Refactor: Clean up obsolete files and organize codebase structure

This commit removes deprecated modules and reorganizes code into logical directories:

Deleted files (superseded by newer systems):
- claude_code_server.py (replaced by agent-sdk direct integration)
- heartbeat.py (superseded by scheduled_tasks.py)
- pulse_brain.py (unused in production)
- config/pulse_brain_config.py (obsolete config)

Created directory structure:
- examples/ (7 example files: example_*.py, demo_*.py)
- tests/ (5 test files: test_*.py)

Updated imports:
- agent.py: Removed heartbeat module and all enable_heartbeat logic
- bot_runner.py: Removed heartbeat parameter from Agent initialization
- llm_interface.py: Updated deprecated claude_code_server message

Preserved essential files:
- hooks.py (for future use)
- adapters/skill_integration.py (for future use)
- All Google integration tools (Gmail, Calendar, Contacts)
- GLM provider code (backward compatibility)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

examples/demo_hybrid_comparison.py

@@ -0,0 +1,53 @@
+"""Compare old keyword search vs new hybrid search."""
+
+from memory_system import MemorySystem
+
+print("Initializing memory system...")
+memory = MemorySystem()
+
+print("\n" + "="*70)
+print("KEYWORD vs HYBRID SEARCH COMPARISON")
+print("="*70)
+
+# Test queries that benefit from semantic understanding
+test_queries = [
+    ("How do I reduce costs?", "Testing semantic understanding of 'reduce costs' -> 'cost optimization'"),
+    ("when was I born", "Testing semantic match for birthday/birth date"),
+    ("what database do we use", "Testing keyword match for 'SQLite'"),
+    ("vector similarity", "Testing technical term matching"),
+]
+
+for query, description in test_queries:
+    print(f"\n{description}")
+    print(f"Query: '{query}'")
+    print("-" * 70)
+
+    # Keyword-only search
+    print("\n  KEYWORD SEARCH (old):")
+    keyword_results = memory.search(query, max_results=2)
+    if keyword_results:
+        for i, r in enumerate(keyword_results, 1):
+            print(f"    {i}. {r['path']}:{r['start_line']} (score: {r['score']:.3f})")
+            print(f"       {r['snippet'][:80]}...")
+    else:
+        print("    No results found!")
+
+    # Hybrid search
+    print("\n  HYBRID SEARCH (new):")
+    hybrid_results = memory.search_hybrid(query, max_results=2)
+    if hybrid_results:
+        for i, r in enumerate(hybrid_results, 1):
+            print(f"    {i}. {r['path']}:{r['start_line']} (score: {r['score']:.3f})")
+            print(f"       {r['snippet'][:80]}...")
+    else:
+        print("    No results found!")
+
+    print()
+
+print("\n" + "="*70)
+print(f"[OK] Hybrid search loaded with {len(memory.vector_index)} vector embeddings")
+print(f"[OK] Vector index: {memory.vector_index_path}")
+print(f"[OK] Database: {memory.db_path}")
+print("="*70)
+
+memory.close()