Gracjan Wojciechowski@gracjanpw723

Rarely people read the whole data corpus of terms of service when they sign up for companies and we came with a solution: The primary objective of Term Aware Guard is to simplify the readability of Terms and Conditions, providing a summarized version and ensuring that users are well-informed about data privacy beforehand. It is a web app with built with Next.js using GCP for hosting and Firebase for storing the companies the user has signed up for. The web app uses Python environment REST API hosted on Digital Ocean for the backend. We use Google Vertex AI PaLM2 on text-bison002 model for inference, a large language model with impressive capabilities. Terms and conditions of companies change constantly, and fine tuning the model everytime that happens could be very costly and inefficient, for that we found a solution. We use Retrieval augmented generation(RAG) to enhance the model's summarization capabilities and to get the context of the latest updated version ToS of companies. The model is evaluated with the help of TruLens to measure its quality with feedback and metrics. We used Apify for gathering the data by web scrapping and Pinecone as a vector store where RAG gets context from.

A web app with built with React and Next.js using GCP for hosting and Firebase for storage. Web app uses a Large Language Model as main feature: Google Vertex AI PaLM2 on text-bison model. For fine tuning we plan to use CUAD dataset and the model was modified with LangChain, for enhancing it's summarization abilities to state of art. The app is aimed to summarize and let the user be aware of the terms and conditions of the companies he signed up for. The model be evaluated with the help of TruLens and The final features will be: Summarization of the company's ToS, Storing previous answers and use them to plot and visualize the the terms you've agreed on, Submit your own terms and conditions which will be summarized and sent into the pipeline for context

While coding, programmers rely heavily on documentation, and the process of switching windows every time you search for something could be obnoxious at times, especially if the device has only one display. We thought about a solution of speeding the process of developing by creating a code helper VSCode extension. Llama2 GPT CodePilot is aiming at helping software developers in building code or debugging their software by prompting the gpt making it coding convenient for developers with only one display. It uses a large language model, CodeLlama-7B-Instruct-GPTQ, takes input from the user, and generates a relevant response based on the text given. It is published on the VSCode extension marketplace under the name of "Llama2-GPT-CodePilot" and it is written in TypeScript

⌚ 3-day AI Hackathon 🚀 Compete to build an AI app powered by Llama 2 💡 Learn how to extend your capabilities with Clarifai! 🎓 Mentors are available to support you during your creative journey

TruLens provides a robust suite of tools for neural network development and monitoring, including LLMs. You can utilize TruLens-Eval for evaluation and TruLens-Explain for deep learning explainability, independently. Explore these valuable tools while taking on the challenge!

Revolutionize AI with TruLens by TruEra - Your Gateway to Building Better LLM Applications! Build with TruLens, LLMs development and explainability tool by TruEra Leverage AI observability for dynamic app enhancement Collaborate with like-minded individuals! For the Hackathon you can work in a team from 1 to 6 members. In this Hackathon, you can also join with a project from the TruLens 48-Hours Hackathon, provided you adhere to the following rules. Consider it a warm-up, and explore the world of technology by participating in the TruLens 48-Hours Hackathon.