## Inspiration

Whether you’re thriving in life or really going through it, research shows that writing down your thoughts throughout the day has many benefits. We wanted to add a social element to this valuable habit and build a sense of community through sharing and acknowledging each other’s feelings. However, even on the internet, we've noticed that it is difficult for people to be vulnerable for fear of judgement, criticism, or rejection.

Thus, we centred our problem around this challenge and asked the question: How might we create a sense of community and connection among journalers without compromising their sense of safety and authenticity when sharing their thoughts?

## What it does

With Yapyap, you can write daily journal entries and share them anonymously with the public. Before posting, our AI model analyzes your written entry and provides you with an emotion, helping to label and acknowledge your feelings.

Once your thoughts are out in the world, you can see how other people's days are going too and offer mutual support and encouragement through post reactions.

Then, the next day comes, and the cycle repeats.

## How we built it

After careful consideration, we recognized that most users of our app would favour a mobile version as it is more versatile and accessible throughout the day. We used Figma to create an interesting and interactive design before implementing it in React Native. On the backend, we created an API using AWS Lambda and API Gateway to read and modify our MongoDB database. As a bonus, we prepared a sentimental analyzer using Tensorflow that could predict the overall mood of the written entry.

## Challenges we ran into

Learning new technologies and figuring out how to deploy our app so that they could all communicate were huge challenges for us.

## Accomplishments that we're proud of

Being able to apply what we learned about the new technologies in an efficient and collaborative way. We're also proud of getting a Bidirectional RNN for sentiment analysis ready in a few hours!

## What we learned

How to easily deal with merge conflicts, what it's like developing software as a group, and overall just knowing how to have fun even when you're pulling an all-nighter!

## What's next for yapyap

More personable AI Chatbots, and more emotions available for analysis!
## Inspiration

The inspiration behind CareerFairy stemmed from a desire to transform traditional career fairs into a more accessible and efficient virtual experience. We aimed to leverage technology to streamline the recruitment process, making it easier for both job seekers and employers to connect in a 2D spatial calling web application.

## What it does

CareerFairy is a revolutionary platform that facilitates virtual career fairs, allowing job seekers to explore booths and engage with recruiters seamlessly. Utilizing React, Flask, PeerJS, WebRTC, SocketIO, and OpenAI's GPT-3.5, the application not only provides a user-friendly interface but also incorporates AI-generated candidate overviews for recruiters, optimizing the hiring process.

## How we built it

We built CareerFairy using React for the front end, Flask for the backend, and integrated PeerJS, WebRTC, and SocketIO for real-time communication. The inclusion of OpenAI's GPT-3.5 ensures that recruiters receive instant, AI-generated insights into candidate qualifications and skills.

## Challenges we ran into

Integrating multiple technologies posed challenges in terms of synchronization and seamless communication. Overcoming the learning curve associated with WebRTC and ensuring a smooth user experience were additional hurdles that our team successfully addressed during the development process.

## Accomplishments that we're proud of

We are proud to have created a functional and innovative platform that enhances the virtual recruitment experience. The successful integration of AI-driven candidate overviews and the collaborative efforts of our team in overcoming technical challenges stand out as notable accomplishments.

## What we learned

Throughout the development of CareerFairy, our team gained valuable insights into the intricacies of real-time communication, virtual environments, and the application of AI in recruitment processes. We refined our skills in React, Flask, PeerJS, WebRTC, and SocketIO, further enhancing our capabilities.

## What's next for Fairy

Looking ahead, CareerFairy aims to expand its features and capabilities. We plan to refine the AI models for more nuanced candidate assessments, incorporate additional interactive elements for job seekers and recruiters, and continually enhance the overall user experience. Our vision is to make CareerFairy the go-to platform for efficient, AI-driven virtual recruitment.
## Inspiration

We got lost so many times inside MIT... And no one could help us :( No Google Maps, no Apple Maps, NO ONE. Since now, we always dreamed about the idea of a more precise navigation platform working inside buildings. And here it is. But that's not all: as traffic GPS usually do, we also want to avoid the big crowds that sometimes stand in corridors.

## What it does

Using just the pdf of the floor plans, it builds a digital map and creates the data structures needed to find the shortest path between two points, considering walls, stairs and even elevators. Moreover, using fictional crowd data, it avoids big crowds so that it is safer and faster to walk inside buildings.

## How we built it

Using k-means, we created nodes and clustered them using the elbow diminishing returns optimization. We obtained the hallways centers combining scikit-learn and filtering them applying k-means. Finally, we created the edges between nodes, simulated crowd hotspots and calculated the shortest path accordingly. Each wifi hotspot takes into account the number of devices connected to the internet to estimate the number of nearby people. This information allows us to weight some paths and penalize those with large nearby crowds.
A path can be searched on a website powered by Flask, where the corresponding result is shown.

## Challenges we ran into

At first, we didn't know which was the best approach to convert a pdf map to useful data.
The maps we worked with are taken from the MIT intranet and we are not allowed to share them, so our web app cannot be published as it uses those maps...
Furthermore, we had limited experience with Machine Learning and Computer Vision algorithms.

## Accomplishments that we're proud of

We're proud of having developed a useful application that can be employed by many people and can be extended automatically to any building thanks to our map recognition algorithms. Also, using real data from sensors (wifi hotspots or any other similar devices) to detect crowds and penalize nearby paths.

## What we learned

We learned more about Python, Flask, Computer Vision algorithms and Machine Learning. Also about frienship :)

## What's next for SmartPaths

The next steps would be honing the Machine Learning part and using real data from sensors.