TalentFlow

TalentFlow is a college platform showing where peers have interned, company reviews, detailed career profiles, and resume analysis to see key skills that land roles and optimize your career strategy.

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TalentFlow

Inspiration

The inspiration for TalentFlow came from the need to bridge the gap between students and potential job opportunities. As STEM students, we recognized the struggles many of our peers face in navigating the internship and job application process. We wanted to create a platform that not only connects students with job opportunities but also provides insights and resources to enhance their employability.

What it does

TalentFlow is a networking application designed to connect students with internship and job opportunities as well as other students who have secured internships. The platform allows students to see a dashboard of companies where their peers, both active students and alumni, have interned or worked. It aggregates reviews, ratings, and career paths, giving users valuable insights into the application process and work experience at different companies.

How we built it

We built TalentFlow using a modern tech stack:

  • Frontend: The frontend was developed using Next.js, TypeScript, and Tailwind CSS for a responsive and user-friendly design.
  • Backend: We utilized Flask for the backend, where we implemented API endpoints to handle user data and interactions.
  • AI Features: We integrated a Hugging Face OS AI model to provide resume improvement suggestions, helping students craft better applications.
  • Authentication: For secure user authentication, we implemented Auth0, making it easy for users to log in and access their profiles.
  • Data Collection: We used Python for LinkedIn profile scraping to populate user profiles and work experience, giving users a seamless onboarding experience.

Challenges we ran into

One of the major challenges we faced was in scraping LinkedIn profiles. We had to navigate the limitations by LinkedIn’s API and find workarounds as to how we go about scraping (as to prevent too many requests). Additionally, integrating the AI model with the backend posed challenges in terms of performance and data handling, which we had to optimize.

Accomplishments that we're proud of

We are proud of successfully creating a functional MVP within the hackathon's time constraints. The integration of AI-powered resume suggestions and the ability to connect students with others who have top experiences and steer them closer to job opportunities in real-time showcases the potential of TalentFlow to empower users in their career journeys.

What we learned

Throughout the development process, we learned a lot about collaborative coding, API integrations, and implement auth with auth0. We also gained valuable experience in deploying a full-stack application using Vercel for frontend hosting and managing backend services with Flask.

What's next for TalentFlow

Looking ahead, we plan to enhance TalentFlow by adding more features, such as personalized job recommendations based on user profiles and an improved UI/UX design. We also want to implement messaging and other community-oriented features.

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