the operational practices that make the difference.
Building browser-side data visualizations in Rust compiled to WebAssembly — particle systems, large-dataset rendering, and the practical wins over a pure-JS implementation.
Built a pipeline that extracts 296 businesses from Chamber of Commerce directories in 9 minutes using a local 7B-parameter model — 100% name/phone capture, no API costs.