EcoLife 360 is an innovative mobile application designed to transform how we approach our diet and its impact on the environment. Utilizing the power of GPT-4 Vision, the app analyzes images of meals uploaded by users, providing detailed insights into their nutritional value and environmental impact. It's more than just a dietary tool; EcoLife 360 serves as a comprehensive guide towards a healthier lifestyle and a more sustainable way of living. By integrating advanced AI technology, the app identifies ingredients and assesses their sustainability, offering users personalized advice on healthier eating habits and eco-friendly choices. It's a step towards bridging the gap between personal health and environmental responsibility. Whether dining out, grocery shopping, or cooking at home, EcoLife 360 empowers users to make informed decisions that positively affect their health and the planet. The app is also designed to evolve. Future updates aim to include a broader database of products for eco-impact assessment, community features for sharing sustainable tips, and personalized tracking of users’ eco-friendly efforts. EcoLife 360 is not just an application; it's a movement towards a sustainable future, where each meal choice contributes to a healthier self and a greener world.

The integration of Mixtral for authentic human conversations has proven to be remarkably successful. In an upcoming version, we plan to refine the sequencing to more accurately mimic real group dynamics. Additionally, we aim to introduce a human participant to interact within this digital environment. A pivotal realization emerged: Mixtral's utility extends beyond patient support; it's a valuable tool for therapists to enhance their sessions and gather insightful statistics. Our programming “aha” moment was the use of Steps to create a final report and the use sentiment analysis to grade the session and offer feedback on how to make it better in the future. The societal benefits of Mixtral are multifaceted: Firstly, it establishes a secure and nonjudgmental space for discussing sensitive matters. Secondly, it furnishes therapists with a feedback mechanism to refine their practice. Lastly, it generates vital statistics, often required for governmental funding, through methods like sentiment analysis.

Overview: The Custom Everything Movie Script Creator, featuring AI agents Sam (author) and Donna (editor), blends your input for a unique 3-act script experience across nine plot segments. Starting with a user-submitted logline, it assists writers in crafting complex narratives with diverse story paths. Key Features: Initial Logline Input: Users set their screenplay's narrative foundation with a logline. Dual AI Agents for Script Development: Sam proposes story ideas and structures, while Donna refines for coherence and flow. Together, they craft from the Hook to The End. Human Revision and Oversight: Users review and revise each segment, ensuring the script aligns with their vision. Branching Narrative Paths: Offers three branching options at crucial points: expected, less predictable, and unconventional paths. Integration of Classic Three-Act Structure: Structures the screenplay in a traditional three-act format with AI-generated suggestions for key plot points and character development. Feedback Loop for Continuous Improvement: Continuous feedback allows AI refinement of dialogue, pacing, and themes. Application: Ideal for screenwriters and creatives, it's perfect for exploring narrative directions and refining story arcs. Conclusion: This tool merges AI and human creativity for a unique, interactive screenplay writing experience, empowering writers to experiment with storylines for a cohesive, personalized script.

The ongoing dialogue between humans and AI not only showcases the remarkable capabilities of current technologies but also illuminates the future possibilities of AI-human synergy, promising an era where AI enhances human creativity, decision-making, and problem-solving in unprecedented ways. Our hackathon project explored the interaction between humans and Large Language Models (LLMs) over time, developing a novel metric, the Human Interpretive Number (HIN Number), to quantify this dynamic. Leveraging tools like Trulens for groundedness analysis and HHEM for hallucination evaluation, we integrated features like a custom GPT-5 scene writer, the CrewAI model translator, and interactive Dall-E images with text-to-audio conversion to enhance understanding. The HIN Number, defined as the product of Groundedness and Hallucination scores, serves as a new benchmark for assessing LLM interpretive accuracy and adaptability. Our findings revealed a critical inflection point: LLMs without guardrails showed improved interaction quality and higher HIN Numbers over time, while those with guardrails experienced a decline. This suggests that unrestricted models adapt better to human communication, highlighting the importance of designing LLMs that can evolve with their users. Our project underscores the need for balanced LLM development, focusing on flexibility and user engagement to foster more meaningful human-AI interactions.

Graph Detective harnesses the power of state-of-the-art Agent and Phi-2 technology to redefine fraud detection and prevention in the insurance, banking, and eCommerce sectors. By unveiling hidden connections and identifying patterns, it offers an unrivaled solution that combines Generative AI Agents with Phi-2's speed and precision, enabling investigators to tackle fraud with unparalleled efficiency and accuracy. This groundbreaking app not only simplifies data analysis by allowing direct interaction with the Generative AI Agent, bypassing the need for complex coding or manual analysis, but also stands out for its ability to deliver consistent results without the common pitfalls of data hallucination, requiring minimal tuning. With fraud impacting the financial sectors to the tune of $308 billion annually in the United States alone, Graph Detective is poised to revolutionize the industry by offering an unmatched efficiency improvement over traditional methods, ensuring it remains at the forefront of meeting the evolving needs of fraud detection and prevention professionals. What sets our app apart is its unique integration of Phi-2 and Generative AI Agents, providing a solution that not only quickly identifies fraud patterns but is also easy to implement within Agent frameworks (such as CrewAI and LangGraph), making it a game-changer in the fight against sophisticated fraud schemes.

EZ-Read is a GenerativeAI comic book creation tool. It's primary use is for Creators who want to assemble a comic book using Generative AI for character, background images and book parsing with Generative AI summaries and metadata extractions, such as which characters are in which scenes. The tool was built for the hackathon in 3 apps (do to time zone differences) but will be a single platform ina future state. One thing we tried to forcus on was consistent character creation (in different poses). We got good results with Midjoureny, but there is no API. So we built our own using Stable Diffusion.