# PAIML Sovereign AI Stack: Vision Sync Document

**Version:** 1.0.0
**Last Updated:** 2025-11-26
**Status:** Active Development

---

## Executive Summary

The **PAIML Sovereign AI Stack** is a comprehensive, pure-Rust ecosystem for building, training, and deploying machine learning systems with **zero external dependencies**. The stack prioritizes:

1. **Sovereignty** - Full control, no cloud lock-in, air-gap deployable
2. **Performance** - SIMD/GPU/WASM acceleration throughout
3. **Quality** - Toyota Way principles, EXTREME TDD (95%+ coverage)
4. **Portability** - Single-binary deployment, WASM-first design

---

## Stack Architecture

```
┌─────────────────────────────────────────────────────────────────────────────┐
│                        ORCHESTRATION LAYER                                   │
│  ┌─────────────┐                                                            │
│  │   batuta    │  Conductor orchestrating all PAIML tools                   │
│  │   (v0.1.0)  │  Python/C/Shell → Rust migration pipeline                  │
│  └─────────────┘                                                            │
├─────────────────────────────────────────────────────────────────────────────┤
│                        TRANSPILER LAYER                                      │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐        │
│  │   depyler   │  │   bashrs    │  │    decy     │  │   ruchy     │        │
│  │  Python→Rust│  │  Bash→Rust  │  │   C→Rust    │  │  DSL→Rust   │        │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘        │
├─────────────────────────────────────────────────────────────────────────────┤
│                        ML/AI LAYER                                           │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐                          │
│  │  aprender   │  │  entrenar   │  │  realizar   │                          │
│  │   (v0.9.1)  │  │   (v0.1.0)  │  │   (GGUF)    │                          │
│  │  ML Algos   │  │  Training   │  │  Inference  │                          │
│  │   .apr ←────┼──┼─────────────┼──┼──→ .gguf    │                          │
│  └─────────────┘  └─────────────┘  └─────────────┘                          │
├─────────────────────────────────────────────────────────────────────────────┤
│                        DATA LAYER                                            │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐                          │
│  │  alimentar  │  │  trueno-db  │  │trueno-graph │                          │
│  │   (v0.1.0)  │  │   (v0.3.3)  │  │   (v0.1.1)  │                          │
│  │  DataLoader │  │ Analytics DB│  │  Graph DB   │                          │
│  │    .ald     │  │   Parquet   │  │ CSR+Parquet │                          │
│  └─────────────┘  └─────────────┘  └─────────────┘                          │
├─────────────────────────────────────────────────────────────────────────────┤
│                        COMPUTE LAYER                                         │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                         trueno (v0.7.3)                               │   │
│  │   SIMD: SSE2 │ AVX │ AVX2 │ AVX-512 │ NEON │ WASM-SIMD128            │   │
│  │   GPU:  Vulkan │ Metal │ DX12 │ WebGPU                               │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│  ┌─────────────┐                                                            │
│  │ trueno-viz  │  Visualization (PNG/SVG/Terminal)                          │
│  │   (v0.1.1)  │                                                            │
│  └─────────────┘                                                            │
├─────────────────────────────────────────────────────────────────────────────┤
│                        QUALITY LAYER                                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐        │
│  │    pmat     │  │   certeza   │  │  verificar  │  │   renacer   │        │
│  │  Analysis   │  │  Coverage   │  │  Test Gen   │  │  Syscall    │        │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘        │
└─────────────────────────────────────────────────────────────────────────────┘
```

---

## Native File Formats

The PAIML stack defines two primary binary formats for model and data interchange.

### `.apr` - Aprender Model Format

**Purpose:** Serialized ML models with security, quality, and optimization features.

**Specification Version:** 1.8.0

```
┌─────────────────────────────────────────┐
│ Header (32 bytes, fixed)                │  Magic: "APRN" (0x4150524E)
├─────────────────────────────────────────┤
│ Metadata (variable, MessagePack)        │  Model card, metrics, hyperparams
├─────────────────────────────────────────┤
│ Chunk Index (if STREAMING flag)         │  JIT access map
├─────────────────────────────────────────┤
│ Salt + Nonce (if ENCRYPTED flag)        │  AES-256-GCM parameters
├─────────────────────────────────────────┤
│ Payload (variable, compressed)          │  Model weights (Zstd/LZ4)
├─────────────────────────────────────────┤
│ Signature Block (if SIGNED flag)        │  Ed25519 provenance
├─────────────────────────────────────────┤
│ License Block (if LICENSED flag)        │  Commercial protection
├─────────────────────────────────────────┤
│ Checksum (4 bytes, CRC32)               │  Integrity verification
└─────────────────────────────────────────┘
```

**Header Structure (32 bytes):**

| Offset | Size | Field | Description |
|--------|------|-------|-------------|
| 0 | 4 | magic | `0x4150524E` ("APRN") |
| 4 | 2 | format_version | Major.Minor (u8.u8) |
| 6 | 2 | model_type | Algorithm identifier (0x0001-0x00FF) |
| 8 | 4 | metadata_size | MessagePack metadata bytes |
| 12 | 4 | payload_size | Compressed payload bytes |
| 16 | 4 | uncompressed_size | Original payload bytes |
| 20 | 1 | compression | 0x00=None, 0x01=Zstd-L3, 0x02=Zstd-L19, 0x03=LZ4 |
| 21 | 1 | flags | Feature bitmask |
| 22 | 2 | schema_size | Reserved |
| 24 | 8 | reserved | Future extensions |

**Feature Flags:**

| Bit | Flag | Description | WASM |
|-----|------|-------------|------|
| 0 | ENCRYPTED | AES-256-GCM encryption | ✓ |
| 1 | SIGNED | Ed25519 signatures | ✓ |
| 2 | STREAMING | Chunked/mmap loading | Native only |
| 3 | LICENSED | Commercial license block | ✓ |
| 4 | TRUENO_NATIVE | 64-byte SIMD alignment | Native only |
| 5 | QUANTIZED | Q4_0/Q8_0 integer weights | ✓ |

**Supported Model Types (17+):**

| Code | Type | Description |
|------|------|-------------|
| 0x0001 | LINEAR_REGRESSION | OLS/Ridge/Lasso |
| 0x0002 | LOGISTIC_REGRESSION | GLM Binomial |
| 0x0003 | DECISION_TREE | CART/ID3 |
| 0x0004 | RANDOM_FOREST | Bagging ensemble |
| 0x0005 | GRADIENT_BOOSTING | Boosting ensemble |
| 0x0006 | KMEANS | Lloyd's clustering |
| 0x0007 | PCA | Principal Components |
| 0x0008 | NAIVE_BAYES | Gaussian NB |
| 0x0009 | KNN | K-Nearest Neighbors |
| 0x000A | SVM | Support Vector Machine |
| 0x0010 | NGRAM_LM | Markov language model |
| 0x0011 | TFIDF | TF-IDF vectorizer |
| 0x0020 | NEURAL_SEQUENTIAL | Feed-forward NN |
| 0x0021 | NEURAL_CUSTOM | Custom architecture |
| 0x0030 | CONTENT_RECOMMENDER | Content-based filtering |
| 0x00FF | CUSTOM | User-defined |

---

### `.ald` - Alimentar Dataset Format

**Purpose:** Portable, secure dataset interchange with streaming support.

**Specification Version:** 1.2.0

```
┌─────────────────────────────────────────┐
│ Header (32 bytes, fixed)                │  Magic: "ALDF" (0x414C4446)
├─────────────────────────────────────────┤
│ Metadata (variable, MessagePack)        │  Dataset card, schema info
├─────────────────────────────────────────┤
│ Schema (variable, Arrow IPC)            │  Column definitions
├─────────────────────────────────────────┤
│ Chunk Index (if STREAMING flag)         │  JIT access map
├─────────────────────────────────────────┤
│ Salt + Nonce (if ENCRYPTED flag)        │  AES-256-GCM parameters
├─────────────────────────────────────────┤
│ Payload (variable, Arrow IPC + zstd)    │  Dataset rows
├─────────────────────────────────────────┤
│ Signature Block (if SIGNED flag)        │  Ed25519 provenance
├─────────────────────────────────────────┤
│ License Block (if LICENSED flag)        │  Commercial protection
├─────────────────────────────────────────┤
│ Checksum (4 bytes, CRC32)               │  Integrity verification
└─────────────────────────────────────────┘
```

**Header Structure (32 bytes):**

| Offset | Size | Field | Description |
|--------|------|-------|-------------|
| 0 | 4 | magic | `0x414C4446` ("ALDF") |
| 4 | 2 | format_version | Major.Minor (u8.u8) |
| 6 | 2 | dataset_type | Type identifier (25+ variants) |
| 8 | 4 | metadata_size | Bytes |
| 12 | 4 | payload_size | Compressed bytes |
| 16 | 4 | uncompressed_size | Original bytes |
| 20 | 1 | compression | Algorithm ID |
| 21 | 1 | flags | Feature flags |
| 22 | 2 | schema_size | Schema block size |
| 24 | 8 | num_rows | Total row count |

**Dataset Types (25+ variants):**

| Category | Types |
|----------|-------|
| Structured | Tabular, TimeSeries, Graph, Spatial |
| Text/NLP | TextCorpus, TextClassification, TextPairs, SequenceLabeling, QuestionAnswering, Summarization, Translation |
| Vision | ImageClassification, ObjectDetection, Segmentation, ImagePairs, Video |
| Audio | AudioClassification, SpeechRecognition, SpeakerIdentification |
| Recommender | UserItemRatings, ImplicitFeedback, SequentialRecs |
| Multimodal | ImageText, AudioText, VideoText |

---

## Storage Formats by Project

| Project | Primary Format | Secondary Formats | Notes |
|---------|---------------|-------------------|-------|
| **trueno** | In-memory | - | Vector<T>, Matrix<T> types |
| **trueno-db** | Arrow/Parquet | SQL queries | 128MB morsel paging |
| **trueno-graph** | CSR (in-memory) | Parquet persistence | Edges + Nodes files |
| **trueno-viz** | Framebuffer | PNG, SVG, Terminal | 64-byte SIMD alignment |
| **alimentar** | `.ald` | Parquet, CSV, JSON | HuggingFace import |
| **aprender** | `.apr` | GGUF export | SafeTensors compatible |
| **verificar** | Parquet | JSON | VerifiedTuple schema |
| **batuta** | YAML config | JSON state | `.batuta-state.json` |
| **entrenar** | - | `.apr` output | Uses aprender format |
| **realizar** | GGUF | `.apr` input | llama.cpp compatible |

---

## Dependency Graph

```
                              ┌─────────────┐
                              │   batuta    │ ← Orchestration
                              └──────┬──────┘
                                     │
          ┌──────────────────────────┼──────────────────────────┐
          │                          │                          │
          ▼                          ▼                          ▼
    ┌───────────┐             ┌───────────┐             ┌───────────┐
    │  depyler  │             │  bashrs   │             │   decy    │
    │  Python   │             │   Bash    │             │    C      │
    └─────┬─────┘             └─────┬─────┘             └─────┬─────┘
          │                         │                         │
          └─────────────────────────┴─────────────────────────┘
                                    │
                                    ▼
    ┌───────────────────────────────────────────────────────────────┐
    │                      ML/AI LAYER                               │
    │                                                                │
    │  ┌───────────┐      ┌───────────┐      ┌───────────┐          │
    │  │ aprender  │◄────►│ entrenar  │◄────►│ realizar  │          │
    │  │  Models   │      │ Training  │      │ Inference │          │
    │  │   .apr    │      │           │      │   .gguf   │          │
    │  └─────┬─────┘      └─────┬─────┘      └─────┬─────┘          │
    │        │                  │                  │                 │
    └────────┼──────────────────┼──────────────────┼─────────────────┘
             │                  │                  │
             └──────────────────┼──────────────────┘
                               │
    ┌──────────────────────────┼──────────────────────────┐
    │                   DATA LAYER                         │
    │                          │                           │
    │  ┌───────────┐    ┌──────┴──────┐    ┌───────────┐  │
    │  │ alimentar │    │  trueno-db  │    │trueno-graph│  │
    │  │   .ald    │    │   Parquet   │    │ CSR+Parquet│  │
    │  └─────┬─────┘    └──────┬──────┘    └─────┬─────┘  │
    │        │                 │                 │        │
    └────────┼─────────────────┼─────────────────┼────────┘
             │                 │                 │
             └─────────────────┼─────────────────┘
                              │
    ┌─────────────────────────┼─────────────────────────┐
    │                  COMPUTE LAYER                     │
    │                         │                          │
    │  ┌──────────────────────┴──────────────────────┐  │
    │  │              trueno (v0.7.3)                 │  │
    │  │  SIMD: SSE2/AVX/AVX2/AVX-512/NEON/WASM      │  │
    │  │  GPU:  Vulkan/Metal/DX12/WebGPU             │  │
    │  └──────────────────────┬──────────────────────┘  │
    │                         │                          │
    │  ┌───────────┐          │                          │
    │  │trueno-viz │──────────┘                          │
    │  │  Charts   │                                     │
    │  └───────────┘                                     │
    └───────────────────────────────────────────────────┘
```

---

## Version Matrix (Current)

| Project | Version | Status | Test Coverage | WASM |
|---------|---------|--------|---------------|------|
| trueno | 0.7.3 | Production | 90.40% | ✓ |
| trueno-db | 0.3.3 | Production | 95.58% | Planned |
| trueno-viz | 0.1.1 | Production | 96.41% | ✓ |
| trueno-graph | 0.1.1 | Production | 98%+ | - |
| alimentar | 0.1.0 | Production | 85%+ | ✓ |
| aprender | 0.9.1 | Production | 96.94% | ✓ |
| verificar | 0.3.2 | Production | 95.48% | - |
| batuta | 0.1.0 | Alpha | 90%+ | ✓ |
| entrenar | 0.1.0 | Spec Phase | - | ✓ |

---

## Feature Capabilities Matrix

| Feature | trueno | trueno-db | trueno-viz | trueno-graph | alimentar | aprender |
|---------|--------|-----------|------------|--------------|-----------|----------|
| SIMD Acceleration | ✓ | via trueno | ✓ | via aprender | - | via trueno |
| GPU Compute | ✓ | ✓ | Planned | ✓ | - | Planned |
| WebGPU/WASM | ✓ | Planned | ✓ | - | ✓ | ✓ |
| Encryption | - | - | - | - | AES-256-GCM | AES-256-GCM |
| Digital Signatures | - | - | - | - | Ed25519 | Ed25519 |
| Streaming/Chunked | - | Morsel | - | Paged | ✓ | ✓ |
| Quantization | - | - | - | - | - | Q4/Q8 |
| Zero-copy I/O | ✓ | Arrow | - | Arrow | Arrow | - |

---

## Quality Standards (Stack-Wide)

All PAIML projects adhere to **Toyota Way** principles:

| Metric | Target | Enforcement |
|--------|--------|-------------|
| Test Coverage | ≥95% | cargo llvm-cov |
| Mutation Score | ≥85% | cargo-mutants |
| TDG Grade | ≥B+ | pmat analyze |
| Cyclomatic Complexity | ≤10 | clippy |
| SATD Comments | 0 | grep TODO/FIXME |
| Clippy Warnings | 0 | -D warnings |
| `unwrap()` calls | 0 | clippy::unwrap_used |

**Pre-commit Hook (All Projects):**
```bash
cargo fmt --check
cargo clippy -- -D warnings
cargo test --lib
pmat analyze tdg src/ --min-score 85
```

---

## Cryptographic Stack (Pure Rust, WASM-Safe)

All security features use pure Rust implementations:

| Algorithm | Crate | Purpose |
|-----------|-------|---------|
| AES-256-GCM | `aes-gcm` | Authenticated encryption |
| Argon2id | `argon2` | Key derivation (GPU-resistant) |
| X25519 | `x25519-dalek` | Key agreement |
| Ed25519 | `ed25519-dalek` | Digital signatures |
| SHA-256 | `sha2` | Hashing |
| HKDF | `hkdf` | Key expansion |
| CRC32 | Built-in | Checksums |

---

## Integration Patterns

### Pattern 1: Data Pipeline (alimentar → aprender → realizar)

```rust
// Load dataset
let dataset = alimentar::load("training.ald")?;

// Train model
let mut model = aprender::RandomForest::new();
model.fit(&dataset.x, &dataset.y)?;

// Save model
aprender::save(&model, ModelType::RandomForest, "model.apr",
    SaveOptions::default().with_compression(Zstd))?;

// Export for inference
realizar::export_gguf(&model, "model.gguf")?;
```

### Pattern 2: Analytics (trueno-db + trueno-viz)

```rust
// Query data
let storage = trueno_db::StorageEngine::load_parquet("sales.parquet")?;
let result = trueno_db::execute("SELECT region, SUM(amount) GROUP BY region", &storage)?;

// Visualize
let chart = trueno_viz::BarChart::new()
    .data(&result)
    .build()?;
chart.save_png("sales_by_region.png")?;
```

### Pattern 3: Graph Analytics (trueno-graph + aprender)

```rust
// Build graph
let mut graph = trueno_graph::CsrGraph::new();
graph.add_edge(0, 1, 1.0)?;
// ... add edges

// Run algorithms
let pagerank = trueno_graph::pagerank(&graph, 20, 1e-6)?;
let communities = trueno_graph::louvain(&graph)?;

// Persist
graph.write_parquet("call_graph").await?;
```

### Pattern 4: Test Generation (verificar → depyler)

```rust
// Generate test corpus
let generator = verificar::Generator::new(Language::Python);
let tests = generator.generate(SamplingStrategy::CoverageGuided {
    max_depth: 5,
    seed: 42
}, 1000)?;

// Verify transpilation
let transpiler = depyler::Transpiler::new();
for test in tests {
    let result = transpiler.transpile(&test.source)?;
    verificar::verify_equivalence(&test.source, &result)?;
}
```

---

## Deployment Targets

| Target | Binary Size | SIMD | GPU | Notes |
|--------|-------------|------|-----|-------|
| x86_64-unknown-linux-gnu | ~5MB | AVX2/AVX-512 | Vulkan | Production default |
| aarch64-unknown-linux-gnu | ~4MB | NEON | Vulkan | ARM servers |
| x86_64-apple-darwin | ~5MB | AVX2 | Metal | macOS Intel |
| aarch64-apple-darwin | ~4MB | NEON | Metal | macOS M1/M2/M3 |
| x86_64-pc-windows-msvc | ~5MB | AVX2 | DX12 | Windows |
| wasm32-unknown-unknown | ~500KB | SIMD128 | WebGPU | Browser |
| thumbv7em-none-eabihf | ~2MB | - | - | Embedded |

---

## Roadmap: Active Development Focus

### trueno (v0.8.0)
- [ ] Multi-dimensional array support
- [ ] Autograd backward operations
- [ ] Training loop primitives

### trueno-db (v0.4.0)
- [ ] Phase 2: Multi-GPU distributed execution
- [ ] Phase 4: WASM browser deployment

### trueno-viz (v0.2.0)
- [ ] Real-time GPU rendering
- [ ] Interactive dashboards

### alimentar (v0.2.0)
- [ ] P2P dataset sharing
- [ ] Streaming transformations

### aprender (v1.0.0)
- [ ] Complete neural network support
- [ ] Production autograd engine

### entrenar (v0.1.0)
- [ ] Autograd engine implementation
- [ ] LoRA/QLoRA fine-tuning
- [ ] Quantization (QAT/PTQ)

---

## Scientific Validation (Code Review)

The following peer-reviewed publications support the architectural decisions and quality standards defined in this specification, particularly regarding the "Toyota Way" (reliability/safety) and "Sovereign AI" (portability/performance) goals.

1. **Rust/Memory Safety (Sovereignty & Quality)**
   *   *Jung, R., et al. (2017). "RustBelt: Securing the Foundations of the Rust Programming Language." Proc. ACM Program. Lang. 2 (POPL).*
   *   **Annotation:** Validates the choice of Rust for a "Sovereign" stack by mathematically proving that Rust's safety guarantees (ownership, type safety) hold even when interacting with "unsafe" low-level code, essential for a secure, zero-dependency AI stack.

2. **WASM-First Design (Portability)**
   *   *Haas, A., et al. (2017). "Bringing the Web up to Speed with WebAssembly." PLDI '17.*
   *   **Annotation:** Supports the `aprender`/`realizar` strategy of treating the browser as a first-class deployment target (Performance/Portability), demonstrating that WASM can achieve near-native execution speeds.

3. **Columnar Storage & Vectorization (trueno-db)**
   *   *Abadi, D., et al. (2008). "Column-stores vs. row-stores: how different are they really?" SIGMOD '08.*
   *   **Annotation:** Justifies the architectural split between `trueno-db` (columnar/Parquet) and transactional memory, confirming that for the analytical workloads (OLAP) typical in AI training/data loading, column-stores provide order-of-magnitude performance benefits.

4. **Graph Storage efficiency (trueno-graph)**
   *   *Sakr, S., et al. (2021). "The Future is Big Graphs: A Community View on Graph Processing Systems." Communications of the ACM.*
   *   **Annotation:** Validates the use of Compressed Sparse Row (CSR) and Parquet for `trueno-graph`, ensuring that the graph processing layer aligns with modern state-of-the-art practices for storage efficiency and traversal speed.

5. **Authenticated Encryption (Security)**
   *   *Bernstein, D. J., et al. (2012). "High-speed high-security signatures." Journal of Cryptographic Engineering.*
   *   **Annotation:** Supports the `aprender` format's use of Ed25519 for model signing, confirming it as a high-performance, side-channel-resistant choice suitable for verifying model provenance without slowing down loading times.

6. **Mutation Testing (Quality/Toyota Way)**
   *   *Jia, Y., & Harman, M. (2011). "An analysis and survey of the development of mutation testing." IEEE Trans. Softw. Eng.*
   *   **Annotation:** Provides the scientific basis for the "EXTREME TDD" and `cargo-mutants` requirement, illustrating that mutation testing is a superior proxy for fault detection capability compared to simple code coverage.

7. **Model Explainability (Ethics/Quality)**
   *   *Arrieta, A. B., et al. (2020). "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI." Information Fusion.*
   *   **Annotation:** Reinforces the requirement for `.apr` metadata (Model Cards) and `trueno-viz` capabilities, emphasizing that sovereignty includes the ability to understand and audit model decision-making processes.

8. **Parameter-Efficient Fine-Tuning (entrenar)**
   *   *Hu, E. J., et al. (2021). "LoRA: Low-Rank Adaptation of Large Language Models." ICLR 2022.*
   *   **Annotation:** Directly supports the `entrenar` roadmap item for "LoRA/QLoRA fine-tuning", proving that massive efficiency gains in training memory/compute are possible without sacrificing model quality.

9. **Quantization Strategies (Inference)**
   *   *Jacob, B., et al. (2018). "Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference." CVPR 2018.*
   *   **Annotation:** Validates the `.apr` flags for Q4_0/Q8_0 quantization, demonstrating that integer-only arithmetic is a viable path for high-performance inference on edge devices (Sovereignty/Portability) with minimal accuracy loss.

10. **Morsel-Driven Parallelism (trueno-db)**
    *   *Leis, V., et al. (2014). "Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age." SIGMOD '14.*
    *   **Annotation:** (Expansion on existing ref) Specifically validates `trueno-db`'s "128MB morsel paging" architecture, showing this fine-grained task scheduling is optimal for modern multi-core CPUs compared to traditional thread-per-query models.

## References

### Academic Papers

1. Gregg & Hazelwood (2011) - PCIe bus bottleneck analysis (5× rule)
2. Harris (2007) - Optimizing parallel reduction in CUDA
3. Leis et al. (2014) - Morsel-driven parallelism
4. Wu et al. (2012) - Kernel fusion execution model
5. Wilkinson (2005) - Grammar of Graphics
6. Godefroid et al. (2008) - Grammar-based fuzzing
7. Mitchell et al. (2019) - Model cards for model reporting

### Specifications

- Apache Arrow IPC Format v5
- Apache Parquet v2.9.0
- GGUF Format (llama.cpp)
- MessagePack Specification
- WebGPU Shading Language (WGSL)

---

## Contact & Resources

- **Repository:** https://github.com/paiml
- **Documentation:** Each project contains detailed specs in `docs/specifications/`
- **Quality Tool:** `certeza` for coverage validation
- **Analysis Tool:** `pmat` for complexity analysis

---

*This document is auto-generated from active development state. Last sync: 2025-11-26*
