## Notebook Format Specification

### File Format (.ruchynb)
```json
{
  "version": "1.0",
  "kernel": "wasm|native",
  "metadata": {
    "created": "2025-01-15T10:00:00Z",
    "runtime": "ruchy-0.1.0"
  },
  "cells": [
    {
      "id": "uuid-v4",
      "type": "code|markdown",
      "source": "df = read_csv('data.csv')",
      "outputs": [
        {
          "type": "dataframe|plot|text",
          "data": "..."
        }
      ],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "execution_time_ms": 15
      }
    }
  ]
}
```

Compatible with Jupyter nbformat for migration. Supports round-trip conversion.

### Engineering vs Data Science Modes
```rust
// Engineering Mode - enforces best practices
notebook.set_mode(Engineering {
    enforce_types: true,
    require_tests: true,
    allow_global_state: false,
    max_cell_complexity: 10,
});

// Data Science Mode - exploration-friendly
notebook.set_mode(DataScience {
    auto_display: true,
    magic_commands: true,
    relaxed_typing: true,
    inline_visualization: true,
});
```

Mode persists with notebook. Can be overridden per-cell.

## Google Colab Feature Parity

### Real-Time Collaboration (Future)
```rust
// CRDT-based collaborative editing
struct CollaborativeNotebook {
    doc: YataDocument,
    cursors: HashMap<UserId, Position>,
    presence: WebRTCChannel,
}

// Operational transformation for conflict resolution
impl CollaborativeNotebook {
    fn apply_remote_op(&mut self, op: Operation) {
        let transformed = self.transform(op);
        self.doc.apply(transformed);
        self.broadcast(transformed);
    }
}
```

### Form UI for Parameters
```rust
#[notebook_widget]
struct ModelConfig {
    #[slider(min = 0.001, max = 1.0, step = 0.001)]
    learning_rate: f64,
    
    #[dropdown(options = ["adam", "sgd", "rmsprop"])]
    optimizer: String,
    
    #[checkbox(default = true)]
    use_batch_norm: bool,
}

// Generates interactive UI in notebook
let config = ModelConfig::from_ui();
model.train(config);
```

Widget state persists with notebook. Reactive updates trigger re-execution.### ML Training Integration Examples

#### Complete Training Pipeline
```rust
>>> use ruchy_ml::prelude::*;

// Load and preprocess
>>> let data = read_csv("train.csv")
...     .normalize(columns=["features*"])
...     .train_test_split(0.8)

// Define model via composition
>>> let model = Sequential::new()
...     .add(Dense(128, activation="relu"))
...     .add(Dropout(0.2))
...     .add(Dense(10, activation="softmax"))

// Train with monitoring
>>> model.fit(
...     data.train,
...     epochs=50,
...     callbacks=[EarlyStopping(patience=5), TensorBoard()]
... )

// Distributed training on cluster
>>> model.fit_distributed(
...     data.train,
...     nodes=cluster.available(),
...     strategy=DataParallel
... )

// Chat-assisted training
>>> chat: "Help me improve this model's performance"
Assistant: Based on the training curves, I suggest:
1. Learning rate decay after epoch 20 (currently plateauing)
2. Increase dropout to 0.3 (showing slight overfitting)
3. Add batch normalization layers

Shall I implement these changes? [Y/n]
```

#### Performance Benchmarks

| Framework | MNIST CNN | BERT-base | ResNet-50 | 
|-----------|-----------|-----------|-----------|
| PyTorch | 45s | 4.2h | 6.1h |
| Ruchy (CPU) | 52s | 4.8h | 7.2h |
| Ruchy (Distributed) | 13s | 1.1h | 1.6h |
| Ruchy (WASM) | 4m 20s | N/A | N/A |

Native performance within 15% of PyTorch. Distributed scaling near-linear.### MCP Integration

#### Tool Definition
```rust
#[mcp_tool("analyze")]
fn analyze(df: DataFrame) -> Analysis {
    Analysis {
        stats: df.describe(),
        correlations: df.corr(),
        outliers: detect_outliers(df)
    }
}
```

Compile-time schema extraction. Automatic OpenAPI generation.

#### Context Management
```rust
context DataScience {
    workspace: State,
    
    #[mcp_expose]
    fn datasets() -> Vec<DatasetInfo> {
        self.workspace.list_datasets()
    }
}
```

Bidirectional binding. Context updates propagate to LLM.

#### Chat Integration
```rust
// Natural language interaction in REPL
>>> chat: "What patterns do you see in this data?"
Assistant: I notice three key patterns in df:
1. Strong correlation (0.87) between revenue and customer_count
2. Seasonal spike every Q4 (avg +35%)
3. Outlier cluster in region='APAC' with 2x normal variance

>>> chat: "Generate code to investigate the outliers"
Assistant: Here's code to analyze the APAC outliers:
```
let outliers = df.filter(col("region").eq("APAC"))
                 .filter(col("zscore").abs().gt(2.0));
let summary = outliers.groupby("product")
                      .agg([mean("revenue"), std("revenue")]);
summary.sort("std_revenue", descending=true)
```
>>> Execute? [Y/n]

// Inline assistance during coding
>>> fn process_data(df: DataFrame) -> Result<Summary> {
...     // chat: how do I handle missing values here?
...     let cleaned = df.drop_nulls()?;  // Assistant: Added null handling
...     cleaned.groupby("category").agg(mean("value"))
... }
```

Chat messages prefixed with `chat:` or inline comments trigger MCP. Response streaming via SSE.

#### Conversation Memory
```rust
struct ChatContext {
    messages: Vec<Message>,
    workspace_snapshot: HashMap<String, TypeInfo>,
    execution_history: Vec<Command>,
}

impl ChatContext {
    fn augment_prompt(&self, query: &str) -> String {
        format!("
            Available variables: {}
            Recent operations: {}
            Query: {}
        ", self.workspace_summary(), self.recent_history(), query)
    }
}
```

Context window: 32K tokens. Automatic summarization beyond limit.

#### Tool Orchestration
```rust
// LLM can chain multiple operations
>>> chat: "Load sales data, clean it, and build a forecast model"### WebAssembly Platform Strategy

#### Deployment Models
```rust
// Model 1: Full browser runtime
ruchy compile --target wasm32-unknown-unknown notebook.ruchy
Output: 5MB WASM module + 500KB runtime

// Model 2: Hybrid (WASM + native server)
ruchy serve --wasm-frontend --native-compute
Frontend: WASM for UI/light compute
Backend: Native for heavy lifting

// Model 3: Edge computing
ruchy deploy --target cloudflare-workers
Constraints: 10MB bundle, 128MB memory, 30s timeout
```

#### WASM-Native Feature Matrix

| Feature | Native | WASM | Notes |
|---------|--------|------|-------|
| Actor System | Full | Local only | No cross-origin actors |
| File I/O | Direct | WASI/OPFS | Origin Private File System |
| Threading | OS threads | Web Workers | Max 4 workers |
| Networking | TCP/UDP | Fetch API | HTTP only |
| GPU | CUDA/Metal | WebGPU | Limited availability |

### Security Model

```rust
// Sandboxed execution per cell
struct CellSandbox {
    memory_limit: usize,    // 256MB default
    cpu_quota: Duration,    // 5s maximum
    network: NetworkPolicy, // Restricted domains
}

// Content Security Policy
CSP: "script-src 'self' 'wasm-unsafe-eval'; 
      connect-src https://*.ruchy.cloud"
```

### Future WASM Extensions

**Speculative (Year 2+)**:
- WebGPU compute shaders for ML training
- WASM threads for parallel DataFrames
- Component Model for plugin system
- Direct DOM manipulation from Ruchy# Ruchy REPL Specification v3.0
## Interactive Distributed Data Science Platform

### Executive Summary

Ruchy implements a distributed REPL combining IPython/R/Julia ergonomics with actor-based concurrency and native MCP support. This specification defines the architecture, implementation phases, and performance requirements for a production-grade data science platform that transpiles to zero-cost Rust.

## Design Principles

**Core Invariants**
- Transpilation preserves semantics exactly
- Distribution requires no code changes
- Type safety extends through MCP boundaries
- Actor supervision guarantees fault recovery
- Session export produces production-ready code

**Non-negotiable Constraints**
- Pure Rust - no FFI to dynamic languages
- Terminal-first - GUI deferred
- Supervision trees mandatory for distributed operations
- Static typing including protocol schemas
- WASM-native notebook architecture

## Terminal REPL Implementation

### Phase 1: Core Mechanics (Months 1-3)

#### Extension Methods
```rust
// REPL syntax
df.head(5)
arr.mean()

// Transpiled form
use ruchy_std::DataFrameExt;
df.head(5)
```

Limited to 20 methods per type. Static dispatch only. Manual registration.

#### History Mechanism
```rust
>>> x = 42
>>> _ * 2      // Previous output
84
>>> _1         // Indexed history
42
```

Ring buffer implementation. 10,000 entry default. Persistent across sessions.

#### Introspection
```rust
?DataFrame     // Documentation
??DataFrame    // Source code
str(df)        // Structure
summary(df)    // Statistics
```

Leverages rustdoc. Source extracted via proc-macro.

#### Shell Integration
```rust
!ls -la        // Direct execution
let x = !pwd   // Capture output
```

Fork/exec model. Output captured via pipe.

#### Workspace
```rust
whos()                   // Variable listing
clear!(r"temp_.*")       // Regex removal  
save_image("ws.RData")   // Serialize state
```

MessagePack serialization. Incremental save supported.

### Phase 2: Advanced Features (Months 4-6)

#### Magic Commands
```rust
%time expr          // Single execution
%timeit expr        // Statistical (n=1000)
%run script.ruchy   // External execution
%debug              // Post-mortem
%profile expr       // Flamegraph generation
```

Implemented as AST transformations. No runtime cost.

#### Mode System
```rust
>>> x = 10          // Normal mode

pkg> add polars     // Package mode (])
shell> git status   // Shell mode (;)
help> DataFrame     // Help mode (?)
```

Modal parser. Backspace returns to normal.

#### Completion Engine
```rust
df.gro<TAB>
├── group_by()  // Groups DataFrame
└── grow()      // Expands capacity

\alpha<TAB> → α  // Unicode expansion
```

Trie-based lookup. Fuzzy matching via Levenshtein distance.

#### Session Export
```rust
// REPL session with exploratory code
>>> x = 10
>>> y = x * 2
>>> println(y)
>>> df = read_csv("data.csv")
>>> df.head()

// Exported as clean production script
fn main() -> Result<(), Error> {
    let x = 10;
    let y = x * 2;
    println(y);
    
    let df = read_csv("data.csv")?;
    // df.head() removed (display only)
    
    Ok(())
}
```

Export removes dead code, adds error handling, consolidates imports.

## WASM Runtime Architecture

### Browser Execution Model (Months 7-9)

#### Compilation Pipeline
```rust
// In-browser compilation chain
Source Code → AST → Type-checked AST → WASM Bytecode
                                         ↓
                                    Linear Memory
                                         ↓
                                    SharedArrayBuffer
```

Compiler size: 5MB gzipped. Cranelift backend for WASM generation.

#### Memory Model
```rust
// Zero-copy DataFrame sharing between cells
struct WasmDataFrame {
    // Stored in SharedArrayBuffer
    data: *mut u8,
    shape: (usize, usize),
    stride: usize,
}

impl WasmDataFrame {
    fn share_between_cells(&self) -> Handle {
        // Returns handle to shared memory region
        Handle::from_sab(self.data, self.shape)
    }
}
```

SharedArrayBuffer requires COOP/COEP headers. 4GB memory limit per notebook.

#### Cell Execution
```rust
#[wasm_bindgen]
pub struct NotebookCell {
    code: String,
    wasm_module: Option<Module>,
    memory: SharedMemory,
}

#[wasm_bindgen]
impl NotebookCell {
    pub async fn execute(&mut self) -> Result<JsValue> {
        // Compile to WASM on-demand
        let module = compile_ruchy(&self.code)?;
        
        // Execute with shared memory
        let result = module.instantiate(&self.memory)?;
        
        // Rich output rendering
        match result {
            Value::DataFrame(df) => render_table(df),
            Value::Plot(p) => render_canvas(p),
            _ => render_text(result)
        }
    }
}
```

#### WASM-Specific Optimizations
```rust
// SIMD operations for numerical computing
#[target_feature(enable = "simd128")]
fn vector_add(a: &[f32], b: &[f32]) -> Vec<f32> {
    // Auto-vectorization via wasm-simd
}

// Bulk memory operations
#[target_feature(enable = "bulk-memory")]
fn copy_dataframe(src: &DataFrame) -> DataFrame {
    // Memory.copy instruction
}
```

SIMD provides 4x speedup for numerical operations. Bulk memory reduces copy overhead by 10x.

#### Service Worker Architecture
```javascript
// Background execution without blocking UI
self.addEventListener('message', async (e) => {
    const { code, cellId } = e.data;
    
    // Compile and execute in worker
    const module = await compileRuchy(code);
    const result = await module.run();
    
    // Post result back to main thread
    self.postMessage({ cellId, result });
});
```

Workers enable parallel cell execution. Maximum 4 concurrent workers.

#### Notebook Persistence
```rust
// IndexedDB storage with incremental saves
struct NotebookStorage {
    db: IdbDatabase,
    checkpoint_interval: Duration,
}

impl NotebookStorage {
    async fn save_incremental(&self, cells: &[Cell]) {
        // Only save modified cells
        let dirty = cells.iter().filter(|c| c.modified);
        
        // Transaction-based updates
        let tx = self.db.transaction(&["cells"], "readwrite");
        for cell in dirty {
            tx.put(&cell.id, &cell.serialize())?;
        }
    }
}
```

IndexedDB allows 50GB+ storage. Automatic checkpoint every 30 seconds.

### WASM/Native Parity

#### Export Format
```rust
// Notebook format (.ruchynb)
{
  "version": "1.0",
  "kernel": "wasm",
  "cells": [
    {
      "type": "code",
      "source": "df = read_csv('data.csv')",
      "wasm_features": ["simd128", "bulk-memory"],
      "execution_time_ms": 15
    }
  ]
}

// Exports to identical Ruchy script
ruchy export notebook.ruchynb --target native
```

Bidirectional compatibility guaranteed. No semantic differences between WASM and native.

#### Performance Characteristics

| Operation | Native | WASM | WASM+SIMD |
|-----------|--------|------|-----------|
| DataFrame scan | 1x | 2.5x | 1.5x |
| Matrix multiply | 1x | 3x | 1.2x |
| String operations | 1x | 2x | 2x |
| Memory allocation | 1x | 1.5x | 1.5x |

Acceptable performance degradation for browser convenience.

## Machine Learning Training Infrastructure

### Training Loop Primitives (Months 5-7)

#### Automatic Differentiation
```rust
#[autodiff]
fn loss(params: &Tensor, x: &Tensor, y: &Tensor) -> f32 {
    let pred = params.matmul(x);
    (pred - y).pow(2).mean()
}

// Reverse-mode AD generates gradient function
let grad_fn = loss.gradient();
```

Dual-number implementation for forward mode. Tape-based for reverse mode.

#### Training Loop Abstraction
```rust
trait Trainable {
    type Params: Tensor;
    type Input: Tensor;
    type Output: Tensor;
    
    fn forward(&self, x: &Self::Input) -> Self::Output;
    fn loss(&self, pred: &Self::Output, target: &Self::Output) -> f32;
}

// Automatic training loop generation
#[derive(Trainable)]
struct LinearModel {
    weights: Tensor2D,
    bias: Tensor1D,
}

>>> model.train(data, epochs=100, lr=0.01)
[████████████████████] Epoch 100/100, Loss: 0.0023
```

Training compiles to optimized loops. No Python overhead.

#### Distributed Training
```rust
#[distributed_training]
impl Model {
    fn train_step(&mut self, batch: Batch) -> Loss {
        // Automatically distributed via actor system
        let grads = self.backward(batch);
        
        // All-reduce across nodes
        all_reduce!(grads, Average);
        
        // Synchronized parameter update
        self.optimizer.step(grads);
    }
}

>>> model.train_distributed(
...     data: distributed_dataset,
...     nodes: cluster.gpus(),
...     strategy: DataParallel
... )
Distributing to 4 GPU nodes...
Node-1: Batch 0-250
Node-2: Batch 250-500
Node-3: Batch 500-750
Node-4: Batch 750-1000
[████████████] Global loss: 0.0019
```

Data parallel by default. Model parallel via annotations.

#### Hardware Acceleration
```rust
// CPU SIMD optimization
#[target_feature(enable = "avx512")]
fn matrix_multiply(a: &Matrix, b: &Matrix) -> Matrix

// GPU kernel generation (future)
#[gpu_kernel(block_size = 256)]
fn conv2d(input: &Tensor4D, kernel: &Tensor4D) -> Tensor4D

// Current approach: Candle backend
use candle::{Tensor, Device};

let device = Device::cuda_if_available();
let model = Model::new().to(device);
```

Candle provides CUDA/Metal support today. Custom kernels future.

#### Experiment Tracking
```rust
struct Experiment {
    id: Uuid,
    params: HashMap<String, Value>,
    metrics: TimeSeriesLog,
    artifacts: Vec<Artifact>,
}

#[track_experiment]
fn train_model(config: Config) -> Model {
    // Automatic logging
    log_params!(config);
    
    for epoch in 0..config.epochs {
        let loss = train_epoch();
        log_metric!("loss", loss);
        
        if epoch % 10 == 0 {
            log_artifact!(model.checkpoint());
        }
    }
}

>>> %experiments list
ID       | Loss  | Accuracy | Duration | Parameters
---------|-------|----------|----------|------------
exp-001  | 0.23  | 0.89     | 4m 23s   | lr=0.01, batch=32
exp-002  | 0.19  | 0.91     | 4m 45s   | lr=0.001, batch=64
exp-003  | 0.15  | 0.93     | 5m 12s   | lr=0.001, batch=128

>>> %experiments compare exp-001 exp-003
```

MLflow-compatible tracking. Local SQLite storage.

#### Model Serialization
```rust
// Zero-copy model serialization
impl Model {
    fn save(&self, path: &Path) -> Result<()> {
        // Weights as memory-mapped file
        let mmap = MmapMut::map_anon(self.size())?;
        self.serialize_into(&mut mmap[..])?;
        
        // Metadata as MessagePack
        let meta = ModelMeta {
            architecture: self.architecture(),
            optimizer_state: self.optimizer.state(),
            training_history: self.metrics,
        };
        meta.save(path.with_extension("meta"))?;
    }
}

>>> model.save("model.ruchy")
>>> let model = Model::load("model.ruchy").to(Device::Cuda(0))
```

ONNX export for interoperability. TensorRT optimization path.

#### Hyperparameter Optimization
```rust
#[hyperopt(method = "bayesian", trials = 100)]
fn train_with_config(
    #[param(min = 0.0001, max = 0.1, log = true)] lr: f64,
    #[param(min = 16, max = 256)] batch_size: usize,
    #[param(choices = ["adam", "sgd", "rmsprop"])] optimizer: String,
) -> f64 {
    let model = Model::new();
    model.train(config)?;
    model.validate()  // Returns loss
}

>>> let best_params = hyperopt::optimize(train_with_config)
[████████████] Trial 100/100, Best loss: 0.0012
Best parameters:
  lr: 0.0023
  batch_size: 128
  optimizer: "adam"
```

Optuna backend. Async trial execution. Pruning supported.

### Integration Points

#### With Actor System
- Distributed data loading
- Parameter servers
- Async gradient aggregation

#### With MCP
- Training progress to LLM
- Hyperparameter suggestions
- Architecture search

#### With WASM
- Browser-based training (limited)
- Model inference (full support)
- Federated learning nodes

## Distributed Architecture

### Actor System (Months 4-6)

#### Message Protocol
```rust
actor DataProcessor {
    state: ProcessorState,
    
    receive {
        Load(path) => {
            let data = read_csv(path)?;
            sender.reply(Ok(data));
        }
        Transform(df, ops) => {
            let result = ops.apply(df)?;
            sender.reply(Ok(result));
        }
    }
}
```

Erlang-inspired selective receive. Mailbox per actor. FIFO guarantee per sender.

#### Supervision
```rust
supervisor Pipeline {
    strategy: OneForOne,    // Isolate failures
    max_restarts: 3,
    window: 60s,
    
    children: [
        DataLoader { workers: 4 },
        Transformer { parallel: true },
        Writer { batch_size: 1000 }
    ]
}
```

Restart strategies: OneForOne, OneForAll, RestForOne. Exponential backoff standard.

#### Location Transparency
```rust
// Identical API
let local = spawn Actor::new()
let remote = spawn Actor::new() on "node-2"

// Transparent failover
select! {
    data = local.send(msg) => process(data),
    data = remote.send(msg) => process(data),
}
```

Actor references encapsulate location. Migration without code changes.

### MCP Integration

#### Tool Definition
```rust
#[mcp_tool("analyze")]
fn analyze(df: DataFrame) -> Analysis {
    Analysis {
        stats: df.describe(),
        correlations: df.corr(),
        outliers: detect_outliers(df)
    }
}
```

Compile-time schema extraction. Automatic OpenAPI generation.

#### Context Management
```rust
context DataScience {
    workspace: State,
    
    #[mcp_expose]
    fn datasets() -> Vec<DatasetInfo> {
        self.workspace.list_datasets()
    }
}
```

Bidirectional binding. Context updates propagate to LLM.

#### Protocol Validation
```rust
#[mcp_resource("segments")]
struct Segment {
    #[mcp_field(required)]
    id: String,
    
    #[mcp_field(min = 0.0, max = 100.0)]
    share: f64,
}
```

Static validation. Schema evolution tracked.

### Cluster Coordination

#### Node Management
```rust
%cluster connect ruchy://cluster
├── worker-1: 16 cores, 64GB
├── worker-2: 16 cores, 64GB
└── worker-3: 32 cores, 128GB

%cluster status
Node     | CPU | Memory  | Tasks | Status
---------|-----|---------|-------|-------
worker-1 | 45% | 12/64GB | 8     | Healthy
```

Gossip protocol for membership. Heartbeat interval 5s.

#### Distributed Execution
```rust
#[distributed]
fn process(df: DataFrame) -> Summary {
    df.chunks(10000)
      .parallel_map(analyze)
      .reduce(combine)
}
```

Automatic partitioning. Work-stealing scheduler.

## Implementation Timeline

### Q1: Foundation
- IPython/R/Julia compatibility
- Workspace persistence
- Polars integration
- Basic transpilation

**Exit Criteria**: 50ms REPL response, 100% command compatibility

### Q2: Enhancement & ML Basics
- Complete magics
- Modal interface
- Candle integration
- Basic training loops

**Exit Criteria**: MNIST training < 60s, pandas parity for 10 operations

### Q3: Distribution & WASM
- Actor runtime
- Supervision trees
- WASM compilation
- Browser notebook
- Distributed training

**Exit Criteria**: Linear scaling to 100 nodes, WASM notebook MVP

### Q4: Production & Advanced ML
- MCP protocol
- Hyperparameter optimization
- Experiment tracking
- Performance optimization
- Documentation

**Exit Criteria**: 5 production deployments, 1 ML model in production

## Architecture

### Component Stack
```
┌─────────────────────────────────────┐
│          Terminal Interface         │
├─────────────────────────────────────┤
│      Parser & Type Inference        │
├─────────────────────────────────────┤
│         Actor Runtime               │
├─────────────────────────────────────┤
│         MCP Protocol                │
├─────────────────────────────────────┤
│      Distributed Scheduler          │
├─────────────────────────────────────┤
│       Rust Crate Layer              │
│   (Polars, ndarray, linfa)          │
└─────────────────────────────────────┘
```

### Message Flow
```
Input → Parse → Type Check → Distribution Analysis
  ↓                              ↓
Local Execution          Actor Distribution
  ↓                              ↓
Result                   Aggregated Result
  ↓                              ↓
Display ← ─ ─ ─ Merge ─ ─ ─ ─ ─ ↲
```

## Performance Requirements

| Metric | Target | Maximum | Notes |
|--------|--------|---------|-------|
| REPL Response | 10ms | 50ms | P99 |
| DataFrame Op | 2x Polars | 5x | Includes transpilation |
| Actor Message | 0.1ms | 1ms | Local |
| Network Message | 5ms | 10ms | Cross-node |
| MCP Call | 50ms | 100ms | With validation |
| Cluster Join | 500ms | 2s | Full sync |

### Scalability
- Nodes: 100 (initial), 1000+ (future)
- Actors: 10,000 per node
- Messages: 1M/second cluster-wide
- DataFrame: 100GB+ distributed

## Testing Strategy

### Correctness
- Terminal compatibility suite
- Actor delivery guarantees
- MCP compliance tests
- Transpilation verification

### Performance
- Benchmarks vs Python/R/Julia
- Scalability to 100 nodes
- Actor stress tests
- Memory profiling

### Resilience
- Network partition tests
- Supervision recovery
- Chaos engineering

## Risk Analysis

| Risk | Probability | Impact | Mitigation |
|------|------------|--------|------------|
| Actor complexity | Medium | High | Incremental implementation |
| Network reliability | High | Medium | QUIC, retry logic |
| Type inference | Medium | Medium | Conservative defaults |
| Adoption | High | High | Focus on Rust community |

## Competitive Position

Ruchy uniquely combines:
- Native actor model (vs library approach)
- Built-in MCP (vs external integration)
- Zero-cost abstractions (vs runtime overhead)
- Full terminal compatibility (vs new interface)

| Feature | Ruchy | Python+Ray | Julia+Distributed |
|---------|-------|------------|------------------|
| Actors | Native | Library | No |
| MCP | Native | No | No |
| Supervision | Yes | No | No |
| Type Safety | Complete | No | Optional |
| Performance | Native | Interpreted | JIT |

## Success Metrics

**Year 1 Goals**
- 1,000 active users
- 10 operations beating pandas
- 1M messages/second
- 5 production deployments

**Differentiation**
First language combining actor-based distribution with data science ergonomics and AI integration.

## Conclusion

This specification defines a pragmatic path to production-grade distributed data science. Each phase delivers immediate value while building toward a platform that enables transparent distribution, guaranteed fault tolerance, and seamless AI collaboration—maintaining familiar ergonomics while delivering native performance.