# Inspiration

Legal research can be daunting, especially for new plaintiffs or lawyers dealing with cases that have historical precedents. We wanted to create a tool that simplifies this process, making it accessible and efficient for everyone.

# What it does

LegalGPT leverages advanced AI technology to provide users with relevant legal precedents and actionable insights. By comparing user input with a comprehensive database of case law, LegalGPT aids in understanding and formulating legal strategies.

# How we built it

We used the OpenAI API as the backbone for our language model. Our database contains archaic case files stored as word embeddings, which the AI references to provide accurate and contextually relevant information. The data was acquired via an API and summarized before generating text embeddings.
We are trying to use the concepts of RAG and LLM to make it customized.

# Challenges we ran into

One of the major challenges was ensuring the accuracy and relevance of the AI's responses. Additionally, handling and processing a large volume of legal documents to create meaningful embeddings was technically demanding along with keeping in mind the rate limits of the API keys.

# Accomplishments that we're proud of

Being in the first Hackathon, we have all come a long way by being able to formulate and successfully develop an AI agent that can assist both new plaintiffs and seasoned lawyers by providing them with vital legal information and strategies. This tool can potentially democratize legal knowledge and make the research process significantly more efficient.

# What we learned

Before getting into the technicalities of the solution, we understood how a hackathon works and what it has to offer to the students. We gained deep insights into natural language processing, particularly in the context of legal documents. The project also taught us the importance of data accuracy and the challenges involved in training AI models with specialized datasets.

# What's next for LegalGPT

We plan to expand LegalGPT's database to include more recent cases and enhance its ability to provide nuanced legal advice. Additionally, we aim to integrate more user-friendly features and expand its accessibility to a broader audience.
## Inspiration

Why do people constantly change their diet?
Health and environment are the most common factors. There has been a 600% increase in people going vegan for the environment, and millions of people changing their eating habits to live a healthier lifestyle.
People want change. But why is it so hard?

## What it does

You already have grocery list.
Grocery-Me edits the list based on your personal profile.

1. Create a personal profile: a selection of up to 29 dietary preferences
2. Add your current grocery list
3. "Personalize" (generate an alternative list automatically)
4. Find relevant recipes

## How We built it

We used React Native for the app development platform, allowing for both IOS and Android customers.

## Challenges We ran into

## Accomplishments that We are proud of

## What We learned

React Native.

## What's next for GroceryMe

...
## 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.