## Inspiration

My team loves traveling. Last summer, we were in India together. However, one problem that kept coming up for us was talking to people over the phone who don't speak English. We would call our Uber driver or a local restaurant owner to be completely unable to understand each other.

## What it does

We translate languages real time between two speakers. In other words, if you choose to speak and listen in French using our call system, your english speaking friend would hear your message in English, and vice-versa.

## How we built it

Our project integrates the Twilio and Google translate API. By using the conference call function and tying Twilio events to recording and saving files, sending them to our translation system built with Google translate, and playing these audio files back to the partner in the call, we are able to play a direct translation of the messages.

## Challenges we ran into

We ran into many challenges using the Twilio API, and it was difficult for us to set up a server and make a web app since none of us had prior web development experience. The mentors were extremely helpful, though (thank you Shariq!!).

## Accomplishments that we're proud of

When we played the audio file in the context of the call, and when we were able to first record specific parts of calls and save the file were very exciting. Learning to use the Google API was also exciting.

## What we learned

We learned tons and tons about web development in a very short amount of time, and will hopefully be able to use these skills in other projects.

## What's next for Small World Connect

We will continue refining the service until it delivers the best possible user experience - this means working out kinks like the translation talking over each other.
## Inspiration

When Humam was in Syria, it was extremely hard for him to guide people to his house since there is no proper address, street number or postal code. It is like that in so many countries; over 70 countries share this problem and most of them are 3rd world countries, which are the countries that don't have it need it most. For that, there is an application that is called What3Words. They assign for each 3m square in the world a unique 3-word address that will never change. The problem that this application faces from most users (UPS drivers, people in need of AAA road assistance, and people with no data) is that this service is only accessible online and users are not allowed to download their database. That is where our SMS service comes in handy for users with no access to the internet when they are in need of it, especially since What3Words is used very often for emergencies.

## What it does

+Map is a messaging service that users are able to send their 3-word location and get the coordinates back or send their coordinates to the phone number and the user will get the 3-word address. Users can use the service in 35 different languages.

## How we built it

We used the Twilio api to create a phone number, which posts to an express.js server that uses the what3words API to translate 3-word addresses to precise coordinates, and vice-versa.

## Challenges we ran into

The Twilio API is very powerful, and it was our first time using it. This meant that there was a lot of documentation to read through, and we had to be smart about finding the right information to get done what we needed to get done.

## Accomplishments that we're proud of

We are most proud that our product can work in over 100 countries and in over 35 languages. We are also proud that we made our first ever SMS service and our first ever fully-featured Node.JS application, and that we made something that we all strongly feel we should continue working on in the future.

## What we learned

We learned to leverage the Twilio and what3words API. We also learned a lot about webhooks, node.JS, express, and Git. We also learned about the benefits and limitations of the SMS protocol.

## What's next for +Map

Twilio supports phone number availability in over 100 countries. This will make our service scalable to almost every country in the world. Twilio also has support for the popular messaging application WhatsApp (some providers support data only to WhatsApp)
## Inspiration

While caught in the the excitement of coming up with project ideas, we found ourselves forgetting to follow up on action items brought up in the discussion. We felt that it would come in handy to have our own virtual meeting assistant to keep track of our ideas. We moved on to integrate features like automating the process of creating JIRA issues and providing a full transcript for participants to view in retrospect.

## What it does

*Minutes Made* acts as your own personal team assistant during meetings. It takes meeting minutes, creates transcripts, finds key tags and features and automates the process of creating Jira tickets for you.

It works in multiple spoken languages, and uses voice biometrics to identify key speakers.

For security, the data is encrypted locally - and since it is serverless, no sensitive data is exposed.

## How we built it

Minutes Made leverages Azure Cognitive Services for to translate between languages, identify speakers from voice patterns, and convert speech to text. It then uses custom natural language processing to parse out key issues. Interactions with slack and Jira are done through STDLIB.

## Challenges we ran into

We originally used Python libraries to manually perform the natural language processing, but found they didn't quite meet our demands with accuracy and latency. We found that Azure Cognitive services worked better. However, we did end up developing our own natural language processing algorithms to handle some of the functionality as well (e.g. creating Jira issues) since Azure didn't have everything we wanted.

As the speech conversion is done in real-time, it was necessary for our solution to be extremely performant. We needed an efficient way to store and fetch the chat transcripts. This was a difficult demand to meet, but we managed to rectify our issue with a Redis caching layer to fetch the chat transcripts quickly and persist to disk between sessions.

## Accomplishments that we're proud of

This was the first time that we all worked together, and we're glad that we were able to get a solution that actually worked and that we would actually use in real life. We became proficient with technology that we've never seen before and used it to build a nice product and an experience we're all grateful for.

## What we learned

This was a great learning experience for understanding cloud biometrics, and speech recognition technologies. We familiarised ourselves with STDLIB, and working with Jira and Slack APIs. Basically, we learned a lot about the technology we used and a lot about each other ❤️!

## What's next for Minutes Made

Next we plan to add more integrations to translate more languages and creating Github issues, Salesforce tickets, etc. We could also improve the natural language processing to handle more functions and edge cases. As we're using fairly new tech, there's a lot of room for improvement in the future.