VanTechBridge

Inspiration The inspiration for VanTechBridge came from the ongoing challenge in Vancouver's tech ecosystem. Despite having a thriving community, events and knowledge-sharing often remain siloed across various disciplines. We wanted to create an app that brings professionals, mentors, and innovators together by leveraging location-based networking, helping bridge the gap between aspiring developers and industry experts.

What it does VanTechBridge is a map-based career app where users can input their LinkedIn URL and project idea. The app analyzes their tech stack and project requirements, then connects them with professionals and mentors nearby who have the expertise needed to help. It allows users to set up coffee chats to network and discuss ideas in person or virtually, making it easier to share knowledge and collaborate.

How we built it We built VanTechBridge using a combination of APIs and GPT-powered natural language processing. Here's the breakdown:

LinkedIn Parsing: We used a parser API to extract the user’s skills and experiences from their LinkedIn profile. GPT Integration: GPT analyzed the project idea to generate the required tech stack. Location Matching: We used location data to find professionals nearby who possess the required skills, using a simple database query and sorting by distance. Coffee Chat Invite: Once matched, users can invite the professional for a coffee chat. Registration is handled through LinkedIn details, and sessions are maintained for seamless interactions. Challenges we ran into One of the main challenges was integrating the various APIs smoothly and ensuring real-time location matching. Maintaining sessions throughout the app for consistent user experience was tricky given the time constraints. Additionally, filtering professionals and mentors by relevance and proximity required careful optimization.

Accomplishments that we're proud of We’re proud of creating a functional MVP in a short timeframe that effectively solves the problem of fragmented tech events and knowledge-sharing. The integration between LinkedIn parsing and GPT worked well, allowing us to map user expertise with project needs and match them to local professionals. Despite the complexities, we managed to create an intuitive experience for networking.

What we learned This project taught us the importance of balancing technical complexity with simplicity. Building a real-time location-based career app in such a short amount of time required streamlining our workflows and focusing on core functionalities. We also learned the power of GPT in mapping skills to project requirements and the importance of having a clear API integration strategy.

What's next for VanTechBridge Next, we plan to expand VanTechBridge by adding features like event recommendations based on user skills and interests. We also aim to refine the matching algorithm for even better mentor-protégé pairing. Additionally, integrating direct calendar syncing and video chat options for virtual coffee meetings could make it more versatile for remote users.

Built With

• canva
• figma
• llm
• nextjs
• python