{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/44c793c47e7d45eaaf02bac7c168a10d\" frameborder=\"0\" width=\"1440\" height=\"1080\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1080,"width":1440,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1080,"thumbnail_width":1440,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/44c793c47e7d45eaaf02bac7c168a10d-4c58c90bdfbb77cb.gif","duration":77.612,"title":"NOUS Hackaton - Presentation - 18 May 2025","description":"In this video, I discuss the importance of fine-tuning larger gauge models to interact effectively with MCPs, which are crucial for intelligent internets. I propose a reinforcement learning framework to help LLMs master MCPs by generating datasets and evaluating use cases. I also highlight the challenges developers face with MSW API calls and the diversity of MCPs. Please take a moment to consider the proposed framework and how it could enhance our projects."}