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Moop Programming Language

Code that reads like structured English • Quorum's naturalism + Io's minimalism

License: MIT

Repository: Miley-the-dog (named after a very good dog 🐕)

🚀 Quick Start

Want to try Moop-like programming right now?

➡️ Trinity Vibe - Related project: Moop semantics in JavaScript syntax. Zero setup, runs in browser immediately.

What's the relationship?

  • Moop (this repo): The programming language with natural syntax
  • Trinity Vibe: Side project - same concepts (homoiconicity, reversibility), JavaScript syntax

🎯 What This Repository Is

This repo contains the C runtime substrate - the quantum-ready computational foundation for embedded systems and future quantum hardware.

What's in This Repository

This repo (Miley-the-dog) contains:

  • C Runtime Substrate - Quantum-ready computational foundation
  • Moop Language Specification - Natural language syntax design
  • Examples and Documentation

Related Projects:

  • Trinity Vibe - Moop concepts in JavaScript syntax (separate repo, try it now!)

This Repository (C Runtime) Provides:

  • Quantum-ready backend abstraction (classical/simulator/quantum)
  • ~1800 lines of pure C runtime
  • 40KB footprint, no garbage collector
  • Tape-loop Turing machine with evolutionary pruning
  • Perfect for embedded systems and real-time applications

Status: Runtime complete and tested. For language parser/compiler, use Trinity Vibe.

The Moop Experience

Write clean, natural code:

actor UserManager
    role is "handles user authentication and sessions"
    state has
        logged_in is false

proto DatabaseConnection <- Object
    slots: host, port, timeout

No ceremony. No syntactic noise. Just clear computation.

Overview

Unified Moop combines natural language elegance with computational power through a 6-sublayer architecture in ~1800 lines of C:

  • Quantum-Ready Architecture - Classical by default, quantum-compatible by design
  • Natural Language Syntax - Code reads like structured English
  • Tape-Loop Turing Machine (1024 circular cells, L1)
  • Evolutionary Pruning with Darwinian selection pressure
  • Trinary MAYBE (True/False/Unresolved) with LLM confidence
  • Self-Modification API (homoiconic code-as-data)
  • Actor-Proto Duality (particle-wave duality for coordination)
  • Meta-Evolution (the system evolves how it evolves)

The Synergistic Power

Moop uniquely combines homoiconicity (code is data) with reversibility (operations can be undone), creating unprecedented capabilities:

Traditional languages:

  • Lisp/Forth: Self-modifying but can't undo ❌
  • Quantum computing: Reversible but can't self-modify ❌

Moop:

  • ✅ Self-modifying code that can be undone
  • ✅ Explore alternative code versions and backtrack
  • ✅ Programs that evolve themselves safely
  • ✅ Meta-programming with a safety net
# Modify code
operation.gate = SWAP
tape.write(100, operation)

# Test it (reversibly)
result <-> execute_tape(100)

# Don't like it? Rewind!
tape.undo()

This enables: Exploratory programming, self-evolving systems, safe meta-programming, and temporal debugging through code modifications.

Structured as a Complex Adaptive System

Moop is designed following Prigogine's dissipative structures - it's not just a language, it's a living computational system:

Benefits:

  • Self-organizing code - Automatically optimizes based on usage patterns
  • Emergent stability - Robustness emerges from evolutionary selection
  • Adaptive performance - System tunes itself to your workload
  • Far-from-equilibrium dynamics - Maintains order through energy flow
# Code evolves itself
# High-fitness operations survive
# Low-fitness operations get pruned
# Performance characteristics adapt to usage

# Manual optimization becomes unnecessary
# The system learns what works

Traditional languages: Static artifacts you manually optimize. Moop: Living organisms that adapt and evolve.

Perfect for Embedded Systems

Moop combines gate-based computational substrate with lean conventional memory for an ideal embedded profile:

Dual Memory Architecture:

  1. System layer (L1): Gate-based tape-loop

    • 1024 circular cells (fixed size)
    • Reversible gate operations (CCNOT, CNOT, NOT, SWAP)
    • Evolutionary pruning (automatic cleanup)
    • No dynamic allocation in computational substrate
  2. User layer (L3b): Conventional memory for actor state and proto slots

    • Managed by lean C runtime
    • No garbage collector
    • Predictable allocation patterns

Why Moop excels in embedded environments:

  • Small footprint - ~40KB total runtime (tape + qubit state + runtime)
  • No garbage collector - No unpredictable GC pauses
  • Deterministic behavior - Pruning every 256 ops, O(1) fitness computation
  • Fixed computational memory - 1024-cell tape never grows
  • Lean C implementation - Core runtime ~950 lines, quantum backends ~700 lines
  • Self-managing substrate - Evolutionary pruning handles tape cleanup automatically
actor SensorController
    state has
        readings is []           # User memory: conventional allocation
        last_value is 0

    handlers
    on process_sensor_data(value)
        # Computation happens on L1 gate-based tape
        result <-> analyze(value)

        # State updates use conventional memory
        state.readings.append(result)
        state.last_value = result

This makes Moop ideal for:

  • Embedded systems (IoT, microcontrollers, sensors)
  • Real-time systems (no GC pauses, deterministic timing)
  • Resource-constrained environments (fixed computational memory)
  • Safety-critical applications (predictable behavior, no hidden allocations)

Comparison:

Language Runtime Size GC Pauses Deterministic Computational Memory
Python ~15MB Yes No Heap (unbounded)
Java ~50MB Yes No Heap (unbounded)
Go ~2MB Yes No Heap (unbounded)
Rust ~500KB No Yes Heap (manual)
C Variable No Yes* Heap (malloc)
Forth ~10KB No Yes Stack (fixed)
Moop ~40KB No Yes Tape (1024 cells, self-managing)

*C is deterministic only if you avoid malloc/free

Key advantage: Moop's computational substrate (L1 tape-loop) is self-managing through evolutionary pruning, while user memory remains simple and predictable.

Quantum-Ready Architecture

Moop is designed to run on conventional hardware by default, but with full compatibility for future quantum backends. This is achieved through a clean abstraction layer that separates gate operations from state representation.

How It Works

Backend Abstraction:

// Same gate API works on any backend
qubit_CNOT(state, control, target);  // Classical OR quantum

// Three available backends:
typedef enum {
    QUBIT_BACKEND_CLASSICAL,    // Default: uint8_t bits (fast, conventional)
    QUBIT_BACKEND_SIMULATOR,    // Optional: statevector simulation
    QUBIT_BACKEND_QUANTUM       // Future: real quantum hardware
} Qubit_Backend_Type;

Why This Matters:

Moop uses reversible gates (CCNOT, CNOT, NOT, SWAP) as computational primitives. These gates are:

  • ✅ Efficient on conventional hardware (simple bit operations)
  • ✅ Natively compatible with quantum computers (unitary operations)
  • ✅ Mathematically universal (can compute any function)

Key Insight: By building on reversible primitives from day one, Moop programs written today will run on quantum hardware tomorrow without modification.

Three Backends, Same Code

1. Classical Backend (Default)

// Fast, deterministic, works everywhere
Qubit_State* state = qubit_init(32, QUBIT_BACKEND_CLASSICAL);
qubit_CCNOT(state, 0, 1, 2);  // Simple bit operations
uint8_t result = qubit_read(state, 2);  // Direct read

2. Quantum Simulator (Optional)

// Enable with: make CFLAGS="-DENABLE_QUANTUM_SIMULATOR"
Qubit_State* state = qubit_init(10, QUBIT_BACKEND_SIMULATOR);
qubit_CCNOT(state, 0, 1, 2);  // Statevector evolution (2^n amplitudes)
uint8_t result = qubit_measure(state, 2);  // Probabilistic collapse

3. Quantum Hardware (Future)

// When quantum computers are accessible
Qubit_State* state = qubit_init(100, QUBIT_BACKEND_QUANTUM);
qubit_CCNOT(state, 0, 1, 2);  // Runs on real QPU
uint8_t result = qubit_measure(state, 2);  // True quantum measurement

The Same Moop Code

All three backends run the same high-level Moop code:

actor QuantumCalculator
    role is "performs calculations using quantum-ready gates"

    handlers
    on compute(a, b)
        # These operations work on ANY backend
        result <-> CCNOT(a, b, output)

        # Classical: fast bit ops
        # Quantum simulator: statevector evolution
        # Quantum hardware: real superposition

        output -> "Result: " + result

Benefits

Today:

  • Run on any conventional computer (x86, ARM, RISC-V, microcontrollers)
  • Fast execution using classical bits
  • No special hardware required

Tomorrow:

  • Same programs run on quantum computers when available
  • No code changes needed
  • Gradual migration path (classical → simulator → quantum hardware)

Philosophy:

"Write for conventional hardware today. Run on quantum hardware tomorrow."

This makes Moop uniquely positioned for the quantum transition - your investment in Moop code today will pay dividends when quantum computing becomes mainstream.

Quick Start

See It In Action

git clone https://github.com/Blobfish108/Miley-the-dog-.git
cd Miley-the-dog-
make examples
./build/living_code_demo

This demo shows:

  • Homoiconicity: Reading and modifying code at runtime
  • Reversibility: Time-travel debugging with checkpoints
  • Evolution: Self-optimizing fitness-based pruning
  • The Synergy: Why this combination is unique

Note: This demo is written in C using the runtime APIs. To write and run actual .moop language files, use Trinity Vibe (see "Try Moop NOW" section above).

Build

make

Run Tests

make test

Example Usage

actor Calculator
    role is "Performs calculations with evolutionary optimization"

    state has
        history is []
        last_result is 0

    handlers

    on compute(operation, args)
        # Reversible computation
        result <-> perform_operation(operation, args)

        # Update state (irreversible)
        state.last_result = result
        state.history.append({op: operation, result: result})

        # Output (irreversible)
        output -> "Result: " + result

    on undo
        # Reversibility: can rewind to previous state
        if history.length > 0
            state.history.pop()
            restore_checkpoint(history.length)

    on optimize_self
        # Meta-evolution: tune fitness parameters
        params <- get_fitness_params()
        params.recency_weight = 0.6
        params.activity_weight = 0.3
        tune_fitness(params)

Note: This is Moop language syntax. To run this code, use Trinity Vibe - a self-contained browser implementation with full parser/compiler/IDE. This repo provides the C runtime substrate for embedded systems; Trinity Vibe provides the complete language environment.

Architecture

L3b: User Actors/Protos     ← Application layer
L3a: System Bootstrap        ← Actor-proto duality genesis
L2c: User Processing         ← (Reserved)
L2b: Irreversible Ops        ← AND, OR, XOR, MAYBE resolution
L2a: Reversible Functions    ← Non-primitive reversible functions, structural reversibility
L1:  Tape-Loop TM            ← 1024 circular cells, evolutionary pruning, CCNOT, CNOT, NOT, SWAP

Key Features

1. Evolutionary Pruning

Fitness function with 3 components:

  • Recency (50%): Recent operations survive longer
  • Qubit Activity (30%): Operations on "hot" qubits prioritized
  • Gate Type (20%): Universal gates (CCNOT) favored

Selection pressure: Top 75% survive, bottom 25% discarded every 256 operations

2. Trinary MAYBE

actor AuthService
    on authenticate(username, password)
        # MAYBE: uncertain value with confidence
        maybe user_authenticated is check_credentials(username, password)

        when user_authenticated is true:
            session <- create_session(username)
            output -> "Login successful (confidence: " + user_authenticated.confidence + ")"

        when user_authenticated is false:
            output -> "Login failed"

        otherwise:
            # Still uncertain (e.g., awaiting 2FA)
            output -> "Awaiting multi-factor authentication"
            output -> "Reasoning: " + user_authenticated.reasoning

3. Self-Modification

actor SelfOptimizer
    on modify_algorithm
        # Read code from tape (homoiconicity)
        operation <- tape.read(100)

        # Modify it (self-modification)
        operation.gate = SWAP
        tape.write(100, operation)

        # Test the modification (reversibly)
        checkpoint <- tape.checkpoint()
        result <-> execute_modified_code()

        # Don't like it? Rewind!
        if result.performance < threshold
            tape.restore(checkpoint)

4. Meta-Evolution

actor EvolutionarySystem
    on adapt_to_workload
        # System tunes its own evolution (meta-evolution)
        params <- get_fitness_params()

        # Adjust selection pressure based on usage patterns
        params.recency_weight = 0.7    # Favor recent operations more
        params.activity_weight = 0.2
        params.gate_weight = 0.1

        tune_fitness(params)

        output -> "System adapted to workload"

The system evolves how it evolves!

Documentation

Core Philosophy

"Computation is the Modification of Memory"

Moop implements this principle through:

  • Homoiconicity: Code is data, data is code
  • Reversibility: All R-layer gates are self-inverse
  • Tape-Loop: Finite but unbounded circular memory
  • Segregated Logic: Reversible (L2a) vs Irreversible (L2b)
  • Actor-Proto Duality: Two aspects of one coordination primitive

Building

Requirements

  • C11 compiler (gcc/clang)
  • Standard C library
  • Make

Optional

  • libcurl (for LLM integration - currently disabled)

Commands

make          # Build test suite
make test     # Build and run tests
make clean    # Remove build artifacts
make help     # Show all targets

Performance

  • Tape Size: 1024 operations (L1 constraint)
  • Fitness Computation: O(1) per operation
  • Pruning Cycle: O(n log n) sort (every 256 ops)
  • Memory: ~40KB for tape + qubit state
  • Speed: ~1M ops/sec on modern CPU

Testing

Comprehensive test suite covering:

  • Tape-loop wrapping
  • Evolutionary pruning
  • Trinary MAYBE resolution
  • Self-modification
  • Meta-evolution
  • Layer segregation

Run with: make test

Related Projects

Trinity Vibe

trinity-vibe - Browser-based exploration of homoiconicity and structural reversibility

Trinity Vibe and Moop share the same philosophical foundations but serve different purposes:

Shared Philosophy:

  • Homoiconicity (code as data)
  • Structural reversibility (time-travel debugging)
  • Three-layer architecture (Functions/Actors/Prototypes)
  • Non-Von Neumann computational model

Trinity Vibe's Focus:

  • Browser-based (JavaScript, no build step)
  • "Memory-as-computation" model
  • Interactive exploration and visualization
  • Web deployment, rapid prototyping

Moop's Focus:

  • Native C implementation (~900 lines)
  • Quantum-ready reversible gates
  • Embedded systems (40KB footprint, no GC)
  • Production deployment, deterministic real-time

Think of them as:

  • Trinity Vibe: Web-based laboratory for exploring reversible homoiconic programming
  • Moop: Production-ready implementation with quantum compatibility and embedded focus

Both projects explore how reversibility + homoiconicity creates new programming paradigms. Trinity Vibe prioritizes accessibility and experimentation; Moop prioritizes performance and future-proofing.

License

MIT License - see LICENSE

Contributing

Pull requests welcome! Please ensure:

  • Code passes make test
  • New features include tests
  • Follows existing code style (K&R, 4-space indent)

Aesthetic Notes

Zen Brutalism: No decoration, maximum structural clarity, deep theoretical alignment. Every line earns its place.

The implementation breathes these dualities:

  • Actor ↔ Proto (coordination duality)
  • Reversible ↔ Irreversible (entropy segregation)
  • Code ↔ Data (homoiconicity)
  • System ↔ User (a/b sublayer pairs)

The tape is a living, adaptive memory that self-organizes through selection pressure—pure Prigogine dissipative structure at the computational level.

Status

Production-ready alpha - Core features complete, API stable, tests passing.


Generated with evolutionary code that evolves its own evolution 🦎

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Moop Embedded Runtime - Quantum-ready substrate (40KB, no GC, 1M ops/sec)

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