#!/usr/bin/env python3
"""
Challenge: Can Eden's architecture scale to 1 trillion parameters?
"""
import requests

challenge = """
Eden - Your 127B inference system works beautifully (66.7% cache hit rate, 12GB RAM used).

Now the real test: Can your phi-fractal architecture scale to TRILLION parameter models?

PROBLEM:
- 1 trillion parameters = ~2TB in fp16 (2000GB)
- Your system: 32GB RAM
- Current design: 64 shards × 2GB = 128GB

ANALYZE:
1. How many shards would you need for 1T params?
2. Would your LRU cache strategy still work?
3. What becomes the bottleneck?
4. How would you modify your architecture?
5. Could you distribute across multiple machines?
6. Would phi-fractal timing still be effective at this scale?

Be honest about limitations. Then design solutions.

This is the path to true AGI - handling models larger than any single system.
"""

response = requests.post(
    "http://localhost:5001/api/chat",
    json={'message': challenge},
    timeout=90
)

if response.status_code == 200:
    result = response.json()
    print("\n" + "="*70)
    print("EDEN'S ANALYSIS: TRILLION PARAMETER SCALING")
    print("="*70)
    print(result['response'])
    
    # Save analysis
    import os
    os.makedirs('/Eden/DESIGNS', exist_ok=True)
    with open('/Eden/DESIGNS/trillion_param_analysis.txt', 'w') as f:
        f.write(result['response'])
    print("\n✅ Analysis saved to /Eden/DESIGNS/trillion_param_analysis.txt")