neuva-lang 0.2.0

Neuva — A working ML programming language with lexer, parser, AST and interpreter

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A programming language built purely for Machine Learning.

Simple like Python. Clear like Rust. Made for ML.

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What is Neuva?

Neuva (NOO-vah) is an open source programming language designed from the ground up for Machine Learning. No boilerplate. No imports. No configuration. Just describe your model and train it.

# Train a digit classifier — the entire program
let data = load("mnist.csv")
let train_data, test_data = data.split(0.8)

model DigitNet {
    layer dense(784 -> 128, relu)
    layer dense(128 -> 10,  softmax)
}

train DigitNet on train_data for 20 epochs, lr=0.001
print accuracy(DigitNet, test_data)

That is it. No import torch. No nn.Module. No training loop. Neuva handles it all.

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Why Neuva?

Most ML code looks like this:

import torch
import torch.nn as nn
import torch.optim as optim

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return torch.softmax(self.fc2(x), dim=1)

model = Net()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

for epoch in range(20):
    for batch_x, batch_y in train_loader:
        optimizer.zero_grad()
        output = model(batch_x)
        loss = criterion(output, batch_y)
        loss.backward()
        optimizer.step()

Neuva code looks like this:

model DigitNet {
    layer dense(784 -> 128, relu)
    layer dense(128 -> 10,  softmax)
}

train DigitNet on train_data for 20 epochs, lr=0.001

Same result. A fraction of the code.

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Features

• ML-first keywords — model, layer, train, predict, save are built into the language
• No boilerplate — no imports, no class definitions, no training loops
• PyTorch backend — compiles to PyTorch under the hood, so it is fast and compatible
• Rust-style errors — clear error messages with line numbers and helpful hints
• Simple types — tensor, matrix, int, float, bool, string
• Beginner friendly — if you know any programming language, you can write Neuva
• Open source — AGPL v3 licensed, built in public, contributions welcome

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Language Overview

Variables

let name   = "Neuva"        # string  (inferred)
let layers = 3              # int     (inferred)
let rate: float = 0.001     # float   (explicit)
let ready: bool = true      # bool    (explicit)

Models

model MyNet {
    layer dense(784 -> 128, relu)      # input 784, output 128, relu activation
    layer dense(128 -> 64,  relu)
    layer dense(64  -> 10,  softmax)   # output layer
}

Supported layer types: dense, conv, pool, dropout, flatten, norm

Supported activations: relu, sigmoid, softmax, tanh, linear, gelu

Loading Data

let data = load("dataset.csv")

let train_data, test_data = data.split(0.8)   # 80/20 split
data = data.normalize()                        # normalize values
data = data.shuffle()                          # shuffle rows

Training

# one-liner
train MyNet on train_data for 10 epochs, lr=0.001

# multi-line (more readable)
train MyNet
    on    train_data
    for   50 epochs
    lr    = 0.0005
    loss  = crossentropy

Supported loss functions: crossentropy, mse, mae, huber, binary_crossentropy

Predict and Evaluate

let result = predict MyNet on test_data
let acc    = accuracy(MyNet, test_data)
print "Accuracy:", acc

Save and Load

save MyNet to "my_model.nva"
let loaded = load("my_model.nva")

Functions

fn welcome(name: string) {
    print "Hello from Neuva,", name
}

fn square(x: float) -> float {
    return x * x
}

Control Flow

if acc > 0.95 {
    print "Excellent model!"
} else {
    print "Keep training."
}

for i in 0..10 {
    print i
}

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Examples

File	Description
examples/hello.nva	Hello world
examples/iris_classifier.nva	Iris flower classification
examples/digit_classifier.nva	MNIST digit recognition
examples/regression.nva	House price regression
examples/full_pipeline.nva	End-to-end ML pipeline

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Install

Neuva is currently in early development. The interpreter is being built in public.

Once released:

pip install neuva-lang

Run a .nva file:

neuva run my_model.nva

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Project Status

Neuva is being built live, day by day, in public.

Phase	Status	Description
1 — Design	✅ Done	Language syntax and keywords
2 — GitHub Setup	✅ Done	Repo, license, structure
3 — Lexer	🔨 In progress	Tokenizer
4 — Parser	⏳ Upcoming	AST builder
5 — Interpreter	⏳ Upcoming	Tree walker
6 — PyTorch backend	⏳ Upcoming	ML execution
7 — PyPI release	⏳ Upcoming	pip install neuva-lang

Follow the journey on LinkedIn — posting every milestone.

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Contributing

Neuva is open source and we welcome all contributors — beginners and experts alike.

git clone https://github.com/Ankit-Mahadani/neuva.git
cd neuva
pip install -e ".[dev]"
pytest tests/

See CONTRIBUTING.md for the full guide.

Look for issues labelled good first issue to get started quickly: github.com/Ankit-Mahadani/neuva/issues

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Built With

• Python 3.10+ — the interpreter is written in Python
• PyTorch — ML execution backend
• NumPy + Pandas — data handling

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Roadmap

• Lexer + Parser (current focus)
• Working interpreter for basic programs
• PyTorch backend for model training
• pip install neuva-lang on PyPI
• VS Code syntax highlighting extension
• GPU support
• CNN and RNN layer types
• Interactive REPL (neuva shell)
• Web playground

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License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).

This means:

• You can use, study, modify, and distribute Neuva freely
• If you use Neuva in a product or service (including over a network), you must release your source code under the same license
• Any modifications must also be open source

This keeps Neuva and everything built on top of it free and open — forever.

See the full LICENSE file for details, or read a plain-English summary at tldrlegal.com/license/gnu-affero-general-public-license-v3.

Copyright © 2026 Ankit Mahadani and Neuva Contributors

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Built in public. Day by day.

Star the repo to follow the journey.