Add metaprogramming example

[critesjosh]

Demonstrate functionality describes on this page: https://noir-lang.org/docs/noir/standard_library/meta

[critesjosh]

Claude recommended these examples:

Beginner Examples

1. Generic Serialization Library

#[derive_via(derive_serialize)]
trait Serialize {
    fn to_bytes(self) -> [u8]
}

// Automatically generates serialization for any struct
#[derive(Serialize)]
struct User {
    id: u32,
    name: [u8; 32],
    age: u8,
}

2. Compile-time Type Validation

comptime fn ensure_numeric_type<T>(x: T) {
    let t = std::meta::type_of(x);
    // Validate that T is a numeric type at compile time
    assert(t == quote { u8 }.as_type() || 
           t == quote { u32 }.as_type() || 
           t == quote { Field }.as_type());
}

Intermediate Examples

3. Automatic Builder Pattern Generator

#[derive_via(derive_builder)]
trait Builder {
    fn build() -> Self;
}

#[derive(Builder)]
struct Config {
    timeout: u32,
    retries: u8,
    debug: bool,
}

// Generates: ConfigBuilder with .timeout(), .retries(), .debug() methods

4. Zero-Knowledge Proof Field Validation

comptime fn generate_range_check<T>(min: T, max: T) -> Quoted {
    quote {
        fn validate_range(value: T) {
            assert(value >= $min);
            assert(value <= $max);
        }
    }
}

// Usage: generates compile-time range validators
comptime {
    let age_validator = generate_range_check(0, 150);
    unquote!(age_validator);
}

Advanced Examples

5. Automatic Merkle Tree Implementation

#[derive_via(derive_merkle_tree)]
trait MerkleProof {
    fn compute_root(leaves: [Self]) -> Field;
    fn verify_proof(self, root: Field, path: [Field], indices: [bool]) -> bool;
}

#[derive(MerkleProof)]
struct Transaction {
    from: Field,
    to: Field,
    amount: Field,
}

6. Domain-Specific Language (DSL) for Constraints

comptime fn constraint_dsl(rules: Quoted) -> Quoted {
    // Parse a custom constraint syntax and generate Noir code
    quote {
        fn verify_constraints(inputs: Inputs) -> bool {
            $rules
        }
    }
}

// Usage:
comptime {
    let validator = constraint_dsl(quote {
        ensure input.age >= 18;
        ensure input.balance > 0;
        ensure input.signature_valid == true;
    });
    unquote!(validator);
}

7. Compile-time Circuit Optimization

comptime fn optimize_circuit<T>(computation: T) -> Quoted {
    let t = std::meta::type_of(computation);
    
    // Analyze the computation type and generate optimized circuit
    if (is_multiplication_heavy(t)) {
        generate_karatsuba_optimization()
    } else if (is_hash_heavy(t)) {
        generate_poseidon_optimization()
    } else {
        quote { $computation }
    }
}

Real-World Applications

8. Automatic Zero-Knowledge Schema Validation

Perfect for validating private data structures while maintaining privacy:

#[derive(ZkValidate)]
struct PrivateProfile {
    #[range(18, 120)]
    age: u8,
    #[hash_verify]
    ssn_hash: Field,
    #[merkle_inclusion]
    credential: Field,
}

9. Privacy-Preserving Database Queries

Generate ZK proofs for database operations:

#[derive(ZkQuery)]
struct UserQuery {
    #[private]
    user_id: Field,
    #[public]
    query_type: u8,
    #[constraint("balance > min_balance")]
    balance_check: Field,
}

Why These Examples Are Powerful

1. Code Reusability: The derive system eliminates boilerplate for common ZK patterns
2. Type Safety: Compile-time validation prevents runtime errors in circuits
3. Performance: Metaprogramming enables circuit-specific optimizations
4. Domain-Specific: Perfect for blockchain/privacy applications where ZK proofs are critical
5. Maintainability: Generated code is consistent and less error-prone

These examples demonstrate how Noir's metaprogramming can automate complex zero-knowledge proof generation, making privacy-preserving applications much more accessible to developers.