Problem: Network outages and equipment failures can have severe consequences, including financial losses, disrupted services, and reputational damage. Traditional maintenance approaches are reactive, addressing issues only after they occur. This leads to inefficiencies, increased costs, and prolonged downtime. There is a critical need for a proactive solution that predicts potential failures and enables timely interventions solution:ProactiveGuard is a cutting-edge predictive maintenance system designed to revolutionize how organizations manage their infrastructure. The system integrates operational data (e.g., sensor readings), connectivity metrics (e.g., network performance), and geospatial data (e.g., location-based environmental factors) to predict failures before they happen. Key features of ProactiveGuard include: Real-Time Risk Prediction : Uses machine learning models to analyze historical and real-time data, providing accurate predictions of failure probabilities. Geospatial Insights : Incorporates location-specific data to identify patterns and correlations between environmental conditions and equipment performance. Actionable Alerts : Generates risk levels (Low, Medium, High) and actionable recommendations to guide maintenance teams. Scalable Architecture : Built with a modular design to support integration with IoT devices, APIs, and third-party systems.

*"Analyze the uploaded CIF (Crystallographic Information File) and generate a comprehensive textual report detailing the structural and chemical properties of the given crystalline material. Begin by identifying the crystal system, space group, and symmetry operations governing the structure. Provide an in-depth examination of the unit cell parameters, including the lattice dimensions (a, b, c) and the interaxial angles (α, β, γ), discussing how these parameters influence the overall stability and geometry of the crystal. Next, extract and interpret the atomic positions, coordination environment, and bonding characteristics within the structure. Identify the unique atomic sites and their respective occupancy, ensuring that the provided atomic coordinates align with the expected symmetry constraints. Discuss the types of chemical elements present in the material, their oxidation states if applicable, and any significant interactions between atoms, such as covalent bonding, ionic bonding, or van der Waals forces. Additionally, analyze any reported thermal parameters, including atomic displacement factors (B-factors), and comment on their implications regarding the vibrational motion of atoms in the crystal. If applicable, review any supplementary data related to the material’s electronic structure, density, or potential defects that may influence its physical and chemical properties. Check for any inconsistencies, missing data, or errors in the CIF file, such as incomplete atomic positions, undefined space groups, or unusual lattice parameters, and provide explanations on how these issues might affect the reliability of the structural analysis. If necessary, suggest potential corrections or refinements that could improve the accuracy of the crystallographic description.

Our team’s vision for this event is driven by a decade of research and over 15 initiatives that highlight a persistent challenge: fragmented and disorganized data and information. Tackling this foundational issue is essential to addressing broader governance challenges, and we’re taking the first step with a transformative prototype: the Knowledge Extraction Model. Whether it’s legal documents, governmental records, or citizen petitions, the inability to structure, interconnect, and analyze information impedes progress across the board. The solution lies in creating a robust, cross-sector platform capable of organizing data and fine-tuning AI models for graph-based knowledge representation. By solving this root issue, we can pave the way for more efficient, transparent, and citizen-focused governance systems. We did a prototype that leverages advanced AI and graph database technology to restructure how data is organized and utilized. Some features: Knowledge Extraction and Graph Databases The model identifies patterns, entities, and relationships within unstructured data to construct a knowledge graph. This structured representation allows for better visualization and analysis of interconnected information, enabling actionable insights. Grok extracts relevant information from diverse data sources. It organizes the extracted data into graph databases, streamlining the process of analysis and relationship mapping. Machine Learning for Legal and Governmental Analysis The model transforms legal and governmental documents into organized, cyclical formats, improving process tracking and management. Knowledge Graph for Recommendations The prototype supports systems that recommend appropriate governmental entities for resolving specific issues. This ensures clearer and more efficient pathways for citizens to interact with government services. More information here: https://docs.google.com/document/d/1yE91oZBmNIqkvarKIu1b6zjswOSHMY_KC9k7GrHl5Vs/edit?usp=sharing

Immortiva stems from the belief that health data is central to understanding and extending human life. Our medical records are fragmented across clinics, insurers, and institutions, creating silos that impede effective care and prevent individuals from having full ownership of their medical history. This fragmentation hinders holistic health insights, an essential component of personalised care and longevity research. Immortiva envisions a future where each individual has seamless access to an integrated and centralised repository of their health records, powered by AI. This repository, Life-Vault, represents the foundational step in the journey so we decided to partner with MediAgil, Volmand, Ciudad Botica, New Digital Trade Coalition, Mozart and other organizations in Life-Vault for this Hackaton. The Importance of Health Records for Longevity and Immortality: Medical histories are critical not just for diagnosing and treating conditions but also for predicting and preventing illnesses. AI thrives on data, and the richer and more complete the dataset, the more accurate its insights. By consolidating medical records into a unified Life-Vault, we enable: 1. Longitudinal Analysis: A continuous view of a person’s health over time to identify patterns and trends that can inform proactive care. 2. Customised Health Recommendations: AI-driven analysis provides personalised advice tailored to an individual's unique history and needs. 3. Precision Medicine: Improved access to comprehensive records allows healthcare providers to offer treatments that are more targeted and effective. 4. Research Advancements: Aggregated anonymised data supports breakthroughs in health, biotechnology, and transhumanism, accelerating the path to longevity. This emphasis on centralised records is critical to Immortiva MTP—extending life expectancy and improving quality of life through systematic innovation. Creating Life-Vault we can start an MVP of data training for our network.

Image Upload: Users upload a diagram of the project structure. AI Processing: IBM watsonx.ai analyzes the image and extracts details. Llama Multimodal AI generates repository files (e.g., folder structure, README.md, starter code). Customization: Users can preview and edit generated content. GitHub Integration: Pushes the finalized repo to GitHub automatically. Streamlit App: Image-to-Repo Generator An AI-powered app that converts project diagrams or sketches into a GitHub repository in minutes. Features: Image Upload: Users upload a diagram of the project structure. AI Processing: IBM watsonx.ai analyzes the image and extracts details. Llama Multimodal AI generates repository files (e.g., folder structure, README.md, starter code). Customization: Users can preview and edit generated content. GitHub Integration: Pushes the finalized repo to GitHub automatically. Benefits: Saves time by automating project setup. Enhances team collaboration and efficiency.

Annuncio addresses the challenge of creating engaging product advertisements efficiently. In a competitive market, businesses often struggle to produce high-quality promotional content that captures the essence of their products. Our solution combines the power of the Aria and Allegro AI models to automate the generation of comprehensive product descriptions and engaging promotional videos. Targeting e-commerce businesses, marketers, and entrepreneurs, Annuncio provides an easy-to-use interface via Streamlit, allowing users to input product details and receive polished outputs instantly. Unique features include automated hashtag generation to enhance social media reach and the ability to create content that can be easily integrated into broader digital marketing strategies. This ensures that users can effectively promote their products across multiple platforms.

HealthPulse is an innovative AI-driven health assistant designed to help users make informed decisions about their health. By analyzing wearable data like sleep, activity, and symptoms, it predicts future health trends and offers personalized supplement recommendations. HealthPulse also allows users to input symptoms, generating detailed health reports to prevent the negative impact of misleading information often found online. Powered by MindsDB’s predictive models, it provides real-time updates and proactive health advice. The system continuously learns and adapts, offering dynamic insights tailored to each user’s unique health needs, ensuring accuracy and relevance over time.

Traditional methods of creating quizzes for educational purposes are time-consuming and require significant effort from educators. This limits their ability to focus on more critical aspects of teaching and student engagement. Additionally, students often face a lack of interactive and personalized learning tools, which can hinder their learning experience and outcomes. The Quiz App leverages the power of Falcon AI to generate quiz questions on any given topic with multiple-choice answers. The app allows educators to specify the topic and the number of questions, and the AI generates relevant questions with four possible choices and the correct answer. Students can take the quiz, select their answers, and submit them to receive instant feedback on their performance. This solution streamlines the quiz creation process for educators and provides an interactive learning tool for students.

Nous proposons une application web facile d'utilisation sur laquelle nous avons déployer un modèle de traduction français-fon pour permettre à tout utilisateur d'importer une vidéo en français de son choix et de voir la transcription de cette vidéo en fongbé. Pour ce fait, lorsque l'utilisateur appuie sur le bouton démarrer sur la page d'accueil, il a la possibilité de glisser-déposer ou de sélectionner une vidéo en français de son répertoire. Après le chargement de la video, la transcription en fon de la vidéo est afficher à côté de la vidéo et il pourra suivre la transcription avec la vidéo. L'autre particularité de notre application est qu'une vidéo présentant le Bénin est disponible sur la page d'accueil avec sa transcription en fongbé. Aussi, l'utilisateur a t il la possibilté de voir les différents textes de l'aplication en fongbé.

🤖 You have 4 weeks to build. Join in at any point! 🌐 Connect with a global tech community. 🎉 Win from a $30,000 cash prize pool. 💾 The winning agent will become the AutoGPT in the 150000 star repository! 🚀 You may continue your startup journey after the Hackathon! 🖥️ Utilize Weaviate as the vector database for your agent with open access.

🔐 Exclusive Access: Only 1,000 slots available for Coral model use. 💡 Spotlight Your Idea: Showcase in this limited-entry event. 💰 Win Big: Cash prizes and Cohere credits up for grabs. 🌐 Collaborate Globally: Team up with AI enthusiasts worldwide.

Fine-tuning is a technique that empowers pre-trained models to perform specific tasks or behaviors, opening up a world of possibilities for customization and specialization. Dive into the details of this cutting-edge technology and showcase your skills in this fast-paced hackathon!