•⁠ ⁠Inspiration
The inspiration for digifoot.ai stemmed from the growing concern about digital footprints and online presence in today's world. With the increasing use of social media, individuals often overlook the implications of their online activities. We aimed to create a tool that not only helps users understand their digital presence but also provides insights into how they can improve it.

•⁠ ⁠What it does
digifoot.ai is a comprehensive platform that analyzes users' social media accounts, specifically Instagram and Facebook. It aggregates data such as posts, followers, and bios, and utilizes AI to provide insights on their digital footprint. The platform evaluates images and content from users’ social media profiles to ensure there’s nothing harmful or inappropriate, helping users maintain a positive online presence.

•⁠ ⁠How we built it
We built digifoot.ai using Next.js and React for the frontend and Node.js for the backend. The application integrates with the Instagram and Facebook Graph APIs to fetch user data securely. We utilized OpenAI's API for generating insights based on the collected social media data.

•⁠ ⁠Challenges we ran into
API Authentication and Rate Limits:
 Managing API authentication for both Instagram and OpenAI was complex. We had to ensure secure
 access to user data while adhering to rate limits imposed by these APIs. This required us to optimize our
 data-fetching strategies to avoid hitting these limits.
Integrating Image and Text Analysis:
 We aimed to analyze both images and captions from Instagram posts using the OpenAI API's
 capabilities. However, integrating image analysis required us to understand how to format requests
 correctly, especially since the OpenAI API processes images differently than text.
 The challenge was in effectively combining image inputs with textual data in a way that allowed the AI to
 provide meaningful insights based on both types of content.

•⁠ ⁠Accomplishments that we're proud of
We are proud of successfully creating a user-friendly interface that allows users to connect their social media accounts seamlessly. The integration of AI-driven analysis provides valuable feedback on their digital presence. Moreover, we developed a robust backend that handles data securely while complying with privacy regulations.

•⁠ ⁠What we learned
Throughout this project, we learned valuable lessons about API integrations and the importance of user privacy and data security. We gained insights into how AI can enhance user experience by providing personalized feedback based on real-time data analysis. Additionally, we improved our skills in full-stack development and project management.

•⁠ ⁠What's next for digifoot.ai
Looking ahead, we plan to enhance digifoot.ai by incorporating more social media platforms for broader analysis capabilities. We aim to refine our AI algorithms to provide even more personalized insights. Additionally, we are exploring partnerships with educational institutions to promote digital literacy and responsible online behavior among students.
## Inspiration

as we have spent some time in our new Covid-19 world, we have noticed work from home and such has resulted in an increase of phishing and fraudulent emails. We noticed this is strongly apparent in our educational community as well noticing that our inboxes and our peers' inboxes get spammed with fraudulent E-mails that pose a risk and threat to our finances.

## What it does

MailSafe allows you to verify the integrity and legitimacy of your emails by generating a QR Code in a Google Chrome Extension pop-up for your phone to scan. The QR Code is scanned by the phone in the MailSafe Mobile App that cross references the QR Code to a list of verified email senders. If present on the list, it means the email is verified and the user can trust the email they received. If it is not on the list, the email is most likely fraudulent as it does not belong to any of your financial service providers.

## How we built it

We had used standard Google Chrome Extension structure. We constructed a main manifest file for the Chrome Extension to function and call a popup.html file upon an Chrome Extension Action that showed a button. Clicking that button generated a QR Code from a standard qrcode.js library that your phone could actually scan. Then we created the way the app would function on your mobile device using Figma.

## Challenges we ran into

The main challenge we ran into was in the Google Chrome Extension. We had to use the email sender ID to generate a QR Code. After some trial and error, and countless stackoverflow guides, we came to a library, qrcode.js, that allowed us to pass the email sender ID and generate a QR Code from it. That QR Code is functional and usable by most devices.

## Accomplishments that we're proud of

We are proud of the functional Google Chrome Extension that generates actual QR Codes for devices to scan. All in all, we are proud to have come up with an idea to protect users from fraudulent E-mails and phishing scams and proud that we have implemented a major element of the system. Now that we have that foundation, we are on our way to complete the mobile application element to have a functional system.

## What we learned

The project was a great learning experience for us at MailSafe. Spending most of our time implementing the Google Chrome Extension we learned to manipulate an existing HTML file, parse it and extract information we specifically needed such as the email sender ID. Using that we learned how to convert it into a QR Code that actually is recognized by anyone's cellular device. All in all, it was an amazing opportunity and learning experience.

## What's next for MailSafe

Our goals for MailSafe is to implement a functional phone application. Furthermore, we would like to implement the the extension add-on for non-chromium based browsers such as Mozilla Firefox. In the future, we are looking forward to wide-spread adoption by many companies, not just Bank Institutions, who would like to provide their users with a seamless and safe experience in communicating over E-mail.
## Inspiration

Social media has been shown in studies that it thrives on emotional and moral content, particularly angry in nature. In similar studies, these types of posts have shown to have effects on people's well-being, mental health, and view of the world. We wanted to let people take control of their feed and gain insight into the potentially toxic accounts on their social media feed, so they can ultimately decide what to keep and what to remove while putting their mental well-being first. We want to make social media a place for knowledge and positivity, without the anger and hate that it can fuel.

## What it does

The app performs an analysis on all the Twitter accounts the user follows and reads the tweets, checking for negative language and tone. Using machine learning algorithms, the app can detect negative and potentially toxic tweets and accounts to warn users of the potential impact, while giving them the option to act how they see fit with this new information. In creating this app and its purpose, the goal is to **put the user first** and empower them with data.

## How We Built It

We wanted to make this application as accessible as possible, and in doing so, we made it with React Native so both iOS and Android users can use it. We used Twitter OAuth to be able to access who they follow and their tweets while **never storing their token** for privacy and security reasons.

The app sends the request to our web server, written in Kotlin, hosted on Google App Engine where it uses Twitter's API and Google's Machine Learning APIs to perform the analysis and power back the data to the client. By using a multi-threaded approach for the tasks, we streamlined the workflow and reduced response time by **700%**, now being able to manage an order of magnitude more data. On top of that, we integrated GitHub Actions into our project, and, for a hackathon mind you, we have a *full continuous deployment* setup from our IDE to Google Cloud Platform.

## Challenges we ran into

* While library and API integration was no problem in Kotlin, we had to find workarounds for issues regarding GCP deployment and local testing with Google's APIs
* Since being cross-platform was our priority, we had issues integrating OAuth with its requirement for platform access (specifically for callbacks).
* If each tweet was sent individually to Google's ML API, each user could have easily required over 1000+ requests, overreaching our limit. Using our technique to package the tweets together, even though it is unsupported, we were able to reduce those requests to a maximum of 200, well below our limits.

## What's next for pHeed

pHeed has a long journey ahead: from integrating with more social media platforms to new features such as account toxicity tracking and account suggestions. The social media space is one that is rapidly growing and needs a user-first approach to keep it sustainable; ultimately, pHeed can play a strong role in user empowerment and social good.