## Inspiration

Jessica here - I came up with the idea for BusPal out of expectation that the skill has already existed. With my Amazon Echo Dot, I was already doing everything from checking the weather to turning off and on my lights with Amazon skills and routines. The fact that she could not check when my bus to school was going to arrive was surprising at first - until I realized that Amazon and Google are one of the biggest rivalries there is between 2 tech giants. However, I realized that the combination of Alexa's genuine personality and the powerful location ability of Google Maps would fill a need that I'm sure many people have. That was when the idea for BusPal was born: to be a convenient Alexa skill that will improve my morning routine - and everyone else's.

## What it does

This skill enables Amazon Alexa users to ask Alexa when their bus to a specified location is going to arrive and to text the directions to a phone number - all hands-free.

## How we built it

Through the Amazon Alexa builder, Google API, and AWS.

## Challenges we ran into

We originally wanted to use stdlib, however with a lack of documentation for the new Alexa technology, the team made an executive decision to migrate to AWS roughly halfway into the hackathon.

## Accomplishments that we're proud of

Completing Phase 1 of the project - giving Alexa the ability to take in a destination, and deliver a bus time, route, and stop to leave for.

## What we learned

We learned how to use AWS, work with Node.js, and how to use Google APIs.

## What's next for Bus Pal

Improve the text ability of the skill, and enable calendar integration.
## 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.
## Inspiration

We were inspired by the numerous Facebook posts, Slack messages, WeChat messages, emails, and even Google Sheets that students at Stanford create in order to coordinate Ubers/Lyfts to the airport as holiday breaks approach. This was mainly for two reasons, one being the safety of sharing a ride with other trusted Stanford students (often at late/early hours), and the other being cost reduction. We quickly realized that this idea of coordinating rides could also be used not just for ride sharing to the airport, but simply transportation to anywhere!

## What it does

Students can access our website with their .edu accounts and add "trips" that they would like to be matched with other users for. Our site will create these pairings using a matching algorithm and automatically connect students with their matches through email and a live chatroom in the site.

## How we built it

We utilized Wix Code to build the site and took advantage of many features including Wix Users, Members, Forms, Databases, etc. We also integrated SendGrid API for automatic email notifications for matches.

## Challenges we ran into

## Accomplishments that we're proud of

Most of us are new to Wix Code, JavaScript, and web development, and we are proud of ourselves for being able to build this project from scratch in a short amount of time.

## What we learned

## What's next for Runway