Stop Wasting LLM Tokens! I Built a Rust CLI to Prune JS/TS Codebases by 80%

Developer Sanjaiyan has introduced 'urai-ecma', a high-performance Rust-based CLI tool designed to optimize JavaScript and TypeScript codebases for LLM consumption. By utilizing the SWC compiler to parse code into Abstract Syntax Trees (AST), the tool performs structural distillation, removing non-essential elements like static Tailwind CSS classes and imperative logic while preserving critical architectural components. The project addresses the 'infinite context' trap, where bloated prompts lead to increased latency, higher costs, and model degradation. Benchmarks demonstrate an impressive 80% reduction in token volume, significantly improving the performance of LLM agents and local models. The tool features a two-tier resolution strategy using existing JSDoc or local Ollama inference for summarization, supported by a hybrid Foyer cache for rapid subsequent runs. This solution provides a more efficient way for developers to feed complex codebases into AI models without sacrificing context or accuracy.