2 posts tagged with "Performance"

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.

Most code analysis tools treat your codebase like a collection of isolated files. They parse each file into an Abstract Syntax Tree (AST), analyze it in isolation, and call it a day. But real software doesn't work in isolation—it's a web of interconnected relationships, dependencies, and data flows that span multiple files, modules, and even languages.

CodePrism takes a radically different approach: graph-based code analysis with a Universal AST. Instead of analyzing files in isolation, we build a unified graph representation of your entire codebase, enabling analysis that understands relationships, patterns, and behaviors that emerge at the system level.

Here's how we built an engine that can index 1000+ files per second and answer complex queries in sub-millisecond time.