Code Hire AI
A Unified AI Tool for Code Assistance and Interview Preparation
1Vaishnavi Mali, 2Mayank Vora, 3Gauri Patil, 4Ketan Ahire–––, Guided by: Ms. Pooja M. Khandar
1,2,3,4 UG Student, 5Assistant Professor Department of Computer Engineering, SSBT’s College of
Engineering and Technology Jalgaon, Maharashtra, India
Abstract: In recent years, the global recognition of the importance of artificial intelligence in software development and technical hiring has grown significantly. With the rise in demand for efficient coding solutions and effective interview preparation tools, there is a need for a unified system that addresses both concerns. This project aims to contribute to this field by leveraging the power of machine learning to improve coding efficiency and technical interview preparation. The proposed system, "Code Hire AI," harnesses the potential of AI-powered code assistance and interview simulation, thereby making a significant contribution to the existing body of knowledge in this field. It utilizes machine learning algorithms to analyze code patterns, detect errors, and simulate realistic technical interviews, enhancing both productivity and confidence for developers and job seekers. The project employs a web-based application built with Next.js and React.js, integrating with the Gemini AI model to provide intelligent responses and suggestions. This approach not only facilitates seamless coding experiences but also aids in comprehensive interview preparation. By integrating advanced AI capabilities, this project aims to ensure a more streamlined development process and improved technical hiring experience. It underscores the potential of technology in addressing software development and recruitment challenges and advocates for its wider application in these domains. One of the key features of this project is the development of a unified platform that handles both code debugging and technical interview simulations. This application is designed to support multiple programming languages and provide personalized feedback. This approach not only enhances coding skills but also boosts interview confidence, which is crucial for career advancement. This feature is particularly useful for developers and job candidates who rely on efficient tools to improve their skills and performance. The project serves as a testament to the power of technological innovation, particularly AI and machine learning, in transforming software development and technical recruitment processes, and paves the way for future research in this field.
Index Terms – Code Assistance, Interview Preparation, AI Model , Gemini AI
