## Inspiration

Have you ever run into the situation where you are trying to find a roommate that you are compatible with and that your current roommates will like as well? Are you tired of having the same discussion with each other roommate trying to pick the best candidate, whether it is online or offline?

## What it does

Crowd vote your next roommate at your fingertips! As someone looking for a place to rent, simply go on bunkieballot.tech, select the listings you are interested in checking out, and click submit. Now the current renters of each of the selected listings will get notified via a text message, which will contain the candidate's profile. Then they can each reply back with a score of 1-10 to indicate how much you like this candidate. After all the votes are collected, Bunkie Ballot will tally the scores for each applicant of each listing. Finally the roommates will see the list of applicants in order of high to low ratings.

## How we built it

We utilized StdLib as a serverless backend to implement the sending and receiving of text messages, mongoDB for database, and React and Javascript for web UI.

## Challenges we ran into

As StdLib is new and our team is also learning it hands on, we ran into a few challenges mainly revolving around connecting JS API calls to StdLib functions, making DB calls to Mongo, and in general how to send and receive text messages from the StdLib functions.

## Accomplishments that we're proud of

We mastered the StdLib technology!

## What we learned

We learned how to use StdLib to build a peer roommate voting system!

## What's next for BunkieBallot

-more fair and robust rating algorithm
-more elaborate user profile
-more user friendly text messages
## Inspiration

When attending crowded lectures or tutorials, it's fairly difficult to discern content from other ambient noise. What if a streamlined pipeline existed to isolate and amplify vocal audio while transcribing text from audio, and providing general context in the form of images? Is there any way to use this technology to minimize access to education? These were the questions we asked ourselves when deciding the scope of our project.

## The Stack

**Front-end :** react-native

**Back-end :** python, flask, sqlalchemy, sqlite

**AI + Pipelining Tech :** OpenAI, google-speech-recognition, scipy, asteroid, RNNs, NLP

## What it does

We built a mobile app which allows users to record video and applies an AI-powered audio processing pipeline.

**Primary use case:** Hard of hearing aid which:

1. Isolates + amplifies sound from a recorded video (pre-trained RNN model)
2. Transcribes text from isolated audio (google-speech-recognition)
3. Generates NLP context from transcription (NLP model)
4. Generates an associated image to topic being discussed (OpenAI API)

## How we built it

* Frameworked UI on Figma
* Started building UI using react-native
* Researched models for implementation
* Implemented neural networks and APIs
* Testing, Testing, Testing

## Challenges we ran into

Choosing the optimal model for each processing step required careful planning. Algorithim design was also important as responses had to be sent back to the mobile device as fast as possible to improve app usability.

## Accomplishments that we're proud of + What we learned

* Very high accuracy achieved for transcription, NLP context, and .wav isolation
* Efficient UI development
* Effective use of each tem member's strengths

## What's next for murmr

* Improve AI pipeline processing, modifying algorithms to decreasing computation time
* Include multi-processing to return content faster
* Integrate user-interviews to improve usability + generally focus more on usability
## Inspiration

Britafull is inspired by the ubiquitous student experience of living with people who may drive us crazy. Some roommates never wash their dishes, some don't take out the trash, but most insidious and aggravating of all... not refilling the Brita!! It's the little things that motivate you to finally get your own place. According to our ***highly*** scientific research, 80% of students face the **SAME** problem!

## What it does

Britafull detects when the Brita's water levels get below a certain point using weight. The next person to grab the Brita and empties it to that point has seconds before the speaker *kindly* reminds them to be a good roommate and fill up that Brita. If that's not incentive enough, waiting even longer triggers a text message reminder. That's right, the whole group chat knows now.

Call it petty, but here at Britafull, we get results.

## How we built it

We used force sensing resistors to detect changes in force accounting for the presence or absence of the Brita. This information (analog) is passed to an Arduino which converts to a digital signal that is sent to our Raspberry Pi. We programmed primarily in Python and implemented functions to check when the Brita is empty based on the input data; if it is, we then trigger an alarm which prompts the user to refill. Lastly, if the Brita remains unfilled, we use Twilio to send a message to the entire roommate group chat that someone needs to get on their Brita game!

## Challenges we ran into

As with many hardware hacks, our challenges were related to finding the right parts to use, and connecting the Raspberry Pi, Arduino, and our personal computers! Once we gathered the resistors that were compatible with the sensors to take the measurements we needed, we ran into compatibility problems that came from running Twilio's API with the rest of our backend interface. Also, we needed a Brita (generously provided by a fellow hacker)!!

When working with Force-Sensing Resistors to obtain the weight of the Brita, we found that results often fluctuated as the water and sensor settled alike. To counter this, we found that gathering data across several seconds in 500ms intervals gave us a reliable set of data points, which we cleaned up with numpy for further accuracy.

## Accomplishments that we're proud of

This was many of our first hackathons and first experiences doing a Hardware Hack! We are proud of ourselves for championing through the in-person experience and coming up with such a funny yet overwhelmingly practical product.

## What we learned

For those of us without hardware experience, we learned how hardware-software system interfaces work! We also learned that the student housing experience is, indeed, universal.

## What's next for Britafull

Britafull will never stop finding ways to manage your household and roommate problems for you. One way we could advance our platform is by implementing a machine learning algorithm that identifies the roommate in violation of the first roommate commandment: thou shalt refill the Brita as SOON AS IT'S EMPTY.

In our pursuit of ensuring nonconfrontational hydration for all, we created a product mockup of Britafull should it ever hit the market. Featuring strong ABS plastic, inductive charging, force-sensing resistors, thin piezoelectric speakers, and ultrathin lithium batteries, we made sure that it would fit in with your daily routine and kitchen aesthetic without a hitch!