In this episode of the Vertica Frontend team podcast, we explore the "Building Code Grader Feedback System Using AI" project, which is revolutionizing code evaluation in e-commerce and frontend development. We discuss how this system, powered by the Meta La-ma model and the Naga A-I A-P-I, provides developers with timely and actionable feedback across various programming languages such as C, C++, Python, JavaScript, Rust, and SQL.We go into detail about the challenges faced during development, including issues with quantizing the Meta La-ma model on Google Colab and the necessary switch to CPU-based execution. We highlight the role of Low-Rank Adaptation (LoRA) in improving efficiency, and the integration of React and Fast API for creating a seamless user interface using Tailwind CSS for optimization.Key features of the Naga A-I A-P-I are discussed, specifically its ability to offer fast code grading, while noting the strengths of the Meta La-ma model in providing nuanced feedback on code structure and maintainability. We also examine the project’s implications for the frontend industry and its impact on e-commerce platforms.Additionally, we touch on the importance of managing memory and performance bottlenecks when switching to a CPU-based environment, as well as the limitations of relying solely on one model for comprehensive feedback.In the second part of the episode, we dive into the world of Bootstrap, a popular frontend framework that simplifies web development with its responsive design capabilities and pre-built components. We guide listeners on how to set up Bootstrap, highlighting its benefits for creating user-friendly e-commerce sites efficiently.We conclude by emphasizing the importance of these advancements in software development practices for Vertica and our clients, as well as providing a link to the main story for further reading.Thank you for listening, and stay tuned for our next update!

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