## Inspiration

Large Language Models (LLMs) are limited by a token cap, making it difficult for them to process large contexts, such as entire codebases. We wanted to overcome this limitation and provide a solution that enables LLMs to handle extensive projects more efficiently.

## What it does

LLM Pro Max intelligently breaks a codebase into manageable chunks and feeds only the relevant information to the LLM, ensuring token efficiency and improved response accuracy. It also provides an interactive dependency graph that visualizes the relationships between different parts of the codebase, making it easier to understand complex dependencies.

## How we built it

Our landing page and chatbot interface were developed using React. We used Python and Pyvis to create an interactive visualization graph, while FastAPI powered the backend for dependency graph content. We've added third-party authentication using the GitHub Social Identity Provider on Auth0. We set up our project's backend using Convex and also added a Convex database to store the chats. We implemented Chroma for vector embeddings of GitHub codebases, leveraging advanced Retrieval-Augmented Generation (RAG) techniques, including query expansion and re-ranking. This enhanced the Cohere-powered chatbot’s ability to respond with high accuracy by focusing on relevant sections of the codebase.

## Challenges we ran into

We faced a learning curve with vector embedding codebases and applying new RAG techniques. Integrating all the components—especially since different team members worked on separate parts—posed a challenge when connecting everything at the end.

## Accomplishments that we're proud of

We successfully created a fully functional repo agent capable of retrieving and presenting highly relevant and accurate information from GitHub repositories. This feat was made possible through RAG techniques, surpassing the limits of current chatbots restricted by character context.

## What we learned

We deepened our understanding of vector embedding, enhanced our skills with RAG techniques, and gained valuable experience in team collaboration and merging diverse components into a cohesive product.

## What's next for LLM Pro Max

We aim to improve the user interface and refine the chatbot’s interactions, making the experience even smoother and more visually appealing. (Please Fund Us)
## Problem Statement

As the number of the elderly population is constantly growing, there is an increasing demand for home care. In fact, the market for safety and security solutions in the healthcare sector is estimated to reach $40.1 billion by 2025.
The elderly, disabled, and vulnerable people face a constant risk of falls and other accidents, especially in environments like hospitals, nursing homes, and home care environments, where they require constant supervision. However, traditional monitoring methods, such as human caregivers or surveillance cameras, are often not enough to provide prompt and effective responses in emergency situations. This potentially has serious consequences, including injury, prolonged recovery, and increased healthcare costs.

## Solution

The proposed app aims to address this problem by providing real-time monitoring and alert system, using a camera and cloud-based machine learning algorithms to detect any signs of injury or danger, and immediately notify designated emergency contacts, such as healthcare professionals, with information about the user's condition and collected personal data.
We believe that the app has the potential to revolutionize the way vulnerable individuals are monitored and protected, by providing a safer and more secure environment in designated institutions.

## Developing Process

Prior to development, our designer used Figma to create a prototype which was used as a reference point when the developers were building the platform in HTML, CSS, and ReactJs.
For the cloud-based machine learning algorithms, we used Computer Vision, Open CV, Numpy, and Flask to train the model on a dataset of various poses and movements and to detect any signs of injury or danger in real time.
Because of limited resources, we decided to use our phones as an analogue to cameras to do the live streams for the real-time monitoring.

## Impact

* **Improved safety:** The real-time monitoring and alert system provided by the app helps to reduce the risk of falls and other accidents, keeping vulnerable individuals safer and reducing the likelihood of serious injury.
* **Faster response time:** The app triggers an alert and sends notifications to designated emergency contacts in case of any danger or injury, which allows for a faster response time and more effective response.
* **Increased efficiency:** Using cloud-based machine learning algorithms and computer vision techniques allow the app to analyze the user's movements and detect any signs of danger without constant human supervision.
* **Better patient care:** In a hospital setting, the app could be used to monitor patients and alert nurses if they are in danger of falling or if their vital signs indicate that they need medical attention. This could lead to improved patient care, reduced medical costs, and faster recovery times.
* **Peace of mind for families and caregivers:** The app provides families and caregivers with peace of mind, knowing that their loved ones are being monitored and protected and that they will be immediately notified in case of any danger or emergency.

## Challenges

One of the biggest challenges have been integrating all the different technologies, such as live streaming and machine learning algorithms, and making sure they worked together seamlessly.

## Successes

The project was a collaborative effort between a designer and developers, which highlights the importance of cross-functional teams in delivering complex technical solutions. Overall, the project was a success and resulted in a cutting-edge solution that can help protect vulnerable individuals.

## Things Learnt

* **Importance of cross-functional teams:** As there were different specialists working on the project, it helped us understand the value of cross-functional teams in addressing complex challenges and delivering successful results.
* **Integrating different technologies:** Our team learned the challenges and importance of integrating different technologies to deliver a seamless and effective solution.
* **Machine learning for health applications:** After doing the research and completing the project, our team learned about the potential and challenges of using machine learning in the healthcare industry, and the steps required to build and deploy a successful machine learning model.

## Future Plans for SafeSpot

* First of all, the usage of the app could be extended to other settings, such as elderly care facilities, schools, kindergartens, or emergency rooms to provide a safer and more secure environment for vulnerable individuals.
* Apart from the web, the platform could also be implemented as a mobile app. In this case scenario, the alert would pop up privately on the user’s phone and notify only people who are given access to it.
* The app could also be integrated with wearable devices, such as fitness trackers, which could provide additional data and context to help determine if the user is in danger or has been injured.
## Inspiration

The inspiration for Hivemind stemmed from personal frustration with the quality of available lectures and resources, which were often insufficient for effective learning. This led us to rely entirely on ChatGPT to teach ourselves course material from start to finish. We realized the immense value of tailored responses and the structured learning that emerged from the AI interactions. Recognizing the potential, this inspired the creation of a platform that could harness collective student input to create smarter, more effective lessons for everyone.

## What it does

Hivemind is an AI-powered learning platform designed to empower students to actively engage with their course material and create personalized, interactive lessons. By allowing students to input course data such as lecture slides, notes, and assignments, Hivemind helps them optimize their learning process through dynamic, evolving lessons. As students interact with the platform, their feedback and usage patterns inform the system, organically improving and refining the content for everyone. This collaborative approach transforms passive learning into an active, community-driven experience, creating smarter lessons that evolve based on the collective intelligence and needs of all users.

## How we built it

* **Backend**: Developed with Django and Django REST Framework to manage data processing and API requests.
* **Data Integration**: Used PyMuPDF for text extraction and integrated course materials into a cohesive database.
* **Contextual Search**: Implemented Chroma for similarity searches to enhance lesson relevance and context.
* **LLM Utilization**: Leveraged Cerebras and TuneAI to transform course content into structured lessons that evolve with user input.
* **Frontend**: Created a React-based interface for students to access lessons and contribute feedback.
* **Adaptive Learning**: Built a system that updates lessons dynamically based on collective interactions, guiding them towards an optimal state.

## Challenges we ran into

* Getting RAG to work with Tune
* Creating meaningful inferences with the large volume of data
* Integrating varied course materials into a unified, structured format that the LLM could effectively utilize
* Ensuring that lessons evolve towards an optimal state based on diverse student interactions and inputs
* Sleep deprivation

## Accomplishments that we're proud of

* Functional Demo
* Integration of advanced technologies
* Team effort

## What we learned

Throughout the development of Hivemind, we gained valuable insights into various advanced topics, including large language models (LLMs), retrieval-augmented generation (RAGs), AI inference, and fine-tuning techniques. We also deepened our understanding of:

* Tools such as Tune and Cerebras
* Prompt Engineering
* Scalable System Design

## What's next for Hivemind

* Easy integration with all LMS for an instant integration with any courses
* Support different types of courses (sciences, liberal arts, languages, etc.)
* Train on more relevant data such as research studies and increase skill level of the model
* Create an algorithm that can generate a large amount of lessons and consolidate them into one optimal lesson
* Implement a peer review system where students can suggest improvements to the lessons, vote on the best modifications, and discuss different approaches, fostering a collaborative learning environment