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Divyesh Mali edited this page Nov 16, 2024 · 1 revision

Welcome to the Maverick AI Wiki! 📖

Introduction

Maverick AI is an open-source, AI-powered content moderation platform designed to create safer online communities by leveraging advanced AI models and real-time analysis. This Wiki serves as a central hub for all the essential information you need to understand, contribute to, and expand upon the Maverick AI project.

Below, you'll find a log of our project’s progress and updates, starting with the Day 1 & 2 developments.

Screenshot 2024-11-16 032234

Day 1 & 2: Setting the Foundation

🛠️ Key Accomplishments

1. Dynamic Contributor Display Using GitHub API

We implemented functionality to dynamically display the list of contributors to Maverick AI by leveraging the GitHub API.

  • Purpose: To ensure real-time updates and proper recognition of all contributors.
  • How It Works: The API fetches and displays contributor profiles directly from the GitHub repository, providing transparency and fostering collaboration.
  • Impact: This encourages active participation from the community by showcasing contributions in a visible and impactful way.

2. Supabase API Integration

We successfully integrated the Supabase API to handle database management and backend operations seamlessly.

  • Purpose: To ensure reliable and efficient data storage and retrieval for moderation tasks and user interactions.
  • How It Works: The Supabase API enables real-time communication between the backend and the front end, simplifying data management processes.
  • Impact: This integration lays the groundwork for robust, scalable backend infrastructure, supporting features like user data storage, moderation logs, and analytics.

🚀 Progress So Far

These foundational developments set the stage for Maverick AI's functionality and scalability:

  • A polished contributor recognition system that showcases our open-source community.
  • A backend framework ready for advanced features like real-time content analysis and data export.

Next Steps

  1. Implement real-time text analysis using TensorFlow.js toxicity models.
  2. Enhance the frontend with responsive UI elements using React 18 and Tailwind CSS.
  3. Build a seamless CI/CD pipeline with GitHub Actions and Docker.

💬 Engage With Us!

Have questions or suggestions? Head over to the Discussions tab and join the conversation!

Stay tuned as we build a safer internet, one step at a time. 🌐

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