## Inspiration

We were inspired by the desire to combat the massive amount of disinformation that is prevalent in society today.

## What it does

Users are able to input URLs for articles that they are interested in having analyzed by our machine learning model. This model then displays confidence percentages for whether it thinks an article is true or false. It also shows a summary of the article generated by the OpenAI API.

## How we built it

We trained the multinomial Naive Bayes model on over 20,000 pieces of article data (author, title, text, and label). We used a count vectorizer to convert the words into numerical representations. Scikit-learn provided us with the necessary machine learning tools. We used Modal to ensure that our back end would run entirely in the cloud. Finally, we used React to create an intriguing and visually interesting user interface that will keep our website visitors engaged and the OpenAI API to provide AI-generated summaries of articles.

## Challenges we ran into

One of our main troubles was with learning Modal and trying to integrate it into all layers of our tech stack. We dealt with properly setting up images, creating stub functions, and handling API calls between our back and front end. We also ran into errors when trying to send and receive JSON objects and display the right visual elements to the user.

## Accomplishments that we're proud of

We are extremely proud that we were able to quickly pick up the basics of Modal and successfully integrate it into our project in a useful way. We are also very proud of the way our front end website looks and how it correctly interacts with our serverless functions. Most of all, we are proud of what we were able to accomplish in our first-ever hackathon!

## What we learned

The most significant thing we learned technology-wise was the foundations of Modal, all thanks to Jonathon Belotti, who was a tremendous help whenever we asked him questions. We also gained a lot of experience in a short amount of time with putting together a website that integrates a machine learning model running in the cloud. And last but not least, we learned about each other and what it means to work together effectively as a team!

## What's next for TruthQuest

Next, we would like to store our model in a Modal shared volume after training it. This way, we will not have to retrain the model every time we call the function. We would only have to retrain the model after gaining new data or after a set amount of time. We could construct a schedule using Modal cron jobs and periodically update our model.
## Inspiration

Considering the rapid growth of technology and the disconnection between many elderly people and their devices, we wanted to use technology for the better and help tackle a prevalent issue that many seniors encounter, for example the "Ore-Ore Sagi" scam in Japan. Our grandparents have fallen victim to scams before, so we felt personally inspired to help senior citizens who are often targeted by scammers for being more trusting and easier to scam.

## What it does

The scam detection works by using NLP to identify whether text resembles scam, and it is connected with an interactive quiz with both audio and text in a website.

## How we built it

We wanted to build everything onto a website, so we spent a lot of time using HTML and Javascript to create a beautiful website and an interesting quiz. As for the NLP model, we decided to use the BERT Machine Learning Framework due to its efficiency and accuracy when running on ambiguous language in text. With BERT, we worked on collecting a dataset (a combination of personally generated work and work from an existing catalog), preprocessing and tokenizing the data, and designing and testing the model.

## Challenges we ran into

We definitely faced a lot of challenges along the way. We decided to dive into a topic and techniques that none of us were extremely familiar with, but we thought that it would be cool. Because of this, we spent a lot of time understanding how frameworks such as BERT work, installing and uninstalling different packages in terminal, and scouring Stack Overflow for ways to debug. We also struggled to find ways to deploy our machine learning model in python to a web app to make them more accessible. While these challenges got infuriating at times, we were able to rise above and create something that we are pretty proud of.

## Accomplishments that we're proud of

I think our biggest accomplishment was creating a cohesive website that connects the various parts that we have made. As stated above, one of the hardest parts was finding a way to connect our model to a website, and creating that was definitely one of our biggest accomplishments. Beyond that, for the majority of us, this was our first hackathon! Completing something that we all feel proud of is a big accomplishment in it of itself.

## What we learned

Learning to integrate different languages across different IDE's and computers with limited experience in a timed environment was stressful, but we enjoyed the learning and collaboration experience. We also learned the importance of time-management.

## What's next for No Scam, Only Ham

Our next steps are including whisper.ai to implement speech-to-text capabilities to increase the impact of our project. We also want to include other languages to make our scam detection more inclusive.
## Inspiration

In the exciting world of hackathons, where innovation meets determination, **participants like ourselves often ask "Has my idea been done before?"** While originality is the cornerstone of innovation, there's a broader horizon to explore - the evolution of an existing concept. Through our AI-driven platform, hackers can gain insights into the uniqueness of their ideas. By identifying gaps or exploring similar projects' functionalities, participants can aim to refine, iterate, or even revolutionize existing concepts, ensuring that their projects truly stand out.

For **judges, the evaluation process is daunting.** With a multitude of projects to review in a short time frame, ensuring an impartial and comprehensive assessment can become extremely challenging. The introduction of an AI tool doesn't aim to replace the human element but rather to enhance it. By swiftly and objectively analyzing projects based on certain quantifiable metrics, judges can allocate more time to delve into the intricacies, stories, and the passion driving each team

## What it does

This project is a smart tool designed for hackathons. The tool measures the similarity and originality of new ideas against similar projects if any exist, we use web scraping and OpenAI to gather data and draw conclusions.

**For hackers:**

* **Idea Validation:** Before diving deep into development, participants can ascertain the uniqueness of their concept, ensuring they're genuinely breaking new ground.
* **Inspiration:** By observing similar projects, hackers can draw inspiration, identifying ways to enhance or diversify their own innovations.

**For judges:**

* **Objective Assessment:** By inputting a project's Devpost URL, judges can swiftly gauge its novelty, benefiting from AI-generated metrics that benchmark it against historical data.
* **Informed Decisions:** With insights on a project's originality at their fingertips, judges can make more balanced and data-backed evaluations, appreciating true innovation.

## How we built it

**Frontend:** Developed using React JS, our interface is user-friendly, allowing for easy input of ideas or Devpost URLs.

**Web Scraper:** Upon input, our web scraper dives into the content, extracting essential information that aids in generating objective metrics.

**Keyword Extraction with ChatGPT:** OpenAI's ChatGPT is used to detect keywords from the Devpost project descriptions, which are used to capture project's essence.

**Project Similarity Search:** Using the extracted keywords, we query Devpost for similar projects. It provides us with a curated list based on project relevance.

**Comparison & Analysis:** Each incoming project is meticulously compared with the list of similar ones. This analysis is multi-faceted, examining the number of similar projects and the depth of their similarities.

**Result Compilation:** Post-analysis, we present users with an 'originality score' alongside explanations for the determined metrics, keeping transparency.

**Output Display:** All insights and metrics are neatly organized and presented on our frontend website for easy consumption.

## Challenges we ran into

**Metric Prioritization:** Given the timeline restricted nature of a hackathon, one of the first challenges was deciding which metrics to prioritize. Striking the balance between finding meaningful data points that were both thorough and feasible to attain were crucial.

**Algorithmic Efficiency:** We struggled with concerns over time complexity, especially with potential recursive scenarios. Optimizing our algorithms, prompt engineering, and simplifying architecture was the solution.

*Finding a good spot to sleep.*

## Accomplishments that we're proud of

We took immense pride in developing a solution directly tailored for an environment we're deeply immersed in. By crafting a tool for hackathons, while participating in one, we felt showcases our commitment to enhancing such events. Furthermore, not only did we conceptualize and execute the project, but we also established a robust framework and thoughtfully designed architecture from scratch.

Another general Accomplishment was our team's synergy. We made efforts to ensure alignment, and dedicated time to collectively invest in and champion the idea, ensuring everyone was on the same page and were equally excited and comfortable with the idea. This unified vision and collaboration were instrumental in bringing HackAnalyzer to life.

## What we learned

We delved into the intricacies of full-stack development, gathering hands-on experience with databases, backend and frontend development, as well as the integration of AI. Navigating through API calls and using web scraping were also some key takeaways. Prompt Engineering taught us to meticulously balance the trade-offs when leveraging AI, especially when juggling cost, time, and efficiency considerations.

## What's next for HackAnalyzer

We aim to amplify the metrics derived from the Devpost data while enhancing the search function's efficiency. Our secondary and long-term objective is to transition the application to a mobile platform. By enabling students to generate a QR code, judges can swiftly access HackAnalyzer data, ensuring a more streamlined and effective evaluation process.