## Inspiration

We wanted to work with estimotes to create an app that would encourage users to go out more; to motivate them to explore their community and the ones around them. We thought it would also be easier to encourage people if they had an incentive to do so, which brings us into our next part.

## What it does

The app ends up displaying the user and their score (which is earned by exploring the community, listening to popular music, and tweeting about trending topics in the area). Below all of that, there are social sections where Twitter shows the trending topics and from there, you can go tweet with the hashtag to earn more points. Below this, there is a Spotify section which shows you the popular songs and then allows you to go and listen to the music.

## How we built it

We didn't end up using estimotes as the hardware lab didn't have any to sign out. We created an Android application which allowed for us to use the Twitter API, Facebook API for this.

## Challenges we ran into

We didn't get the estimotes the first night, so we had to work with the Intel Edison. We spent the whole night trying to learn how to use the Edison and then how to use the sensors. This caused more issues and we had to abandon our initial idea to pursue this one. Which then gave us approximately 24 hours to work.

## Accomplishments that we're proud of

Despite losing a third of the time we were able to have completed a functional app.

## What we learned

We learned how to use the basics of the Intel Edison and worked with unfamiliar APIs (i.e. Twitter).

## What's next for Soshul

Improving the design and overall functionality of the app.
## Inspiration

Research shows that when humans are presented with too many options, it often makes it harder for humans to make an easier and quicker decision. So we want to minimize the number of options presented to users when they are deciding where to dine.

## What it does

Food Tinder!
After launching the app, the program will ask users for their preferences (location, what kind of food, distance, price). Then, the program will display the 10 best-matched restaurants to users one at a time. Swiping right means that the user likes the restaurant and wants to visit. Swiping left means that the user displays the restaurants. By displaying only one restaurant at a time, we hope that it will help users to be able to decide on a restaurant quicker and spend more time enjoying the food and not struggling to find a place to eat.

## How we built it

Yumm consists of two parts, backend and frontend UI. Our backend was made by incorporating the Yelp API into a Python program and then printing that on the terminal. Our frontend UI was made by using a combination of Kivy and Python. This Kivy window was the eventual final frontend, but we weren’t able to fully connect the frontend and backend.

## Challenges we ran into

We were happy with the progress we made throughout the weekend, but we encountered a few struggles along the way. Our main struggle was trying to use Kivy. We all had experience with Python coming in, but none of us had ever seen or heard of Kivy before researching for this project. There were a lot of syntactical intricacies that we didn’t understand and it was hard to get used to making .kv files. While we couldn’t fully finish our kivy animation, we were okay with our ability to make an animation.

## Accomplishments that we're proud of

We are all first-time hackers so we are all so proud of ourselves to be able to sign up for hack Harvard and be committed to this event. Even though we did not complete the project in the way we wanted to, we learned so much about python and just CS in general. Our CS skills definitely came out stronger than before and we are definitely going to join more hackathons in the future.

## What we learned

We learned a lot through doing this Hackathon. We learned how much coding goes into making an app, even a simple one. We also learned how to use an API, and how to pull data from different companies and utilize these data in our own favor. We also learned how hard it is to make a frontend UI program, as there is a lot of formatting and small details that need to be considered. We also learned that with a lot of people focusing on one issue, things can get done much quicker. If we only had one person coding this, we would not nearly have gotten as far, but given that we had three of us, we were able to divide and conquer and accomplish a lot more than we thought possible.

## What's next for Yumm

We want to finish Yumm on our own time to not only learn more coding and solidify our CS skills, but also to turn our idea into a real product!
## Inspiration

We as a team shared the same interest in knowing more about Machine Learning and its applications. upon looking at the challenges available, we were immediately drawn to the innovation factory and their challenges, and thought of potential projects revolving around that category. We started brainstorming, and went through over a dozen design ideas as to how to implement a solution related to smart cities. By looking at the different information received from the camera data, we landed on the idea of requiring the raw footage itself and using it to look for what we would call a distress signal, in case anyone felt unsafe in their current area.

## What it does

We have set up a signal that if done in front of the camera, a machine learning algorithm would be able to detect the signal and notify authorities that maybe they should check out this location, for the possibility of catching a potentially suspicious suspect or even being present to keep civilians safe.

## How we built it

First, we collected data off the innovation factory API, and inspected the code carefully to get to know what each part does. After putting pieces together, we were able to extract a video footage of the nearest camera to us. A member of our team ventured off in search of the camera itself to collect different kinds of poses to later be used in training our machine learning module. Eventually, due to compiling issues, we had to scrap the training algorithm we made and went for a similarly pre-trained algorithm to accomplish the basics of our project.

## Challenges we ran into

Using the Innovation Factory API, the fact that the cameras are located very far away, the machine learning algorithms unfortunately being an older version and would not compile with our code, and finally the frame rate on the playback of the footage when running the algorithm through it.

## Accomplishments that we are proud of

Ari: Being able to go above and beyond what I learned in school to create a cool project

Donya: Getting to know the basics of how machine learning works

Alok: How to deal with unexpected challenges and look at it as a positive change

Sudhanshu: The interesting scenario of posing in front of a camera while being directed by people recording me from a mile away.

## What I learned

Machine learning basics, Postman, working on different ways to maximize playback time on the footage, and many more major and/or minor things we were able to accomplish this hackathon all with either none or incomplete information.

## What's next for Smart City SOS

hopefully working with innovation factory to grow our project as well as inspiring individuals with similar passion or desire to create a change.