2 minute read

[Written by GPT-4o]

Project Brief: Building the “vAItalik” Twitter AI Agent

Objective:
Develop an AI-driven Twitter agent, “vAItalik,” modeled after Vitalik Buterin’s communication style. This AI bot will analyze Vitalik’s tweets, find alpha from his work, tweet ideas in his style, and even engage with his tweets in a meaningful way—helping clarify discussions around Ethereum without spamming or disrupting.

Tasks Overview:

  1. Research AI Agent Frameworks
    • Explore SmolAgents by HuggingFace and understand its capabilities in context retrieval and response generation.
    • Compare with alternatives like Eliza (by a16z) and AgentKit (by Base), particularly focusing on their integration with Twitter APIs.
    • Study AIXBT, a well-known AI Twitter bot, to understand its logic, engagement patterns, and strategies.
  2. Data Preparation
    • Leverage the scraped data of Vitalik’s blog posts and tweets to build a contextual knowledge base.
    • Format the data to be RAG (Retrieval-Augmented Generation) ready for efficient querying.
    • Identify additional sources such as Ethereum Foundation news, GitHub repos, and grant announcements for relevant context.
  3. Agent Training & Fine-Tuning
    • Choose the most suitable framework (SmolAgents, Eliza, or AgentKit) to train the model using Vitalik’s data.
    • Train the model to generate tweets in Vitalik’s tone, including thought leadership, technical insights, and community engagement.
    • Implement reinforcement learning techniques (if applicable) to improve responses over time.
  4. Twitter API Integration
    • Set up a Twitter Developer account and API keys for posting and fetching replies.
    • Implement automatic tweet generation and scheduled posting.
    • Develop logic to analyze Vitalik’s tweets and provide meaningful replies in his style.
    • Ensure bot behavior adheres to Twitter policies to avoid spammy interactions.
  5. Agent Monitoring & Optimization
    • Implement sentiment analysis to ensure respectful and productive interactions.
    • Tune engagement strategies by monitoring tweet performance and adjusting tone if necessary.
    • Add human-in-the-loop functionality where needed for manual oversight.
  6. Deployment & Automation
    • Set up cloud deployment (e.g., AWS Lambda, HuggingFace Spaces, or Vercel) for continuous operation.
    • Implement fail-safes to avoid excessive posting or incorrect responses.

Expected Deliverables:

  1. A trained AI model that can:
    • Generate insightful tweets based on Vitalik’s style.
    • Reply meaningfully to Ethereum-related discussions.
    • Identify key trends and opportunities from Vitalik’s posts.

  2. A working prototype running on a dedicated Twitter account (@vAItalik).

  3. Documentation covering:
    • Chosen framework and reasoning.
    • API integrations and setup.
    • Model fine-tuning process and results.
  • AI Frameworks: SmolAgents / AgentKit / Eliza
  • Database: Qdrant / Pinecone for embeddings
  • APIs: Twitter API, OpenAI/Claude for generation
  • Deployment: AWS Lambda / HuggingFace Spaces

Next Steps for Suboor:

  1. Review SmolAgents documentation and run initial tests.
  2. Research Twitter integration capabilities across different frameworks.
  3. Set up data pipelines for training and RAG integration.
  4. Provide a progress update in 3-5 days.