A guest post by Tian Jin discusses a novel approach to enhancing recommendation systems using large language models (LLMs) to address issues like lack of transparency, configurability, and conflicts of interest inherent in current systems. Highlighting the drawbacks of existing systems, such as prioritizing profit over user welfare and making inferences based on current preferences without considering personal growth, the post introduces RecAlign, an open-source Chrome extension that uses LLMs to act as smart content filters for social media feeds. Users can specify their preferences in simple language, allowing the LLM to assess and filter content accordingly, offering a solution that is configurable, transparent, and flexible. The project, developed by two Ph.D. students from MIT and Harvard, utilizes LangChain for efficient communication with the OpenAI backend, enabling rapid prototyping and iteration. The authors encourage others to try out RecAlign on GitHub and follow its development.