## Inspiration

We were inspired to create Watch w/ Me because of how hard it can be to find people who have the same taste in movies as you.

## What it does

Watch w/ Me uses an already established business model and framework made popular by Tinder, to connect people to others with similar tastes in movies. Users can swipe left or right on a number of different profiles.

## How we built it

We built the frontend of this project using React Js, CSS and HTML. For the backend, we started off with connecting to CockroachDB using Sequelize. Then, we defined a user model to store the data, including username, profile images. We then query the database and set up backend routes to send the data, such as all the users near you. You can decide whether to accept them or not based on their profile. The frontend can make a GET request to get the data from these backend routes.

## Challenges we ran into

The most challenging part of our project was connecting to the Cockroach database. We ran into many problems implementing it on Windows. However, with the help of our mentors, we managed to connect to the database with Sequelize.

## Accomplishments that we're proud of

The accomplishments we're most proud of are the card swiping mechanics we built.

## What we learned

We learned a lot about using a number of different technologies together.

## What's next for Watch w/ Me

We plan to add a DM's section soon, something we weren't able to do in our given time.
## Inspiration

There is a problem with social media networks, they aren't run by users. Companies have an invisible hand that determines which content makes the front page. There is also no tangible incentive to make positive comments and posts with value. Content creators have to comply with strict and sometimes arbitrary guidelines in order to monetize their content. Having users be able to earn tokens through content creation, commenting, and curating provides incentives for the community to put forth good and meaningful posts.

## What it does

We built a blog that a user can use to submit posts to the blockchain. It takes a private key from the user to be able to update the blockchain.

## How we built it

This app is on the HIVE blockchain using Hivenet and Django.

## Challenges we ran into

We had initially trouble setting up the test network and deploying it to the docker container.

## Accomplishments that we're proud of

We were eventually able to submit our posts to the main network.

## What we learned

We learned HIVE and Flask and what DPoS protocol is.

## What's next for beem.it

We want to implement a user reputation system as well as setting up nodes for crypto wallets to be able to receive payments. We figured out how to send Hive tokens to accounts on the test network, so we want to implement them into the main network.
## Inspiration

My father put me in charge of his finances and in contact with his advisor, a young, enterprising financial consultant eager to make large returns. That might sound pretty good, but to someone financially conservative like my father doesn't really want that kind of risk in this stage of his life. The opposite happened to my brother, who has time to spare and money to lose, but had a conservative advisor that didn't have the same fire. Both stopped their advisory services, but that came with its own problems. The issue is that most advisors have a preferred field but knowledge of everything, which makes the unknowing client susceptible to settling with someone who doesn't share their goals.

## What it does

Resonance analyses personal and investment traits to make the best matches between an individual and an advisor. We use basic information any financial institution has about their clients and financial assets as well as past interactions to create a deep and objective measure of interaction quality and maximize it through optimal matches.

## How we built it

The whole program is built in python using several libraries for gathering financial data, processing and building scalable models using aws. The main differential of our model is its full utilization of past data during training to make analyses more wholistic and accurate. Instead of going with a classification solution or neural network, we combine several models to analyze specific user features and classify broad features before the main model, where we build a regression model for each category.

## Challenges we ran into

Our group member crucial to building a front-end could not make it, so our designs are not fully interactive. We also had much to code but not enough time to debug, which makes the software unable to fully work. We spent a significant amount of time to figure out a logical way to measure the quality of interaction between clients and financial consultants. We came up with our own algorithm to quantify non-numerical data, as well as rating clients' investment habits on a numerical scale. We assigned a numerical bonus to clients who consistently invest at a certain rate. The Mathematics behind Resonance was one of the biggest challenges we encountered, but it ended up being the foundation of the whole idea.

## Accomplishments that we're proud of

Learning a whole new machine learning framework using SageMaker and crafting custom, objective algorithms for measuring interaction quality and fully utilizing past interaction data during training by using an innovative approach to categorical model building.

## What we learned

Coding might not take that long, but making it fully work takes just as much time.

## What's next for Resonance

Finish building the model and possibly trying to incubate it.