## Inspiration

Are you a bad texter? Do you not know what to say to your friends over text? MessageMinder tries to help maintain meaningful connections with friends by suggesting responses to difficult texts.

## What it does

MessageMinder finds meaningful responses to difficult texts. MessageMinder gets your friend's texts that you haven't replied to, and based on your thoughts on the texts and from learning your texting style/mannerisms, suggests responses to text your friends back.

## How we built it

We built the frontend using NextJS and power our response suggestion engine with a LLM using Together.ai.

## Challenges we ran into

Prompt engineering was a large challenge, and a lot of effort was put into fine-tuning our prompts to yield the most clean and relevant response suggestions.

## What's next for MessageMinder

We want to integrate MessageMinder with messaging platforms such as iMessage, Whatsapp, etc, so you can view all your messages in one place, and directly send suggestions from the app.
## Inspiration

It can be difficult to find teammates for hackathons. No one wants to have a teammate that uses the wrong editor (vim master race), and sometimes you just really need someone who understands UI/UX a little better.

## What it does

HackrFindr allows users to create a profile with some basic information and then "join" the HackrFindr page for different hackathons. They are then presented with the profiles of other hackers at the hackathon, and can swipe left to reject, or swipe right to accept, these other hackers. Users that mutually accept each other will be emailed with each other's contact info.

## How I built it

Backend is built using Flask and Elasticsearch; frontend is simple bootstrap, jquery, and underscorejs
If we take a moment to stop and think of those who can't speak or hear, we will realize and be thankful for what we have. To make the lives of these differently ables people, we needed to come up with a solution and here we present you with Proximity.

Proximity uses the Myo armband for sign recognition and an active voice for speech recognition. The armband is trained on a ML model reads the signs made by human hands and interprets them, thereby, helping the speech impaired to share their ideas and communicate with people and digital assistants alike. The service is also for those who are hearing impaired, so that they can know when somebody is calling their name or giving them a task.

We're proud of successfully recognizing a few gestures and setting up a web app that understands and learns the name of a person. Apart from that we have calibrated a to-do list that can enable the hearing impaired people to actively note down tasks assigned to them.

We learned an entirely new language, Lua to set up and use the Myo Armband SDK. Apart from that we used vast array of languages, scripts, APIs, and products for different parts of the product including Python, C++, Lua, Js, NodeJs, HTML, CSS, the Azure Machine Learning Studio, and Google Firebase.

We look forward to explore the unlimited opportunities with Proximity. From training it to recognize the entire American Sign Language using the powerful computing capabilities of the Azure Machine Learning Studio to advancing our speech recognition app for it to understand more complex conversations. Proximity should integrate seamlessly into the lives of the differently abled.