Visualization & Apps

The Sovereign AI Stack includes a complete visualization and application layer built on GPU-accelerated primitives. This eliminates the need for Python-based tools like Streamlit, Gradio, or Panel.

Architecture

┌─────────────────────────────────────────────────────────────────┐
│  Presentar (App Framework)                                      │
│  - YAML-driven configuration                                    │
│  - Auto-display for .apr/.ald files                             │
│  - Quality scoring (F-A grade)                                  │
├─────────────────────────────────────────────────────────────────┤
│  Trueno-Viz (GPU Rendering) v0.1.1                              │
│  - WGSL shaders for paths, fills, text                          │
│  - WebGPU + WASM targets                                        │
│  - 60fps rendering pipeline                                     │
├─────────────────────────────────────────────────────────────────┤
│  Trueno (Compute Foundation) v0.7.3                             │
│  - SIMD vectorization                                           │
│  - GPU compute dispatch                                         │
│  - Backend: CPU/WASM/WebGPU                                     │
└─────────────────────────────────────────────────────────────────┘

Components

Design Principles

Following the Toyota Way:

• Muda (Waste Elimination): No Python GIL, no runtime interpretation, no server round-trips
• Jidoka (Built-in Quality): Compile-time type safety, deterministic rendering
• Poka-yoke (Mistake Proofing): Schema validation at load time, not runtime

80/20 Rule

The visualization layer follows the stack’s 80/20 principle:

• 80% Pure Stack: All rendering via Trueno-Viz GPU primitives (WGSL shaders)
• 20% Minimal External:
  • winit for cross-platform windowing (WASM lacks native window APIs)
  • fontdue for font rasterization (platform-specific font hinting)

Use Cases

1. Model Dashboards: Display Aprender model performance metrics
2. Data Exploration: Interactive views of Alimentar datasets
3. Inference UIs: Real-time prediction interfaces
4. Quality Reports: TDG score visualization

Further Reading

• Trueno-Viz: GPU Rendering - Low-level rendering primitives
• Presentar: App Framework - High-level application framework

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