## Inspiration

My teammates and I all save posts on Facebook, but never remember to go back to them. We wanted to create a feature that reminded the user to go back to their saved posts.

## What it does

Our app sets a reminder at the time the user selects after they save a post on Facebook for later. They have the option of choosing between being reminded later that day, or in a week, or not at all. We analyze the most active time for the user on facebook, and set the reminder to display at that time.

## How we built it

We used mongodb to store posts from the newsfeed to the user's saved for later posts list.
We used python for the backend.
We also implemented bootstrap for the frontend as well as html, css, and javascript.
We used paralleldots, and pymongo in our app as well.

## Challenges we ran into

We couldn't access the newsfeed of the user. However, we found a way around this, by taking the user's likes and using the id's of the pages the user has liked to generate a newsfeed of posts for the user to add to saved for later.

## Accomplishments that we're proud of

We're proud of the time analysis feature that determines when the best time to display the reminder for the user is.
We're proud of our app because it's our first hackathon. We're also proud of staying up, and giving it our best effort.

## What we learned

We learned that creating a Design Document, having regular team meetings, and checking up on the schedule regularly are very important to the engineering process.

## What's next for faceboot

We hope to see our feature implemented on facebook. We hope to attend more hackathons, and improve our skills!
## Inspiration

In response to the challenges faced by the elderly and disabled who rely on traditional, costly service dogs, our team drew inspiration to create SpotAssist. Traditional service dogs demand substantial resources, extensive training, and are limited to one user at a time. SpotAssist leverages Boston Dynamics’ Spot to overcome these challenges, offering advanced functionalities through technologies like machine learning, computer vision, and natural language processing. This innovative robotic companion not only surpasses traditional service dogs but also holds the potential for mass deployment in settings like senior care homes, addressing financial and logistical constraints. SpotAssist embodies our commitment to making a significant impact by combining technology, compassion, and scalability to enhance the lives of those in need.

## What it does / How we built it

SpotAssist is an assistive robot meant to achieve *better* functionality than a regular service dog.
First, Spot will take an image of its surroundings and generate a list of objects detected along with their corresponding bounding boxes (using YOLO). Then, Spot will wait for the user to make a verbal query including a target landmark to approach; an example of such a command would be “Take me to the nearest chair.” Finally, Spot will gently guide the user to their desired destination through noting the difference between the center x-coordinate of the bounding box and the actual center of the image. Spot will utilize this quantity in order to align its heading angle to be in line with the desired object and subsequently move forward, stopping at a reasonable distance from the object in question. Furthermore, we built health-first integrations from the ground-up for Spot. Using our custom ML and analytical methods and Terra API integrations, we are able to use Spot as an alert for a medical event, such as abnormal heart rhythms and/or heart rate.

## Challenges we ran into

There were a number of technical challenges we encountered throughout the development process.
Firstly, a major hurdle was the restrictions placed on the compute module attached to Spot; we were not allowed to perform any commands directly to Spot itself (we instead had to communicate through the module). Furthermore, we were not allowed to SSH into the module (nor were we given elevated privileges), so we had to build our own version of a remote communication system between our remote servers and Spot. This led to many creative avenues being explored for development.

## Accomplishments that we're proud of

We’re especially proud of building upon a system/environment that was entirely new to the whole team, especially without any documentation on the compute module we built upon. Furthermore, none of the team knew each other prior to the hackathon, so we’re proud of the teamwork skills we were able to utilize to accomplish our success this weekend.

## What's next for SpotAssist

Mobile integration!
## Inspiration

With the recent Corona Virus outbreak, we noticed a major issue in charitable donations of equipment/supplies ending up in the wrong hands or are lost in transit. How can donors know their support is really reaching those in need? At the same time, those in need would benefit from a way of customizing what they require, almost like a purchasing experience.

With these two needs in mind, we created Promise. A charity donation platform to ensure the right aid is provided to the right place.

## What it does

Promise has two components. First, a donation request view to submitting aid requests and confirm aids was received. Second, a donor world map view of where donation requests are coming from.

The request view allows aid workers, doctors, and responders to specificity the quantity/type of aid required (for our demo we've chosen quantity of syringes, medicine, and face masks as examples) after verifying their identity by taking a picture with their government IDs. We verify identities through Microsoft Azure's face verification service. Once a request is submitted, it and all previous requests will be visible in the donor world view.

The donor world view provides a Google Map overlay for potential donors to look at all current donation requests as pins. Donors can select these pins, see their details and make the choice to make a donation. Upon donating, donors are presented with a QR code that would be applied to the aid's packaging.

Once the aid has been received by the requesting individual(s), they would scan the QR code, confirm it has been received or notify the donor there's been an issue with the item/loss of items. The comments of the recipient are visible to the donor on the same pin.

## How we built it

Frontend: React  
Backend: Flask, Node  
DB: MySQL, Firebase Realtime DB  
Hosting: Firebase, Oracle Cloud  
Storage: Firebase  
API: Google Maps, Azure Face Detection, Azure Face Verification  
Design: Figma, Sketch

## Challenges we ran into

Some of the APIs we used had outdated documentation.
Finding a good of ensuring information flow (the correct request was referred to each time) for both the donor and recipient.

## Accomplishments that we're proud of

We utilized a good number of new technologies and created a solid project in the end which we believe has great potential for good.

We've built a platform that is design-led, and that we believe works well in practice, for both end-users and the overall experience.

## What we learned

Utilizing React states in a way that benefits a multi-page web app
Building facial recognition authentication with MS Azure

## What's next for Promise

Improve detail of information provided by a recipient on QR scan. Give donors a statistical view of how much aid is being received so both donors and recipients can better action going forward.

Add location-based package tracking similar to Amazon/Arrival by Shopify for transparency