{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/f5b3e4e9c23941638e08a9c308c9cf0b\" 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/f5b3e4e9c23941638e08a9c308c9cf0b-59750f7e67cbc64d.gif","duration":127.928,"title":"AI-Powered SQL Query Optimization and Risk Analysis","description":"In this video, I showcase a full-stack web application I've built that uses AI to help developers optimize their SQL queries and identify performance risks. I demonstrate how users can input SQL queries and receive analysis on potential risks, such as scanning entire tables without filtering. For example, I show how adding a WHERE clause can mitigate risks, and I highlight that even with optimizations, issues may arise as data grows. I encourage viewers to try out the application with their own queries and explore the recommendations provided. Overall, it’s a practical tool for improving SQL query performance."}