## Inspiration

Moving out of residence is a tedious part for students. Why? Because we own a bunch of stuffs we no longer need and we don't know where or how to donate them. That is why our team create an app that students can share their food, shampoo, chair,.. anything to the others.

## What it does

An individual makes a post about stuffs that they have spare and want to share. They also choose the preferred meeting time and a location on campus that they can deliver. If someone wants to take it, they will inform the poster by hitting the "Take it" button on the post.

## How we built it

* Figma: we used the platform to brainstorm the idea, draft the app's layout and design
* React.js: we use React, especially React-Bootstrap library to create a responsive and well-organized website.
* Firebase: besides connecting the app with a real-time storage, we use Firebase Authentication for log-in page.

## Challenges we ran into

We are all beginners in React.js. Hence, it took time for us to learn the basics of the library and implement it to the website.
We also had some conflicts in the design and task distribution process.

## Accomplishments that we're proud of

Picking up React.js and create a responsive website

## What we learned

* Authentication using Firebase
* React.js
* Web development
* Teamwork

## What's next for ShareWithMe

Initially, the app works locally in the university residence so that it's safe to share privacy information and for students to contact others. Later on, when it has stricter security and scam detection, we want to expand the region to outside of the university. We also plan to finalize the profile page so user can check what they had lent and taken.
## Inspiration

Dating apps are the same old concept, beat to death in a million ways. Whether it's one gender getting to text first, or you having a limited number of "roses" a day, the principle remains the same. Your photos are placed front and centre for others to judge. You cannot have a profile without a photo, and you can't see a profile without seeing their photo first. There really is no better way to breed a more superficial "match", even if I were trying!

Yet, people don't choose to go on dating apps for superficial matches. They choose to swipe time and time again for the possibility of a deep connection — be it friendship, or more. This begs the question: how do we eliminate the superficial nature of dating, without the discomfort that comes with unknown information? How would you create a tool for bonding that doesn't require plastering your face on every inch of your profile?

Catfish is our answer.

## What it does

Catfish is a pet-centered dating app. Yes, pet-centered. No, the pets are not the ones being paired up. It's you, and some other animal lover out in the world. The only images we allow on our platform are about your pets or your ideal future pet. In a dating world that's all about looks, we're choosing to take a step back and connect over the things we care about the most. I'm sure that for many of you out there, your furry friends definitely fall into this category!

You write a profile for your pet, and show your personality in a light-hearted and humorous way. Let your match know all about how you train your dog to win agility competitions, or just about how you can't resist giving your little buddy some scraps from the table! We believe that you can tell a lot about someone's personality by the way they talk about something that they really care about — this is the space to wax poetic about your animal companion.

After you match with someone else, you can take it away! Whether its organising animal playdates, or moving the conversation to yourselves, the possibilities are endless. Get Catfished, in the best way.

## How we built it

This is an iOS app, built on React Native. We hosted the code locally with xcode (which takes an inordinate time to install).

## Challenges we ran into

We... did not know React Native or React or basically any kind of web/app development before this hackathon. As you can imagine, we did not get much sleep. Our demo was basically hacked together to be able to fully show you what our vision is for Catfish.

## Accomplishments that we're proud of

We're exceedingly proud of being able to submit a demo and show the fruits of our hard work, all while repping our little fluffy pals!

## What we learned

1. How to read React Native code (oh god)
2. How to write React Native code (ish)
3. How to ask for help when we need it (thanks Yath)
4. How to persevere (through suffering)
5. The power of Google (and also its limits ;-;)
6. The spirit of hackathons (making something happen!)

## What's next for Catfish, the less super-fish-ial dating app

Even when we really really wanted to quit, we knew Catfish **had** to be unleashed into the world. Call it an utter meme, or social critique, or a wonderful business idea, I know we plan to keep learning more and more React Native until we can make it happen! But for now, time to close 40 tabs and sleep (at least till the demo).
## Inspiration

Prolonged covid restrictions have caused immense damage to the economy and local markets alike. Shifts in this economic landscape have led to many individuals seeking alternate sources of income to account for the losses imparted by lack of work or general opportunity. One major sector that has seen a boom, despite local market downturns, is investment in the stock market. While stock market trends at first glance, seem to be logical, and fluid, they're in fact the opposite. Beat earning expectation? New products on the market? *It doesn't matter!*, because at the end of the day, a stock's value is inflated by speculation and **hype**. Many see the allure of rapidly increasing ticker charts, booming social media trends, and hear talk of town saying how someone made millions in a matter of a day *cough* **GameStop** *cough* , but more often then not, individual investors lose money when market trends spiral. It is *nearly* impossible to time the market. Our team sees the challenges and wanted to create a platform which can account for social media trends which may be indicative of early market changes so that small time investors can make smart decisions ahead of the curve.

## What it does

McTavish St. Bets is a platform that aims to help small time investors gain insight on when to buy, sell, or hold a particular stock on the DOW 30 index. The platform uses the recent history of stock data along with tweets in the same time period in order to estimate the future value of the stock. We assume there is a correlation between tweet sentiment towards a company, and it's future evaluation.

## How we built it

The platform was build using a client-server architcture and is hosted on a remote computer made available to the team. The front-end was developed using react.js and bootstrap for quick and efficient styling, while the backend was written in python with flask. The dataset was constructed by the team using a mix of tweets and article headers. The public Twitter API was used to scrape tweets according to popularity and were ranked against one another using an engagement scoring function. Tweets were processed using a natural language processing module with BERT embeddings which was trained for sentiment analysis. Time series prediction was accomplished through the use of a neural stochastic differential equation which incorporated text information as well. In order to incorporate this text data, the latent representations were combined based on the aforementioned scoring function. This representation is then fed directly to the network for each timepoint in the series estimation in an attempt to guide model predictions.

## Challenges we ran into

Obtaining data to train the neural SDE proved difficult. The free Twitter API only provides high engagement tweets for the last seven days. Obtaining older tweets requires an enterprise account costing thousands of dollars per month. Unfortunately, we didn’t feel that we had the data to train an end-to-end model to learn a single representation for each day’s tweets. Instead, we use a weighted average tweet representation, weighing each tweet by its importance computed as a function of its retweets and likes. This lack of data extends to the validation side too, with us only able to validate our model’s buy/sell/hold prediction on this Friday's stock price.
Finally, without more historical data, we can only model the characteristics of the market this week, which has been fairly uncharacteristic of normal market conditions. Adding additional data for the trajectory modeling would have been invaluable.

## Accomplishments that we're proud of

* We used several API to put together a dataset, trained a model, and deployed it within a web application.
* We put together several animations introduced in the latest CSS revision.
* We commissioned McGill-themed banner in keeping with the /r/wallstreetbets culture. Credit to Jillian Cardinell for the help!
* Some jank nlp

## What we learned

Learned to use several new APIs, including Twitter and Web Scrapers.

## What's next for McTavish St. Bets

Obtaining much more historical data by building up a dataset over several months (using Twitters 7-day API). We would have also liked to scale the framework to be reinforcement based which is data hungry.