RO-Lang is a Representation-Oriented language. It treats data and representations as core objects that you manipulate mathematically. Stop wiring neural networks by hand; let the compiler build the graph.
Move from explicit instructions to continuous spaces.
int + int = int (Standard math)word + word = word (Dumb concatenation: "hello" + "world" = "helloworld")image + image = ? (Crashes)int + int = int (Embeddings map to numeric concepts)word + word = word (Semantic math: King - Man + Woman = Queen)image + image = image (Semantic overlay: Person + Glasses = Person w/ Glasses)In RO-Lang, you don't write logic for complex tasks. You define the signature and provide the data.
Under the hood, RO-Lang spins up a multi-headed PyTorch FeedForwardNet, dynamically
generating VAEs (Variational Autoencoders), categorical heads, and continuous embedding spaces tailored exactly to your types.
from rolang.decorators import learnable
from rolang.rtypes import RInt, RString
# 1. Define the interface and data
@learnable(train_epochs=32, data=fizzbuzz_dataset)
def fizzbuzz(i: RInt) -> Union[RInt, RString]:
pass # The compiler writes the rest!
# 2. Under the hood:
# RO-Lang dynamically generates a PyTorch
# graph with RInt inputs, and Union-compatible
# categorical/embedding output heads.
# 3. Execution runs the forward pass:
result = fizzbuzz(RInt(15))
print(result) # RString("fizzbuzz")