<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/3ab3271cc054487dbfab066de96a603e&quot; frameborder=&quot;0&quot; width=&quot;1910&quot; height=&quot;1432&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1432</height><width>1910</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1432</thumbnail_height><thumbnail_width>1910</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/3ab3271cc054487dbfab066de96a603e-e19b3c5b4d3dafc4.gif</thumbnail_url><duration>213.536</duration><title>RustSmith </title><description>In this video, I introduce our project RustSmith, which addresses the limitations of small language models in programming, particularly in Rust. We developed a number guessing game that showcases how our system utilizes multiple agents to generate and compile code without manual prompting. I explain the roles of the master agent and the Smith agent in this process, ensuring that any errors are automatically corrected. I encourage you to explore how this project democratizes AI usage and enhances the capabilities of small models.</description></oembed>