# Inspiration

Traditional startup fundraising is often restricted by stringent regulations, which make it difficult for small investors and emerging founders to participate. These barriers favor established VC firms and high-networth individuals, limiting innovation and excluding a broad range of potential investors. Our goal is to break down these barriers by creating a decentralized, community-driven fundraising platform that democratizes startup investments through a Decentralized Autonomous Organization, also known as DAO.

# What It Does

To achieve this, our platform leverages blockchain technology and the DAO structure. Here’s how it works:

* **Tokenization**: We use blockchain technology to allow startups to issue digital tokens that represent company equity or utility, creating an investment proposal through the DAO.
* **Lender Participation**: Lenders join the DAO, where they use cryptocurrency, such as USDC, to review and invest in the startup proposals.
-**Startup Proposals**: Startup founders create proposals to request funding from the DAO. These proposals outline key details about the startup, its goals, and its token structure. Once submitted, DAO members review the proposal and decide whether to fund the startup based on its merits.
* **Governance-based Voting**: DAO members vote on which startups receive funding, ensuring that all investment decisions are made democratically and transparently. The voting is weighted based on the amount lent in a particular DAO.

# How We Built It

### Backend:

* **Solidity** for writing secure smart contracts to manage token issuance, investments, and voting in the DAO.
* **The Ethereum Blockchain** for decentralized investment and governance, where every transaction and vote is publicly recorded.
* **Hardhat** as our development environment for compiling, deploying, and testing the smart contracts efficiently.
* **Node.js** to handle API integrations and the interface between the blockchain and our frontend.
* **Sepolia** where the smart contracts have been deployed and connected to the web application.

### Frontend:

* **MetaMask** Integration to enable users to seamlessly connect their wallets and interact with the blockchain for transactions and voting.
* **React** and **Next.js** for building an intuitive, responsive user interface.
* **TypeScript** for type safety and better maintainability.
* **TailwindCSS** for rapid, visually appealing design.
* **Shadcn UI** for accessible and consistent component design.

# Challenges We Faced, Solutions, and Learning

### Challenge 1 - Creating a Unique Concept:

Our biggest challenge was coming up with an original, impactful idea. We explored various concepts, but many were already being implemented.

**Solution**:

After brainstorming, the idea of a DAO-driven decentralized fundraising platform emerged as the best way to democratize access to startup capital, offering a novel and innovative solution that stood out.

### Challenge 2 - DAO Governance:

Building a secure, fair, and transparent voting system within the DAO was complex, requiring deep integration with smart contracts, and we needed to ensure that all members, regardless of technical expertise, could participate easily.

**Solution**:

We developed a simple and intuitive voting interface, while implementing robust smart contracts to automate and secure the entire process. This ensured that users could engage in the decision-making process without needing to understand the underlying blockchain mechanics.

## Accomplishments that we're proud of

* **Developing a Fully Functional DAO-Driven Platform**: We successfully built a decentralized platform that allows startups to tokenize their assets and engage with a global community of investors.
* **Integration of Robust Smart Contracts for Secure Transactions**: We implemented robust smart contracts that govern token issuance, investments, and governance-based voting bhy writing extensice unit and e2e tests.
* **User-Friendly Interface**: Despite the complexities of blockchain and DAOs, we are proud of creating an intuitive and accessible user experience. This lowers the barrier for non-technical users to participate in the platform, making decentralized fundraising more inclusive.

## What we learned

* **The Importance of User Education**: As blockchain and DAOs can be intimidating for everyday users, we learned the value of simplifying the user experience and providing educational resources to help users understand the platform's functions and benefits.
* **Balancing Security with Usability**: Developing a secure voting and investment system with smart contracts was challenging, but we learned how to balance high-level security with a smooth user experience. Security doesn't have to come at the cost of usability, and this balance was key to making our platform accessible.
* **Iterative Problem Solving**: Throughout the project, we faced numerous technical challenges, particularly around integrating blockchain technology. We learned the importance of iterating on solutions and adapting quickly to overcome obstacles.

# What’s Next for DAFP

Looking ahead, we plan to:

* **Attract DAO Members**: Our immediate focus is to onboard more lenders to the DAO, building a large and diverse community that can fund a variety of startups.
* **Expand Stablecoin Options**: While USDC is our starting point, we plan to incorporate more blockchain networks to offer a wider range of stablecoin options for lenders (EURC, Tether, or Curve).
* **Compliance and Legal Framework**: Even though DAOs are decentralized, we recognize the importance of working within the law. We are actively exploring ways to ensure compliance with global regulations on securities, while maintaining the ethos of decentralized governance.
## Inspiration

There are 1.1 billion people without Official Identity (ID). Without this proof of identity, they can't get access to basic financial and medical services, and often face many human rights offences, due the lack of accountability.

The concept of a Digital Identity is extremely powerful.
In Estonia, for example, everyone has a digital identity, a solution was developed in tight cooperation between the public and private sector organizations.

Digital identities are also the foundation of our future, enabling:

* P2P Lending
* Fractional Home Ownership
* Selling Energy Back to the Grid
* Fan Sharing Revenue
* Monetizing data
* bringing the unbanked, banked.

## What it does

Our project starts by getting the user to take a photo of themselves. Through the use of Node JS and AWS Rekognize, we do facial recognition in order to allow the user to log in or create their own digital identity. Through the use of both S3 and Firebase, that information is passed to both our dash board and our blockchain network!

It is stored on the Ethereum blockchain, enabling one source of truth that corrupt governments nor hackers can edit.

From there, users can get access to a bank account.

## How we built it

Front End: | HTML | CSS | JS

APIs: AWS Rekognize | AWS S3 | Firebase

Back End: Node JS | mvn

Crypto: Ethereum

## Challenges we ran into

Connecting the front end to the back end!!!! We had many different databases and components. As well theres a lot of accessing issues for APIs which makes it incredibly hard to do things on the client side.

## Accomplishments that we're proud of

Building an application that can better the lives people!!

## What we learned

Blockchain, facial verification using AWS, databases

## What's next for CredID

Expand on our idea.
## Inspiration

Medical imaging, in this case histopathological tissue images, is generally incomprehensible to a layperson, even though these tissue images are extremely vital in diagnosing breast cancer and estimating the aggressiveness of the cancer. To even begin understanding these images and ensuring thorough, accurate diagnoses requires second opinions from multiple gold-standard pathologists and doctors who can manually identify tumor types and grades, which is oftentimes financially unsustainable and also inefficient for medical practitioners with many patients.

## What it does

Bean provides an effective way for individuals with little to no medical experience to understand histopathological tissue imaging for breast cancer, reducing economic costs associated with having to get many second opinions as well as informing the patient about their condition in a straightforward manner. It analyzes the cancer grades and therefore the aggressiveness of the cancer using a modified Nottingham grading system, and identifies and analyzes the tissue as benign or malignant based off of nuclear pleomorphism, tubular formation, and mitotic count. The images are annotated in a way such that a layperson can understand the issues present in the images in a clear and efficient way.

## How we built it

We used Flask to build the web app, along with a combination of HTML/CSS/JavaScript. We use Google Cloud to train a machine learning model that is capable of regional classification. Then, using REST API, we integrate the model with our web app, creating a seamless experience for the user to upload images and analyze histology images.

## Challenges we ran into

It was difficult to find a sufficient data set in the time that we had, because most medical images are only partially publicly available to preserve patient privacy. We ran into challenges training our model and connecting the front-end and back-end. We were also initially unfamiliar with Google Cloud and how to use it. Also, at first, we were unable to run the web server and implement Flask to create the app.

## Accomplishments that we're proud of

Our model is trained to be 78% accurate, which, although is not extremely high, is impressive given the amount of time (< 1 day) of training and lack of pre-processing data. While we achieved this accuracy using Google Cloud's ML toolkit, we believe that we can achieve higher values in the future through original research. In addition, we are proud of how robust our software is. Not only is it able to point out individual cells, but it is also able to point out the type of tumor, lesions, and frequency of mitotic events.

## What we learned

This experience proved to be a challenging feat for us. All of us came in with barely any background in medical imaging, and it was our first time utilizing a machine learning based cloud software. As a diverse team, our group split into groups of two: front-end and back-end for figuring out how to improve the UI/UX and getting the machine-learning based model to work, respectively. After working for a weekend, we learned about how we can all come together in software engineering from our diverse background, and how the diversity in thought between us sparked innovation and creativity.

## What's next for Bean

In the future, we hope to expand Bean to medical practitioners as well as the current audience of laypeople, because not only would an accurate model that effectively and efficiently reads histopathological images reduce costs, it would also make an accurate diagnosis more efficient and available to more people. With more time and data, we could also perfect our model and make it even more accurate, as well as implement more precise grading techniques.