{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/dc0df4028d0a43c296ddbf83cd4db091\" frameborder=\"0\" width=\"1726\" height=\"1294\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1294,"width":1726,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1294,"thumbnail_width":1726,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/dc0df4028d0a43c296ddbf83cd4db091-7523a805ea1910a9.gif","duration":302.064,"title":"Enhancing the Glitch Investigator Game 🤖","description":"Hello everyone, this is Steven Gobran. In my final project for A1-110, I transformed the original buggy number guessing game into a full-applied AI system with two new features: a RAG-powered bug pattern lookup and an automated reliability evaluator. I demonstrated how the bug pattern lookup retrieves relevant documentation for common Python errors and how the reliability evaluator tests the system's performance with seven checks. I encourage you to explore these features and provide any feedback on the project."}