Avidia Features Document

1. Engaging Learning Platform

• Overview: Avidia is an innovative learning platform that blends quizzes, assessments, live sessions, and hands-on projects to create an engaging and interactive educational experience. This platform is designed to cater to various learning styles and provide students with practical skills and knowledge.
• Implementation:
  • Quizzes and Assessments:
    • Backend: Built on Express with TypeScript, the backend handles user authentication, quiz management, scoring, and feedback mechanisms. It ensures scalability and security through microservices architecture.
    • Frontend: The frontend, developed with Next.js and ShadCN, provides a responsive and interactive interface. Quizzes are dynamic, offering instant feedback and progress tracking, while assessments are designed to evaluate deeper understanding and practical skills.
    • Database: PostgreSQL or MongoDB stores quiz questions, answers, student scores, and progress data. Real-time updates are managed using WebSockets or GraphQL subscriptions.
  • Live Sessions:
    • Integration with Video Platforms: Avidia integrates with popular video conferencing tools like Zoom or WebRTC for live sessions, providing real-time interaction between students and instructors.
    • Scheduling and Reminders: Integrated calendar and notification systems help students keep track of live sessions, ensuring they never miss a learning opportunity.
  • Hands-on Projects:
    • Project Management: The platform offers a project management tool where students can select projects, collaborate with peers, and submit their work. This is integrated with the GIT-based verification system.
    • Collaboration Features: Shared workspaces, version control, and peer review options enable students to work together effectively on projects.

2. Virtual Cloud Labs

• Overview: Avidia’s virtual cloud labs allow students to run software in the cloud via a browser, eliminating the need for powerful local machines and providing access to industry-standard tools.
• Implementation:
  • Kubernetes for Cloud Labs:
    • Cluster Setup: Kubernetes orchestrates containerized applications, allowing each student to have a personalized and isolated environment. The setup ensures that resources are allocated efficiently, scaling up or down based on demand.
    • Environment Configuration: Docker containers are pre-configured with necessary software, providing a consistent development environment across all users.
    • Resource Management: Kubernetes handles load balancing and resource allocation dynamically, ensuring optimal performance even during peak usage times.
  • Frontend Integration:
    • User Interface: The Next.js and ShadCN-based frontend provides an intuitive interface for launching and managing cloud labs. The platform integrates with a VNC client, enabling students to interact with the cloud-based environment as if it were running on their local machine.
    • Security: HTTPS and WebSocket Secure (WSS) protocols ensure secure communication between the client and the cloud labs.

3. AI Guidance and Mentorship

• Overview: Avidia’s AI-driven guidance and mentorship system is personalized for each student, utilizing fine-tuned models based on data from previous cohorts and bootcamps.
• Implementation:
  • AI Engine:
    • Fine-Tuned Llama 3: The AI system is powered by a fine-tuned version of Llama 3. The model is trained on data collected from previous bootcamps and cohorts, where mentors manually assessed student code. The fine-tuning process focuses on several key areas to ensure that the AI can provide meaningful insights:
      • Code Quality Metrics: The model evaluates code quality based on industry standards and best practices. This includes assessing code readability, maintainability, and adherence to design patterns.
      • Common Bad Coding Practices: The model is trained to recognize and flag common bad coding practices such as hardcoding, excessive nesting, lack of modularity, and improper error handling.
      • Improvement Rates Over Time: The model tracks and analyzes a student's improvement over time, measuring how their coding skills have progressed in terms of efficiency, code structure, and problem-solving ability.
      • Error Patterns: By identifying recurring errors and debugging patterns, the AI can provide targeted advice to help students overcome specific challenges.
      • Feedback on Assignments: The AI offers detailed feedback on assignments, highlighting both strengths and areas for improvement, and providing suggestions for further learning.
    • Contextual Awareness: Every interaction with the AI is stored as embeddings in a vector database (such as Pinecone or Faiss). This allows the AI to maintain context and personalize its responses according to each student's progress and learning style.
  • Mentorship Features:
    • Conversational AI: The AI provides real-time guidance, answering questions, offering suggestions, and helping students overcome challenges. This is achieved through a custom NLP model that understands the nuances of student queries.
    • Analytics and Reporting: The AI tracks and analyzes student performance, providing detailed reports to both students and instructors. These reports highlight strengths, areas for improvement, and suggest personalized learning paths.

4. GIT-Based Verification System

• Overview: The GIT-based verification system in Avidia automatically analyzes every commit that a student makes, ensuring that code meets quality standards and is free of common issues.
• Implementation:
  • Code Analysis:
    • Integration with Git: The system is tightly integrated with Git, monitoring student repositories for new commits.
    • Automated Analysis Tools: Tools like SonarQube, ESLint, and custom scripts are used to analyze code for bad practices, vulnerabilities, and redundancy. These tools are integrated into a CI/CD pipeline, ensuring that analysis is continuous and automated.
    • Custom Rule Sets: Instructors can define specific rules for code quality, security, and efficiency. The system checks each commit against these rules and provides feedback to students.
  • Feedback Mechanism:
    • Real-Time Feedback: Students receive immediate feedback after each commit, with detailed explanations of any issues found. This helps students learn from their mistakes and improve their coding practices.
    • Reporting and Progress Tracking: The system generates detailed reports on student progress, highlighting improvement over time and areas that need further work.

5. Custom Hardware Device

• Overview: Avidia offers a low-cost hardware device designed to integrate seamlessly with the platform’s cloud labs. This device is connected to the internet via AWS Private 5G and runs a lightweight Linux distribution capable of accessing cloud services.
• Implementation:
  • Hardware Specifications:
    • Device: The device is based on Raspberry Pi 4, offering a balance of performance and affordability. It is powerful enough to run a lightweight Linux distribution and a VNC client.
    • Operating System: Arch Linux is chosen for its minimalism and flexibility, allowing the device to run efficiently on limited hardware.
    • VNC Client: A lightweight VNC client is pre-installed, allowing users to connect to cloud labs and run resource-intensive software remotely.
  • AWS IoT Greengrass:
    • Edge Computing: AWS IoT Greengrass is deployed on the device to enable edge processing, reducing latency and enhancing performance.
    • Device Management: AWS IoT Core manages the devices, providing secure and scalable communication between the devices and cloud services.
  • AWS Private 5G Network:
    • Connectivity: The device connects to the internet via an AWS Private 5G network, ensuring high-speed, low-latency connections even in areas with poor public network coverage.
    • Security: End-to-end encryption ensures that data transmitted between the device and the cloud remains secure, protecting student information and intellectual property.

6. Tech Stack Overview

• Frontend:
  • Next.js + ShadCN: Provides a responsive, dynamic user interface that is both easy to use and visually appealing.
• Backend:
  • Express with TypeScript: Offers a scalable and robust API, supporting microservices architecture and providing the necessary backend functionality.
• Cloud Infrastructure:
  • Kubernetes: Manages and scales the cloud labs, ensuring that resources are used efficiently and environments are consistent.
  • AWS Services: Including AWS IoT Greengrass, AWS IoT Core, and AWS Private 5G for secure, scalable device connectivity and management.
• Hardware:
  • Raspberry Pi: Provides an affordable yet capable hardware platform for running the lightweight Linux distribution and connecting to cloud labs.
  • Arch Linux: A minimalistic operating system optimized for performance on low-power devices.
• AI Integration:
  • Llama 3 Fine-Tuned: Provides personalized AI mentorship based on historical data, with models fine-tuned using data from previous cohorts and bootcamps.
  • Vector Database: Stores and retrieves contextual embeddings, enabling the AI to provide personalized and context-aware responses.
• Code Verification:
  • GIT Integration: Ensures that all code pushed by students is analyzed for quality and security, providing immediate feedback and detailed reports.

Conclusion

Avidia’s feature set and implementation strategy are designed to provide a comprehensive, scalable, and personalized learning experience. By leveraging cutting-edge technology, Avidia aims to deliver a platform that not only educates but also empowers students to reach their full potential. The combination of cloud labs, AI-driven mentorship, and a GIT-based verification system ensures that students receive a well-rounded education, with tools and resources that are both accessible and effective.