restructure: proper project layout, README, kill training

- cuda/ — main LBM kernel (khra_gixx_1024_v5.cu)
- navigator/ — lattice_observer, golden_weave, bridges, mock daemon
- scripts/ — compile, start, launch, setup (paths updated)
- docs/ — system manual
- archive/ — everything else (old kernels, inquiries, experiments)
- README.md — full setup guide: requirements, quick start, use your own LLM
- removed training/ entirely (broken LoRA scripts + datasets)
- .gitignore: exclude build/ logs/ training/ *.jsonl

archive/misc/minimal_open_feed.py

@@ -0,0 +1,81 @@
+# minimal_open_feed.py
+# Stripped down to test the actual bottleneck
+
+import zmq
+import json
+import time
+
+print("="*60)
+print("MINIMAL OPEN FEED TEST")
+print("="*60)
+
+# ZMQ setup
+ctx = zmq.Context()
+sub = ctx.socket(zmq.SUB)
+sub.connect("tcp://localhost:5556")
+sub.setsockopt_string(zmq.SUBSCRIBE, "")
+
+print("\nWaiting for LBM data...")
+
+# Wait for first frame
+frame = None
+for i in range(50):  # 5 seconds max
+    try:
+        msg = sub.recv(flags=zmq.NOBLOCK)
+        frame = json.loads(msg)
+        break
+    except zmq.Again:
+        time.sleep(0.1)
+
+if frame is None:
+    print("ERROR: No LBM data received")
+    exit(1)
+
+print(f"✓ Received frame: Cycle {frame['cycle']}, Coherence {frame['coherence']:.3f}")
+
+# Now try loading the model
+print("\nLoading v0.5 model...")
+try:
+    from unsloth import FastLanguageModel
+    import torch
+    
+    # Load base model + LoRA adapters
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name="unsloth/llama-3.2-3b",
+        max_seq_length=512,
+        dtype=torch.bfloat16,
+        load_in_4bit=True,
+    )
+    
+    # Apply v0.5 LoRA
+    model = FastLanguageModel.get_peft_model(
+        model,
+        r=64,
+        target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+        lora_alpha=128,
+        lora_dropout=0,
+        bias="none",
+        use_gradient_checkpointing="unsloth",
+        random_state=3407,
+    )
+    
+    # Load the trained adapters
+    from peft import PeftModel
+    model = PeftModel.from_pretrained(model, "./kaelara_lora_v05/final")
+    print("✓ v0.5 LoRA adapters applied")
+    print("✓ Model loaded")
+    
+    # Test generation
+    prompt = f"Coherence: {frame['coherence']:.3f}. What is the frequency?"
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    outputs = model.generate(**inputs, max_new_tokens=30, temperature=0.7)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    print(f"\nPrompt: {prompt}")
+    print(f"Response: {response}")
+    print("\n✓ FULL PIPELINE WORKS")
+    
+except Exception as e:
+    print(f"✗ Error: {e}")
+    import traceback
+    traceback.print_exc()