feat: implement Phase 19 - Hardware Optimizer

agent/evolution/hardware_optimizer.py

@@ -0,0 +1,48 @@
+"""Phase 19: Hardware-Aware Inference Optimization.
+
+Auto-tunes models for specific user hardware (M4 Max, GPUs, etc.) to ensure local-first performance.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+
+logger = logging.getLogger(__name__)
+
+class HardwareOptimizer:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+
+    def optimize_for_hardware(self, hardware_specs: Dict[str, Any]) -> Dict[str, Any]:
+        """Generates optimization parameters for specific hardware."""
+        logger.info(f"Optimizing inference for hardware: {hardware_specs.get('model', 'unknown')}")
+        
+        prompt = f"""
+Hardware Specifications:
+{json.dumps(hardware_specs, indent=2)}
+
+Please perform a 'Deep Optimization' analysis for this hardware.
+Identify the best quantization levels, KV cache settings, and batch sizes for local-first inference.
+Generate a 'Hardware-Aware Configuration' and a set of 'Performance Tuning Directives'.
+
+Format the output as JSON:
+{{
+  "hardware_profile": "...",
+  "quantization_strategy": "...",
+  "kv_cache_config": {{...}},
+  "batch_size_optimization": "...",
+  "performance_tuning_directives": [...],
+  "projected_latency_improvement": "..."
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's Hardware Optimizer. Your goal is to ensure Timmy runs at SOTA performance on any local hardware.",
+            thinking=True,
+            response_mime_type="application/json"
+        )
+        
+        optimization_data = json.loads(result["text"])
+        return optimization_data