## Inspiration

With the rise of meme stocks taking over the minds of gen-z, vast amounts of young people are diving into the world of finance. We wanted to make a platform to make it easy for young people to choose stocks based on what matters most: the environment.

## What it does

Loraxly speaks for the trees: it aggregates realtime stock data along with articles about the environmental impact a company has on the world. It then uses OpenAI's powerful GPT-3 api to summarize and classify these articles to determine if the company's environmental impact is positive or not.

## How we built it

Figma, React, Javascript, Kumbucha, Python, Selenium, Golang, goroutines, Cally, firebase, pandas, OpenAI API, Alphavantage stock api, Doppler, rechart, material-ui, and true love.

## Challenges we ran into

some goroutines being ahead of others, fixed this with channels.
article summaries not making since, we had to be more granular with our article selection.
the chart was tough to setup.
we didn't get going until saturday afternoon.

## Accomplishments that we're proud of

getting things working

## What we learned

alphavantage api has some major rate limiting.

## What's next for Lorax.ly

Adding a trading function and creating an ETF comprised of environmentally friendly companies that people can invest in.
## Inspiration

As university students and soon to be graduates, we understand the financial strain that comes along with being a student, especially in terms of commuting. Carpooling has been a long existing method of getting to a desired destination, but there are very few driving platforms that make the experience better for the carpool driver. Additionally, by encouraging more people to choose carpooling at a way of commuting, we hope to work towards more sustainable cities.

## What it does

FaceLyft is a web app that includes features that allow drivers to lock or unlock their car from their device, as well as request payments from riders through facial recognition. Facial recognition is also implemented as an account verification to make signing into your account secure yet effortless.

## How we built it

We used IBM Watson Visual Recognition as a way to recognize users from a live image; After which they can request money from riders in the carpool by taking a picture of them and calling our API that leverages the Interac E-transfer API. We utilized Firebase from the Google Cloud Platform and the SmartCar API to control the car. We built our own API using stdlib which collects information from Interac, IBM Watson, Firebase and SmartCar API's.

## Challenges we ran into

IBM Facial Recognition software isn't quite perfect and doesn't always accurately recognize the person in the images we send it. There were also many challenges that came up as we began to integrate several APIs together to build our own API in Standard Library. This was particularly tough when considering the flow for authenticating the SmartCar as it required a redirection of the url.

## Accomplishments that we're proud of

We successfully got all of our APIs to work together! (SmartCar API, Firebase, Watson, StdLib,Google Maps, and our own Standard Library layer). Other tough feats we accomplished was the entire webcam to image to API flow that wasn't trivial to design or implement.

## What's next for FaceLyft

While creating FaceLyft, we created a security API for requesting for payment via visual recognition. We believe that this API can be used in so many more scenarios than carpooling and hope we can expand this API into different user cases.
## Inspiration

Many investors looking to invest in startup companies are often overwhelmed by the sheer number of investment opportunities, worried that they will miss promising ventures without doing adequate due diligence. Likewise, since startups all present their data in a unique way, it is challenging for investors to directly compare companies and effectively evaluate potential investments. On the other hand, thousands of startups with a lot of potential also lack visibility to the right investors. Thus, we came up with Disruptive as a way to bridge this gap and provide a database for investors to view important insights about startups tailored to specific criteria.

## What it does

Disruptive scrapes information from various sources: company websites, LinkedIn, news, and social media platforms to generate the newest possible market insights. After homepage authentication, investors are prompted to indicate their interest in either Pre/Post+ seed companies to invest in. When an option is selected, the investor is directed to a database of company data with search capabilities, scraped from Kaggle. From the results table, a company can be selected and the investor will be able to view company insights, business analyst data (graphs), fund companies, and a Streamlit Chatbot interface. You are able to add more data through a DAO platform, by getting funded by companies looking for data. The investor also has the option of adding a company to the database with information about it.

## How we built it

The frontend was built with Next.js, TypeScript, and Tailwind CSS. Firebase authentication was used to verify users from the home page. (Company scraping and proxies for company information) Selenium was used for web scraping for database information. Figma was used for design, authentication was done using Firebase.
The backend was built using Flask, StreamLit, and Taipy. We used the Circle API and Hedera to generate bounties using blockchain. SQL and graphQL were used to generate insights, OpenAI and QLoRa were used for semantic/similarity search, and GPT fine-tuning was used for few-shot prompting.

## Challenges we ran into

Having never worked with Selenium and web scraping, we found understanding the dynamic loading and retrieval of web content challenging. The measures some websites have against scraping were also interesting to learn and try to work around. We also worked with chat-GPT and did prompt engineering to generate business insights - a task that can sometimes yield unexpected responses from chat-GPT!

## Accomplishments that we're proud of + What we learned

We learned how to use a lot of new technology during this hackathon. As mentioned above, we learned how to use Selenium, as well as Firebase authentication and GPT fine-tuning.

## What's next for Disruptive

Disruptive can implement more scrapers for better data in terms of insight generation. This would involve scraping from other options than Golden once there is more funding. Furthermore, integration between frontend and blockchain can be improved further. Lastly, we could generate better insights into the format of proposals for clients.