Masa is a decentralized network for Fair AI, empowering developers to train AI using real-time data from various sources. Masa’s competitive global network allows you to build innovative AI agents with ease.
Key Points About Masa:
🏆 First AI project to debut on CoinList Launchpad in 2024: Masa launched via a viral, 17-minute token sale.
💼 Supported by Leading Investors: Backed by DCG, Anagram, Animoca, and incubated by Binance and Hashkey.
📊 Extensive Data Sources: Provides real-time and static (indexed), structured, annotated, and vectorized data from a variety of sources critical for AI development.
Data Sources accessible via Masa Include:
🐦 X-Twitter: ~350 million tweets daily
💬 Discord: ~4 billion messages daily
📱 Telegram: ~15 billion messages daily
🌐 Web pages: ~250,000 new websites launched daily
🎙️ Diarized speech: ~120,000 new podcast episodes daily; ~720,000 hours of new video content uploaded daily
Use Case: Quickly scrape and access data via the Masa Network to create AI powered applications or AI agents like real-time trading predictions or sentiment analysis.
Welcome to the Masa Bounty for the Redacted Hackathon!
Prize:
You have the freedom to create your own AI agent idea or use the examples below to get started. Focus on building an MVP that showcases core functionality, whether it’s based on one of these examples or your own unique concept. The only requirement is to use Masa Twitter data in your AI agent. Note that you can also leverage Masa Twitter data to build AI agents for other bounties, and earn multiple rewards.
Feel free to innovate and push the boundaries—originality is highly encouraged!
Ideation:
Objective: Use Masa’s Twitter data and tools to build an AI agent that excites you.
Key Tasks:
Identify a problem or opportunity where real-time Twitter data can provide value.
Implement core features that address this problem.
Focus on creating a working prototype within the timeframe.
Why This Matters: Innovative ideas can lead to groundbreaking solutions. Let your creativity guide you!
Example 1: KOL AI Agent with Masa Twitter Data (video demo)🐦
Objective: Track and analyze Crypto Twitter Key Opinion Leaders (KOLs) for real-time market insights.
Key Tasks:
Sentiment Monitoring: Analyze sentiment from KOL tweets.
Topic and Trend Analysis: Identify and track trending topics.
Market Sentiment Insights: Provide actionable insights based on KOL activities.
Why This Matters: Stay ahead of market trends driven by influential crypto figures.
Example 2: Memecoin AI Agent with Masa Twitter Data (video demo)🚀
Objective: Predict memecoin market movements using real-time Twitter data.
Key Tasks:
Spike Detection: Identify spikes in mentions, hashtags, and emojis.
Trend Tracking Interface: Create a dashboard to monitor trends.
Data Analysis: Rank memecoins based on activity metrics.
Why This Matters: Capitalize on the unpredictable growth of memecoins with real-time insights.
Decide whether you want to scrape real-time Twitter data using the Masa protocol or utilize existing Twitter datasets from Masa's developer playground.
Option A: Real-time Twitter Data Scraping via Masa Twitter Worker Node
Run a Masa Protocol Twitter Worker Node
Setup the Masa Protocol Node: Setup Documentation
Stake with Test Masa: Staking Documentation.
Setup Your Twitter Worker: Twitters scraper node setup
Option B: Large Amounts of Real-time Twitter Data Scraping via Masa Twitter Worker Node
Run a Masa Protocol Twitter Worker Node
Setup the Masa Protocol Node: Setup Documentation
Stake with Test Masa: Staking Documentation.
Setup Your Twitter Worker: Twitters scraper node setup
Setting up the Masa SDK: The SDK will allow you to more easily scrape larger datasets. You will need a Masa protocol node running to use the SDK. Note: You are also able to scrape the data with the Masa protocol alone but can run into limitations on how much data you scrape.
Install the SDK
Access SDK Documentation
Option C: Pre-Scraped Twitter Datasets
Access Existing Datasets
After completing the setup, you'll be ready to start working with Twitter data using the Masa protocol. If you encounter any issues or have questions, please refer to our comprehensive documentation or reach out to our support team.
Data Processing: Use simple scripts to clean and analyze data. Focus on implementing basic sentiment analysis or spike detection.
Model Application: Use pre-trained models (e.g., LLaMA 3.1) to apply sentiment analysis or trend detection. Keep the implementation simple to fit the timeframe.
User Interface: Use Streamlit or a preferred solution to create a basic, functional dashboard that visualizes the insights generated by your AI agent.
Ensure your core features work smoothly. Quickly iterate based on test results to refine functionality.
Prepare Your Demo: Focus on showcasing the core features and real-time capabilities of your AI agent.
Record a Loom Video: Create a short Loom video (3-5 minutes) demonstrating your project. Walk through your AI agent's key features, explain how it works, and highlight any innovative aspects.
Document Your Work: Ensure your code is well-documented, and include any setup instructions needed to run your AI agent.
Submit Your Work: Please email your work to calanthia@masa.ai.