<?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/58a31109e21247c4ab1b1beb0ab0009e&quot; frameborder=&quot;0&quot; width=&quot;1918&quot; height=&quot;1438&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1438</height><width>1918</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1438</thumbnail_height><thumbnail_width>1918</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/58a31109e21247c4ab1b1beb0ab0009e-5907b5886a386d68.gif</thumbnail_url><duration>238.266</duration><title>Enhancing ChatGPT with Real-Time Data 🌐</title><description>I discuss a unique feature of SecureGPT that allows for real-time data access in language models. By enabling Python code execution, we can fetch live data into the models, overcoming the limitation of static training data. I demonstrate how this feature works using tools like a code-driven calculator and real-time weather API calls. Viewers can leverage this capability for various applications, even non-technical tasks, making it a powerful tool for data integration.</description></oembed>