Show HN: Using LLMs to make the Emacs Web Browser great again (github.com)

A new Emacs package aims to enhance the Emacs Web Browser by using machine learning to simplify complex modern web pages, rendering them more compatible with Emacs' text-based interface. This innovative tool translates contemporary HTML to a simpler, 1990s-style format, enabling faster and more accurate page loading. By generating a program that modifies the webpage's source via a background task, users can experience improved browsing without the clutter of modern web design, which often relies heavily on JavaScript and intricate layouts. This development is significant for the AI/ML community as it demonstrates a practical application of large language models (LLMs) to bridge the gap between old and new web technologies. Although the initial process requires substantial token usage—suggesting users maintain appropriate subscription plans—the ability to automate the modification of web content presents an exciting avenue for dynamic browsing experiences. The package leverages browser sandboxing and emphasizes user privacy, although caution is advised when using it with sensitive information. Future updates are planned to include support for a broader range of models, enhancing its flexibility and effectiveness in web browsing.