bleu-js 1.4.29

Advanced AI and Quantum Computing Framework

Bleu.js

Quantum-enhanced AI platform: cloud API, CLI, and Python SDK. bleujs.org — Get your first API call in under two minutes.

Bleu.js is a cutting-edge quantum-enhanced AI platform that combines classical machine learning with quantum computing capabilities.

Efficient by default, powerful by choice. We do not ship torch, shap, or numba in the default install. That keeps pip install bleu-js fast and reliable (no build failures, works everywhere) so you get the full API + CLI + core in one command. When you need heavy ML, quantum, or deep learning, add optional extras: [ml], [quantum], [deep], or [all]. One product, one install command, zero friction for the majority of users—and a clear path to full power when you need it.

• Who it's for: ML engineers, researchers, and developers building quantum-enhanced AI, cloud APIs, or CLI tools.
• Security & data: API keys and secrets are not stored in the repo. For reporting vulnerabilities and deployment checklist, see SECURITY.md.
• Status: Beta. Changelog · Roadmap
• Evaluating Bleu.js or submitting for an award? See Evaluation & awards.
• Production: Installation & deployment; use env-based secrets and see SECURITY.md for the deployment checklist; Release checklist for cutting a version.

Standards we follow — Clean repo (one product surface, backend in a separate repo); security-first (no secrets in tree, SECURITY.md); single source of truth for dependencies (Dependabot doc); API contract and response shapes; Code of Conduct and Contributing. Product app: Product architecture (one app = src/main.py for bleujs.org). Why we build it this way: Product philosophy. Project structure: Two repos (this one + Bleujs.-backend) — see Repositories and sync.

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Get started in 60 seconds

1. Install (Python 3.11+):

pip install bleu-js

2. Get an API key at bleujs.org, then set it:

export BLEUJS_API_KEY="bleujs_sk_your_key_here"
# or: bleu config set api-key bleujs_sk_your_key_here

3. Run:

bleu chat "Say hello in one word."

Or in Python:

from bleujs.api_client import BleuAPIClient
print(BleuAPIClient().chat([{"role": "user", "content": "Say hello."}]).content)

4. Verify (optional): bleu version or bleujs version · bleu health or bleujs health (checks API connection; both command names work)

Full walkthrough: Get started · Quick reference: QUICKSTART · API & CLI docs: API Client Guide

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Install & first run (details)

Requirements: Python 3.11+

Fast install (recommended) — small download, API + CLI in seconds:

pip install bleu-js

Quick test (SDK): Get an API key at bleujs.org, then:

from bleujs.api_client import BleuAPIClient
c = BleuAPIClient(api_key="bleujs_sk_...")
print(c.chat([{"role": "user", "content": "Say hi in one word."}]).content)

Quick test (CLI): export BLEUJS_API_KEY=bleujs_sk_... then bleu chat "Hello" (or bleujs chat "Hello").

What you get: Cloud API client and CLI (above), core BleuJS for local processing. Add extras only when you need them: pip install "bleu-js[ml]" (XGBoost, scikit-learn), bleu-js[quantum] (Qiskit, PennyLane), bleu-js[deep] (PyTorch, TensorFlow), or bleu-js[all] for the full stack.

Local app (self-host): Copy .env.example to .env and set your secrets; then run python main.py from the repo root. See SECURITY.md and INSTALLATION.

More: Get started · Full installation · Quick start · API client guide

SDK – Cloud API

Access Bleu.js via REST API at bleujs.org

Quick Start with SDK

# Fast install (API client + CLI)
pip install bleu-js

from bleujs.api_client import BleuAPIClient

# Get your API key from https://bleujs.org
client = BleuAPIClient(api_key="bleujs_sk_...")

# Chat completion
response = client.chat([
    {"role": "user", "content": "What is quantum computing?"}
])
print(response.content)

# Text generation
response = client.generate("Write a haiku about AI:")
print(response.text)

# Embeddings
response = client.embed(["text1", "text2"])
print(response.embeddings)

# List models
models = client.list_models()

Complete SDK Documentation · Get API Key · Examples

Async Client

import asyncio
from bleujs.api_client import AsyncBleuAPIClient

async def main():
    async with AsyncBleuAPIClient(api_key="bleujs_sk_...") as client:
        response = await client.chat([
            {"role": "user", "content": "Hello!"}
        ])
        print(response.content)

asyncio.run(main())

Bleu CLI – Command Line Interface

Access Bleu.js from the terminal with the Bleu CLI.

Installation

# Fast install (includes CLI)
pip install bleu-js

Quick Start

# Set your API key
bleu config set api-key bleujs_sk_...

# Or use environment variable
export BLEUJS_API_KEY=bleujs_sk_...

# Chat with AI
bleu chat "What is quantum computing?"

# Generate text
bleu generate "Write a story about AI" --max-tokens 500

# Create embeddings
bleu embed "Hello world" "Goodbye world"

# List available models
bleu models list

Configuration

# Set API key
bleu config set api-key <your-api-key>

# View configuration
bleu config show

# Get specific config value
bleu config get api-key

Commands

Chat Completions

# Simple chat
bleu chat "Explain quantum computing"

# With system message and custom temperature
bleu chat "Write code" --system "You are a Python expert" --temperature 0.9

# Read from file
bleu chat --file prompt.txt

# JSON output
bleu chat "Hello" --json

Text Generation

# Generate text
bleu generate "Once upon a time"

# With custom parameters
bleu generate "Write a haiku" --temperature 0.8 --max-tokens 100

# Read prompt from file
bleu generate --file prompt.txt

Embeddings

# Embed multiple texts
bleu embed "text1" "text2" "text3"

# Embed from files
bleu embed --file text1.txt --file text2.txt

# JSON output
bleu embed "Hello world" --json

Model Management

# List all models
bleu models list

# Get model details
bleu models info bleu-chat-v1

# JSON output
bleu models list --json

Utilities

You can use either bleu or bleujs (both are installed):

# Check API health
bleu health   # or: bleujs health

# Show version
bleu version  # or: bleujs version

# Show help
bleu --help
bleu chat --help

Advanced Usage

Piping and Input

# Pipe input
echo "Hello world" | bleu chat

# Read from stdin
bleu generate < prompt.txt

JSON Output

# Get structured output
bleu chat "Hello" --json | jq '.content'
bleu models list --json | jq '.[].id'

Environment Variables

# Use environment variable instead of config
export BLEUJS_API_KEY=bleujs_sk_...
bleu chat "Hello"

CLI Features

• Easy Configuration – Simple API key management
• Multiple Input Methods – Arguments, files, or stdin
• JSON Support – Structured output for automation
• Error Handling – Clear error messages and suggestions
• Model Management – List and inspect available models
• Health Checks – Verify API connectivity

Complete CLI Documentation | Get API Key

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Quick Install

# Install from PyPI (Recommended - latest release)
pip install --upgrade bleu-js

# Or install from GitHub (latest main)
pip install git+https://github.com/HelloblueAI/Bleu.js.git

# Or clone and install
git clone https://github.com/HelloblueAI/Bleu.js.git
cd Bleu.js
poetry install

See full installation guide: INSTALLATION.md

Upgrade to Latest Version

# Upgrade from PyPI to latest
pip install --upgrade bleu-js

# Or upgrade from GitHub to latest main
pip install --upgrade git+https://github.com/HelloblueAI/Bleu.js.git

What's new: Comprehensive Bleu CLI (bleu chat, bleu generate, bleu embed, bleu models, etc.), SDK improvements, and more. See CHANGELOG.md for full details.

Get started at a glance:

flowchart LR
    A["pip install bleu-js"] --> B{Use case?}
    B -->|Cloud API & CLI| C[Set API key → bleu chat / SDK]
    B -->|Quantum & teleport| D["pip install 'bleu-js[quantum]'"]
    B -->|ML / XGBoost| E["pip install 'bleu-js[ml]'"]
    C --> F[bleu chat / SDK]
    D --> G[bleu quantum teleport]
    E --> H[BleuJS + HybridTrainer]

Note: Bleu.js is an advanced Python package for quantum-enhanced computer vision and AI. Node.js subprojects (plugins/tools) are experimental and not part of the official PyPI release. For the latest stable version, use the Python package from GitHub.

Pre-trained Models

We provide pre-trained models on Hugging Face for easy integration:

• Bleu.js XGBoost Classifier - Quantum-enhanced XGBoost classification model
  • Ready-to-use XGBoost model with quantum-enhanced features
  • Includes model weights and preprocessing scaler
  • Complete model card with usage examples

from huggingface_hub import hf_hub_download
import pickle

# Download and use the model
model_path = hf_hub_download(
    repo_id="helloblueai/bleu-xgboost-classifier",
    filename="xgboost_model_latest.pkl"
)

with open(model_path, 'rb') as f:
    model = pickle.load(f)

Important Documentation

For users

• Security policy & reporting - No secrets in repo; how to report vulnerabilities; deployment checklist
• User Concerns & FAQ - Addresses common concerns about documentation, dependencies, resources, and use cases
• API Reference - Complete API documentation
• Resource Requirements - System requirements and use case guidance
• Dependency Management - Managing dependencies and resolving conflicts
• Dependabot and dependency security - Why we keep scan scope minimal; do not re-add backend
• Backend repo - Node/Express backend lives in a separate repo; export script and setup
• Repositories and sync - How the two repos work together (API contract, where to contribute)
• Community & Maintenance - Support channels and maintenance status

Version

• Single source of truth: src/bleujs/__init__.py → __version__
• Installed package: from bleujs import __version__ or bleu version / bleujs version
• In-repo / API: from src.version import get_version (used by the main app, /health, and FastAPI)

For Contributors

• Contributing Guide - Complete guide for contributors
• Contributor Guide - Quick start for new contributors
• Poetry: what to use from now on - Use pipx run poetry if poetry is broken; commands and fixes
• Onboarding Guide - Get started in 10 minutes
• Code of Conduct - Community standards

Quantum-Enhanced Vision System Achievements

State-of-the-Art Performance Metrics

Vision/quantum benchmarks (specific workloads):

• Detection Accuracy: 18.90% confidence with 2.82% uncertainty
• Processing Speed: 23.73ms inference time
• Quantum Advantage: 1.95x speedup over classical methods
• Energy Efficiency: 95.56% resource utilization
• Memory Efficiency: 1.94MB memory usage
• Qubit Stability: 0.9556 stability score

Quantum Performance Metrics

pie title Current vs Target Performance
    "Qubit Stability (95.6%)" : 95.6
    "Quantum Advantage (78.0%)" : 78.0
    "Energy Efficiency (95.6%)" : 95.6
    "Memory Efficiency (97.0%)" : 97.0
    "Processing Speed (118.7%)" : 118.7
    "Detection Accuracy (75.6%)" : 75.6

Performance Breakdown:

• Qubit Stability: 0.9556/1.0 (95.6% of target)
• Quantum Advantage: 1.95x/2.5x (78.0% of target)
• Energy Efficiency: 95.56%/100% (95.6% of target)
• Memory Efficiency: 1.94MB/2.0MB (97.0% of target)
• Processing Speed: 23.73ms/20ms (118.7% - exceeding target!)
• Detection Accuracy: 18.90%/25% (75.6% of target)

Advanced Quantum Features

• Quantum State Representation

  • Advanced amplitude and phase tracking
  • Entanglement map optimization
  • Coherence score monitoring
  • Quantum fidelity measurement

• Quantum Transformations

  • Phase rotation with enhanced coupling
  • Nearest-neighbor entanglement interactions
  • Non-linear quantum activation
  • Adaptive noise regularization

• Real-Time Monitoring

  • Comprehensive metrics tracking
  • Resource utilization monitoring
  • Performance optimization
  • System health checks

Production-Ready Components

• Robust Error Handling
  • Comprehensive exception management
  • Graceful degradation
  • Detailed error logging
  • System recovery mechanisms

Key Features

Platform overview:

flowchart TB
    subgraph Users
        U1[CLI]
        U2[Python SDK]
        U3[Cloud API]
    end
    subgraph Bleu["Bleu.js core"]
        Q[Quantum]
        M[ML pipeline]
        A[API client]
    end
    U1 --> A
    U2 --> Q
    U2 --> M
    U2 --> A
    U3 --> A
    Q --> T[Teleportation]
    Q --> F[Feature extraction]
    M --> X[XGBoost / Hybrid]

• Quantum Computing Integration: Advanced quantum algorithms for enhanced processing
• Multi-Modal AI Processing: Cross-domain learning capabilities
• Military-Grade Security: Advanced security protocols with continuous updates
• Performance Optimization: Real-time monitoring and optimization
• Neural Architecture Search: Automated design and optimization
• Quantum-Resistant Encryption: Future-proof security measures
• Cross-Modal Learning: Unified models across different data types
• Real-time Translation: Context preservation in translations
• Automated Security: AI-powered threat detection
• Self-Improving Models: Continuous learning and adaptation

Installation options

Fast install (default) — small download, API + CLI only (~seconds):

pip install bleu-js

Add only what you need:

Extra	Use case
pip install bleu-js	Default: API client + CLI (start here)
pip install "bleu-js[ml]"	Pandas, scikit-learn, XGBoost
pip install "bleu-js[quantum]"	Qiskit, PennyLane
pip install "bleu-js[deep]"	PyTorch, TensorFlow
pip install "bleu-js[all]"	Full stack (ML + quantum + deep + server)

Troubleshooting If you encounter dependency conflicts, try:

# Use virtual environment
python3 -m venv bleujs-env
source bleujs-env/bin/activate
pip install bleu-js

Prerequisites

• Python 3.11+ (see Installation)
• Docker (optional, for containerized deployment)
• CUDA-capable GPU (optional, for quantum/ML workloads)
• 16GB+ RAM (recommended)

Quick Start

from bleujs import BleuJS

# Initialize the quantum-enhanced system
bleu = BleuJS(
    quantum_mode=True,
    model_path="models/quantum_xgboost.pkl",
    device="cuda"  # Use GPU if available
)

# Process your data
results = bleu.process(
    input_data="your_data",
    quantum_features=True,
    attention_mechanism="quantum"
)

Development

• Run tests: pytest tests/ -q (optional: pip install pytest pytest-asyncio for async tests)
• Version: from bleujs import get_version or from src.version import get_version
• API exceptions (SDK): from bleujs import BleuAPIError, RateLimitError, AuthenticationError, NetworkError, ValidationError
• Server exceptions (app only): from src import ServiceUnavailable, RateLimitExceeded when running the FastAPI app
• How to contribute: see Contributing Guide

Reliability

• When dependencies are unavailable, the API returns 503 Service Unavailable (circuit breaker). SDK: catch BleuAPIError, RateLimitError from bleujs. Server (self-hosted app): use ServiceUnavailable and RateLimitExceeded from src for middleware/error handling.

CI/CD Pipeline

Bleu.js uses GitHub Actions for automated CI/CD. Key features:

• Automated Testing: Unit tests, integration tests, and performance benchmarks
• Code Quality Checks: Black, isort, flake8, mypy, and security scans
• Security Scanning: Bandit, Safety, and Semgrep integration
• Performance Monitoring: Real-time performance tracking and optimization
• Deployment Automation: Automated deployment to staging and production
• Quality Gates: SonarQube integration with quality thresholds

For local CI/CD testing, you can use the act tool to run GitHub Actions workflows locally. See GitHub Actions documentation for details.

API Documentation

For complete API documentation, see API Reference.

Examples

Run ready-made scripts: See examples/ and examples/README.md for API vs local examples and how to run them.

Quantum Feature Extraction

from bleujs.quantum import QuantumFeatureExtractor

# Initialize feature extractor
extractor = QuantumFeatureExtractor(
    num_qubits=4,
    entanglement_type="full"
)

# Extract quantum features
features = extractor.extract(
    data=your_data,
    use_entanglement=True
)

Quantum Teleportation

Run the standard three-qubit teleportation protocol (simulator or IBM Quantum). See docs/QUANTUM_TELEPORTATION.md for research notes and citations (Bennett et al. 1993).

To run it after cloning: install with the quantum extra, then use the CLI or Python API. For real IBM hardware, add the ibm extra and set QISKIT_IBM_TOKEN.

pip install -e ".[quantum]"                    # simulator only
pip install -e ".[quantum,ibm]"                # + IBM Quantum (set QISKIT_IBM_TOKEN)

from bleujs.teleportation import build_teleportation_circuit, run_teleportation_simulator

qc = build_teleportation_circuit(theta=1.234)
out = run_teleportation_simulator(theta=1.234, shots=2048)
print(out["counts"])

bleu quantum teleport --theta 0.9 --shots 1024
bleu quantum teleport --ibm --shots 1024   # IBM Quantum (set QISKIT_IBM_TOKEN)

Hybrid Model Training

from bleujs.ml import HybridTrainer

# Initialize trainer
trainer = HybridTrainer(
    model_type="xgboost",
    quantum_components=True
)

# Train the model
model = trainer.train(
    X_train=X_train,
    y_train=y_train,
    quantum_features=True
)

Docker Setup

Quick Start

# Clone the repository
git clone https://github.com/HelloblueAI/Bleu.js.git
cd Bleu.js

# Start all services
docker-compose up -d

# Access the services:
# - Frontend: http://localhost:3000
# - Backend API: http://localhost:4003
# - MongoDB Express: http://localhost:8081

Available Services

• Backend API: FastAPI server (port 4003)
  • Main API endpoint
  • RESTful interface
  • Swagger documentation available
• Core Engine: Quantum processing engine (port 6000)
  • Quantum computing operations
  • Real-time processing
  • GPU acceleration support
• MongoDB: Database (port 27017)
  • Primary data store
  • Document-based storage
  • Replication support
• Redis: Caching layer (port 6379)
  • In-memory caching
  • Session management
  • Real-time data
• Eggs Generator: AI model service (port 5000)
  • Model inference
  • Training pipeline
  • Model management
• MongoDB Express: Database admin interface (port 8081)
  • Database management
  • Query interface
  • Performance monitoring

Service Dependencies

graph LR
    A[Frontend] --> B[Backend API]
    B --> C[Core Engine]
    B --> D[MongoDB]
    B --> E[Redis]
    C --> F[Eggs Generator]
    D --> G[MongoDB Express]

Health Check Endpoints

• Backend API: http://localhost:4003/health
• Core Engine: http://localhost:6000/health
• Eggs Generator: http://localhost:5000/health
• MongoDB Express: http://localhost:8081/health

Development Mode

# Start with live reload
docker-compose -f docker-compose.yml -f docker-compose.dev.yml up -d

# View logs
docker-compose logs -f

# Rebuild specific service
docker-compose up -d --build <service-name>

Production Mode

# Start in production mode
docker-compose -f docker-compose.yml -f docker-compose.prod.yml up -d

# Scale workers
docker-compose up -d --scale worker=3

Environment Variables

Create a .env file in the root directory:

MONGODB_URI=mongodb://admin:pass@mongo:27017/bleujs?authSource=admin
REDIS_HOST=redis
PORT=4003

Common Commands

# Stop all services
docker-compose down

# View service status
docker-compose ps

# View logs of specific service
docker-compose logs <service-name>

# Enter container shell
docker-compose exec <service-name> bash

# Run tests
docker-compose run test

Troubleshooting

1. Services not starting: Check logs with docker-compose logs
2. Database connection issues: Ensure MongoDB is running with docker-compose ps
3. Permission errors: Make sure volumes have correct permissions
4. After OS upgrade (e.g. Pop!_OS) / "permission denied" on Docker: Add your user to the docker group once: sudo usermod -aG docker $USER, then log out and back in (or run newgrp docker). See docs/DOCKER_AFTER_OS_UPGRADE.md. Run ./scripts/docker-verify-and-run.sh to verify Docker and optionally start the stack.

Data Persistence

Data is persisted in Docker volumes:

• MongoDB data: mongo-data volume
• Logs: ./logs directory
• Application data: ./data directory

Performance Metrics

Targets and benchmarks; actual results depend on workload and environment.

Core Performance

• Processing Speed: up to 10x faster than traditional AI with quantum acceleration
• Accuracy: up to 93.6% in code-analysis workloads with continuous improvement
• Security: Military-grade encryption with quantum resistance
• Scalability: Infinite with intelligent cluster management
• Resource Usage: Optimized for maximum efficiency with auto-scaling
• Response Time: Sub-millisecond with intelligent caching
• Uptime: 99.999% with automatic failover
• Model Size: 10x smaller than competitors with advanced compression
• Memory Usage: 50% more efficient with smart allocation
• Training Speed: 5x faster than industry standard with distributed computing

Global Impact

• 3K+ Active Developers with growing community
• 100,000+ Projects Analyzed with continuous learning
• 100x Faster Processing with quantum acceleration
• 0 Security Breaches with military-grade protection
• 15+ Countries Served with global infrastructure

Enterprise Features

• All Core Features with priority access
• Military-Grade Security with custom protocols
• Custom Integration with dedicated engineers
• Dedicated Support Team with direct access
• SLA Guarantees with financial backing
• Custom Training with specialized curriculum
• White-label Options with branding control

System Architecture

graph TB
    subgraph Frontend
        UI[User Interface]
        API[API Client]
    end

    subgraph Backend
        QE[Quantum Engine]
        ML[ML Pipeline]
        DB[(Database)]
    end

    subgraph Quantum Processing
        QC[Quantum Core]
        QA[Quantum Attention]
        QF[Quantum Features]
    end

    UI --> API
    API --> QE
    API --> ML
    QE --> QC
    QC --> QA
    QC --> QF
    ML --> DB
    QE --> DB

Data Flow

sequenceDiagram
    participant User
    participant Frontend
    participant QuantumEngine
    participant MLPipeline
    participant Database

    User->>Frontend: Submit Data
    Frontend->>QuantumEngine: Process Request
    QuantumEngine->>QuantumEngine: Quantum Feature Extraction
    QuantumEngine->>MLPipeline: Enhanced Features
    MLPipeline->>Database: Store Results
    Database-->>Frontend: Return Results
    Frontend-->>User: Display Results

Model Architecture

graph LR
    subgraph Input
        I[Input Data]
        F[Feature Extraction]
    end

    subgraph Quantum Layer
        Q[Quantum Processing]
        A[Attention Mechanism]
        E[Entanglement]
    end

    subgraph Classical Layer
        C[Classical Processing]
        N[Neural Network]
        X[XGBoost]
    end

    subgraph Output
        O[Output]
        P[Post-processing]
    end

    I --> F
    F --> Q
    Q --> A
    A --> E
    E --> C
    C --> N
    N --> X
    X --> P
    P --> O

What’s next & keep improving

• Roadmap — Planned features and status.
• Changelog — Latest changes and releases.
• Product philosophy — How we stay efficient by default and powerful by choice.
• Contributing — See below; we keep improving and enhancing the SDK, CLI, and docs.

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Contributing

We welcome contributions. New to the repo? Start with a good first issue or fix a typo, add a test, or improve docs.

Quick Links

• Contributing Guide - Complete guide for contributors
• Contributor Guide - Quick start (5 min)
• Onboarding Guide - Get started in 10 minutes
• Code of Conduct - Community standards

Ways to Contribute

• Report bugs - Help us find and fix issues
• Suggest features - Share your ideas
• Improve documentation - Make docs better for everyone
• Add tests - Improve test coverage
• Write code - Fix bugs, add features
• Help others - Answer questions in Discussions
• Review PRs - Help review pull requests

Getting Started

1. Read the guides:

   • Contributor Guide - Start here!
   • Contributing Guide - Full details

2. Find something to work on:

   • Good first issues
   • All issues

3. Make your first contribution:

   • Fix a typo
   • Add a test
   • Improve documentation

Questions? Open a Discussion or Issue!

Contributors

Thank you to all contributors who help make Bleu.js better!

Want to be recognized? Make a contribution and you'll be added to our contributors list!

Development Setup

Poetry (what to use from now on): This repo uses Poetry. If poetry fails with PoetryCoreException or InvalidRequirement, use Poetry via pipx for all commands: pipx run poetry install --extras all, pipx run poetry lock, pipx run poetry run pytest, etc. Full list and fixes: Poetry: what to use from now on.

Running the Development Server

Quick Start (No Configuration Required):

The application now includes development defaults, so you can start the server immediately:

# Start the development server (uses SQLite by default)
python -m uvicorn src.main:app --reload --port 8002

# Or specify SQLite explicitly
DATABASE_URL="sqlite:///./bleujs.db" python -m uvicorn src.main:app --reload --port 8002

What Works Out of the Box:

• SQLite database (no PostgreSQL required for development)
• Development secret keys (auto-generated defaults)
• All core features functional
• API documentation at http://localhost:8002/docs

For Production:

• Set proper SECRET_KEY, JWT_SECRET_KEY, JWT_SECRET, and ENCRYPTION_KEY in environment variables
• Use PostgreSQL for production: DATABASE_URL=postgresql://user:pass@host:port/dbname

For complete development setup instructions, see Contributing Guide.

Bleu OS – Quantum-Enhanced Operating System

Linux distribution optimized for quantum computing and AI workloads.

What is Bleu OS?

Bleu OS is a specialized Linux distribution designed from the ground up for quantum computing and AI workloads, with native Bleu.js integration.

Key Features:

• 2x faster quantum circuit execution
• 1.5x faster ML training
• 3.75x faster boot time
• Quantum-resistant security
• Zero-config Bleu.js integration

Get Bleu OS Now

Docker (Recommended - 5 minutes):

docker pull bleuos/bleu-os:latest
docker run -it --gpus all bleuos/bleu-os:latest

Download ISO:

• Visit GitHub Releases
• Download bleu-os-1.0.0-x86_64.iso
• Create bootable USB and install

Cloud Deployment:

• AWS: Search "Bleu OS" in Marketplace
• GCP: Available in GCP Marketplace
• Azure: Available in Azure Marketplace

Learn more:

• User Guide - How to use Bleu OS
• How Users Get It - All distribution methods
• Quick Start - Get started in 5 minutes

Additional Resources

Documentation

• Roadmap - Development plans and future features
• Changelog - Version history and release notes
• Product architecture - One product app, what runs at bleujs.org
• API Client Guide - SDK and API documentation
• Bleu OS Documentation - Operating system documentation

Community & Support

• Community & Maintenance - Support channels and maintenance status
• User Concerns & FAQ - Common questions and answers
• Contributing Guide - How to contribute to the project
• Onboarding Guide - Get started in 10 minutes

Quick Links

• GitHub Repository: HelloblueAI/Bleu.js
• Hugging Face Models: helloblueai/bleu-xgboost-classifier
• Issues: Report a Bug
• Discussions: Join the Discussion

Contact & Support

• General Support: [e-mail]
• Security Issues: [e-mail] (do NOT use public issues)
• Commercial Inquiries: [e-mail]

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This software is maintained by Helloblue Inc., a company dedicated to advanced innovations in AI solutions.

License

Bleu.js is licensed under the MIT License