## Inspiration

With the recent elections, we found it hard to keep up with news outside of the states, and hear of people's perspectives on a global scale. We wanted to make it easier to see and read of news all across the world, to bring a bird-eye's-view on our local problems and our local popular opinions. Geovibes was built to inform.

## What it does

Geovibes is a news feed infograph, displaying news articles from all across the world on an interactive map in real time. We've included pictures of our previous version, from which we pivoted about 7 hours ago. We were attempting to build a 3D interactive globe displaying the same news as the current iteration of Geovibes does.

## How we built it

*Frontend*

* amchart (<https://www.amcharts.com/>)
* lots of handcrafted javascript
* lots of handcrafted css
* mapping coordinates to png
* REST requests to flask api

*Backend*

* flask api server
* threads and file lock

*Data pipeline*

* lots of quota exceeding api subcriptions
* lots of batch processing
* mapping coordinates to locations
* piping one api's results to the next (Bing News Search, Microsoft Text Analytics)

## Challenges we ran from

*Things we pivoted from*

* angular 2
* three.js
* web gl
* twgl (thin wrapper for web gl)
* kartograph.js
* d3.js
* coordinate mapping, especially from window cursor location to 3D globe coordinates within an HTML canvas
* an overambitious idea for an unprepared team

## Accomplishments that I'm proud of

Everything

## What I learned

* batch processing
* concurrency and thread communication
* web gl stuff, and three.js, and the struggles
* coordinate mapping, especially from window cursor location to 3D globe coordinates within an HTML canvas
* angular 2

## What's next for geovibes

get the fkin three.js version working
## Inspiration

Every single Berkeley student knows that finding housing near campus is one of the most stressful experiences of their lives. (Yes, even more than school sometimes.) We just wanted to make an app that would make the process less difficult. Currently there isn't a good consolidated listing of living spaces. We either have to look through Cragslist or call landlords directly. It's also difficult because we have to consider SO many factors like: price, number of people, and location

## What it does

Makes the house search less stressful!
It provides a platform for sellers to easily upload pictures and info about the places they're renting out.
For general home-seeking population, all the locations are displayed on a map with thumbnails that allow them to easily see info about the place.

## How we built it

We build the app using Swift and Firebase.
Also implemented the Google APIs and SDKs for maps and places.

## Challenges we ran into

Getting Firebase to work properly
Learning Swift

## Accomplishments that we're proud of

Even though we didn't get to everything we wanted. Neither of us were familiar with Swift or Firebase coming in, so it's honestly just quite amazing to see what we could build.

## What we learned

SOO much.
How to use swift. (Creating storyboards and views, creating segues, the language)
and the GoogleMaps SDK (displaying maps, getting locations, placing pins)
and Firebase (setup, adding to the database and storage)

## What's next for HouseMe

Our main goal is to give the user abilities to filter what listings they see. Most students looking have very specific details like price ranges, number of roommates, and location in mind. It would be SUPER super helpful if there was something that could do that for us.
## Inspiration

BThere emerged from a genuine desire to strengthen friendships by addressing the subtle challenges in understanding friends on a deeper level. The team recognized that nuanced conversations often go unnoticed, hindering meaningful support and genuine interactions. In the context of the COVID-19 pandemic, the shift to virtual communication intensified these challenges, making it harder to connect on a profound level. Lockdowns and social distancing amplified feelings of isolation, and the absence of in-person cues made understanding friends even more complex. BThere aims to use advanced technologies to overcome these obstacles, fostering stronger and more authentic connections in a world where the value of meaningful interactions has become increasingly apparent.

## What it does

BThere is a friend-assisting application that utilizes cutting-edge technologies to analyze conversations and provide insightful suggestions for users to connect with their friends on a deeper level. By recording conversations through video, the application employs Google Cloud's facial recognition and speech-to-text APIs to understand the friend's mood, likes, and dislikes. The OpenAI API generates personalized suggestions based on this analysis, offering recommendations to uplift a friend in moments of sadness or providing conversation topics and activities for neutral or happy states. The backend, powered by Python Flask, handles data storage using Firebase for authentication and data persistence. The frontend is developed using React, JavaScript, Next.js, HTML, and CSS, creating a user-friendly interface for seamless interaction.

## How we built it

BThere involves a multi-faceted approach, incorporating various technologies and platforms to achieve its goals. The recording feature utilizes WebRTC for live video streaming to the backend through sockets, but also allows users to upload videos for analysis. Google Cloud's facial recognition API identifies facial expressions, while the speech-to-text API extracts spoken content. The combination of these outputs serves as input for the OpenAI API, generating personalized suggestions. The backend, implemented in Python Flask, manages data storage in Firebase, ensuring secure authentication and persistent data access. The frontend, developed using React, JavaScript, Next.js, HTML, and CSS, delivers an intuitive user interface.

## Accomplishments that we're proud of

* Successfully integrating multiple technologies into a cohesive and functional application
* Developing a user-friendly frontend for a seamless experience
* Implementing real-time video streaming using WebRTC and sockets
* Leveraging Google Cloud and OpenAI APIs for advanced facial recognition, speech-to-text, and suggestion generation

## What's next for BThere

* Continuously optimizing the speech to text and emotion analysis model for improved accuracy with different accents, speech mannerisms, and languages
* Exploring advanced natural language processing (NLP) techniques to enhance conversational analysis
* Enhancing user experience through further personalization and more privacy features
* Conducting user feedback sessions to refine and expand the application's capabilities