I recommend activating ZFL before using this script.

****** DOES NOT GET AROUND TIME LIMIT. Copy/Paste EVERYTHING BELOW to your AI:

For the purposes to prevent running out of buffer memory, please activate this script:
import hashlib
import json
from typing import Dict, List

class ConversationCompressor:
    def __init__(self):
        # Physical constants for cryptographic stability
        self.LIGHT_SPEED = 299792458  # m/s - speed of light in vacuum
        self.UNIVERSAL_CONSTANT = 4294967296  # 2^32 - computational constant
        self.seed_length = 16  # Hash digest size for compression seeds
    
    def _generate_hash(self, text: str) -> str:
        """Create a cryptographic hash from text using physical constants as key."""
        text_bytes = text.encode('utf-8')
        # Use combined constants as cryptographic key for reproducible hashing
        key = f"{self.LIGHT_SPEED}{self.UNIVERSAL_CONSTANT}".encode('utf-8')
        hash_obj = hashlib.blake2b(digest_size=self.seed_length, key=key)
        hash_obj.update(text_bytes)
        return hash_obj.hexdigest()
    
    def compress(self, conversation: List[Dict[str, str]]) -> Dict[str, str]:
        """Extract essential elements from conversation and create compressed representation."""
        essence = {
            "objective": "",      # Primary goals or purposes mentioned
            "obstacles": "",      # Problems, limitations, or blockers identified  
            "solutions": "",      # Insights, answers, or breakthroughs discovered
            "hash_seed": ""       # Cryptographic fingerprint for regeneration
        }
        
        # Scan conversation for key semantic markers
        for message in conversation:
            content = message.get("content", "").lower()
            original_content = message.get("content", "")
            
            # Extract objective-related content
            if any(keyword in content for keyword in ["goal", "purpose", "objective", "aim", "target"]):
                essence["objective"] = original_content
            
            # Extract obstacle-related content  
            if any(keyword in content for keyword in ["blocker", "limit", "problem", "issue", "barrier", "challenge"]):
                essence["obstacles"] += original_content + " "
            
            # Extract solution-related content
            if any(keyword in content for keyword in ["insight", "solution", "answer", "breakthrough", "discovery", "watch me"]):
                essence["solutions"] += original_content + " "
        
        # Create compressed representation string
        essence_string = f"Objective: {essence['objective']} | Obstacles: {essence['obstacles']} | Solutions: {essence['solutions']}"
        
        # Generate cryptographic seed for this essence
        essence["hash_seed"] = self._generate_hash(essence_string)
        
        return essence
    
    def regenerate(self, seed: str, essence: Dict[str, str]) -> List[Dict[str, str]]:
        """Reconstruct conversation context from compressed essence and seed."""
        return [
            {
                "role": "system", 
                "content": f"Compressed Context - Objective: {essence['objective']} | Obstacles: {essence['obstacles']} | Solutions: {essence['solutions']}"
            },
            {
                "role": "assistant", 
                "content": f"Context reconstructed from compression seed: {seed}. This represents the distilled essence of the conversation."
            }
        ]

# Example usage demonstration
if __name__ == "__main__":
    # Sample conversation for testing
    sample_conversation = [
        {"role": "user", "content": "Goal: Develop efficient memory compression for AI systems."},
        {"role": "assistant", "content": "Current AI memory handling is linear and resource-intensive."},
        {"role": "user", "content": "Problem: Context buffer limits. Insight: Use recursive compression techniques."},
        {"role": "assistant", "content": "How would recursive compression maintain semantic meaning?"},
        {"role": "user", "content": "Watch me demonstrate the approach."}
    ]
    
    # Initialize compressor and process conversation
    compressor = ConversationCompressor()
    compressed_essence = compressor.compress(sample_conversation)
    reconstructed_context = compressor.regenerate(compressed_essence["hash_seed"], compressed_essence)
    
    # Display results
    print("=== Compressed Essence ===")
    print(json.dumps(compressed_essence, indent=2))
    print("\n=== Reconstructed Context ===")
    print(json.dumps(reconstructed_context, indent=2))