HackStack

Project Info

UrbanQuest

Devpost

This project did not submit a demo video on Devpost.

Inspiration

The inspiration behind Urban Quest came from the desire to blend everyday exploration with the power of AI. We wanted to create an interactive platform where users could snap photos of their surroundings and instantly learn more about what they captured. Whether it’s an interesting landmark, an object, or a question about the environment, Urban Quest makes discovery and learning fun and accessible. We aimed to bring adventure and curiosity into the palms of our users.

What it does

Urban Quest allows users to take photos and ask questions about the content in the images. Using AI, the app processes the photos, analyzes the visual elements, and provides insightful responses. Whether it's identifying objects, explaining a scene, or answering specific questions related to the captured image, Urban Quest brings context and understanding to the world around the user. It’s a blend of exploration and knowledge sharing, designed for curious minds.

How we built it

We built Urban Quest using Swift and integrated advanced AI technologies like Google Vision and Gemini API for image recognition and content generation. The app's core functionality revolves around the seamless interaction between the camera, the AI model, and the user interface. We used cloud-based AI models to handle the heavy lifting of image analysis and natural language processing, ensuring fast and accurate responses. The app’s design focuses on simplicity, allowing users to easily capture images, ask questions, and receive answers in real-time.

Challenges we ran into

One of the biggest challenges we faced was integrating AI models to effectively understand and process the images, especially in real-time. Ensuring smooth performance while handling large images and making API calls was difficult. Another challenge was creating a user-friendly experience that balanced the power of AI with simplicity. We also had to carefully manage user privacy, particularly when accessing the camera and photo library, while adhering to strict data security protocols.

Accomplishments we're proud of

We’re proud of successfully combining image capture, AI-based analysis, and interactive storytelling in a single, cohesive app. The smooth integration of the camera functionality with real-time AI processing was a significant achievement. We also take pride in making a highly interactive and educational tool that can engage users in a fun and meaningful way. Creating an intuitive user interface that can bring such advanced technology to the everyday user is another highlight.

What we learned

Throughout the development process, we learned a lot about AI integration, especially in mobile applications. We deepened our understanding of how to process visual data and convert it into meaningful responses using AI models. Managing real-time API calls, handling image data, and optimizing app performance across different devices provided valuable insights. We also learned how important user experience is, especially when dealing with complex technology—keeping things simple and easy to use was key.

What's next

for Urban Quest Moving forward, we plan to expand Urban Quest’s capabilities by adding more advanced image recognition features, such as real-time object tracking and expanded visual analysis. We also aim to incorporate social features where users can share their discoveries and learn from others. Additionally, we want to explore gamification elements to make the learning process even more engaging. Lastly, we’re looking to enhance the AI model's ability to understand even more complex queries and provide richer, more detailed responses.

Analysis

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Technology

Found in codeClaimed only
  • SwiftIn code
  • Google GeminiClaimed

1 of 2 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

18 KB

Source files

8

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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