## Inspiration

Aravind doesn't speak Chinese. When Nick and Jon speak in Chinese Aravind is sad. We want to solve this problem for all the Aravinds in the world -- not just for Chinese though, for any language!

## What it does

TranslatAR allows you to see English (or any other language of your choice) subtitles when you speak to other people speaking a foreign language. This is an augmented reality app which means the subtitles will appear floating in front of you!

## How we built it

We used Microsoft Cognitive Services's Translation APIs to transcribe speech and then translate it. To handle the augmented reality aspect, we created our own AR device by combining an iPhone, a webcam, and a Google Cardboard. In order to support video capturing along with multiple microphones, we multithread all our processes.

## Challenges we ran into

One of the biggest challenges we faced was trying to add the functionality to handle multiple input sources in different languages simultaneously. We eventually solved it with multithreading, spawning a new thread to listen, translate, and caption for each input source.

## Accomplishments that we're proud of

Our biggest achievement is definitely multi-threading the app to be able to translate a lot of different languages at the same time using different endpoints. This makes real-time multi-lingual conversations possible!

## What we learned

We familiarized ourselves with the Cognitive Services API and were also able to create our own AR system that works very well from scratch using OpenCV libraries and Python Imaging Library.

## What's next for TranslatAR

We want to launch this App in the AppStore so people can replicate VR/AR on their own phones with nothing more than just an App and an internet connection. It also helps a lot of people whose relatives/friends speak other languages.
## Our Inspiration

We were inspired by apps like Duolingo and Quizlet for language learning, and wanted to extend those experiences to a VR environment. The goal was to gameify the entire learning experience and make it immersive all while providing users with the resources to dig deeper into concepts.

## What it does

EduSphere is an interactive AR/VR language learning VisionOS application designed for the new Apple Vision Pro. It contains three fully developed features: a 3D popup game, a multi-lingual chatbot, and an immersive learning environment. It leverages the visually compelling and intuitive nature of the VisionOS system to target three of the most crucial language learning styles: visual, kinesthetic, and literacy - allowing users to truly learn at their own comfort. We believe the immersive environment will make language learning even more memorable and enjoyable.

## How we built it

We built the VisionOS app using the Beta development kit for the Apple Vision Pro. The front-end and AR/VR components were made using Swift, SwiftUI, Alamofire, RealityKit, and concurrent MVVM design architecture. 3D Models were converted through Reality Converter as .usdz files for AR modelling. We stored these files on the Google Cloud Bucket Storage, with their corresponding metadata on CockroachDB. We used a microservice architecture for the backend, creating various scripts involving Python, Flask, SQL, and Cohere. To control the Apple Vision Pro simulator, we linked a Nintendo Switch controller for interaction in 3D space.

## Challenges we ran into

Learning to build for the VisionOS was challenging mainly due to the lack of documentation and libraries available. We faced various problems with 3D Modelling, colour rendering, and databases, as it was difficult to navigate this new space without references or sources to fall back on. We had to build many things from scratch while discovering the limitations within the Beta development environment. Debugging certain issues also proved to be a challenge. We also really wanted to try using eye tracking or hand gesturing technologies, but unfortunately, Apple hasn't released these yet without a physical Vision Pro. We would be happy to try out these cool features in the future, and we're definitely excited about what's to come in AR/VR!

## Accomplishments that we're proud of

We're really proud that we were able to get a functional app working on the VisionOS, especially since this was our first time working with the platform. The use of multiple APIs and 3D modelling tools was also the amalgamation of all our interests and skillsets combined, which was really rewarding to see come to life.
## Inspiration

Our inspiration for this project was to empower individuals, enabling them to make well-informed financial choices effortlessly. Our goal was to eliminate the need for extensive hours of sifting through intricate credit card terms and conditions, making financial decision-making accessible to everyone.

## What it does

The application stores information about the various credit cards used by the client. When a client wishes to make a purchase, they can simply open the app. It will then utilize location data to automatically identify the type of store they are in and recommend the most suitable credit card for that specific transaction, optimizing their benefits and rewards.

## How we built it

The framework of the mobile application is React Native. We used Python for information processing/retrieval to and from Google Places API as well as OpenAI API. We also used JavaScript for app functionality.

## Challenges we ran into

We had an issue because when we retrieved users' location data, we could initially only map their location to an address that could have multiple stores in its area depending on the density of the stores (in plazas/malls, etc). We solved this problem by making the program get the closest store within a 100m radius of the retrieved address.

## Accomplishments that we're proud of

Building a React-Native app with no prior experience with the framework. Working around hurdles within our techstack and maintaining the original project idea. Utilizing and learning how to use new API's (OpenAI, Google-Places).

## What we learned

How to work efficiently in a team. By finding what needs to be done and delegating tasks among the group.

## What's next for Blitz

Future additions for Blitz is to add a database of credit cards and their rewards so the user can search for what card they have and add it to their account. Index our own map data to make it more accurate for our use case. Track user spending to recommend new cards. Create a physical card that stores all the other credit card data to automatically choose the card for you. A Chrome extension for online checkout.