QuackTrack was built with the goal of assisting college students in overcoming the challenges of the job search process. As students ourselves, we encountered several obstacles when trying to find the right job, refine our resumes, and prepare for interviews. QuackTrack is designed to simplify and guide users through these stages by providing AI-driven recommendations and actionable feedback.
- AI-Driven Job Matching: QuackTrack analyzes your resume and uses the Empllo API to find the most relevant job listings based on your experience and preferences.
- Application Guidance: The platform helps you understand the application process and offers step-by-step guidance on how to apply for your ideal roles.
- Interview Simulations: QuackTrack simulates both behavioral and technical interviews, providing detailed feedback to help you improve your interviewing skills.
- Personalized Recommendations: The tool gives feedback on how to improve your resume and interview techniques, making your job search more efficient and effective.
- Frontend: React, Node.js
- Backend: Flask (Python)
- APIs: Empllo API for job listings
- Web Scraping: Data scraping from LinkedIn, Glassdoor, and Indeed for job listings and interview questions.
The frontend was developed using React and Node.js, which allowed us to create an interactive, user-friendly interface with five unique pages. The backend was built with Flask in Python, enabling us to scrape job listings from major platforms such as LinkedIn, Glassdoor, and Indeed. The backend also generates mock interview questions and personalized roadmaps for job seekers.
One of the main challenges faced during development was unifying the frontend design. With two different developers working on the UI, there were discrepancies in styling. It took additional time and effort to align the design and create a consistent look and feel across the app.
We are proud of the comprehensive features we were able to develop within a short timeframe. QuackTrack offers job matching, interview simulations, resume feedback, and personalized recommendations, all powered by AI.
Throughout the project, each team member gained experience with new technologies and frameworks. Two of us worked with React for the first time, while the other two focused on Flask and backend development. This project helped expand our technical skill sets and improved our collaboration.
We plan to further enhance QuackTrack by refining our AI algorithms for resume improvement and job matching. With more data, we aim to provide even more precise, actionable recommendations to help students succeed in their job search and career goals.