## Inspiration and What it does

We often go out with a lot of amazing friends for trips, restaurants, tourism, weekend expedition and what not. Every encounter has an associated messenger groupchat. We want a way to split money which is better than to discuss on the groupchat, ask people their public key/usernames and pay on a different platform. We've integrated them so that we can do transactions and chat at a single place.
We (our team) believe that **"The Future of money is Digital Currency "** (-Bill Gates), and so, we've integrated payment with Algorand's AlgoCoins with the chat. To make the process as simple as possible without being less robust, we extract payment information out of text as well as voice messages.

## How I built it

We used Google Cloud NLP and IBM Watson Natural Language Understanding apis to extract the relevant information. Voice messages are first converted to text using Rev.ai speech-to-text. We complete the payment using the blockchain set up with Algorand API. All scripts and database will be hosted on the AWS server

## Challenges I ran into

It turned out to be unexpectedly hard to accurately find out the payer and payee. Dealing with the blockchain part was a great learning experience.

## Accomplishments that I'm proud of

that we were able to make it work in less than 24 hours

## What I learned

A lot of different APIs

## What's next for Mess-Blockchain-enger

Different kinds of currencies, more messaging platforms
## Inspiration

Civic authorities are constantly working to improve transport for their cities. Their efforts, however, often lack an important piece: access to accurate data about how current transport systems are used. A better understanding of how people commute, share rides, and take public transport today will inform how these systems can be better tomorrow.

## What it does

In essence, Argonaut is a marketplace: it helps users part with any personal data that they're willing to share, in return for monetary incentives in the form of Algorand coins (Algos). Authorities can buy data packs - which are anonymised and aggregated clusters of such data submitted by multiple users, for a price, which is then distributed amongst each of the contributors equally. Here's the key: the blockchain keeps a track record of what organisation is requesting and accessing what data, even while maintaining complete anonymity on the part of the sellers.

## How I built it

We used React.js to build the frontend, a Node.js/Express powered backend; Python to scrape through personal data dumps accessed via Google Takeout, and the Algorand JavaScript SDK to implement the blockchain-based transaction management system.

## Challenges I ran into

The principal challenge we faced was how to implement Blockchain for managing access to personal data. Another important challenge was to do with making sure that the anonymity of users sharing their data is maintained, whilst also keeping any shared data private.

## Accomplishments that I'm proud of

This was our first deep dive into Blockchain technology, and we were glad to be able to use Algorand's APIs and Dev Tools to be able to build a platform aimed at enhancing urban decision making, while simultaneously helping people like you and us take back control of their personal data.

## What I learned

We learned about the power of the blockchain, and how decentralised ledgers have applications far and beyond the traditional financial markets that we've currently seen them in.

## What's next for Argonaut

Argonaut can be scaled up to include the ability for users to connect Google Maps/Uber/Lyft and a lot of other apps directly to the platform so that their periodic data dumps can be seamlessly and automatically streamed into the platform, versus the current requirement of having to upload personal data dumps.
## Inspiration

To break the language barrier and let people with similar interests from any part of the world communicate with each other.

## What it does

* Allows users all over the world to chat with users with similar interests without knowing the other person's language.
* Its easier to make friends and professional connections for guidance or experience sharing.
* Real time translation and sending of messages without any delays makes it seamless and the user feels like he/she is talking to someone who speaks the same language.

## How we built it

* Google Cloud Machine Learning Translation API for real time translation of each message instantly
* Google App Engine for hosting the server
* Google Cloud Datastore API for storing user profile
* Android Studio for building the client facing app
* Firebase Realtime Database to sync messages between devices and server

## Challenges we ran into

* Using Google Cloud stack for the first time.
* Managing order of messages between server and multiple clients.

## Accomplishments that we're proud of

* We were able to build and deploy a chat application in less than 36 hours.
* We successfully utilized new technologies and services that we had never used before for our ideas.
* We were able to contribute to the society by an attempt to bring people closer to each other beyond languages, countries or continents. Just passion and interests!

## What we learned

* Developing scalable apps in the Google Cloud Tech Stack.
* Handling message ordering in a client server chat system.
* Using machine learning APIs for improving the user experience.

## What's next for Hello World

* Deploying soon on the Google Play Store and hopefully an iOS app soon!