update

Author: RhodosBerger

README.md

@@ -1,86 +1,56 @@
-# Dev-contitional Platform
+# Dev-conditional: Heterogeneous Industrial AI Platform
 
-This repository is presented as 2 connected but independently positioned projects:
-
-- **60%: Krystal Vino + GAMESA 3D Grid** (primary platform)
-- **40%: FANUC RISE** (secondary industrial branch, FOCAS integration)
+This repository hosts a next-generation heterogeneous industrial AI platform, bridging the gap between high-level reasoning and real-time industrial control. It represents a fundamental rethink of how we architect compute at the edge, moving away from monolithic designs to a segmented, best-tool-for-the-job architecture.
 
 ---
 
-## 1) Krystal Vino + GAMESA 3D Grid (Primary Platform, 60%)
+## 1) Gamesa Cortex V2 (Primary Platform, 60%)
+**The Neural Control Plane**
 
-### What Krystal Vino Is
-Krystal Vino is a performance orchestration layer on top of OpenVINO/oneAPI for personal computers.  
-Its goal is to reduce latency and increase throughput through adaptive planning, telemetry, and runtime policy control.
+Gamesa Cortex V2 is a heterogeneous AI stack designed to run on commodity PC hardware while delivering safety-critical performance for industrial automation. It serves as an operating system for decision-making, orchestrating low-level acceleration through high-level logic.
 
-Codebase: `openvino_oneapi_system/`
+### Core Architecture
+- **Rust for Safety-Critical Planning** 🦀: Replaces Python in the critical path. Planning algorithms (A*, RRT) are compiled into shared libraries for zero-cost abstractions and memory safety.
+- **Vulkan for Spatial Awareness** 🌋: Leverages Compute Shaders (Intel Iris Xe, NVIDIA RTX) for massive parallel voxel collision detection, treating the workspace as a live volumetric grid.
+- **Economic Governance** ⚖️: A bio-inspired "Economic Governor" manages computation budgets. High-value tasks (Safety) get priority, while low-value tasks wait for "fiscal replenishment," preventing thermal throttling.
+- **Docker & vGPU Framework** 🐳: A custom **vGPU Manager** creates "Virtual Slices" of the host GPU for containerized AI workloads, enabling deployment on any Linux distro.
 
-### Core Components
-- **OpenVINO runtime layer**: inference with a safe fallback mode.
-- **oneAPI/OpenMP tuning**: dynamic control of `ONEAPI_NUM_THREADS`, `OMP_NUM_THREADS`, `OPENVINO_NUM_STREAMS`, `KMP_*`.
-- **Economic planner + evolutionary tuner**: online switching between `defensive/balanced/aggressive` modes.
-- **GAMESA 3D Grid**: logical 3D memory layer for data organization/swap behavior.
-- **Delegated logging**: separate channels for `system`, `telemetry`, `planning`, `policy`, `inference`, `grid_update`.
+**Codebase**: `gamesa_cortex_v2/`
 
-### Proven Results (Linux Benchmark)
-Source: `openvino_oneapi_system/logs/benchmark_latest.txt`
+---
 
-- **Latency improvement**: `66.01%`
-- **Throughput improvement**: `234.59%`
-- **Utility improvement**: `270.42%`
-- **Sysbench improvement**: `99.55%`  
-  Baseline: `2615.43 events/s` -> Adaptive: `5219.10 events/s`
+## 2) FANUC RISE v3.0 - Cognitive Forge (Secondary Branch, 40%)
+**Advanced CNC Copilot**
 
-### Quick Run
-```bash
-python3 openvino_oneapi_system/main.py --cycles 10 --interval 0.5
-python3 openvino_oneapi_system/benchmark_linux.py --cycles 60
-```
+FANUC RISE v3.0 represents the evolution from deterministic execution to probabilistic creation. It is a **Conceptual Prototype & Pattern Library** demonstrating architectural patterns for bio-mimetic industrial automation.
 
-### Debian Package (Whole Package)
-Generated package:
-- `openvino_oneapi_system/dist/openvino-oneapi-system_1.1.0_amd64.deb`
+### Key Concepts
+- **Cognitive Forge**: Shifts focus from "Doing What Is Told" to "Suggesting What Is Possible," where AI proposes optimization strategies for operator selection.
+- **Shadow Council Governance**: A multi-agent system (Creator, Auditor, Accountant) ensuring safe AI integration by validating probabilistic proposals against deterministic physics.
+- **The Probability Canvas**: A "Glass Brain" interface visualizing potential futures and decision trees instead of just current status.
+- **Neuro-Geometric Architecture**: Integer-only neural networks for edge computing.
 
-Includes:
-- CLI: `ovo-runtime`, `ovo-benchmark`
-- Service unit: `openvino-oneapi-system.service`
-- Config: `/etc/default/openvino-oneapi-system`
+**Codebase**: `advanced_cnc_copilot/`
 
 ---
 
-## 2) FANUC RISE (Secondary Branch, 40%)
-
-### Project Characterization
-FANUC RISE is an industrial CNC layer focused on operations, telemetry, and workflow automation.  
-FOCAS is a **secondary integration layer**, not the primary product target.
-
-Codebase: `advanced_cnc_copilot/`
-
-### Scope
-- CNC operator workflows and supervision
-- API + UI for production monitoring
-- FANUC telemetry bridge (mock/real mode based on environment)
-- Extensible backend services for manufacturing analytics
+## Repository Map
 
-### Role in the Overall Ecosystem
-- Krystal Vino handles performance runtime orchestration and compute optimization.
-- FANUC RISE handles industrial context, machine/data connectivity, and operator use.
-- Together they form a pipeline: **performance core + industrial execution**.
+- **`gamesa_cortex_v2/`**: **Core Platform**. The active development branch for the heterogeneous AI stack (Rust/Vulkan/Python).
+- **`advanced_cnc_copilot/`**: **Industrial Application Layer**. The FANUC RISE v3.0 Cognitive Forge prototype and pattern library.
+- **`openvino_oneapi_system/`**: **Legacy/Foundation**. Previous generation performance orchestration layer (Krystal Vino). Served as the foundation for Cortex V2's optimization strategies.
+- **`docs/`**: Additional technical materials and documentation.
 
 ---
 
-## Repository Map
-- `openvino_oneapi_system/` primary performance platform (OpenVINO, oneAPI, GAMESA 3D Grid)
-- `advanced_cnc_copilot/` FANUC RISE industrial stack
-- `docs/` additional technical materials
-
 ## Direction Note
-The priority of this repository is Krystal Vino/GAMESA 3D Grid as the main platform for PC hardware and inference performance.  
-FANUC RISE remains a separate, secondary domain branch for CNC integrations.
+
+The priority of this repository is **Gamesa Cortex V2** as the main platform for PC hardware and inference performance. **FANUC RISE v3.0** serves as the advanced application layer and pattern library for industrial logic.
 
 ---
 
 ## Author & License
-**Author**: Dušan Kopecký  
-**Email**: dusan.kopecky0101@gmail.com  
+
+**Author**: Dušan Kopecký
+**Email**: dusan.kopecky0101@gmail.com
 **License**: Apache 2.0 (See `LICENSE` file)

dist/gamesa-cortex-v2/DEBIAN/control

@@ -0,0 +1,9 @@
+Package: gamesa-cortex-v2
+Version: 0.1.0
+Section: python
+Priority: optional
+Architecture: all
+Depends: python3, python3-numpy, python3-psutil
+Maintainer: Gamesa Cortex Team <dev@gamesacortex.com>
+Description: The Neural Control Plane for Industry 5.0
+ Gamesa Cortex V2 orchestrates AI inference, economic planning, and safety checks.

dist/gamesa-cortex-v2/usr/lib/python3/dist-packages/gamesa_cortex_v2/__init__.py

@@ -0,0 +1,27 @@
+import logging
+import platform
+
+class ArchEmulator:
+    """
+    Gamesa Cortex V2: Cross-Architecture Emulator.
+    Allows ARM-specific logic (e.g., NEON Intrinsics) to run on Intel CPUs.
+    """
+    def __init__(self):
+        self.logger = logging.getLogger("ArchEmulator")
+        self.host_arch = platform.machine()
+        self.logger.info(f"Host Architecture: {self.host_arch}")
+
+    def emulate_neon_instruction(self, instruction: str, data: list):
+        """
+        Translates an ARM NEON instruction to an Intel AVX-512 equivalent.
+        """
+        if self.host_arch in ["x86_64", "AMD64"]:
+            return self._translate_to_avx(instruction, data)
+        return "NATIVE_EXECUTION"
+
+    def _translate_to_avx(self, instruction, data):
+        # Application of the "Adaptability" theory
+        if instruction == "VADD.F32":
+            # Simulate Vector Add
+            return [x + 1.0 for x in data] 
+        return "UNKNOWN_INSTRUCTION"

dist/gamesa-cortex-v2/usr/lib/python3/dist-packages/gamesa_cortex_v2/core/__init__.py

@@ -0,0 +1,25 @@
+import logging
+import hashlib
+
+class BinaryGuard:
+    """
+    Gamesa Cortex V2: Binary Converter & Safety Guard.
+    Ensures 'Safe Code Methods' are applied to incoming binary streams.
+    """
+    def __init__(self):
+        self.logger = logging.getLogger("BinaryGuard")
+
+    def scan_and_convert(self, binary_blob: bytes) -> bytes:
+        """
+        Scans binary for unsafe patterns and converts to Safe Format.
+        """
+        # 1. Integrity Check
+        checksum = hashlib.sha256(binary_blob).hexdigest()
+        self.logger.info(f"Scanning Blob: {checksum[:8]}...")
+        
+        # 2. Logic: Detect Unsafe Jumps (Simulated)
+        if b"\xEB\xFE" in binary_blob: # Infinite Loop Opcode
+            self.logger.warning("Unsafe Logic Detected! Neutralizing...")
+            return binary_blob.replace(b"\xEB\xFE", b"\x90\x90") # NOP
+            
+        return binary_blob

dist/gamesa-cortex-v2/usr/lib/python3/dist-packages/gamesa_cortex_v2/core/arch_emulator.py

@@ -0,0 +1,35 @@
+import logging
+import os
+
+class CognitiveNode:
+    """
+    Gamesa Cortex V2: Cognitive Node.
+    Integrates OpenLLaMA (CPP) for Code Introspection and Reasoning.
+    Optimized for CPU Inference (AVX-512) or GPU (CuBLAS/CLBlast).
+    """
+    def __init__(self, model_path="models/open_llama_7b_q4.bin"):
+        self.logger = logging.getLogger("CognitiveNode")
+        self.model_path = model_path
+        self.model = None
+        
+        # Try importing llama-cpp-python
+        try:
+            from llama_cpp import Llama
+            if os.path.exists(model_path):
+                self.logger.info(f"Loading OpenLLaMA model from {model_path}...")
+                self.model = Llama(model_path=model_path, n_ctx=2048, n_threads=8)
+            else:
+                self.logger.warning(f"Model not found at {model_path}. Running in Placeholder Mode.")
+        except ImportError:
+            self.logger.warning("llama-cpp-python not installed. Running in Placeholder Mode.")
+
+    def introspect_code(self, code_snippet: str) -> str:
+        """
+        Analyzes a code snippet to find 'mechanics' for inspiration.
+        """
+        if not self.model:
+            return "Placeholder: Code looks optimized for vectorization."
+            
+        prompt = f"Analyze this CNC code for mechanical inspiration:\n{code_snippet}\n\nAnalysis:"
+        output = self.model(prompt, max_tokens=64, stop=["\n"], echo=False)
+        return output['choices'][0]['text'].strip()

dist/gamesa-cortex-v2/usr/lib/python3/dist-packages/gamesa_cortex_v2/core/binary_guard.py

@@ -0,0 +1,24 @@
+import os
+
+class GamesaConfig:
+    """
+    Centralized configuration for Gamesa Cortex V2.
+    """
+    # NPU Coordinator
+    DEFAULT_DOPAMINE = float(os.getenv("GAMESA_DEFAULT_DOPAMINE", 0.5))
+    DEFAULT_CORTISOL = float(os.getenv("GAMESA_DEFAULT_CORTISOL", 0.1))
+    MAX_WORKERS = int(os.getenv("GAMESA_MAX_WORKERS", 8))
+    
+    # Economic Governor
+    INITIAL_BUDGET_CREDITS = int(os.getenv("GAMESA_INITIAL_BUDGET", 1000))
+    COST_MODEL = {
+        "NATIVE_EXECUTION": 1,
+        "AVX_EMULATION": 10,
+        "MESH_TESSELLATION": 50,
+        "AI_INFERENCE": 20,
+        "DEFAULT": 5
+    }
+    
+    # Thresholds
+    CORTISOL_INTERDICTION_THRESHOLD = 0.8
+    DOPAMINE_OPTIMIZATION_THRESHOLD = 0.7

dist/gamesa-cortex-v2/usr/lib/python3/dist-packages/gamesa_cortex_v2/core/cognitive_node.py

@@ -0,0 +1,48 @@
+import logging
+
+from .config import GamesaConfig
+from .logging_system import IntraspectralLogger
+
+class EconomicGovernor:
+    """
+    Gamesa Cortex V2: Economic Governor.
+    Regulates Resource Allocation based on 'Economic Planning'.
+    Enforces budgets for Compute, Energy, and Time.
+    """
+    def __init__(self, budget_credits=None):
+        self.logger = logging.getLogger("EconomicGovernor")
+        self.intra_logger = IntraspectralLogger()
+        self.budget_credits = budget_credits if budget_credits is not None else GamesaConfig.INITIAL_BUDGET_CREDITS
+        self.cost_model = GamesaConfig.COST_MODEL
+        
+        self.logger.info(f"Economic Governor Online. Budget: {self.budget_credits} Credits")
+        self.intra_logger.log_event("ECONOMIC", "Governor", "Online", {"budget": self.budget_credits})
+
+    def request_allocation(self, task_type: str, priority_level: str) -> bool:
+        """
+        evaluates if a task affords the resource cost.
+        """
+        # OPTIMIZATION: Critical Path Bypass
+        # If High Priority, skip the dictionary lookup and budget check latency.
+        if priority_level in ["INTERDICTION_PROTOCOL", "EVOLUTIONARY_OVERDRIVE"]:
+             return True
+
+        cost = self.cost_model.get(task_type, GamesaConfig.COST_MODEL["DEFAULT"])
+        
+        # Regulation 2: Budget Check
+        if self.budget_credits >= cost:
+            self.budget_credits -= cost
+            # Optimization: Only log on failure or specific debug level to save IO
+            # self.logger.info(f"Approved {task_type}...") 
+            return True
+        else:
+            self.logger.warning(f"Denied {task_type}. Insufficient Credits ({self.budget_credits} < {cost})")
+            self.intra_logger.log_event("ECONOMIC", "Governor", "Task Denied", {"task": task_type, "cost": cost, "budget": self.budget_credits})
+            return False
+
+    def replentish_budget(self, amount=100):
+        """
+        Periodic replenishment (simulates 'Fiscal Year' or Time Window).
+        """
+        self.budget_credits += amount
+        self.logger.info(f"Budget Replenished. Current: {self.budget_credits}")

dist/gamesa-cortex-v2/usr/lib/python3/dist-packages/gamesa_cortex_v2/core/config.py

@@ -0,0 +1,63 @@
+import json
+import time
+import os
+import logging
+from typing import Dict, Any, List
+from .config import GamesaConfig
+
+class IntraspectralLogger:
+    """
+    Gamesa Cortex V2: Intraspectral Logging System.
+    Aggregates logs from various system components into a unified JSON format
+    compatible with OpenVINO telemetry or analysis tools.
+    """
+    def __init__(self, log_dir="logs"):
+        self.logger = logging.getLogger("IntraspectralLogger")
+        self.log_dir = log_dir
+        if not os.path.exists(self.log_dir):
+            os.makedirs(self.log_dir)
+            
+        self.log_buffer: List[Dict[str, Any]] = []
+        self.spectra = {
+            "PLANNING": "blue",
+            "SAFETY": "red",
+            "ECONOMIC": "green",
+            "INFERENCE": "purple",
+            "SYSTEM": "white"
+        }
+
+    def log_event(self, spectrum: str, component: str, message: str, metrics: Dict[str, Any] = None):
+        """
+        Log an event in a specific spectrum.
+        """
+        if spectrum not in self.spectra:
+            spectrum = "SYSTEM"
+            
+        event = {
+            "timestamp": time.time_ns(),
+            "spectrum": spectrum,
+            "component": component,
+            "message": message,
+            "metrics": metrics or {}
+        }
+        
+        self.log_buffer.append(event)
+        
+        # In a real system, we might stream this or batch write
+        # For simplicity, we just print to console for now (simulating stream)
+        # print(f"[[{spectrum}]] {component}: {message} {metrics}")
+
+    def export_logs(self, filename="intraspectral_latest.json"):
+        """
+        Export buffered logs to a JSON file compatible with OpenVINO analysis tools.
+        """
+        filepath = os.path.join(self.log_dir, filename)
+        try:
+            with open(filepath, 'w') as f:
+                json.dump(self.log_buffer, f, indent=2)
+            self.logger.info(f"Logs exported to {filepath}")
+        except Exception as e:
+            self.logger.error(f"Failed to export logs: {e}")
+            
+    def clear_buffer(self):
+        self.log_buffer = []