Project Showcase

Engineered a distributed agent framework capable of delegating sub-tasks across specialized AI agents. Built with stateful memory trees, vector retrieval, and automated fallback execution mechanisms.

Architected an event-driven data streaming engine using Apache Kafka and Python. Features automated schema validation, windowed aggregations, and sub-second metrics publishing to PostgreSQL.

Handcrafted with bold neo-brutal aesthetics, high contrast, hard drop shadows, and accessible components. Features horizontal project carousel, smooth multi-page routing, and instant page transitions.

Created an enterprise-grade retrieval pipeline utilizing semantic chunking, reciprocal rank fusion (RRF), and cross-encoder reranking to minimize hallucinations and deliver precise answers.

Developed a telemetry and monitoring suite designed for production ML models. Calculates Kolmogorov-Smirnov drift statistics on live inference payloads and alerts engineers before degradation occurs.

Analyzes Git diffs, parses abstract syntax trees, and produces line-by-line review comments with proposed diff patches directly on GitHub pull requests.

Built a high-performance tick data storage layer for backtesting algorithmic models. Leveraged columnar compression to achieve 8x reduction in disk usage while keeping sub-10ms analytical query speeds.

A fast, lightweight developer companion web application featuring zero-latency client-side data parsing, diffing, and formatting tools wrapped in high-contrast neo-brutalist styling.