## Inspiration

Despite the advent of the information age, misinformation remains a big issue in today's day and age. Yet, mass media accessibility for newer language speakers, such as younger children or recent immigrants, remains lacking. We want these people to be able to do their own research on various news topics easily and reliably, without being limited by their understanding of the language.

## What it does

Our Chrome extension allows users to shorten and simplify and any article of text to a basic reading level. Additionally, if a user is not interested in reading the entire article, it comes with a tl;dr feature. Lastly, if a user finds the article interesting, our extension will find and link related articles that the user may wish to read later. We also include warnings to the user if the content of the article contains potentially sensitive topics, or comes from a source that is known to be unreliable.

Inside of the settings menu, users can choose a range of dates for the related articles which our extension finds. Additionally, users can also disable the extension from working on articles that feature explicit or political content, alongside being able to disable thumbnail images for related articles if they do not wish to view such content.

## How we built it

The front-end Chrome extension was developed in pure HTML, CSS and JavaScript. The CSS was done with the help of [Bootstrap](https://getbootstrap.com/), but still mostly written on our own. The front-end communicates with the back-end using REST API calls.

The back-end server was built using [Flask](https://flask.palletsprojects.com/en/2.0.x/), which is where we handled all of our web scraping and natural language processing.

We implemented text summaries using various NLP techniques (SMMRY, TF-IDF), which were then fed into the OpenAI API in order to generate a simplified version of the summary. Source reliability was determined using a combination of research data provided by [Ad Fontes Media](https://www.adfontesmedia.com/) and [Media Bias Check](https://mediabiasfactcheck.com/).

To save time (and spend less on API tokens), parsed articles are saved in a [MongoDB](https://www.mongodb.com/) database, which acts as a cache and saves considerable time by skipping all the NLP for previously processed news articles.

Finally, [GitHub Actions](https://github.com/features/actions) was used to automate our builds and deployments to [Heroku](https://www.heroku.com/), which hosted our server.

## Challenges we ran into

Heroku was having issues with API keys, causing very confusing errors which took a significant amount of time to debug.

In regards to web scraping, news websites have wildly different formatting which made extracting the article's main text difficult to generalize across different sites. This difficulty was compounded by the closure of many prevalent APIs in this field, such as Google News API which shut down in 2011.

We also faced challenges with tuning the prompts in our requests to OpenAI to generate the output we were expecting. A significant amount of work done in the Flask server is pre-processing the article's text, in order to feed OpenAI a more suitable prompt, while retaining the meaning.

## Accomplishments that we're proud of

This was everyone on our team's first time creating a Google Chrome extension, and we felt that we were successful at it. Additionally, we are happy that our first attempt at NLP was relatively successful, since none of us have had any prior experience with NLP.

Finally, we slept at a Hackathon for the first time, so that's pretty cool.

## What we learned

We gained knowledge of how to build a Chrome extension, as well as various natural language processing techniques.

## What's next for OpBop

Increasing the types of text that can be simplified, such as academic articles. Making summaries and simplifications more accurate to what a human would produce.

Improving the hit rate of the cache by web crawling and scraping new articles while idle.

## Love,

## FSq x ANMOL x BRIAN
## Inspiration

Our mission is rooted in the **fight against fake news, misinformation, and disinformation,** which are increasingly pervasive threats in today’s digital world. As the saying goes, "the pen is mightier than the sword," which underscores the power of words and information. We aim to ensure that no one falls victim to digital deception.

While technology has contributed to the spread of misinformation, we believe it can also be a powerful ally in promoting the truth. By leveraging AI for good, we aim to combat falsehoods and uphold the integrity of information.

*Fun fact: Moodeng is a pygmy hippopotamus born on July 10, 2024, living in Khao Kheow Open Zoo, Thailand. She became a viral internet sensation during a busy political season in the US. Amid the flood of true and half-true information, Moodeng, symbolizing purity and honesty, stood as a beacon of clarity. Like Moodeng, our tool is here to cut through the noise and keep things transparent. So, Vote for Moodeng!*

## What it does

Social media platforms are now major sources of rapidly shared information. Our Chrome extension, MD FactFarm, simplifies fact-checking through AI-driven content analysis and verification. Initially focused on YouTube, our tool offers **real-time fact-checking** by scanning video content to **identify and flag misinformation** while providing reliable sources for users to verify accuracy.

## How we built it

* At the core of our system is a Large Language Model (LLM) that we trained and optimized to accurately understand and interpret various forms of misinformation, powering our fact-checking capabilities.
* We integrated an AI agent using Fetch.ai and built services and APIs to enable seamless communication with the agent.
* Our front-end, built with HTML, CSS, and JavaScript, was designed and deployed as a Chrome extension.

## Challenges we ran into

* One of the major challenges we encountered was ensuring that the AI could accurately differentiate between fact, opinion, and misleading content. Early on, the outputs were inconsistent, making it difficult to trust the results.



To achieve this, we had to rethink our approach to prompt engineering. We provided the AI with more detailed context and built a structured framework to clearly separate different types of content. Additionally, we implemented a formula for the AI to use to determine a confidence score for each output. These changes helped us generate more consistent and reliable results, enabling the AI to better recognize the subtle distinctions between fact, opinion, and misleading content.
* Another challenge was integrating multiple agent frameworks into a unified system that could operate seamlessly. Managing the intricacies of coordinating tasks and data flow between these diverse components contributed to a complex integration process.

## Accomplishments that we're proud of

* We successfully developed a Chrome extension that that provides real-time fact-checking for YouTube, empowering users to make informed decisions.
* We crafted prompts that effectively leverage the LLM's ability to detect misinformation.
* We successfully integrated Fetch.ai, utilizing agents that lay the foundation for scalability.

## What we learned

We learned the importance of defining the problem clearly and deciding on a minimum viable product (MVP) within a limited timeframe. Additionally, we focused on framing our work to align with the AI agent framework, which has been crucial in improving our approach to misinformation detection.

## What's next for MD FactFarm

Moving forward, we plan to expand our platform to include other social networks, such as Twitter and Facebook, where misinformation spreads rapidly. We aim to gather a wider range of information sources to ensure more comprehensive fact-checking and cover more diverse content. Moreover, we are working on enhancing our AI's fact-checking mechanics, utilizing more advanced techniques to improve accuracy.
## Inspiration

Nowadays, the payment for knowledge has become more acceptable by the public, and people are more willing to pay to these truly insightful, cuttting edge, and well-stuctured knowledge or curriculum. However, current centalized video content production platforms (like YouTube, Udemy, etc.) take too much profits from the content producers (resaech have shown that content creators usually only receive 15% of the values their contents create) and the values generated from the video are not distributed in a timely manner. In order to tackle this unfair value distribution, we have this decentralized platform EDU.IO where the video contents will be backed by their digital assets as an NFT (copyright protection!) and fractionalized as tokens, and it creates direct connections between content creators and viewers/fans (no middlemen anymore!), maximizing the value of the contents made by creators.

## What it does

EDU.IO is a decentralized educational video streaming media platform & fractionalized NFT exchange that empowers creator economy and redefines knowledge value distribution via smart contracts.

* As an educational hub, EDU.IO is a decentralized platform of high-quality educational videos on disruptive innovations and hot topics like metaverse, 5G, IoT, etc.
* As a booster of creator economy, once a creator uploads a video (or course series), it will be mint as an NFT (with copyright protection) and fractionalizes to multiple tokens. Our platform will conduct a mini-IPO for the each content they produced - bid for fractionalized NFTs. The value of each video token is determined by the number of views over a certain time interval, and token owners (can be both creators and viewers/fans/investors) can advertise the contents they owned to increase it values, and trade these tokens to earn monkey or make other investments (more liquidity!!).
* By the end of the week, the value generated by each video NFT will be distributed via smart contracts to the copyright / fractionalized NFT owners of each video.
Overall we’re hoping to build an ecosystem with more engagement between viewers and content creators, and our three main target users are:
* 1. Instructors or Content creators: where the video contents can get copyright protection via NFT, and they can get fairer value distribution and more liquidity compare to using large centralized platforms
* 2. Fans or Content viewers: where they can directly interact and support content creators, and the fee will be sent directly to the copyright owners via smart contract.
* 3. Investors: Lower barrier of investment, where everyone can only to a fragment of a content. People can also to bid or trading as a secondary market.

## How we built it

* Frontend in HTML, CSS, SCSS, Less, React.JS
* Backend in Express.JS, Node.JS
* ELUV.IO for minting video NFTs (eth-based) and for playing quick streaming videos with high quality & low latency
* CockroachDB (a distributed SQL DB) for storing structured user information (name, email, account, password, transactions, balance, etc.)
* IPFS & Filecoin (distributed protocol & data storage) for storing video/course previews (decentralization & anti-censorship)

## Challenges we ran into

* Transition from design to code
* CockroachDB has an extensive & complicated setup, which requires other extensions and stacks (like Docker) during the set up phase which caused a lot of problems locally on different computers.
* IPFS initially had set up errors as we had no access to the given ports → we modified the original access files to access different ports to get access.
* Error in Eluv.io’s documentation, but the Eluv.io mentor was very supportive :)
* Merging process was difficult when we attempted to put all the features (Frontend, IPFS+Filecoin, CockroachDB, Eluv.io) into one ultimate full-stack project as we worked separately and locally
* Sometimes we found the documentation hard to read and understand - in a lot of problems we encountered, the doc/forum says DO this rather then RUN this, where the guidance are not specific enough and we had to spend a lot of extra time researching & debugging. Also since not a lot of people are familiar with the API therefore it was hard to find exactly issues we faced. Of course, the staff are very helpful and solved a lot of problems for us :)

## Accomplishments that we're proud of

* Our Idea! Creative, unique, revolutionary. DeFi + Education + Creator Economy
* Learned new technologies like IPFS, Filecoin, Eluv.io, CockroachDB in one day
* Successful integration of each members work into one big full-stack project

## What we learned

* More in depth knowledge of Cryptocurrency, IPFS, NFT
* Different APIs and their functionalities (strengths and weaknesses)
* How to combine different subparts with different functionalities into a single application in a project
* Learned how to communicate efficiently with team members whenever there is a misunderstanding or difference in opinion
* Make sure we know what is going on within the project through active communications so that when we detect a potential problem, we solve it right away instead of wait until it produces more problems
* Different hashing methods that are currently popular in “crypto world” such as multihash with cid, IPFS’s own hashing system, etc. All of which are beyond our only knowledge of SHA-256
* The awesomeness of NFT fragmentation, we believe it has great potential in the future
* Learned the concept of a decentralized database which is directly opposite the current data bank structure that most of the world is using

## What's next for EDU.IO

* Implement NFT Fragmentation (fractionalized tokens)
* Improve the trading and secondary market by adding more feature like more graphs
* Smart contract development in solidity for value distribution based on the fractionalized tokens people owned
* Formulation of more complete rules and regulations - The current trading prices of fractionalized tokens are based on auction transactions, and eventually we hope it can become a free secondary market (just as the stock market)