Why grep is failure when it comes to quality and token saving (github.com)

A new context engine has been introduced that significantly optimizes the performance of AI code completion tools like Claude Code and GitHub Copilot, making them 30-45% cheaper while enhancing the quality of generated code. By constructing a semantic graph of a project’s codebase, this engine preloads relevant files into each prompt, allowing the AI to focus its tokens on reasoning instead of exploring the codebase. This advancement is especially notable as it supports a wide array of programming languages, including TypeScript, Python, and Java, and operates across macOS, Linux, and Windows systems. The significance of this tool lies in its ability to compound token savings across user sessions; previous reads and edits are prioritized in future interactions, optimizing both the cost and efficiency of responses. Benchmark tests demonstrate a reduction in average prompt cost from $0.46 to $0.27, and improvements in response quality, making it a compelling choice for developers. The local processing of data ensures privacy, and since all project files remain on the user's machine, it addresses common data security concerns in AI development.