🤖 STARK Language — Core AI Types Specification

This document defines the fundamental AI/ML data structures in STARKLANG. These are not extensions but core language primitives, designed to make AI deployment efficient, safe, and expressive.

📦 Core AI Types

1️⃣ Tensor<T>[...Shape] - Core Primitive

The fundamental data structure for all AI computations in STARK.

let features: Tensor<Float32>[batch, 128]
let weights: Tensor<Float32>[128, 10] 
let image: Tensor<UInt8>[3, 224, 224]

Compile-Time Shape Safety:

• Shape mismatches caught at compile time
• Automatic shape inference in operations
• Zero-cost abstractions for tensor operations

Core Methods:

tensor.shape() -> [Int; N]           // Compile-time known shape
tensor.matmul(other: Tensor<T>[M, K]) -> Tensor<T>[N, K]
tensor.map(fn: T -> U) -> Tensor<U>[...Shape]
tensor.reduce_sum(axis: Int) -> Tensor<T>[...ReducedShape]

Memory Layout:

• Contiguous memory with configurable layouts (row-major, column-major)
• GPU memory support with automatic transfers
• Zero-copy views and slicing

2️⃣ Dataset<T> - Streaming Data Pipeline

Efficient, lazy-evaluated data processing for AI workflows.

let train_data: Dataset<(Tensor<Float32>[128], Int32)>
let images: Dataset<Tensor<UInt8>[3, 224, 224]>

Pipeline Operations:

dataset
  .batch(32)                                    // Create batches
  .map(|x| preprocess(x))                      // Transform data
  .shuffle(1000)                               // Shuffle with buffer
  .prefetch(4)                                 // Async prefetching

Memory Efficiency:

• Lazy evaluation - only process when consumed
• Streaming from disk/network without loading everything
• Automatic memory management for large datasets
• GPU pipeline support for zero-copy transfers

3️⃣ Model<Input, Output> - Inference Engine

Type-safe model abstraction with shape validation and performance optimization.

let classifier: Model<Tensor<Float32>[batch, 128], Tensor<Float32>[batch, 10]>
let embedder: Model<Vec<String>, Tensor<Float32>[batch, 384]>

Model Loading:

let model = load_pytorch_model("classifier.pt")  // Automatic shape inference
let onnx_model = load_onnx_model("model.onnx")
let huggingface_model = load_hf_model("bert-base-uncased")

Inference Methods:

model.predict(input: Input) -> Output
model.batch_predict(inputs: Vec<Input>) -> Vec<Output>
model.stream_predict(inputs: Dataset<Input>) -> Dataset<Output>

Performance Features:

• Automatic batching for throughput optimization
• Device placement (CPU/GPU) with automatic transfers
• Model quantization and compression
• Execution profiling and optimization hints

🧠 Supporting Types (Future Extensions)

• LossFunction: e.g., CrossEntropy, MSE
• Optimizer: e.g., SGD, Adam
• MLGraph: DAG of models/operators
• Metric: Evaluation and scoring abstractions

✨ AI Type Design Philosophy

Goal	Mechanism
Type Safety	Shape-checked tensors
Performance	Backend-neutral tensor engine
Composability	Pipelines, map/filter DSL
Portability	Export to ONNX, deploy as service
ML-Native Semantics	Model + Dataset + Pipeline cohesion

📌 Example: Complete AI Deployment Pipeline

// Load pre-trained model with automatic shape inference
let model = load_pytorch_model("sentiment_classifier.pt")
// Type: Model<Tensor<Float32>[batch, 512], Tensor<Float32>[batch, 3]>

// Create streaming data pipeline
let text_stream: Dataset<String> = load_text_stream("reviews.jsonl")
let processed = text_stream
    .map(|text| tokenize_and_embed(text))  // String -> Tensor<Float32>[512]
    .batch(32)                             // Batch for efficiency
    .prefetch(2)                          // Async prefetching

// Run inference with automatic batching
for batch in processed {
    let predictions = model.predict(batch)  // Tensor<Float32>[32, 3]
    let labels = argmax(predictions, axis=1) // Tensor<Int32>[32]
    
    // Process results
    for (text, label) in zip(batch.unbatch(), labels.unbatch()) {
        handle_classification(text, label)
    }
}

These core AI types make STARK uniquely suited for production ML deployment, providing type safety, performance optimization, and seamless integration with existing ML ecosystems. They are fundamental language primitives, not library additions, enabling compile-time optimizations and safety guarantees that are impossible in general-purpose languages.

🧱 STARK Language — Advanced & Future-Oriented Types

This document outlines additional powerful types in STARKLANG designed for safety, flexibility, and next-gen programming paradigms. These types enhance developer ergonomics, ensure runtime robustness, and support emerging paradigms such as functional programming, streaming data, and safe concurrency.

✅ 1. Option

A type-safe replacement for null. Guarantees explicit handling of absent values.

let maybe_user: Option<UserProfile> = None

Variants:

enum Option<T>:
    Some(value: T)
    None

Methods:

• .is_some()
• .unwrap() (with panic fallback)
• .unwrap_or(default: T)
• .map(fn)

✅ 2. Result<T, E>

Robust error handling and propagation using algebraic types.

fn divide(a: Float32, b: Float32) -> Result<Float32, String>:
    if b == 0.0:
        return Err("Division by zero")
    return Ok(a / b)

Variants:

enum Result<T, E>:
    Ok(value: T)
    Err(error: E)

Methods:

• .is_ok(), .is_err()
• .unwrap(), .unwrap_or()
• .map(fn), .map_err(fn)

🔷 3. Union<T1 | T2 | ...>

A flexible type that can hold one of several types.

let mixed: Union<Int | Float32 | String>

Handled via pattern matching:

match mixed:
  Int(i): print("Int: " + str(i))
  Float32(f): print("Float: " + str(f))
  String(s): print("String: " + s)

🔷 4. Stream

A lazy, async-capable, chainable data processing abstraction.

let stream: Stream<Int> = range(0, 100)
  .map(fn(x) => x * x)
  .filter(fn(x) => x % 2 == 0)

Methods:

• .map(fn), .filter(fn)
• .reduce(fn, init)
• .collect()

Supports both lazy evaluation and stream-based pipelines.

🔷 5. TaggedRecord

Schema-aware struct with tagged metadata.

record Invoice:
    id: String @tag("primary")
    total: Float32 @tag("currency:USD")

Useful for serialization, schema validation, and ETL/data pipelines.

⚪ 6. Ref / Pointer

Low-level reference to memory-allocated object (useful for FFI/systems integration).

let ptr: Ref<UserProfile> = ref user

Supports dereferencing, pointer equality, and pass-by-ref semantics.

⚪ 7. Bitfield

Compact bit-based flag structures for systems/embedded use.

bitfield Flags:
    READ: 0
    WRITE: 1
    EXEC: 2

Bitwise manipulation: flags.set(READ), flags.has(EXEC)

⚪ 8. Persistent Collections

Immutable data structures for functional-style programming.

• PersistentList<T>
• PersistentMap<K, V>

Enable structural sharing and safe concurrent reads.

⏳ 9. Channel

Concurrency primitive for actor systems, thread isolates, and message-passing models.

let ch: Channel<Int> = channel()
spawn fn producer():
    ch.send(42)

let val = ch.recv()

⏳ 10. Future

Async task result holder, supports chaining and callbacks.

let result: Future<Int> = async fn compute(): return 99

Support methods:

• .await()
• .then(fn)
• .catch(fn)

These types extend STARKLANG's capability beyond a typical language into a developer-first, AI-native, concurrency-safe ecosystem—built for the real-world challenges of the future.