## Inspiration

We were inspired by the resilience of freelancers, particularly creative designers, during the pandemic. As students, it's easy to feel overwhelmed and not value our own work. We wanted to empower emerging designers and remind them of what we can do with a little bit of courage. And support.

## What it does

Bossify is a mobile app that cleverly helps students adjust their design fees. It focuses on equitable upfront pay, which in turn increases the amount of money saved. This can be put towards an emergency fund. On the other side, clients can receive high-quality, reliable work. The platform has a transparent rating system making it easy to find quality freelancers.

It's a win-win situation.

## How we built it

We got together as a team the first night to hammer out ideas. This was our second idea, and everyone on the team loved it. We all pitched in ideas for product strategy. Afterwards, we divided the work into two parts - 1) Userflows, UI Design, & Prototype; 2) Writing and Testing the Algorithm.

For the design, Figma was the main software used. The designers (Lori and Janice) used a mix iOS components and icons for speed. Stock images were taken from Unsplash and Pexels. After quickly drafting the storyboards, we created a rapid prototype. Finally, the pitch deck was made to synthesize our ideas.

For the code, Android studio was the main software used. The developers (Eunice and Zoe) together implemented the back and front-end of the MVP (minimum viable product), where Zoe developed the intelligent price prediction model in Tensorflow, and deployed the trained model on the mobile application.

## Challenges we ran into

One challenge was not having the appropriate data immediately available, which was needed to create the algorithm. On the first night, it was a challenge to quickly research and determine the types of information/factors that contribute to design fees. We had to cap off our research time to figure out the design and algorithm.

There were also technical limitations, where our team had to determine the best way to integrate the prototype with the front-end and back-end. As there was limited time and after consulting with the hackathon mentor, the developers decided to aim for the MVP instead of spending too much time and energy on turning the prototype to a real front-end. It was also difficult to integrate the machine learning algorithm to our mini app's backend, mainly because we don't have any experience with implement machine learning algorithm in java, especially as part of the back-end of a mobile app.

## Accomplishments that we're proud of

We're proud of how cohesive the project reads. As the first covid hackathon for all the team members, we were still able to communicate well and put our synergies together.

## What we learned

Although a simple platform with minimal pages, we learned that it was still possible to create an impactful app. We also learned the importance of making a plan and time line before we start, which helped us keep track of our progress and allows us to use our time more strategically.

## What's next for Bossify

Making partnerships to incentivize clients to use Bossify! #fairpayforfreelancers
## Inspiration

Partially inspired by the Smart Cities track, we wanted our app to have the direct utility of ordering food, while still being fun to interact with. We aimed to combine convenience with entertainment, making the experience more enjoyable than your typical drive-through order.

## What it does

You interact using only your voice. The app automatically detects when you start and stop talking, uses AI to transcribe what you say, figures out the food items (with modifications) you want to order, and adds them to your current order. It even handles details like size and flavor preferences. The AI then generates text-to-speech audio, which is played back to confirm your order in a humorous, engaging way. There is absolutely zero set-up or management necessary, as the program will completely ignore all background noises and conversation. Even then, it will still take your order with staggering precision.

## How we built it

The frontend of the app is built with React and TypeScript, while the backend uses Flask and Python. We containerized the app using Docker and deployed it using Defang. The design of the menu is also done in Canva with a dash of Harvard colors.

## Challenges we ran into

One major challenge was getting the different parts of the app—frontend, backend, and AI—to communicate effectively. From media file conversions to AI prompt engineering, we worked through each of the problems together. We struggled particularly with maintaining smooth communication once the app was deployed. Additionally, fine-tuning the AI to accurately extract order information from voice inputs while keeping the interaction natural was a big hurdle.

## Accomplishments that we're proud of

We're proud of building a fully functioning product that successfully integrates all the features we envisioned. We also managed to deploy the app, which was a huge achievement given the complexity of the project. Completing our initial feature set within the hackathon timeframe was a key success for us. Trying to work with Python data type was difficult to manage, and we were proud to navigate around that. We are also extremely proud to meet a bunch of new people and tackle new challenges that we were not previously comfortable with.

## What we learned

We honed our skills in React, TypeScript, Flask, and Python, especially in how to make these technologies work together. We also learned how to containerize and deploy applications using Docker and Docker Compose, as well as how to use Defang for cloud deployment.

## What's next for Harvard Burger

Moving forward, we want to add a business-facing interface, where restaurant staff would be able to view and fulfill customer orders. There will also be individual kiosk devices to handle order inputs. These features would allow *Harvard Burger* to move from a demo to a fully functional app that restaurants could actually use. Lastly, we can sell the product by designing marketing strategies for fast food chains.
## Inspiration

Acting on the wrong information can kill. In the first 3 months of 2020, nearly 6 000 people around the globe were hospitalized because of coronavirus misinformation, recent research suggests. During this period, researchers say at least 800 people may have died due to misinformation related to COVID-19\*.

At its extreme, death can be the tragic outcome of what the World Health Organization has termed the infodemic, an overabundance of information - some accurate, some not that spreads alongside a disease outbreak. False information runs the gamut, from discrediting the threat of COVID-19 to conspiracy theories that vaccines could alter human DNA.
Preventing false information can :

1. Reduce anxiety and stress among people
2. Reduce the spread of unproven remedies
3. It will prevent people from entering panic mode
4. Prevent contamination of true facts

## What it does

Corona Bot delivers credible and updates information related to COVID 19 regarding the number of cases, recoveries, precautionary measures, and a lot more.
It has :

1. Audio responses and speech recognition. [User commands]
2. Choice for male / female voice assistant.
3. Advanced error handling conditions.
4. Mental health support.
5. Includes basic support for North American slang.
6. Accepts voice commands for a hands-free experience.
7. Accessible to blind and deaf people. [Provides output as both audio and text]
8. Includes exception handling for personal questions.
9. Includes the ability to search for off-topic questions.

## How we built it

Corona Bot is a webscraping project built using python and a few python libraries like speech recognition API's and text-to-speech conversion libraries [pyttsx3].

Corona Bot uses an algorithm that was built by us to deliver appropriate responses to each question. It breaks down the user input to separate words and coverts them to lowercase and then matches the word to the keywords from the database. Then based on the tags identified in the user response, the algorithm obtains the latest information through web scraping and delivers customized outputs through the device audio.

## Challenges we ran into

Neither of us had used selenium before so that was a bit tricky. Creating our own algorithm and database gave us a few logical errors. Which we were able to overcome in the end.

## Accomplishments that we're proud of

We are very proud of successfully creating and implementing my own response generation algorithm and implementing a fully functional exception handling backend.

## What we learned

We learned to do web scraping using selenium and to work with python libraries like pyttsx3 and speech\_recogniton.

## What's next for Corona Bot

We want to create a web/ app interface for the corona bot to be easily accessible to everyone.