## Inspiration

We were trying for an IM cross MS paint experience, and we think it looks like that.

## What it does

Users can create conversations with other users by putting a list of comma-separated usernames in the To field.

## How we built it

We used Node JS combined with the Express.js web framework, Jade for templating, Sequelize as our ORM and PostgreSQL as our database.

## Challenges we ran into

Server-side challenges with getting Node running, overloading the server with too many requests, and the need for extensive debugging.

## Accomplishments that we're proud of

Getting a (mostly) fully up-and-running chat client up in 24 hours!

## What we learned

We learned a lot about JavaScript, asynchronous operations and how to properly use them, as well as how to deploy a production environment node app.

## What's next for SketchWave

We would like to improve the performance and security of the application, then launch it for our friends and people in our residence to use. We would like to include mobile platform support via a responsive web design as well, and possibly in the future even have a mobile app.
## Inspiration

Globally, one in ten people do not know how to interpret their feelings. There's a huge global shift towards sadness and depression. At the same time, AI models like Dall-E and Stable Diffusion are creating beautiful works of art, completely automatically. Our team saw the opportunity to leverage AI image models and the emerging industry of Brain Computer Interfaces (BCIs) to create works of art from brainwaves: enabling people to learn more about themselves and

## What it does

A user puts on a Brain Computer Interface (BCI) and logs in to the app. As they work in front of front of their computer or go throughout their day, the user's brainwaves are measured. These differing brainwaves are interpreted as indicative of different moods, for which key words are then fed into the Stable Diffusion model. The model produces several pieces, which are sent back to the user through the web platform.

## How we built it

We created this project using Python for the backend, and Flask, HTML, and CSS for the frontend. We made use of a BCI library available to us to process and interpret brainwaves, as well as Google OAuth for sign-ins. We made use of an OpenBCI Ganglion interface provided by one of our group members to measure brainwaves.

## Challenges we ran into

We faced a series of challenges throughout the Hackathon, which is perhaps the essential route of all Hackathons. Initially, we had struggles setting up the electrodes on the BCI to ensure that they were receptive enough, as well as working our way around the Twitter API. Later, we had trouble integrating our Python backend with the React frontend, so we decided to move to a Flask frontend. It was our team's first ever hackathon and first in-person hackathon, so we definitely had our struggles with time management and aligning on priorities.

## Accomplishments that we're proud of

We're proud to have built a functioning product, especially with our limited experience programming and operating under a time constraint. We're especially happy that we had the opportunity to use hardware in our hack, as it provides a unique aspect to our solution.

## What we learned

Our team had our first experience with a 'real' hackathon, working under a time constraint to come up with a functioning solution, which is a valuable lesson in and of itself. We learned the importance of time management throughout the hackathon, as well as the importance of a storyboard and a plan of action going into the event. We gained exposure to various new technologies and APIs, including React, Flask, Twitter API and OAuth2.0.

## What's next for BrAInstorm

We're currently building a 'Be Real' like social media plattform, where people will be able to post the art they generated on a daily basis to their peers. We're also planning integrating a brain2music feature, where users can not only see how they feel, but what it sounds like as well
## Inspiration

In the wake of the COVID-19 pandemic, more people than ever suffer from poor mental health and social anxiety. Being out-of-state freshmen at a large public university, the founders of OpenTalk have experienced this social anxiety firsthand: we've all stressed over how we would build new connections in our new environment after a mostly-online high school experience. In addition to regular interpersonal connections, we've also had countless interview, presentations, meetings with professors, and more higher-stress encounters.

With all of these situations, we handled our stress in different ways: pacing back and forth, thinking to ourselves, talking to a wall, etc. If only there were an app to help us practice these scenarios...

And that's where we come in. With language-learning model technology reaching an inflection point, we pounced on the opportunity to use generative AI and augmented reality to provide a realistic environment for simulating social interactions that all users can find useful.

## What it does

OpenTalk is an app that allows the user to practice social situations with an AI chatbot in augmented reality. Currently, we have implemented three scenarios which the user can simulate: a mock job interview, ordering coffee from a barista, and free conversation. We plan to implement more situations in the future, as well as customizable characters and voices.

## How we built it

The core of our iOS app is built in the Swift programming language, using built-in packages for frontend design (SwiftUI) and augmented reality (ARKit). We used AVKit to interpret human language, and the OpenAI API to generate humanlike responses. Finally, the response from OpenAI was sent to the Google Speech-to-Text API, and spoken to the user.

## Challenges we ran into

We ran into many technical challenges along the journey. One of the first issues we encountered was one of skills: none of us had ever used AR/VR technologies, nor 3D modeling software such as Blender. Secondly, we ran into many issues using the Google Speech-to-Text API, and getting our AR model to "speak" back to us at the end of the day.

## Accomplishments that we're proud of

We are extremely proud to have produced a working prototype of the product we envisioned. It was exhilarating to see our AR model spring to life and speak for the first time.

## What we learned

Over the course of the project, we learned many in-demand technologies: ARKit, OpenAI API, and SwiftUI to name a few. Besides technical skills, however, we learned more about what goes on behind-the-scenes in ChatGPT from OpenAI and what makes a successful idea from YCombinator in their respective HackerX sessions.

## What's next for OpenTalk

1. Improvements to AR models and animations for more immersion
2. A more effective feedback system with additional features such as analysis of users tone and body language
3. General quality of life changes and finalization into the app we envisioned