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Integration & DevOps Roadmap: Neuro-Visual System

Project: Krystal-Stack / Neural Compositor
Focus: Long-Term Integration, Machine Learning Pipeline, and Deployment
Date: Feb 17, 2026
Author: Dušan Kopecký

This document defines the strategic roadmap for evolving the Neuro-Visual Transduction Engine from a standalone prototype into a fully integrated, self-learning ecosystem. It bridges the gap between raw GPU data (Vulkan), Neural Generation, and high-level Reasoning Patterns.

1. System Architecture: The "Neuro-Visual Loop"

The long-term vision is a closed-loop system where the engine learns from its own operation and the underlying graphics pipeline.

graph TD
    A[VULKAN API] -->|Draw Calls/Shaders| B[Vulkan Learner]
    B -->|Metadata & Hints| C[Reasoning Core]
    
    D[Sensory Input] -->|Video/Audio/Thermal| E[Neural Art Engine]
    
    C -->|Style Weights| E
    E -->|ASCII Output| F[Display]
    
    E -->|Performance Logs| G[Logging System]
    G -->|Training Data| H[Machine Learning Model]
    H -->|Refined Weights| C
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1.1 Components & Roles

  • Vulkan Learner: The raw eye. Inspection of GPU primitives.
  • Neural Art Engine: The brush. Generates the ASCII structure.
  • Logging System: The memory using IntraspectralLogger. Connects pipeline to reasoning.
  • Reasoning Core: The brain. Decides which style fits the current context (e.g., "Combat requires High-Refresh Sketch Mode").

2. Integration Roadmap (Functional)

Phase 1: Signal Unification (Q2 2026)

Goal: Connect all isolated modules into a single data stream.

  • Consolidation: Move neural_art_engine and subsystems to image_generator/.
  • Unified Telemetry: Update main_ar_system.py to log events to the central Gamesa Logging System or Kafka.
  • Vulkan Bridge: Replace the mock vulkan_learner.py with a C++ Shared Library (vulkan_hook.so) that intercepts real draw calls.

Phase 2: The feedback Loop (Q3 2026)

Goal: Enable the system to self-adjust based on performance.

  • Auto-Tuning: If latency > 33ms, the Reasoning Core automatically downgrades the Neural Art Engine's kernel size.
  • Context Awareness: If audio_reactor detects high BPM, video_processor switches to "Glitch/Cyberpunk" mode automatically.

Phase 3: Machine Learning (2027)

Goal: Train a custom model on the "Reasoning Patterns".

  • Data Harvesting: Collect 10,000 hours of gameplay metadata + generated ASCII.
  • Training: Train a lightweight Transformer model to predict the perfect ASCII character for any given GPU state.

3. DevOps Roadmap (Operational)

Stage 1: Local Deployment (Development)

Current State.

  • Environment: virtualenv (venv).
  • Dependencies: requirements.txt.
  • Testing: Manual script execution (python3 system/main_ar_system.py).

Stage 2: Containerization (Docker)

Next Step.

  • Action: Create Dockerfile for the image_generator.
  • Base Image: openvino/ubuntu20_runtime (for acceleration).
  • Service: Deploy as a microservice offering an HTTP API (POST /process_frame).

Stage 3: CI/CD Pipeline (GitHub Actions)

  • Linting: Automatic pylint on commit.
  • Safety: Scan requirements.txt for vulnerabilities.
  • Artifacts: Build .deb package for Debian/Ubuntu deployment.

4. Reasoning Patterns & logging

To facilitate Machine Learning, every major decision must be logged with Context.

Log Structure Example:

{
  "timestamp": "2026-02-17T20:30:00Z",
  "vulkan_state": { "vertex_count": 50000, "shader": "compute" },
  "thermal_state": { "cpu_temp": 65.0, "penalty": 0.2 },
  "decision": { "mode": "cyberpunk", "kernel": "sobel_v2" },
  "outcome": { "latency": 15.4, "user_rating": "implied_positive" }
}

This structured data allows the ML model to learn: "When vertex count is high and temp is moderate, Cyberpunk mode is sustainable."

5. Summary

We are moving from a set of cool scripts to a Cognitive Graphics System. The key is to treat the ASCII output not just as art, but as the result of a Reasoned Decision made by the system based on hardware and software telemetry.