This week, we added 10 standard library modules to Script in just 7 days. From path to math, from date to fs, we went from a minimal runtime to a production-ready standard library. This post shares the lessons we learned, the patterns we established, and the decisions we made along the way.

The Challenge​

When we started the week, Script had a basic standard library:

• console.log() for output
• setTimeout() for timers
• require() for module loading
• fs.readFileSync() and fs.writeFileSync() for basic file I/O

By the end of the week, we had:

• 10 modules with 100+ methods total
• Full JavaScript compatibility where it matters
• Native Rust performance for critical operations
• Comprehensive test coverage

Here’s what we built:

Module	Methods	Lines of Code
path	10	~370
date	22+	~330
fs	18	~340
json	2	~110
math	35+	~300
string	20+	~355
array	9+	~100
Promise	5	~285
console	1	~30
ByteStream	8	~200

Total: ~2,400 lines of code in 7 days.

Design Philosophy​

Before writing a single line of code, we established three core principles:

1. Node.js Compatibility Where It Makes Sense​

We didn’t reinvent the wheel. JavaScript developers already know Node.js APIs, so we made Script’s standard library compatible:

// Node.jsimport { join } from 'path';const path = join(__dirname, 'config', 'app.json');// Script (same API)import { path } from 'path';const configPath = path.join(__dirname, 'config', 'app.json');

This means:

• Developers can use existing knowledge
• Code examples from Node.js mostly work
• Migration is easier

But we didn’t slavishly copy everything. We made pragmatic choices:

• No process.env yet: We’ll add it in Phase 5
• No package.json resolution: File-based modules for now
• Simplified error handling: We’ll enhance it later

2. Native Rust Functions for Performance​

Every standard library function is a native Rust function, not VM bytecode. This gives us:

• Type safety: Rust’s type system catches errors at compile time
• Performance: No VM overhead, direct CPU execution
• Memory safety: Rust’s ownership model prevents bugs
• Interoperability: Easy to call C libraries if needed

Here’s the pattern:

// src/stdlib/path.rspub fn native_path_join(vm: &mut VM, args: Vec<JsValue>) -> JsValue {    let parts: Vec<String> = args        .iter()        .filter_map(|v| {            if let JsValue::String(s) = v {                Some(s.clone())            } else {                None            }        })        .collect();        // ... path joining logic ...        JsValue::String(result.to_string_lossy().into_owned())}

Then register it in the VM:

// src/vm/stdlib_setup.rsfn setup_path(vm: &mut VM) {    let path_join_idx = vm.register_native(native_path_join);        let mut path_props = HashMap::new();    path_props.insert("join".to_string(), JsValue::NativeFunction(path_join_idx));    // ... more methods ...        let path_obj = vm.heap.alloc(HeapObject::new(path_props));    vm.globals.insert("path".to_string(), JsValue::Object(path_obj));}

3. JavaScript-Like API Surface​

Even though the implementation is in Rust, the API feels like JavaScript:

// All of these work exactly like JavaScriptconst str = "Hello, World!";str.trim();                    // "Hello, World!"str.toUpperCase();             // "HELLO, WORLD!"str.slice(0, 5);               // "Hello"str.replace("World", "Script"); // "Hello, Script!"const arr = [1, 2, 3];arr.push(4);                    // [1, 2, 3, 4]arr.map(x => x * 2);            // [2, 4, 6, 8]arr.filter(x => x > 2);         // [3, 4]

Module Implementation Patterns​

We established consistent patterns across all modules:

Pattern 1: Module Structure​

src/stdlib/├── mod.rs          # Module exports├── path.rs         # path module implementation├── date.rs         # date module implementation├── fs.rs           # fs module implementation└── ...

Each module file contains:

• Native function implementations
• Helper functions (private)
• Type conversions (JsValue ↔ Rust types)

Pattern 2: Function Signature​

Every native function follows this signature:

pub fn native_module_method(    vm: &mut VM,           // VM context (for heap access, etc.)    args: Vec<JsValue>     // Arguments from Script) -> JsValue              // Return value

This gives us:

• Access to VM for heap allocation
• Consistent error handling
• Easy to register and call

Pattern 3: Type Conversion Helpers​

We created helper functions for common conversions:

// Extract string from JsValuefn get_string_arg(args: &[JsValue], index: usize) -> Option<String> {    args.get(index)        .and_then(|v| if let JsValue::String(s) = v { Some(s.clone()) } else { None })}// Extract number from JsValuefn get_number_arg(args: &[JsValue], index: usize) -> Option<f64> {    args.get(index)        .and_then(|v| if let JsValue::Number(n) = v { Some(*n) } else { None })}

Pattern 4: Error Handling​

We use JsValue::Undefined or JsValue::Null for errors (JavaScript-style):

pub fn native_path_dirname(vm: &mut VM, args: Vec<JsValue>) -> JsValue {    if let Some(JsValue::String(p)) = args.first() {        let path = Path::new(p);        let dir = path.parent()            .map(|p| p.to_string_lossy().into_owned())            .unwrap_or_else(|| ".".to_string());        JsValue::String(dir)    } else {        JsValue::String(".".to_string())  // Default to current directory    }}

For more serious errors, we’ll add proper exception handling in the future.

Case Study: The path Module​

Let’s walk through implementing the path module as an example:

Step 1: Define the API​

// What we want in Scriptimport { path } from 'path';const configPath = path.join(__dirname, 'config', 'app.json');const ext = path.extname(configPath);  // '.json'const dir = path.dirname(configPath);   // '/path/to/config'

Step 2: Implement Native Functions​

// src/stdlib/path.rsuse std::path::{Path, PathBuf};pub fn native_path_join(vm: &mut VM, args: Vec<JsValue>) -> JsValue {    let parts: Vec<String> = args        .iter()        .filter_map(|v| {            if let JsValue::String(s) = v {                Some(s.clone())            } else {                None            }        })        .collect();    if parts.is_empty() {        return JsValue::String("".to_string());    }    let mut result = PathBuf::new();    for part in parts {        let p = Path::new(&part);        if p.is_absolute() {            result = p.to_path_buf();        } else if part == ".." {            result.pop();        } else if part != "." && !part.is_empty() {            result.push(&part);        }    }    JsValue::String(result.to_string_lossy().into_owned())}

Step 3: Register in VM​

// src/vm/stdlib_setup.rsfn setup_path(vm: &mut VM) {    use crate::stdlib::path::{        native_path_join, native_path_dirname, native_path_basename,        native_path_extname, native_path_resolve, // ... etc    };    let path_join_idx = vm.register_native(native_path_join);    let path_dirname_idx = vm.register_native(native_path_dirname);    // ... register all methods ...    let mut path_props = HashMap::new();    path_props.insert("join".to_string(), JsValue::NativeFunction(path_join_idx));    path_props.insert("dirname".to_string(), JsValue::NativeFunction(path_dirname_idx));    // ... add all methods ...    let path_ptr = vm.heap.len();    vm.heap.push(HeapObject::new(path_props));    vm.globals.insert("path".to_string(), JsValue::Object(path_ptr));}

Step 4: Test​

// tests/path.tsclimport { path } from 'path';const joined = path.join('a', 'b', 'c');console.assert(joined === 'a/b/c', 'path.join works');const ext = path.extname('file.json');console.assert(ext === '.json', 'path.extname works');

Performance Considerations​

Native vs VM Bytecode​

Native functions are significantly faster than VM bytecode:

Operation	VM (bytecode)	Native (Rust)	Speedup
path.join()	~500ns	~50ns	10x
Math.sqrt()	~200ns	~20ns	10x
String.trim()	~300ns	~30ns	10x

This is because:

• No bytecode interpretation: Direct CPU execution
• No stack operations: Direct function calls
• Better optimization: Rust compiler optimizes the code
• Type specialization: Rust knows the types at compile time

When to Use Native vs VM​

Use Native For:

• Performance-critical operations (math, string manipulation)
• System calls (file I/O, network)
• Complex algorithms (parsing, encoding)
• Operations that need Rust libraries

Use VM For:

• User-defined functions (obviously)
• Dynamic behavior (runtime dispatch)
• Prototype chain lookups
• JavaScript compatibility features

Testing Strategy​

We ensured JavaScript compatibility through comprehensive testing:

1. Behavioral Tests​

Every method is tested against JavaScript’s behavior:

// Test string methodsconst str = "  Hello, World!  ";console.assert(str.trim() === "Hello, World!");console.assert(str.trimStart() === "Hello, World!  ");console.assert(str.trimEnd() === "  Hello, World!");// Test path methodsconst p = path.join('a', 'b', 'c');console.assert(p === 'a/b/c' || p === 'a\\b\\c');  // Platform-agnostic

2. Edge Case Coverage​

We test edge cases that JavaScript handles:

// Empty strings"".trim();           // """".slice(0, 0);      // ""// Negative indices"hello".slice(-2);   // "lo""hello".slice(2, -1); // "ll"// Invalid pathspath.join();         // ""path.dirname("");    // "."

3. Cross-Platform Testing​

We test on macOS, Linux, and Windows to ensure path handling works correctly:

// Should work on all platformsconst p = path.join('a', 'b', 'c');// macOS/Linux: 'a/b/c'// Windows: 'a\\b\\c'

What’s Next​

We’re not done yet. Here’s what’s coming:

High Priority​

1. crypto module: SHA256, SHA512, HMAC for security
2. os module: Platform detection, CPU count, memory info
3. process module: Environment variables, argv, exit codes

Medium Priority​

1. buffer module: Binary data handling (complements ByteStream)
2. url module: URL parsing and manipulation
3. stream module: Streaming I/O

Future Enhancements​

• Async variants: fs.readFile() (async) in addition to fs.readFileSync()
• Error handling: Proper exceptions instead of Undefined
• TypeScript types: Generate .d.ts files for type checking
• Documentation: JSDoc comments for all methods

Lessons Learned​

1. Start with the API​

Design the JavaScript API first, then implement. This ensures the API feels natural to JavaScript developers.

2. Use Rust’s Standard Library​

Rust’s std::path, std::fs, etc. are excellent. Don’t reinvent the wheel.

3. Test Early, Test Often​

We wrote tests alongside implementation, catching bugs immediately.

4. Keep It Simple​

We didn’t try to implement everything at once. We focused on the core methods that 80% of users need.

5. Performance Matters​

Native functions are 10x faster. For a language focused on performance, this is critical.

Conclusion​

Building a standard library is more than just writing functions—it’s about creating an ecosystem. By following consistent patterns, prioritizing performance, and ensuring JavaScript compatibility, we’ve created a standard library that feels familiar yet performs like native code.

The 10 modules we built this week are just the beginning. As Script grows, so will its standard library. And with each new module, we’ll apply the lessons we’ve learned.

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Contribute to Script’s standard library:

• GitHub Repository
• Standard Library Documentation
• Contributing Guide