nymya

This is still a work in progress. It may be changed in the future.

nymya (NymyaLang)

Designed by	User:Nymessence
Appeared in	2025
Dimensions	one-dimensional
Computational class	Turing complete
Major implementations	Original
Influenced by	Swift, Rust, C, C++, Python
File extension(s)	.nym

Overview

nymya is an experimental language that blends classical compilation with symbolic mathematics, quantum simulation tools, AI and ML components, and a GUI system inspired by declarative layout frameworks. The design mixes technical structures with a thematic philosophy that emphasizes balance, intuition and consciousness driven computation. The reference implementation lives on GitHub and is actively evolving.

The language includes a compiler pipeline, a growing standard library and several specialized modules covering quantum algorithms, symbolic numerology, sacred geometry, classical neural networks and low level bit or memory operations.

Philosophy

NymyaLang pairs strict structural rules with a more intuitive layer. This balance is reflected throughout the ecosystem. Some parts of the standard library incorporate symbolic or mystical mappings including repeating numbers, geometric patterns and number based traits. These systems coexist with traditional modules like math, ML and networking.

Features

GUI System

The GUI library is built around declarative constructs similar to Vstack, HStack, ZStack and grid containers. The system includes:

• Window and widget abstractions
• Declarative layout
• View components for mathematical, symbolic or visual patterns
• Experimental visualizations derived from sacred geometry and numerological mappings

Compiler and Executable System

The compiler includes:

• A tokenizer, parser and AST builder
• Error reporting with positional information
• A code generation pipeline intended to emit C++ or native builds
• A command line tool that compiles and runs .nym files

Symbolic Mathematics

The symbolic library supports:

• Number reduction systems
• Repeating digit classification
• Prime properties
• Pattern detection across culturally significant or mathematically interesting values
• A geometry engine that models various classical geometric structures

These symbolic patterns integrate with other modules so that a single value can trigger multiple overlays or traits.

Quantum Tools

A built in quantum simulator supports:

• Tensor product operations
• Single, two and three qubit gates
• Measurement and state vector updates
• Parameterized circuits
• Quantum neural and variational models

Some example programs mix quantum features with symbolic or classical ML.

Machine Learning Library

The ML library includes:

• Multi dimensional tensor operations
• Classical neural layers
• Loss functions and data utilities
• Optional quantum enhanced models such as variational classifiers and quantum SVM style components

Networking

Two sets of networking modules exist:

• Classical networking for ping tests, bandwidth, port scanning and TCP operations
• A quantum themed set of functions for experimental entanglement or communication simulations

Low Level and Math Libraries

Supporting libraries provide:

• Bit manipulation
• Memory operations in a simulated environment
• High precision math and special functions
• Complex arithmetic and hypercalc style operations

Execution Model

Nymya code follows a block based syntax influenced by Swift and Rust. Programs compile into an intermediate layer handled by the toolchain. The standard library provides modules that can be imported directly such as:

• math
• symbolic
• ml
• quantum
• networking
• gui

The ecosystem includes a notebook style interface that executes Nymya code cells interactively with syntax highlighting.

Example

A minimal example illustrating the general style of the language:

import ml
import ml.classical
import crystal

func example() -> Void {
    var nn = ml.classical.NeuralNetwork()

    var l1 = ml.classical.Layer(4, 8, "relu")
    var l2 = ml.classical.Layer(8, 2, "sigmoid")

    nn.add_layer(l1)
    nn.add_layer(l2)

    var input = ml.classical.Tensor(1, 4)
    input.set_value(0, 0, 0.5)
    input.set_value(0, 1, 0.3)
    input.set_value(0, 2, 0.8)
    input.set_value(0, 3, 0.1)

    var output = nn.predict(input)
    crystal.manifest("Prediction: " + output.get_value(0, 0))
}

Status

Many subsystems are under active development including the compiler backend, GUI components, quantum algorithms and symbolic engines. Modules may change structure as the project grows.

External links

• GitHub repository