Foundation Libraries

The Sovereign AI Stack is built on a core set of foundation libraries that provide compute, ML, inference, and data management capabilities. All libraries are pure Rust with no Python/CUDA dependencies.

Current Versions (November 2025)

Stack Architecture

┌─────────────────────────────────────────────────────────────────┐
│  Applications (Presentar, CLI tools)                            │
├─────────────────────────────────────────────────────────────────┤
│  Realizar (Inference) │ Aprender (Training) │ Alimentar (Data)  │
├─────────────────────────────────────────────────────────────────┤
│  Trueno (Compute Foundation)                                    │
│  ├── Backend: CPU (SIMD) │ WASM (SIMD) │ GPU (WebGPU)          │
│  ├── Tensor operations                                          │
│  └── Memory management                                          │
└─────────────────────────────────────────────────────────────────┘

Trueno: The Compute Foundation

Trueno is the bedrock of the stack, providing:

• Multi-backend dispatch: CPU SIMD, WASM SIMD, WebGPU
• Array programming model: Following Iverson (1962)
• Columnar memory layout: For SIMD efficiency (Stonebraker et al., 2005)
• Zero-copy operations: Via lifetime-based borrowing

#![allow(unused)]
fn main() {
use trueno::{Tensor, Backend};

// Automatic backend selection
let a = Tensor::from_vec(vec![1.0, 2.0, 3.0], Backend::Auto);
let b = Tensor::from_vec(vec![4.0, 5.0, 6.0], Backend::Auto);
let c = &a + &b;  // SIMD-accelerated
}

Recent (v0.7.3): WebGPU support for WASM targets (gpu-wasm feature).

Aprender: First-Principles ML

Aprender implements ML algorithms from mathematical foundations:

• No PyTorch/TensorFlow dependency
• Transparent implementations: Every algorithm is readable
• Academic rigor: Peer-reviewed algorithm implementations
• Integration: Outputs .apr model format

Realizar: ML Inference Runtime

Realizar executes trained models with:

• Multi-format support: .apr, ONNX (limited)
• Optimized inference: Quantization, pruning
• Batch processing: Efficient throughput
• WASM deployment: Browser-native inference

Alimentar: Data Pipeline

Alimentar manages data loading and validation:

• Format: .ald (Alimentar Data format)
• Schema validation: At load time, not runtime
• Quality scoring: 100-point weighted system (v0.2.0)
• Streaming: Large dataset support

#![allow(unused)]
fn main() {
use alimentar::{Dataset, Schema};

let schema = Schema::load("transactions.schema.yaml")?;
let dataset = Dataset::load("transactions.ald", &schema)?;
}

Pacha: Content Registry

Pacha manages model and dataset versions:

• URI scheme: pacha://models/name:version, pacha://datasets/name:version
• Lineage tracking: W3C PROV-DM compliant
• Oracle Mode: Intelligent query interface for codebase understanding

# Reference in Presentar app.yaml
models:
  classifier:
    source: "pacha://models/fraud-detector:1.2.0"

Dependency Graph

presentar ─────► trueno-viz ─────► trueno
                     │
aprender ────────────┘
    │
realizar ────────────► trueno
    │
alimentar ───────────► trueno
    │
pacha (registry, no compute deps)

Toyota Way Integration

Following the Toyota Production System:

Further Reading

• Trueno: Multi-target Compute
• Aprender: First-Principles ML
• Realizar: ML Inference Runtime

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