## Inspiration

How much carbon does farming really sequester ? This is one question that inspired us to create this solution. With rising interest of governments around the world to start taxing farmers for their emissions, we wanted to find a way to calculate them.

## What it does

A drone with a variety of sensors measure the CO2, CH4 and albedo of the land underneath it to estimate the actual carbon offset. The data collected in drone is sent to our server online which is fetched by MATLAB to calculate the carbon offset.
The drone also has sensors to calculate water quality.

In future the drone will also have soil moisture detection capability using microwaves similar to remote sensing satellites.

With the offset we are able to calculate the carbon credits which can then trade over the pi platform. By using blockchain we enable :
1) No double counting of credits
2) Ensure wider participation from around the world (pi has already over 35 million users)
3) Ensure only algorithmic calculated credits are there.

## How we built it

Using Arduino, PI, MATLAB

## Challenges we ran into

PI was a tough challenge to implement. Loading sensors on drone was another big challenge.

## Accomplishments that we're proud of

We were able to get all sensors to work, collect data in real time and run matlab analysis on it

## What we learned

## What's next for We Are Sus Farms
## 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)
## Inspiration

As we know, demand for natural resources is at an all-time high and continues to grow for food, clothing, water, housing, infrastructure, and other aspects of life. Resource extraction has more than tripled since 1970, including a 45% increase in fossil fuel use. We are using the equivalent of 1.6 Earths to maintain our current way of life, and ecosystems cannot keep up with our demands. This is where we thought, reducing carbon footprint and emissions should be included in one's lifestyle. Hence we decided to create an application to encourage teens and young adults for them to become self-aware and induce some of the greener, eco-friendly ways of doing things in life.

## What it does

Commune is a social networking site, a place to discuss green activities, and, of course, a new way of communicating. Commune gives chores to users every week, and each one is designed to be more environmentally friendly in order to ensure sustainability. Each user receives tasks that are personalized precisely to them based on their data. Users may use the app to send back photographs as they complete tasks, and they can even create a timeline to track their progress over time. It is vital to clarify that Commune supports all green organizations, events, and individuals and wishes to attract more people to the cause. Users can support ongoing, significant, or noteworthy causes by selecting the donation option at the bottom of our list.

## How we built it

Our application was build on a flask backend, which is a scalable framework for creating microservices. We used Cloud Vision API for object recognition in the pictures and CheckBook API for facilitating carbon-offsetting transactions between our users and the organizations. MongoDB was the database of choice for storing user data as well as their timelines.

## Challenges we ran into

One of the challenges we faced during this hackathon was assessing good ideas. On the implementation side, we wanted to recommend custom tasks for each person based on their history, and we found very little or no public data for this use case.

## Accomplishments that we're proud of

We're really proud of the fact that all 3 of us had a passion for sustainability and were determined to build something for the social good. We're really happy to see how the end product turned out to be and hope to continue working on this going ahead.

## What we learned

This initiative helped us to expand our ideas about sustainability, carbon emissions, and the future ramifications.

## What's next for Untitled

There are a set of features that we want to implement to enhance the community experience and increase engagement. We want the people using our application to be educated about the potential dangers, so we'd like to use generative AI to help them learn more about carbon emissions, carbon offsetting, and climate change.