{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/aea10780b01f4071affb1275ba223c9c\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/aea10780b01f4071affb1275ba223c9c-63ce94524ebb8858.gif","duration":298.0025,"title":"Smart Outbound Call Detection System Demo 📞","description":"Hi everyone, this is Aditya Coomar, and in this video, I present my second assignment for Attack Capital, which is a smart outbound call detection system. The system utilizes multiple AMD strategies, including Twilio's native AMD, Jamboz, a Hugging Face model, and Gemini testing. I demonstrate the functionality by testing two AMD strategies, showcasing how the system effectively detects whether a call is answered by a human or a machine, such as a voicemail. The tech stack includes Next.js, Tailwind CSS, and FastAPI for the Hugging Face model. I encourage you to review the demo and provide feedback on the current dashboard and features."}