Getting rid of your social media addiction doesn't have to be a painful process. Turn it into a fun challenge with your friends and see if you can win the pot!

## Inspiration

We recognize that social media addiction is a very real issue among today's youth. However, no steps are taken to curb these addictions as it is a painful and unrewarding process. We set out to fix this by turning the act of quitting social media into a fun group activity with a touch of monetary incentives. 😉

## What it does

Start a party with your friends and contribute a small amount of money into the party's prize pool. Once the showdown starts, no accessing social media! Whoever lasts until the end will win the pot!

Users will top up their account using Interac payments (thanks to the Paybilt API) and they will then start a showdown for a certain period of time, with each contributing some amount of money to the prize pool. The winners will split the pot amongst them and if they so choose they can pay out their account balance using Interac (once again thanks to Paybilt).

If you lose the showdown, no worries! Our AI feature will ~~name and shame~~ gently scold you so that you are encouraged to do better!

## How we built it

We used **React.js** for the frontend, and **Express.js** for the backend with **Prisma** ORM to handle our database.
We also used **Paybilt**'s API to handle monetary transactions as well as **Meta**'s API to fetch user data and link accounts through the OAuth & Webhook API's.
**Cohere** was used to generate dynamic status messages for each game.

Our backend is hosted on **Google Cloud Platform** and our client is hosted on **GitHub Pages**. Our domain is a free .tech domain from MLH (thanks!!).

## Challenges we ran into

Meta's API documentation was very obscure, partially inaccurate, and difficult to implement. The meta API integration portion of our platform took one person the entire duration of the hackathon to work out.

Before this hackathon, we were unfamiliar with Paybilt, especially since their API is private. During this hackathon, we ran into some challenges using their API and receiving callbacks. However in the end we were able to successfully integrate Paybilt into our platform.

## Accomplishments that we're proud of

We are proud of creating a fully functional platform that not only contains the core features necessary for a minimum viable product, but implements additional ✨fun✨ features to enhance the experience such as AI-generated messages in the party.

## What we learned

We learned how to use Paybilt, Meta's Webhook & Oauth API's, and Cohere. Overall we found this an enjoyable experience as we all gained a lot of knowledge and experience without sacrificing our project or our vision.

## What's next for Screentime Showdown

We plan to integrate other platforms such as TikTok and YouTube Shorts and introduce a more robust set of party configurations to suit various needs (including the ability to toggle how aggressive you want the shaming to be).
## Inspiration

As students, we have found that there are very few high-quality resources on investing for those who are interested but don't have enough resources. Furthermore, we have found that investing and saving money can be a stressful experience. We hope to change this for those who want to save better with the help of our app, hopefully making it fun in the process!

## What it does

Our app first asks a new client a brief questionnaire about themselves. Then, using their banking history, it generates 3 "demons", aka bad spending habits, to kill. Then, after the client chooses a habit to work on, it brings them to a dashboard where they can monitor their weekly progress on a task. Once the week is over, the app declares whether the client successfully beat the mission - if they did, they get rewarded with points which they can exchange for RBC Loyalty points!

## How we built it

We built the frontend using React + Tailwind, using Routes to display our different pages. We used Cohere for our AI services, both for generating personalized weekly goals and creating a more in-depth report. We used Firebase for authentication + cloud database to keep track of users. For our data of users and transactions, as well as making/managing loyalty points, we used the RBC API.

## Challenges we ran into

Piecing the APIs together was probably our most difficult challenge. Besides learning the different APIs in general, integrating the different technologies got quite tricky when we are trying to do multiple things at the same time!

Besides API integration, definitely working without any sleep though was the hardest part!

## Accomplishments that we're proud of

Definitely our biggest accomplishment was working so well together as a team. Despite only meeting each other the day before, we got along extremely well and were able to come up with some great ideas and execute under a lot of pressure (and sleep deprivation!) The biggest reward from this hackathon are the new friends we've found in each other :)

## What we learned

I think each of us learned very different things: this was Homey and Alex's first hackathon, where they learned how to work under a small time constraint (and did extremely well!). Paige learned tons about React, frontend development, and working in a team. Vassily learned lots about his own strengths and weakness (surprisingly reliable at git, apparently, although he might have too much of a sweet tooth).

## What's next for Savvy Saver

Demos! After that, we'll just have to see :)
## Inspiration

Given the track of sustainability as well as the growing issues of climate change, one of the biggest problem is that individual action often seems daunting. How can someone truly live a carbon neutral and climate friendly life? Eco food aims to help the climate change issue on this personal level by simplifying and changing the way you think about food.

## What it does

By taking a picture on your phone, the picture is then analyzed through a convolutional neural network to classify the picture into multiple categories. Based on these categories, a range of values for the carbon emission impact of the food is displayed.

## How we built it

For the convolutional neural network, a dataset of images classified into 25 different food categories was used. The CNN machine learning model was implemented in PyTorch.

## Challenges we ran into

Inspiration for an idea was a challenge, especially finding a problem that could be coded in 24 hours (preferably less so that we could sleep) and had enough relevant data to train a model on. Then, the training phase of the model was also challenging in varying parameters to get the highest accuracy. Finally, the ML demo app was a challenge as it was our first time using Xcode.

## Accomplishments that we're proud of

We are proud of being able to use neutral networks to address an issue of global relevance as well as being able to learn new skills.

## What we learned

We learned about the implementation of machine learning as well as Xcode.

## What's next for eco food

We hope to add more food categories.