AI code search tool promises 'where's authentication?' in 100ms—delivers after 1.3GB download and fixing its own next.js alias bugs

A recent study published on January 19, 2026, evaluated the accuracy and limitations of llm-tldr, a code analysis tool featuring semantic search capabilities. The tool allows users to search for code using natural language queries, identifying functions based on vague keywords like "authentication" or "PDF generation" by analyzing actual behavior. Tested in a Next.js project consisting of 269 files, llm-tldr achieved a 95% reduction in tokens and 155 times faster processing. The tool uses a 5-layer code analysis architecture and supports 16 languages, including TypeScript, Python, and JavaScript. With a latency of 100ms, llm-tldr outperforms traditional tools like grep and find. The study confirmed the tool's effectiveness in searching for features like authentication and PDF generation, making it ideal for exploring large codebases and providing context to large language models. Developed by an unnamed researcher, llm-tldr has substantial value for implementation in large projects, particularly in microservice architectures and legacy code analysis, with potential applications in AI-assisted development.