## Inspiration

Our app idea came from the experience of one of our team members when he was having lunch in one of the many food courts of Toronto's CBD. At the time, he was looking around and despite being surrounded by hundreds of people - he knew nothing about any of them. Of course, there were incredible people amongst the crowd, but there was no way to know who was who, who did what, and who was open to chatting with a stranger.

Everyday, we walk past hundreds of people from all walks of life, with thousands of different (or common!) experiences. Yet, perhaps because we've grown wary of being surrounded by so many people, we casually dismiss this opportunity to learn something new. Our app aims to unearth the incredible value of people and their stories, and make connecting with strangers, easier.

## What it does

In short, Peopledex allows you to find other users within a 1 minute walking distance to them and strike up a conversation about their favourite topics. Every user's profile consists of their name, a profile photo, occupation, and three topics that they'd like people to ask them about. Nearby users are viewable in a collection view, ordered by proximity.

After viewing a nearby user's profile, you can find them via GPS-based augmented reality, which places an arrow above their current location. Then, you make an introduction! No instant-messaging allowed - only real connections. When you don't want to be approached (e.g. you're busy meeting with friends), you can enable incognito mode so you won't be discoverable.

**Use cases:**

* Finding relevant connections at large conferences or expos
* Finding potential teammates at hackathons
* Finding someone to have lunch with in a food court
* Finding friendly strangers to talk to while travelling

## How we built it

Our team split off into two groups, one responsible for building the AR functionality and the other for UI/UX. The initial wireframe was built on Balsamiq Cloud's collaborative wireframing tool. The wireframe was then developed using Xcode Interface Builder & Swift. Login and signup functionality was achieved using Firebase authentication.

The AR functionality involved two components. The front end client, using ARKit API for iOS, consumed GPS coordinates in the form of longitude and latitude, and rendered it into 3D space in the augmented reality view. The GPS coordinates were supplied by another client, which sent a POST request to a Python based API that pushed updates to the AR client. These updates were pushed in realtime, using a web socket to lower latency.

## Challenges we ran into

None of our team members came into this hackathon with iOS experience, and only half of our group had developer experience. The AR component proved to be extremely challenging as well, as overcoming the inaccuracies of location finding made us use many engineering techniques. Specifically, rendering assets in real time based on a non-deterministically moving object caused us to take many hours of calibration in order to have a demoable product.

## Accomplishments that we're proud of

We had to apply a high pass filter on the noise from the GPS to smooth out the movement of both clients. This enabled us to have a relatively stable location for the AR client to track and render. We were also able to create a smoothly running and aesthetically-pleasing user interface. We're proud of the fact that all of us learned how to use new and challenging tools in an extremely short amount of time.

## What we learned

We learned:

* ARKit
* That real-time location tracking is hard
* Xcode & Swift
* Firebase
* Balsamiq

## What's next for Peopledex

Get users onboard!
## Inspiration

We recognize that small businesses lose revenue simply because they aren't able to answer all incoming calls. Yet, for many, hiring a receptionist or outsourcing an answering service is too expensive. We believe there is a better way.

## What it does

R2D-Talk automates your small business's reception, allowing you to answer incoming calls you would have missed. Trained on your business data — including company PDFs and Word Docs (e.g. Restaurant menus), Databases and Google Apps (e.g. Restaurant reservations system), and even business website content — we provide a conversational phone agent that does all the work of a receptionist.

## How we built it

1. Data → Vector DB: Upload business data and we’ll embed it.
2. LangChain: We use retrieval-augmented generation with LLMs to augment responses with live business database data and unstructured documents/PDFs
3. Text ↔ Speech: With Twilio, we convert text to speech and vice-versa to create a conversational phone agent that can deploy the LangChain LLM.

Our data is collected with BeautifulSoup and PostgreSQL with PyPDF and PythonDocx. Our backend is based in a microservice architecture with Flask. Our Vector store utilizes LangChain and FAISS. Our text-to-speech from Vosk integrates into Twilio and connects to Google Calendar API.

## Accomplishments that we're proud of

* We have a fully working live demo and commercializable use case with integrations
* We were able to create a high-impact product that could be relevant for multiple industries
* Lasya is a first-time hacker and learned a lot
## Inspiration

The inspiration for Nova came from the overwhelming volume of emails and tasks that professionals face daily. We aimed to create a solution that simplifies task management and reduces cognitive load, allowing users to focus on what truly matters.

## What it does

Nova is an automated email assistant that intelligently processes incoming emails, identifies actionable items, and seamlessly adds them to your calendar. It also sends timely text reminders, ensuring you stay organized and on top of your commitments without the hassle of manual tracking.

## How we built it

We built Nova using natural language processing algorithms to analyze email content and extract relevant tasks. By integrating with calendar APIs and SMS services, we created a smooth workflow that automates task management and communication, making it easy for users to manage their schedules.

## Challenges we ran into

One of the main challenges was accurately interpreting the context of emails to distinguish between urgent tasks and general information. Additionally, ensuring seamless integration with various calendar platforms and messaging services required extensive testing and refinement.

## Accomplishments that we're proud of

We are proud of developing a fully functional prototype of Nova that effectively reduces users' daily load by automating task management. Initial user feedback has been overwhelmingly positive, highlighting the assistant's ability to streamline workflows and enhance productivity.

## What we learned

Throughout the development process, we learned the importance of user feedback in refining our algorithms and improving the overall user experience. We also gained insights into the complexities of integrating multiple services to create a cohesive solution.

## What's next for Nova

Moving forward, we plan to enhance Nova's capabilities by incorporating machine learning to improve task recognition and prioritization. Our goal is to expand its features and ultimately launch it as a comprehensive productivity tool that transforms how users manage their daily tasks.