UrbanSpark

"UrbanSpark: Connecting young minds to local opportunities – discover internships, educational programs, and volunteer work tailored to youth interests, all in one place."

Comment

Inspiration

The inspiration for UrbanSpark came from our desire to connect young adults with relevant opportunities and resources within their cities. In large urban settings, it’s easy to overlook or miss out on programs, internships, educational workshops, and community resources that could be life-changing. We wanted to create an accessible platform that uses AI to provide personalized recommendations for activities, jobs, and learning opportunities tailored to individual interests and needs. It is especially helpful if the individual is low-income or has a disability. Our goal is to empower users to engage more with their city, find support, and build skills in a way that feels customized for them.

What it does

UrbanSpark serves as a personalized recommendation platform that leverages AI to analyze user input and deliver curated suggestions for urban opportunities. Users start by completing a quick onboarding form where they share details such as their location, interests, and goals. From there, UrbanSpark’s AI engine generates a series of queries tailored to their profile. These queries are then processed by the SerpAPI to retrieve up-to-date information on educational programs, jobs, volunteering, events, and more. Finally, the information is summarized into concise recommendations, which are presented on a user-friendly dashboard, guiding users toward the opportunities best suited to their aspirations.

How we built it

UrbanSpark was built with a React front-end that provides a streamlined user experience for entering data, viewing recommendations, and navigating through results. We used FastAPI to handle backend API calls to the SerpAPI, ensuring we could retrieve high-quality, real-time information. For natural language processing, we integrated the OpenAI API, which generates relevant search queries and later summarizes the search results. This system allows UrbanSpark to tailor results to each user’s unique profile while maintaining responsiveness and accuracy. The project involved multiple API interactions, custom state management, and an asynchronous flow to handle real-time data requests and responses smoothly.

Challenges we ran into

One of our biggest challenges was managing multiple API interactions while maintaining data integrity and responsiveness. Ensuring that user input could seamlessly translate into effective search queries and relevant results required precise data parsing, especially as we passed data between different API calls. Additionally, handling asynchronous API calls and managing response times were complex, particularly when orchestrating the AI-powered query generation, the SerpAPI calls, and the summarization step. Data formatting and handling the variability of external data also posed some difficulties, requiring careful validation and error handling.

Accomplishments that we're proud of

We’re proud to have created an application that dynamically combines user input with AI-driven recommendations and real-time data, effectively helping users connect with urban resources. The seamless flow of data between the front-end, AI-based query generation, and the summarization engine is a technical achievement that enhances user experience. We’re also excited about the interface we developed, which remains simple yet highly functional, allowing users to quickly find opportunities and resources that align with their interests. Lastly, seeing our project evolve from a concept to a fully functioning platform within a short timeframe was an incredible accomplishment. We truly think this can help high schoolers find valuable opportunities within the city.

What we learned

Throughout this project, we deepened our understanding of integrating multiple APIs, managing asynchronous data flows, and using AI to enhance user experience. We also gained practical experience with state management in complex, data-driven applications and learned how to design a UI that supports responsive, real-time data updates. Additionally, we discovered the importance of precise data validation, especially when working with AI-generated and third-party data sources, which taught us valuable lessons in error handling and data consistency.

What's next for UrbanSpark

Moving forward, we aim to expand UrbanSpark’s capabilities by incorporating additional APIs to offer a wider range of recommendations. We’d like to add features that allow users to save opportunities and receive personalized notifications about new programs or events in their area. We’re also considering adding a social feature where users can connect with peers interested in similar opportunities, fostering a community of engagement and support. Finally, enhancing the AI’s personalization capabilities to account for user feedback and preferences will make UrbanSpark an even more powerful resource for urban discovery and growth.

Built With

Share this project:

Updates