MCP server powered by a code analysis API that improves coding agents with Code Graphs.
A specialized benchmarking and evaluation harness for MCP tools, specifically focused on validating the reliability of static code analysis in agentic workflows.
Integration of llama.cpp and JUCE that brings local LLMs directly into DAWs and music production software.
Monitoring system for near-real-time visibility into millions of events built with TypeScript and AWS CDK.
Astro starter kit featuring schema-first development, structured data, and Decap CMS integration.
I am a software engineer and cloud architect with over 12 years of experience, including a tenure at Amazon Web Services (AWS) where I specialized in building resilient, enterprise-grade systems. Currently pursuing a Master of Science in Computer Science at Georgia Tech with a focus on machine learning, my work sits at the intersection of robust cloud architecture and the emerging frontier of agentic AI.
I am particularly interested in the Model Context Protocol (MCP) and building tools that enable AI agents to safely and effectively interact with the physical and digital world. Whether it's developing autonomous security patching agents or integrating LLMs into creative software, I am driven by the challenge of moving AI from theoretical research into practical, production-ready applications.
Visit greynewell.com to learn more about me and my work!




