<?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/67dd1db910ae424eb89e249e676bbaf0&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/67dd1db910ae424eb89e249e676bbaf0-f6b17965900d31f5.gif</thumbnail_url><duration>274.8381</duration><title>Introducing DSL Model Framework! 🚀</title><description>https://github.com/seanchatmangpt/dslmodel

Hey DSLModel community, it&apos;s Sean Chatman here. I&apos;m excited to introduce you to DSL Model, a framework that revolutionizes structured text and language models. With DSL Model, you can effortlessly generate mock synthetic data using Jinja, create structured text with Pydantic 2, and automate workflows with a built-in engine. No need for external dependencies like servers or databases - it&apos;s all-in-one! Plus, every DSL model can serialize itself back to YAML for easy manipulation in a chat GPT session. Exciting features like state machines and data handlers make working with CSVs and IPython notebooks a breeze. Check it out and let me know what you think!</description></oembed>