Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Gemma

Gemma is a lightweight, open large language model (LLM) from Google, optimized for efficient AI applications. As part of the Google Gemma family, it uses a transformer-based architecture tailored for responsible and accessible AI usage. Developed as a foundational model, Gemma serves various basic language processing needs, including chatbots, content summarization, and multilingual support.

General
Relese dateFebruary 2024​
AuthorGoogle DeepMind in collaboration with Google AI teams
Website[Google AI Gemma]https://ai.google.dev/gemma
RepositoryGoogle AI Developer Resources​
TypeOpen-source AI, transformer-based LLM

Key Features

  • Efficient Deployment: Available in parameter sizes like 2.5B and 7B, Gemma balances capability with efficiency, enabling deployments on both edge devices and cloud infrastructure​.

  • Flexible Tuning Options: Offers pre-trained and instruction-tuned variants, allowing developers to optimize for specific use cases or deploy as-is.

  • Decoder-Only Transformer Architecture: Uses a streamlined decoder-only design, enabling Gemma 1 to process up to 8192 tokens in one pass for better handling of long-form text​.

  • Safety and Accessibility Tools: Integrates responsible AI features, promoting transparency and safety in AI outputs​.

Applications:

  • Chatbot Development: Optimized for conversational tasks, Gemma provides foundational capabilities for chatbot applications.

  • Summarization and Paraphrasing: Its pre-trained model structure makes it suitable for summarizing content across languages and contexts.

  • Multilingual Processing: Supports multilingual inputs, making it adaptable for global applications and translation services​.

Get started building with Gemma:

Developers can quickly integrate Gemma into applications by accessing its model weights on Google AI Studio and Kaggle. The model’s lightweight design ensures that it can run efficiently on most hardware configurations, including mobile and edge devices. For optimal performance, utilize frameworks such as Keras or JAX to customize and deploy Gemma for your specific use case. Get started today by exploring the tools and resources available on the Google AI Gemma platform​.

Google Gemma AI technology Hackathon projects

Discover innovative solutions crafted with Google Gemma AI technology, developed by our community members during our engaging hackathons.

RoboGripAI

RoboGripAI

This project presents a simulation-first robotic system designed to perform structured physical tasks through reliable interaction with objects and its environment. The system focuses on practical task execution rather than complex physics modeling, ensuring repeatability, robustness, and measurable performance across varied simulated conditions. Simulation-first robotic system performing structured physical tasks such as pick-and-place, sorting, and simple assembly. Designed for repeatable execution under varied conditions, with basic failure handling, environmental interaction, and measurable performance metrics. A key emphasis of the system is reliability under dynamic conditions. The simulation introduces variations such as object position changes, minor environmental disturbances, and task sequence modifications. The robot is designed to adapt to these variations while maintaining consistent task success rates. Basic failure handling mechanisms are implemented, including reattempt strategies for failed grasps, collision avoidance corrections, and task state recovery protocols. The framework incorporates structured task sequencing and state-based control logic to ensure deterministic and repeatable behavior. Performance is evaluated using clear metrics such as task completion rate, execution time, grasp accuracy, recovery success rate, and system stability across multiple trials. The modular system design allows scalability for additional tasks or integration with advanced planning algorithms. By prioritizing repeatability, robustness, and measurable outcomes, this solution demonstrates practical robotic task automation in a controlled simulated environment, aligning with real-world industrial and research use cases. Overall, the project showcases a dependable robotic manipulation framework that bridges perception, decision-making, and action in a simulation-first setting, delivering consistent and benchmark-driven task execution.

Navicare AI

Navicare AI

Navicare AI is an innovative healthcare application leveraging artificial intelligence to enhance patient journey mapping and address key challenges in the healthcare ecosystem. By focusing on improving communication, efficiency, and patient outcomes, Navicare AI strives to support healthcare professionals in delivering more effective and personalized care. The platform utilizes the Gemma 2 API, which powers it with advanced AI capabilities to analyze and interpret patient data. This facilitates insights into patient needs and creates tailored care pathways that adapt dynamically based on individual circumstances. While the application is not yet fully functional with features like real-time predictive analytics or population health management, it has been designed to lay the groundwork for these capabilities in future iterations. The application’s core focus includes bridging the communication gap between patients and healthcare providers, addressing system inefficiencies, and fostering informed decision-making processes. With an intuitive interface and backend functionalities currently under development, Navicare AI envisions integrating data from wearable devices and hospital systems in the future to ensure a comprehensive and robust approach to patient care. Ultimately, Navicare AI aims to transform healthcare delivery by making it more efficient, patient-centric, and data-driven. Its vision includes scaling to a solution capable of supporting healthcare systems globally in navigating their operational challenges and improving overall outcomes.

EduAI - Democratizing education through AI

EduAI - Democratizing education through AI

ENEM (Exame Nacional do Ensino Médio) is Brazil's National High School Exam - a standardized test used as the primary entrance exam for most Brazilian universities. EduAI addresses a critical gap in Brazil's educational landscape. While private school students often have access to personalized education and extensive practice, public school students face significant barriers in preparing for ENEM in general, specially ENEM's essay component, a crucial element for university admission. Our web platform leverages artificial intelligence to provide instant, detailed feedback on student essays following ENEM's official scoring criteria. Unlike traditional methods that require teacher availability, EduAI offers unlimited practice opportunities and immediate guidance. Students can submit essays 24/7, receiving comprehensive feedback across all five ENEM competencies, detailed suggestions for improvement, and personalized learning paths. The platform democratizes access to quality essay preparation through three key features: 1. Instant essay analysis: Detailed scoring and feedback based on ENEM's official rubric 2. Guided improvement: Specific recommendations highlighting strengths and areas for development By making high-quality essay preparation accessible to all students, regardless of their socioeconomic background, EduAI aims to level the playing field in Brazilian education and increase opportunities for public school students to access higher education.