
Andrew Macalister enhanced the charmbracelet/crush repository by delivering targeted performance and stability improvements for large-scale Go backend systems. He replaced regex-based gitignore matching with a glob-based approach, introducing two-level caching and pre-built matchers to reduce CPU usage from 80% to 2% during file-walking in large repositories. Andrew also strengthened the LSP manager by adding safeguards against nil clients and implementing state checks, which improved reliability and error handling. His work leveraged Go, performance optimization techniques, and robust software development practices, resulting in faster indexing, lower resource consumption, and a more stable developer experience for large monorepo environments.
February 2026: Delivered performance and stability improvements in charmbracelet/crush. Implemented glob-based gitignore matching with two-level caching and pre-built matchers, reducing CPU usage from ~80% to ~2% in large repos and speeding file-walking. Strengthened LSP manager by guarding against nil clients and adding state checks, boosting reliability. These changes improved repository operations, responsiveness in large monorepos, and overall developer experience. Technologies demonstrated include Go, optimized caching patterns, glob-based ignore parsing, and robust error handling. Business value includes faster indexing, lower CPU costs, and more stable editor features for large-scale projects.
February 2026: Delivered performance and stability improvements in charmbracelet/crush. Implemented glob-based gitignore matching with two-level caching and pre-built matchers, reducing CPU usage from ~80% to ~2% in large repos and speeding file-walking. Strengthened LSP manager by guarding against nil clients and adding state checks, boosting reliability. These changes improved repository operations, responsiveness in large monorepos, and overall developer experience. Technologies demonstrated include Go, optimized caching patterns, glob-based ignore parsing, and robust error handling. Business value includes faster indexing, lower CPU costs, and more stable editor features for large-scale projects.

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