## Inspiration

I'm a very social, outgoing person, and oftentimes I find myself willing to spend a few dollars for an adventure but with no idea where to go.

That's not to say that there isn't a place for adventure...

I've lived in Berkeley for 2 years and even now a friend introduces me to a whole new world that's only a short car ride from my apartment.

I've discovered many new, exciting things to do this way:

Tango

Fishing,

Hiking

Horse Racing

Skating

But asking friends or maybe even strangers for suggestions on where to go is always pretty time consuming.

I thought about an app that aggregates locals' suggestions into a list accessible by anyone in an area, and that I could get to pretty easily.

## What it does

Concierge is an app we created that uses crowdsourcing to find and suggest popular destinations and off the path nooks known to locals, and filters them according to how much it would cost to Uber to them, providing accessibility to highly rated recreational and social venues for people to discover new places to go visit in their area.

## How I built it

Concierge is a native Android app that stores users' recommendations for places on a Parse database in the cloud and uses Google's Location Services to find where you are and suggest places near you that locals in your area have highly rated. It also uses Uber's API to calculate the cost for an Uber and can even call an Uber for you to take you there.

## Challenges I ran into

Setting up and figuring out the various API's involved in the app. We also had some trouble with setting up the frontend of the app as well.

## Accomplishments that I'm proud of

Having a sleek, clean looking UI for the app, as well as figuring out how to use Uber's API

## What I learned

How to work with Uber's API

## What's next for Concierge

The next step is to take Concierge global, with users all over the world suggesting local haunts in their area and other users learning about these new places. This is a very ambitious vision, but we believe that Concierge will have a place among the rest of the other popular, social based apps that have taken the world by storm.
## Inspiration

There have been countless days when I have stepped into a car with my friends upon the realization that we had no idea as to what we wanted to do. Thus, my team and I came up with the idea of an app that would give a simple recommendation for a night out and learn from its users' preferences. We are all familiar with the examples of Tinder's swipe left/swipe right technique as well as with the concept of the like button. In Take Me Out, we strived to combine the simplicity of these features with machine learning tactics that would learn to give better and better recommendations to users.

## What it does

Take Me Out presents the user with an option for a night out, simply a button click away. From user response through like and dislike buttons, the app collects data from the user and uses it to make more informed decisions from recommendation to recommendation

## How we built it

We used the Yelp API to search for entertainment opportunities around our location as well as the Lyft API to get a ride there. We developed the app to be cross platform, with versions in both iOS Swift and Android Java.

## Challenges we ran into

Our primary challenges were in properly integrating the API's that we wanted to use and in getting the machine learning techniques that we used to be responsive enough to be useful.

## Accomplishments that we're proud of

We were able to develop the app for both iOS and Android, as well as use a variety of API's and other resources in order to get it done.

## What we learned

It is incredibly helpful to strip an application down to what it's core components are and not to over embellish it with extraneous menus and settings. Having simple settings and API usage allow us to make the most of our user experience and machine learning tactics.

## What's next for Take Me Out

We will most likely add some cloud integration (probably with Firebase) to allow user data to be stored on the cloud. We didn't do this here because of a desire for simplicity and immediate access, but use of cloud storage is probably the next step for this application.
## Inspiration

We know the struggles of students. Trying to get to that one class across campus in time. Deciding what to make for dinner. But there was one that stuck out to all of us: finding a study spot on campus. There have been countless times when we wander around Mills or Thode looking for a free space to study, wasting our precious study time before the exam. So, taking inspiration from parking lots, we designed a website that presents a live map of the free study areas of Thode Library.

## What it does

A network of small mountable microcontrollers that uses ultrasonic sensors to check if a desk/study spot is occupied. In addition, it uses machine learning to determine peak hours and suggested availability from the aggregated data it collects from the sensors. A webpage that presents a live map, as well as peak hours and suggested availability .

## How we built it

We used a Raspberry Pi 3B+ to receive distance data from an ultrasonic sensor and used a Python script to push the data to our database running MongoDB. The data is then pushed to our webpage running Node.js and Express.js as the backend, where it is updated in real time to a map. Using the data stored on our database, a machine learning algorithm was trained to determine peak hours and determine the best time to go to the library.

## Challenges we ran into

We had an **life changing** experience learning back-end development, delving into new frameworks such as Node.js and Express.js. Although we were comfortable with front end design, linking the front end and the back end together to ensure the web app functioned as intended was challenging. For most of the team, this was the first time dabbling in ML. While we were able to find a Python library to assist us with training the model, connecting the model to our web app with Flask was a surprising challenge. In the end, we persevered through these challenges to arrive at our final hack.

## Accomplishments that we are proud of

We think that our greatest accomplishment is the sheer amount of learning and knowledge we gained from doing this hack! Our hack seems simple in theory but putting it together was one of the toughest experiences at any hackathon we've attended. Pulling through and not giving up until the end was also noteworthy. Most importantly, we are all proud of our hack and cannot wait to show it off!

## What we learned

Through rigorous debugging and non-stop testing, we earned more experience with Javascript and its various frameworks such as Node.js and Express.js. We also got hands-on involvement with programming concepts and databases such as mongoDB, machine learning, HTML, and scripting where we learned the applications of these tools.

## What's next for desk.lib

If we had more time to work on this hack, we would have been able to increase cost effectiveness by branching four sensors off one chip. Also, we would implement more features to make an impact in other areas such as the ability to create social group beacons where others can join in for study, activities, or general socialization. We were also debating whether to integrate a solar panel so that the installation process can be easier.