2 posts tagged with "Production Engineering"

Building a code intelligence system that scales isn't just about theoretical performance numbers—it's about solving real problems that emerge when you encounter actual codebases. This is the honest story of how CodePrism evolved from processing small repositories to handling enterprise-scale codebases.

Starting from our first commit on June 16, 2025, we've learned that scaling isn't just about making things faster—it's about making thoughtful architectural decisions, handling edge cases gracefully, and building systems that can recover from failures.

"It works on my machine" — the most dangerous phrase in software development. When you're building tools that AI assistants depend on to understand and analyze code, "works on my machine" isn't good enough. You need production-ready reliability.

CodePrism started like many projects: with good intentions, prototype code, and placeholder implementations. But we didn't ship until we achieved something remarkable: 18 tools with a 100% success rate. No broken tools. No placeholder responses. No "this feature is coming soon."

Here's the engineering story of how we went from prototype to production, the challenges we faced implementing the Model Context Protocol (MCP), and the testing methodologies that enabled zero failures at launch.