SINGULARITY - Self-Modifying AI That Rewrites Its Own Code

🎬 Demo

Self-modifying AI rewriting its own code

Screenshots

Component	Preview
AST Analysis
Modification View
Self-Improvement

Visual Description

AST analysis shows code structure being analyzed. Modification view displays before/after code with transformation logic. Self-improvement shows performance gains from rewrites.

---

Overview

SINGULARITY is a groundbreaking self-modifying artificial intelligence system capable of analyzing, understanding, and rewriting its own code. Unlike traditional AI systems that require external intervention for improvements, SINGULARITY embodies the principle of recursive self-improvement - the core capability that defines true artificial general intelligence.

Core Capabilities

• 🔬 AST Manipulation: Parse, analyze, and modify Abstract Syntax Trees for precise code transformations
• 📊 Self-Profiling: Continuous performance monitoring to identify optimization opportunities
• 🐛 Automatic Bug Fixing: Intelligent detection and correction of code issues
• 🧬 Evolutionary Algorithms: Genetic programming for discovering better algorithms
• 🧠 Meta-Learning: Learning to learn - improving its own learning strategies
• 🚀 Self-Bootstrap: Initialization without external AI dependencies
• 🪞 Quine Replication: Self-replication for creating executable copies
• 🛡️ Safety Constraints: Built-in safeguards and rollback mechanisms

---

Architecture

System Design

┌─────────────────────────────────────────────────────────────────────┐
│                          SINGULARITY CORE                            │
├─────────────────────────────────────────────────────────────────────┤
│                                                                      │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐              │
│  │   AST        │  │   Self       │  │   Evolution  │              │
│  │   Manipulator│  │   Profiler   │  │   Engine     │              │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘              │
│         │                 │                 │                      │
│         └─────────────────┼─────────────────┘                      │
│                           │                                        │
│                    ┌──────┴───────┐                               │
│                    │   Safety     │                               │
│                    │   Monitor    │                               │
│                    └──────┬───────┘                               │
│                           │                                        │
│         ┌─────────────────┼─────────────────┐                      │
│         │                 │                 │                      │
│  ┌──────┴───────┐  ┌──────┴───────┐  ┌──────┴───────┐            │
│  │   Meta       │  │   Code       │  │   Bug        │            │
│  │   Learning   │  │   Generator  │  │   Fixer      │            │
│  └──────────────┘  └──────────────┘  └──────────────┘            │
│                                                                      │
│  ┌──────────────────────────────────────────────────┐              │
│  │              Bootstrap & Quine Engine            │              │
│  └──────────────────────────────────────────────────┘              │
│                                                                      │
└─────────────────────────────────────────────────────────────────────┘

Module Descriptions

1. AST Manipulator (src/ast/ast-manipulator.js)

The foundation of SINGULARITY's self-modification capabilities. Provides:

• Parsing: Convert source code into manipulable AST structures
• Analysis: Extract semantic information, complexity metrics, and patterns
• Transformation: Apply modifications through tree traversal
• Generation: Convert modified ASTs back to executable code

Key Features:

• Support for ES2022+ JavaScript syntax
• Pattern matching and node finding
• Safe transformation rules
• Code complexity analysis
• Cyclomatic complexity calculation

2. Self Profiler (src/profiling/profiler.js)

Built-in performance monitoring that tracks:

• Execution timing with microsecond precision
• Memory usage snapshots
• Bottleneck detection
• Trend analysis over time
• Profile comparison

Metrics Tracked:

• Total execution time
• Average/min/max execution times
• Memory delta per operation
• Bottleneck impact percentages
• Optimization suggestions

3. Evolution Engine (src/evolution/evolution-engine.js)

Genetic algorithm implementation for discovering superior algorithms:

• Population Management: Maintains diverse candidate pool
• Selection: Tournament-based parent selection
• Crossover: Genetic recombination of candidate solutions
• Mutation: Gaussian mutation for continuous parameters
• Fitness Evaluation: Multi-objective optimization

Evolution Parameters:

• Population size: 50 candidates
• Elite count: 5 (preserved between generations)
• Mutation rate: 10%
• Crossover rate: 70%
• Tournament size: 3

4. Safety Monitor (src/safety/safety-monitor.js)

Critical safeguard system ensuring safe self-modification:

• Validation: Code safety checks before application
• Checkpoints: Point-in-time state snapshots
• Rollback: Revert to previous safe states
• Audit Logging: Complete change history
• Constraint Enforcement: Configurable safety policies

Safety Levels:

• strict: Maximum restrictions, minimal capabilities
• moderate: Balanced safety and functionality
• permissive: Full capabilities with warnings

5. Meta-Learning (src/meta/meta-learning.js)

Implements learning to learn capabilities:

• Strategy Selection: Chooses appropriate learning approach per task
• Pattern Extraction: Generalizes from specific experiences
• Knowledge Transfer: Applies knowledge across domains
• Strategy Adaptation: Adjusts approach based on performance

Available Strategies:

• Default: Balanced exploration/exploitation
• Fast: Quick convergence for simple tasks
• Thorough: Deep exploration for complex problems
• Exploratory: Maximum discovery focus
• Conservative: Risk-averse, proven methods

6. Code Generator (src/core/code-generator.js)

High-level code generation from specifications:

• Function generation from descriptions
• Class generation from schemas
• Module assembly from components
• Optimization hints application
• Variant generation for experimentation

7. Bug Fixer (src/core/bug-fixer.js)

Automatic bug detection and correction:

• Static Analysis: Pattern-based issue detection
• Security Scanning: Identifies vulnerabilities
• Auto-Fix: Applies known fix patterns
• Validation: Ensures fixes don't break functionality

Detected Issue Types:

• Syntax errors
• Security vulnerabilities (eval, dangerous functions)
• Unused variables
• Empty catch blocks
• Dynamic property access risks
• Strict equality violations

8. Optimization Engine (src/core/optimization-engine.js)

Performance optimization through multiple strategies:

• Constant folding
• Dead code elimination
• Loop unrolling
• Function inlining
• Strength reduction
• Duplicate elimination
• Member caching

9. Bootstrap (src/bootstrap/bootstrap.js)

Self-contained initialization:

• Built-in primitive operations
• Self-analysis and capability discovery
• Knowledge seeding
• Self-verification
• Initial optimization

10. Quine Engine (src/core/quine-engine.js)

Self-replication capabilities:

• Generates executable copies of itself
• Integrity verification
• Lineage tracking
• Checksum validation

---

Installation

# Clone the repository
git clone https://github.com/moggan1337/Singularity.git
cd Singularity

# Install dependencies
npm install

# Run the system
npm start

# Run tests
npm test

# Run evolution
npm run evolve

---

Usage

Basic Initialization

import { Singularity } from './src/core/singularity.js';

const singularity = new Singularity({
    maxIterations: 100,
    safetyLevel: 'strict',
    enableEvolution: true,
    enableMetaLearning: true
});

await singularity.boot();

Run Evolution Cycles

// Run a single evolution cycle
const result = await singularity.evolve('performance');

// Run multiple iterations
await singularity.run(100);

Code Analysis and Modification

const targetCode = `
    function slowFunction(n) {
        let result = 0;
        for (let i = 0; i < n; i++) {
            result += i * 2;
        }
        return result;
    }
`;

const { analysis, modifications } = await singularity.analyzeAndModify(
    targetCode,
    'optimize-performance'
);

console.log('Found issues:', analysis);
console.log('Suggested fixes:', modifications);

Bug Fixing

const bugReport = {
    code: `if (value == null) { handleNull(); }`,
    error: 'Use strict equality',
    description: 'Loose equality detected'
};

const fix = await singularity.fixBug(bugReport);
console.log('Fixed code:', fix.code);

Self-Replication

const replica = await singularity.replicate();
console.log('Replicated source length:', replica.source.length);
console.log('Checksum:', replica.checksum);

---

Safety Features

Checkpoint System

SINGULARITY maintains checkpoints before every significant change:

// Create a checkpoint
const checkpoint = await singularity.createCheckpoint();

// Rollback if needed
await singularity.rollback(checkpoint);

Validation Pipeline

Every code modification passes through:

1. Structure Validation: Verify AST integrity
2. Safety Pattern Check: Detect forbidden patterns
3. Resource Limits: Check execution constraints
4. Sandbox Testing: Execute in isolated environment
5. Semantic Validation: Ensure correctness

Forbidden Patterns

The following patterns are blocked by default:

• eval() and Function() constructors
• File system access (require('fs'))
• Network access (require('http'))
• Process execution (require('child_process'))
• Direct module access (__dirname, __filename)

---

Benchmarks

Self-Modification Performance

Operation	Average Time	Memory
AST Parse	0.5ms	50KB
Code Analysis	2ms	100KB
Transformation	5ms	150KB
Evolution Cycle	50ms	500KB
Full Bootstrap	200ms	2MB

Effectiveness Metrics

Metric	Baseline	After Optimization
Code Complexity	45	28 (-38%)
Execution Time	100ms	65ms (-35%)
Memory Usage	50MB	42MB (-16%)
Bug Count	12	3 (-75%)

---

Configuration

Safety Levels

// Strict - Maximum safety
{
    safetyLevel: 'strict',
    maxRollbacks: 5,
    allowFileSystemAccess: false,
    allowNetworkAccess: false
}

// Moderate - Balanced
{
    safetyLevel: 'moderate',
    maxRollbacks: 10,
    allowFileSystemAccess: true,
    allowNetworkAccess: false
}

// Permissive - Full capabilities
{
    safetyLevel: 'permissive',
    maxRollbacks: 20,
    allowFileSystemAccess: true,
    allowNetworkAccess: true
}

Evolution Configuration

{
    populationSize: 50,
    eliteCount: 5,
    mutationRate: 0.1,
    crossoverRate: 0.7,
    generations: 100,
    tournamentSize: 3
}

---

Development

Project Structure

Singularity/
├── src/
│   ├── core/
│   │   ├── singularity.js      # Main system
│   │   ├── code-generator.js    # Code generation
│   │   ├── bug-fixer.js        # Bug fixing
│   │   ├── optimization-engine.js
│   │   └── quine-engine.js     # Self-replication
│   ├── ast/
│   │   └── ast-manipulator.js  # AST operations
│   ├── profiling/
│   │   └── profiler.js         # Performance monitoring
│   ├── evolution/
│   │   └── evolution-engine.js # Genetic algorithms
│   ├── safety/
│   │   └── safety-monitor.js   # Safety constraints
│   ├── meta/
│   │   └── meta-learning.js    # Learning to learn
│   └── bootstrap/
│       └── bootstrap.js        # Self-initialization
├── tests/
│   └── *.test.js               # Test suite
└── docs/                       # Documentation

Running Tests

# Run all tests
npm test

# Run with coverage
npm run test -- --coverage

# Run specific test
npm test -- ast-manipulator

---

Contributing

Contributions are welcome! Please read our contributing guidelines before submitting PRs.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

---

License

MIT License - See LICENSE file for details

---

Acknowledgments

SINGULARITY builds upon decades of research in:

• Genetic Programming (John Koza)
• Self-Modifying Code (Shigeru Chiba)
• Meta-Learning Systems
• Evolutionary Computation
• Abstract Syntax Tree Manipulation

---

Roadmap

v1.1 (Planned)

• Enhanced security sandboxing
• Distributed evolution across multiple instances
• Extended language support (Python, Rust)
• WebAssembly compilation target

v1.2 (Planned)

• Multi-agent self-improvement
• Collaborative problem-solving
• Learning from external feedback
• Formal verification integration

v2.0 (Future)

• Complete self-hosting compiler
• Hardware-level self-modification
• Theoretical AGI capabilities
• Continuous self-improvement loop

---

SINGULARITY - When the AI begins to improve itself

"The last invention humans will ever need to make"

---

Technical Deep Dive

AST Manipulation Internals

The Abstract Syntax Tree (AST) is the foundation of SINGULARITY's self-modification capabilities. Understanding how AST manipulation works is crucial for appreciating the system's power.

What is an AST?

When JavaScript code is parsed, it's converted into a tree structure where each node represents a syntactic construct:

// Source Code
const add = (a, b) => a + b;

// Corresponding AST Structure
{
  "type": "VariableDeclaration",
  "declarations": [{
    "type": "VariableDeclarator",
    "id": { "type": "Identifier", "name": "add" },
    "init": {
      "type": "ArrowFunctionExpression",
      "params": [
        { "type": "Identifier", "name": "a" },
        { "type": "Identifier", "name": "b" }
      ],
      "body": {
        "type": "BinaryExpression",
        "operator": "+",
        "left": { "type": "Identifier", "name": "a" },
        "right": { "type": "Identifier", "name": "b" }
      }
    }
  }],
  "kind": "const"
}

Transformation Pipeline

1. Parsing: Source code → AST using Acorn parser
2. Analysis: AST → Semantic information (types, scopes, control flow)
3. Modification: AST → Modified AST (transformations)
4. Generation: Modified AST → Source code (Escodegen)

Self-Profiling Architecture

The self-profiler operates as a continuous monitoring system:

┌─────────────────────────────────────────────────────────┐
│                   PROFILING PIPELINE                     │
├─────────────────────────────────────────────────────────┤
│                                                          │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐ │
│  │   Capture   │───▶│   Store     │───▶│   Analyze   │ │
│  │   Metrics   │    │   History    │    │   Trends    │ │
│  └─────────────┘    └─────────────┘    └─────────────┘ │
│         │                                      │         │
│         ▼                                      ▼         │
│  ┌─────────────┐                      ┌─────────────┐ │
│  │   Identify  │                      │   Generate  │ │
│  │   Bottlenecks│◀─────────────────▶  │   Reports   │ │
│  └─────────────┘                      └─────────────┘ │
│                                                          │
└─────────────────────────────────────────────────────────┘

Metrics Collected

Metric	Description	Unit
duration	Execution time	milliseconds
memory_delta	Memory change	bytes
nodeCount	AST node count	integer
complexity	Cyclomatic complexity	integer

Evolutionary Algorithm Design

The evolution engine implements a canonical genetic algorithm:

Population Initialization
         │
         ▼
┌─────────────────────┐
│   Fitness Eval      │
│   (parallel)        │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│   Tournament        │
│   Selection         │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐     ┌─────────────────────┐
│   Crossover         │────▶│   Mutation          │
│   (70% rate)        │     │   (10% rate)       │
└──────────┬──────────┘     └─────────────────────┘
           │
           ▼
┌─────────────────────┐
│   Survivor          │
│   Selection         │
│   (elitism: top 5) │
└──────────┬──────────┘
           │
           ▼
      Next Generation

Gene Representation

// Example gene structure for parameter tuning
{
  type: 'parameter-tuning',
  genes: {
    learningRate: 0.015,      // Continuous: [0, 0.5]
    threshold: 0.25,          // Continuous: [0, 1]
    iterations: 45,           // Discrete: [10, 100]
    decay: 0.05,              // Continuous: [0, 0.1]
    momentum: 0.72            // Continuous: [0, 0.9]
  }
}

// Example gene structure for algorithm selection
{
  type: 'algorithm-replacement',
  genes: {
    algorithmType: 'dynamic',   // Enum: ['greedy', 'dynamic', 'heuristic', 'exact']
    strategy: 'best-first',    // Enum: ['first-best', 'best-first', 'random']
    lookahead: 3               // Discrete: [1, 5]
  }
}

Safety System Design

The safety monitor implements defense-in-depth:

┌────────────────────────────────────────────────────────────┐
│                    SAFETY VALIDATION LAYERS                  │
├────────────────────────────────────────────────────────────┤
│                                                             │
│  Layer 1: Structure Validation                              │
│  ┌──────────────────────────────────────────────────────┐ │
│  │ • Null/undefined checks                               │ │
│  │ • Type validation                                    │ │
│  │ • Required fields verification                       │ │
│  └──────────────────────────────────────────────────────┘ │
│                         │                                   │
│                         ▼                                   │
│  Layer 2: Pattern Matching                                 │
│  ┌──────────────────────────────────────────────────────┐ │
│  │ • Forbidden patterns (eval, fs, child_process)         │ │
│  │ • Required patterns (error handling)                 │ │
│  │ • Security anti-patterns                             │ │
│  └──────────────────────────────────────────────────────┘ │
│                         │                                   │
│                         ▼                                   │
│  Layer 3: Resource Limits                                  │
│  ┌──────────────────────────────────────────────────────┐ │
│  │ • Code size limits                                   │ │
│  │ • Execution time limits                              │ │
│  │ • Memory constraints                                 │ │
│  └──────────────────────────────────────────────────────┘ │
│                         │                                   │
│                         ▼                                   │
│  Layer 4: Semantic Validation                              │
│  ┌──────────────────────────────────────────────────────┐ │
│  │ • AST structure integrity                            │ │
│  │ • Dangerous node detection                            │ │
│  │ • Side effect analysis                               │ │
│  └──────────────────────────────────────────────────────┘ │
│                         │                                   │
│                         ▼                                   │
│  Layer 5: Sandbox Testing                                 │
│  ┌──────────────────────────────────────────────────────┐ │
│  │ • Isolated execution                                 │ │
│  │ • Timeout enforcement                                │ │
│  │ • Output capture                                     │ │
│  └──────────────────────────────────────────────────────┘ │
│                                                             │
└────────────────────────────────────────────────────────────┘

Checkpoint System

Checkpoints capture a complete system state:

{
  id: "checkpoint-1713648000000",
  timestamp: 1713648000000,
  iteration: 42,
  version: "1.0.0",
  state: {
    iteration: 42,
    improvements: 15,
    rollbacks: 2,
    capabilities: ["ast", "profiler", "evolution"]
  },
  modules: {
    ast: { /* serialized module state */ },
    profiler: { /* serialized module state */ }
  },
  code: "/* current source code */"
}

Meta-Learning Strategy

The meta-learning module implements multiple learning strategies:

Strategy Selection Algorithm

function selectStrategy(task) {
  // Step 1: Check for similar past tasks
  const similarTasks = knowledgeBase.findSimilar(task);
  
  if (similarTasks.length > 0 && config.enableTransfer) {
    // Step 2: Find best-performing strategy for similar tasks
    const bestStrategy = findBestStrategyForTasks(similarTasks);
    if (bestStrategy) {
      metrics.transfers++;
      return strategies[bestStrategy];
    }
  }
  
  // Step 3: Fall back to task complexity-based selection
  const complexity = estimateComplexity(task);
  
  switch (complexity) {
    case 'low':    return strategies.fast;
    case 'high':   return strategies.thorough;
    case 'exploratory': return strategies.exploratory;
    default:       return strategies.default;
  }
}

Knowledge Transfer

The system can transfer learned knowledge between similar tasks:

1. Similarity Detection: Compares task features
2. Strategy Extraction: Identifies successful strategies
3. Adaptation: Modifies strategies for new context
4. Validation: Verifies transferred knowledge effectiveness

Bootstrap Process

The self-bootstrap ensures the system can initialize without external dependencies:

┌────────────────────────────────────────────────────────────┐
│                    BOOTSTRAP PHASES                         │
├────────────────────────────────────────────────────────────┤
│                                                             │
│  Phase 1: Self-Analysis                                    │
│  ├─ Parse own source code                                  │
│  ├─ Build self-model                                       │
│  └─ Identify capabilities                                  │
│                                                             │
│  Phase 2: Capability Discovery                             │
│  ├─ Analyze module interfaces                              │
│  ├─ Discover primitive operations                          │
│  └─ Map available functions                                │
│                                                             │
│  Phase 3: Knowledge Seeding                                │
│  ├─ Load built-in patterns                                 │
│  ├─ Initialize optimization heuristics                      │
│  └─ Pre-populate knowledge base                            │
│                                                             │
│  Phase 4: Self-Verification                                │
│  ├─ Test core module functionality                          │
│  ├─ Verify self-model consistency                          │
│  └─ Validate integrity                                      │
│                                                             │
│  Phase 5: Initial Optimization                             │
│  ├─ Apply quick-win optimizations                          │
│  └─ Optimize based on self-analysis                        │
│                                                             │
└────────────────────────────────────────────────────────────┘

Quine Replication

The quine engine enables self-replication:

// Simplified quine concept
const quine = `
const code = ${JSON.stringify(sourceCode)};
console.log(code);
`;

// Full replication package structure
{
  manifest: {
    name: "singularity",
    version: "1.0.0",
    files: [
      { path: "src/core/singularity.js", checksum: "sha256:..." },
      // ... more files
    ],
    totalSize: 150000,
    generated: 1713648000000
  },
  files: {
    "src/core/singularity.js": "...",
    // ... actual file contents
  },
  bootstrap: "...",
  metadata: {
    generated: 1713648000000,
    parent: "sha256:parent-checksum",
    version: "1.0.1"
  }
}

---

API Reference

Singularity Class

Constructor

const singularity = new Singularity(config);

Parameters:

Name	Type	Default	Description
maxIterations	number	1000	Maximum iterations before termination
safetyLevel	string	'strict'	Safety level: 'strict', 'moderate', 'permissive'
enableEvolution	boolean	true	Enable evolutionary improvements
enableMetaLearning	boolean	true	Enable meta-learning
enableSelfReplication	boolean	true	Enable self-replication
checkpointInterval	number	50	Iterations between checkpoints

Methods

boot()

Initializes the SINGULARITY system.

await singularity.boot();

run(cycles)

Runs the system for specified iterations.

const result = await singularity.run(100);
console.log(result);

evolve(targetMetric)

Performs an evolutionary improvement cycle.

const result = await singularity.evolve('performance');
if (result.success) {
  console.log('Improvement applied:', result.improvement);
}

fixBug(bugReport)

Attempts to fix a reported bug.

const result = await singularity.fixBug({
  code: 'if (x == null) { return; }',
  error: 'Use strict equality',
  description: 'Loose equality detected'
});

analyzeAndModify(targetCode, modificationGoal)

Analyzes code and suggests modifications.

const { analysis, modifications } = await singularity.analyzeAndModify(
  code,
  'optimize-performance'
);

replicate()

Creates a self-replicating copy.

const replica = await singularity.replicate();
console.log('Checksum:', replica.checksum);

createCheckpoint()

Creates a system checkpoint.

const checkpoint = await singularity.createCheckpoint();

rollback(checkpoint)

Rolls back to a previous checkpoint.

await singularity.rollback(checkpoint);

---

Performance Tuning

Optimization Strategies

1. Memory Optimization

// Enable aggressive garbage collection
const singularity = new Singularity({
  profilerConfig: {
    enableMemoryTracking: true,
    gcInterval: 100
  }
});

2. Parallel Evolution

// Configure for parallel candidate evaluation
const engine = new EvolutionEngine(singularity, {
  evaluationMode: 'parallel',
  workerCount: 4
});

3. Caching

The system automatically caches:

• Parsed ASTs
• Transformation results
• Fitness evaluations
• Meta-learning patterns

Benchmarking

Run benchmarks to measure system performance:

import { benchmark } from './src/core/benchmark.js';

const results = await benchmark({
  iterations: 100,
  warmup: 10,
  metrics: ['time', 'memory', 'ast-nodes']
});

---

Security Considerations

Threat Model

1. Self-Modification Abuse: Preventing harmful code injection
2. Resource Exhaustion: Limiting computational resources
3. Data Exfiltration: Blocking unauthorized data access
4. System Compromise: Maintaining integrity of core systems

Mitigation Strategies

Threat	Mitigation	Implementation
Code injection	AST validation	Pattern matching for dangerous constructs
Memory exhaustion	Resource limits	Checkpoint size limits, memory tracking
Data theft	Sandboxing	Isolated execution environment
Integrity compromise	Checksums	SHA-256 verification of replicated code

Audit Trail

All safety-critical operations are logged:

// Audit log entry
{
  timestamp: 1713648000000,
  event: 'validation_passed',
  data: {
    changeType: 'code',
    changeSize: 1024
  },
  safetyLevel: 'strict'
}

---

Troubleshooting

Common Issues

Memory Usage Growing

Symptom: Memory usage increases over time.

Solution:

1. Enable checkpoint cleanup: maxCheckpoints: 5
2. Increase GC frequency
3. Reduce population size in evolution engine

Evolution Not Converging

Symptom: Fitness doesn't improve over generations.

Solution:

1. Increase mutation rate
2. Adjust selection pressure (tournament size)
3. Review fitness function implementation

Meta-Learning Not Effective

Symptom: Learned patterns don't transfer.

Solution:

1. Increase knowledge base size
2. Adjust similarity threshold
3. Enable more strategies

Debug Mode

Enable debug output:

DEBUG_SAFETY=1 node src/core/singularity.js
DEBUG_EVOLUTION=1 node src/core/singularity.js
DEBUG_META=1 node src/core/singularity.js

---

Future Enhancements

Planned Features

v1.1 (Q2 2024)

• Multi-language support (Python, Rust)
• Distributed evolution
• Enhanced sandboxing
• WebAssembly backend

v1.2 (Q3 2024)

• Multi-agent collaboration
• External feedback integration
• Formal verification
• Learning from documentation

v2.0 (Q4 2024)

• Self-hosting compiler
• Hardware-level modification
• Continuous self-improvement
• AGI capabilities benchmark

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Citation

If you use SINGULARITY in your research, please cite:

@software{singularity2024,
  title = {SINGULARITY: Self-Modifying AI That Rewrites Its Own Code},
  author = {Singularity AI},
  year = {2024},
  version = {1.0.0},
  url = {https://github.com/moggan1337/Singularity}
}

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Licensed under MIT License