🧠 LIQUBIT (NO TOKEN YET)

Advancing on-chain intelligence through LLM-powered market analysis

STATUS

1. MVP for terminal product is developed with in context crypto data context -> LLM processing layer
2. Cookie.fun integration in process (REQUEST TO DAO OUT); Data swarms to track mindshare and other analytics to be a core functionality
3. No token yet. Much work to be done enhancing data layer. The MVP is capable of processing LLM chat completions with llama-3.3-70b-versatile
4. Socials presence in process
5. No compute or paid API investments. Bootstrapped. Currently not deployed.
6. Contact: [email protected]

🎯 Mission

LIQUBIT combines state-of-the-art language models with real-time on-chain analytics to create a sophisticated market intelligence protocol. By leveraging the LLaMA-3.3-70B model through Groq inference, we're pushing the boundaries of what's possible in AI crypto market analysis.

🔬 Technical Overview

Core Components

graph TD
    A[Data Ingestion] --> B[LLM Processing]
    B --> C[Analysis Engine]
    C --> D[Terminal Interface]
    
    subgraph "Data Layer"
    A1[cookie.fun API] --> A
    A2[On-chain Data] --> A
    A3[Social Metrics] --> A
    end
    
    subgraph "AI Layer"
    B1[Groq Inference] --> B
    B2[Context Processing] --> B
    B3[Prompt Engineering] --> B
    end

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Key Technologies

• LLM Integration: LLaMA-3.3-70B with Groq inference
• Data Sources:
  • cookie.fun for AI agent analytics
  • Native RPC nodes for on-chain data
  • Social sentiment via X API
• Networks: Solana & Base
• Processing: Real-time data correlation and analysis

🛠 Development Stack

AI/ML Pipeline

from transformers import LlamaTokenizer, LlamaForCausalInference
import groq

# Example of our inference pipeline
class LiqubitInference:
    def __init__(self):
        self.tokenizer = LlamaTokenizer.from_pretrained("liqubit/llama-3.3-70b")
        self.client = groq.Client()
    
    async def process_market_data(self, context):
        # Implementation details in docs/inference.md
        pass

Data Integration

interface DataSource {
  fetchMarketData(): Promise<MarketData>;
  fetchOnChainMetrics(): Promise<ChainMetrics>;
  fetchSocialSentiment(): Promise<SentimentData>;
}

Analysis Engine

class MarketAnalysis:
    def __init__(self, model: LiqubitInference):
        self.model = model
        self.metrics = MetricsAggregator()
    
    async def analyze_token(self, token: str) -> Analysis:
        # Implementation details in docs/analysis.md
        pass

🔄 Current Focus Areas

1. LLM Optimization -Enhancing the data layer

2. Data Integration

   • Real-time data pipeline optimization
   • Social sentiment analysis improvements

3. Analysis Capabilities

   • Advanced pattern recognition
   • Risk assessment models
   • Predictive analytics
   • Whale watching
   • Capital flows assessment

🤝 Contributing

We're actively seeking contributors with expertise in:

• Large Language Models

  • Prompt engineering
  • Model fine-tuning
  • Inference optimization

• Market Analysis

  • On-chain analytics
  • Sentiment / mindshare analysis

• Development

  • Solana/Base development
  • High-performance TypeScript
  • Python ML/Data pipelines

Getting Started

1. Development Environment

git clone https://github.com/liqubit/liqubit-core.git
cd liqubit-core
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
npm install

2. Local Development

# Start development environment
npm run dev

📈 Roadmap Q1 2024

• LLaMA model fine-tuning for market analysis
• Cookie.fun integration
• Dexscreener integration
• X Agent Deployment
• Token launch

🤖 Join the Development

We're building the future of on-chain intelligence. If you're passionate about:

• Large Language Models
• Blockchain
• AI Agents
• Market Analysis
• High-Performance Computing

Contact: [email protected]

📜 License

MIT License - see LICENSE for details