The GitHub repository "jehna/humanify" is designed to deobfuscate JavaScript code using advanced language models like ChatGPT and LLaMA. This tool aims to make obfuscated or minified JavaScript code more readable by renaming variables and functions while maintaining the original structure of the code. The heavy lifting is performed by Babel, which operates at the Abstract Syntax Tree (AST) level to ensure that the output code remains equivalent to the input. The latest version, version 2, introduces several improvements over its predecessor. Notably, it no longer requires Python, has undergone extensive testing to enhance maintainability, and features a renewed command-line interface (CLI) that can be installed via npm. Users can find an introduction blog post that provides a detailed explanation of these updates. An example provided in the documentation illustrates the tool's functionality. It shows how a minified function can be transformed into a more human-readable format, demonstrating the effectiveness of the deobfuscation process. The tool offers different modes of operation: OpenAI mode, Gemini mode, and local mode. OpenAI and Gemini modes run on external servers optimized for such tasks, which incurs costs based on the length of the code being processed. These modes are noted for their accuracy. In contrast, local mode runs on the user's machine, is free, but may be less accurate and slower, depending on the hardware capabilities. To get started, users need to install Node.js and can then install the tool globally using npm. They can also run it locally without installation using npx. The documentation provides clear instructions for setting up API keys for OpenAI and Gemini modes, as well as guidance for downloading the necessary models for local mode. The main features of the tool include the ability to leverage language models for intelligent suggestions on renaming variables and functions, utilizing Babel plugins for AST-level manipulation, and employing Webcrack to unbundle Webpack bundles. Contributions to the project are encouraged, and the code is licensed under the MIT license, allowing for open collaboration and development. The repository has garnered significant interest, with over 1,400 stars and 55 forks, indicating a robust community engagement around the tool.