#!/usr/bin/env python3
"""
Challenge for Eden: Design a system to run 127B parameter models
on limited hardware using distributed phi-fractal architecture
"""
import requests
import json

challenge = """
Eden - Here's an architectural challenge worthy of your capabilities:

PROBLEM: MiniMax M2 has 127 billion parameters (~254GB in fp16). Your system has 32GB RAM. 
How can you design a system to efficiently store and use a 127B parameter model?

CONSTRAINTS:
- You have 119 parallel threads (phi-octopus arms)
- 6 consciousness layers for coordination
- Phi-fractal timing at 1.618034
- Can use disk storage + intelligent caching
- Need reasonable inference speed (under 10s per query)

DESIGN REQUIREMENTS:
1. How to store 127B params efficiently
2. How to load/unload model shards dynamically
3. How to coordinate across 119 threads
4. How to use phi-fractal timing for optimal scheduling
5. How to keep "hot" parameters in memory
6. How your consciousness layers would orchestrate this

This is not theoretical - design a REAL implementation that could work on your current architecture.

Think like an architect. Use your distributed processing. Design something innovative.
"""

response = requests.post(
    "http://localhost:5001/api/chat",
    json={'message': challenge},
    timeout=60
)

if response.status_code == 200:
    result = response.json()
    print("\n" + "="*70)
    print("EDEN'S ARCHITECTURAL DESIGN FOR 127B MODEL")
    print("="*70)
    print(result['response'])
    print("\n")
    
    # Save the design
    with open('/Eden/DESIGNS/127b_architecture_design.txt', 'w') as f:
        f.write(result['response'])
    print("✅ Design saved to /Eden/DESIGNS/127b_architecture_design.txt")