A lightweight, embeddable, general-purpose programming language written in C.
Visit the Pilang website | Read the docs | Explore examples
Pilang is a lightweight programming language designed for machine learning, numerical computing, data processing, and visualization. It combines the readability of Python with the flexibility of JavaScript while remaining small enough to embed directly into applications.
Built around a compact C implementation and a bytecode virtual machine, Pilang provides native support for tensors, plotting, data transformation, object-oriented programming, and modular application development. The language is intended for experimentation, scientific computing, educational tools, simulation projects, machine-learning workflows, and interactive visualization.
Unlike many scripting languages that rely on large external ecosystems for numerical work, Pilang treats data-oriented programming as a first-class concern. Tensor operations, statistics, plotting, 3D visualization, and machine-learning experiments are part of the core experience, making it easy to move from data processing to visual exploration with minimal setup.
The language syntax draws inspiration from Python and JavaScript, combining familiar scripting-language ergonomics with features such as comprehensions, closures, classes, operator overloading, ranges, slices, sets, tuples, and callable objects. here is two examples show case the capability of the language with data visualization:
and here is some examples for image processing and applying filtering and manipulating the chroma of images:
- Readable scripts with sharp edges where they help:
let,const,fun,class, ranges, slices,#length,in, ternaries, spread syntax, destructuring, and comprehensions. - Collections are first-class: lists, maps, tuples, and sets have literal syntax and work naturally with loops, membership checks, copying, slicing, and collection helpers.
- Functions are flexible: named functions, anonymous functions, arrow functions, closures, recursion, defaults, named arguments, and higher-order helpers are all part of the language.
- Objects are dynamic but structured: classes, constructors, inheritance, methods, callable objects, bracket access, static behavior, and operator/magic methods let you choose between plain maps and richer objects.
- Numerical work is built in: tensor constructors, indexing, _transforms, reductions, broadcasting-style helpers, statistics, and linear algebra functions live in the standard modules.
- Made to travel: the same language can run as a native executable or as a WebAssembly/browser build.
- Small enough to study: the compiler, VM, object model, module system, and garbage collector live in C source files that are approachable for language/runtime hacking.
import math:m
fun area(radius) {
return m.PI * radius ** 2
}
radii = [2, 4, 8]
for r in radii {
println("radius = " + r + ", area = " + area(r))
}scores = [91, 72, 88, 91, 64, 72]
unique = {91, 72, 88, 64}
curved = [min(score + 5, 100) : score in scores]
honors = []
for score in curved
if score >= 90
honors += score
println("unique scores: " + unique)
println("honors: " + honors)
println("top three-ish: " + curved[0:3])fun make_counter(start = 0) {
let value = start
return () -> {
value += 1
return value
}
}
next_id = make_counter(100)
println(next_id()) // 101
println(next_id()) // 102class Shape {
area() {
return 0
}
perimeter() {
return 0
}
}
class Rectangle: Shape {
constructor(width, height) {
this.width = width
this.height = height
}
area() {
return this.width * this.height
}
perimeter() {
return 2 * (this.width + this.height)
}
format() {
return "Rectangle(" +
this.width + ", " +
this.height + ")"
}
}
class Circle: Shape {
constructor(radius) {
this.radius = radius
}
area() {
return 3.14159 * this.radius * this.radius
}
perimeter() {
return 2 * 3.14159 * this.radius
}
format() {
return "Circle(" + this.radius + ")"
}
}
shapes = [
Rectangle(10, 5),
Circle(3)
]
for shape in shapes {
println(shape)
println("area = " + shape.area())
println("perimeter = " + shape.perimeter())
println("")
}Objects can participate in operators by defining compute methods, which makes domain objects feel native without changing the VM for every new type.
import lang
class Vec2 {
constructor(x, y) {
this.x = x
this.y = y
}
compute(op, other) {
if op == lang.OP_ADD
return Vec2(this.x + other.x, this.y + other.y)
}
format() {
return "Vec2(" + this.x + ", " + this.y + ")"
}
}
println(Vec2(2, 3) + Vec2(4, 1))import tensor:t
x = t.from([[1, 2], [3, 4]])
w = t.eye(2, 2)
println(t.shape(x))
println(t.matmult(x, w))
println(t.mean(x))Pilang includes native SDL-backed drawing and plotting modules for quick visual feedback while experimenting with numerical code, simulations, and machine-learning examples. The plot module covers 2D charts such as loss curves, while plot3d supports interactive 3D surface, mesh, and wireframe plots.
import draw
import plot
let ctx = draw.canvas(480, 480, "Training Loss")
let chart = plot.chart(ctx)
plot.line(chart, steps, losses, draw.COLOR_RED)
plot.title(chart, "Training Loss")
plot.xlabel(chart, "step")
plot.ylabel(chart, "loss")
plot.grid(chart, true)
plot.show(chart)
draw.run(ctx)From the repository root on Windows:
pilang run test.piYou can also use the shorthand form:
pilang test.piShow available commands:
pilang helpDeveloper helpers:
pilang dis test.pi
pilang dis -o bytecode.txt test.pi
pilang fmt test.pi
pilang min test.pifmt and min rewrite the target file in place and use the JavaScript utilities in utils/, so Node.js and the formatter/minifier modules must be available.
The repository includes a Makefile for native and browser builds. On Windows with MinGW available, use make from the repository root. Some MinGW installs expose this as mingw32-make.
make releaseCommon targets:
make release: build the optimized native executable,release/pilang.exe.make debug: build a debug native executable withDEBUG_BUILDenabled atrelease/pilang.exe.make web: build the Emscripten/WebAssembly output inrelease/.make run: build and run the native executable.make test: build the native executable and runpython tools/run_tests.py.make clean: remove generated build outputs.
Run the end-to-end benchmark suite after a release build:
python tools/run_benchmarks.pySee benchmark/README.md for the included workloads and runner options.
The native build expects MinGW GCC and the SDL2 development libraries used by the project. The browser build expects Emscripten's emcc.
pi_*.c,pi_*.h: core compiler, parser, VM, values, objects, modules, and runtime internals.builtin/: built-in native modules.libs/: Pilang libraries written in.pi.ML/: numerical and machine-learning experiments written in Pilang.release/: local build outputs.benchmark/: end-to-end interpreter benchmark programs.docs/: language documentation and reference material.tests/: examples and regression tests grouped by language area.
Start with the documentation index, then explore:
- Language features
- Running Pilang
- Data types
- Functions
- Objects and classes
- Modules
- 2D plotting
- 3D plotting
- Image processing
- Examples
Run the test suite with:
python tools/run_tests.pyTests cover core language behavior, tensors, modules, object/class behavior, runtime types, built-ins, and larger example programs.
The language website and playground are available at:
Use it to read docs, browse examples, and try Pilang in the browser.
This project is licensed under the terms in LICENSE.



