{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/ed62ee967f4b4fae96a3f97773e6df4e\" 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/ed62ee967f4b4fae96a3f97773e6df4e-841adbdd798dcc69.gif","duration":729.157,"title":"PowPal AI Pit Care Planner Demo 🤖","description":"I’m presenting my PowPal AI powered Pit Care Planner. It started as a Python class model for an Owner Pit Task Scheduler, then I added agentic workflow so the system can validate, rank, schedule, explain tasks, and assign a confidence level. The architecture uses app, agent, schedule, evaluation, and testing layers, and it detects conflicts against owner availability and constraints. In the demo I created an owner with availability from 8 to 12 p.m, entered multiple pits and tasks, then generated a daily schedule with 55 unit tests passing, edge case handling, and performance metrics. I did not request any action from viewers."}