Next-10 Survey
We need your feedback to help us shape Bleu.js

AI-Powered JavaScript Framework
Optimized for Speed
& Scalability
Built for the Future

Bleu.js is a next-level, AI-powered JavaScript framework built for speed, seamless integrations, and intelligent features that adapt to your development workflow.

import { createBleu } from 'bleujs';
const app = createBleu({  mode: 'streaming',  optimizations: 'auto'});
app.generate('Hello, Bleu.js!')  .then(result => {    console.log(result);  });
3,000+
Developers Using Bleu.js
~30ms
Ultra-Low API Latency
99.3%
High Reliability & Uptime
Learn more what Bleu.js is able to offer with our Learning materials.
Real-time AI assistance
Performance-optimized
Developer-friendly
NEWNeural UI Generation

Why Choose Bleu.js?

The next-generation JavaScript framework designed for speed, AI-powered enhancements, and developer ease.

Lightning Fast Performance

Experience unmatched speed with Bleu.js, built for efficiency and optimized rendering.

Developer-Friendly API

Bleu.js provides an intuitive and clean API, making development smoother and more enjoyable.

AI-Powered Enhancements

Leverage AI-driven optimizations to enhance performance, debugging, and code suggestions.

Smart Asset Optimization

AI-powered optimization of images, scripts, and other assets for optimal loading performance.

Real-time Performance Monitoring

Advanced monitoring tools that provide real-time insights into your application's performance and health.

Intelligent Error Handling

AI-driven error detection and recovery system that helps prevent and resolve issues before they impact users.

Intelligent AI Components

Experience the future of AI with our cutting-edge components designed to enhance your development workflow

Core AI Capabilities

Our AI components provide powerful capabilities for modern applications:

Natural Language Processing

  • Advanced text understanding and generation
  • Real-time language translation and analysis
  • Sentiment analysis and intent recognition

Code Intelligence

  • Smart code completion and suggestions
  • Automated code optimization
  • Intelligent bug detection and fixes

Performance Optimization

  • Dynamic resource allocation
  • Automated performance tuning
  • Smart caching strategies
Response Time
15ms
Average
Accuracy
99.8%
NLP Tasks
Memory Usage
45MB
Per Instance
Uptime
99.99%
SLA

Live AI Metrics

Real-time monitoring of AI components and neural network performance:

🧠
(Processing)
Model Accuracy
98.5%
Training Progress
Epoch 45/100
(Layer 3/5)
GPU Utilization
78%
Memory Usage
4.2GB
📊
(2 Training, 1 Inference)
Bleu.js v1.1.3
AI Mode Active

Implementation Guide

// Initialize AI components const ai = new BleuAI({ models: ['gpt-4', 'llama-2'], options: { gpu: true, batchSize: 32 } }); // Monitor performance ai.on('metrics', (data) => { console.log('AI Metrics:', data); });

Python Integration

Seamlessly integrate Python with Bleu.js for enhanced development capabilities and AI-powered features.

Integration

Seamlessly integrate Python with our advanced AI components and debugging tools.

# Install the pack
pip install bleujs

from bleujs_utils import ai_query

# Use AI query tools
response = ai_query("What is the weather today?")

Models

Access our rule-based AI framework with advanced debugging and code quality tools.

from bleujs_utils import some_utility_function

# Use utility functions for AI integration
result = some_utility_function(input_data)
print(result)

Implementation

Simple and intuitive API design for quick integration and code quality assurance.

# Use the CLI tool
bleujs-utils-cli --help

# Or use the Python API
from bleujs_utils.quality import analyze_code
result = analyze_code(code)

Live Integration Metrics

Real-time monitoring of Python integration with Bleu.js v1.1.3:

🔄
(Connected)
API Response Time
45ms
Memory Usage
128MB
📊
(3 active)
CPU Usage
12%
Queue Size
2
(Processing batch 3/5)
Bleu.js v1.1.3
Integration Mode

Next Steps

Run Tests

Execute the test suite to verify the functionality of the application.

pnpm test

Running the Application

Start the core engine to run the application in development mode.

cd core-engine
node src/index.mjs

Testing the API

Use these commands to test the API endpoints and verify integration.

api_curl_tests.sh

XGBoost Model Training

Bleu.js includes a robust machine learning pipeline with XGBoost integration for high-performance model training and deployment.

XGBoost Training Pipeline

Our advanced training pipeline includes comprehensive features for optimal model performance:

Data Preprocessing

  • Automated feature scaling and normalization
  • Missing value handling with advanced imputation
  • Feature selection and importance analysis

Model Training

  • Hyperparameter optimization with Optuna
  • Cross-validation with stratified sampling
  • Early stopping and model checkpointing

Performance Monitoring

  • Real-time training metrics visualization
  • Resource usage tracking and optimization
  • Model performance benchmarking
Best Accuracy
0.9450
±0.002
Best ROC-AUC
0.9869
±0.001
Best F1 Score
0.9488
±0.003
Best Precision
0.9444
±0.002

Implementation Guide

1. Environment Setup

Activate the Virtual Environment and install dependencies:

source ~/Bleu.js/bleujs-env/bin/activate
pip install -r requirements.txt

2. Model Training

Train a new XGBoost model with custom parameters:

python train_xgboost.py \ --data_path data/processed/train.csv \ --model_path models/xgboost_v1.1.3.json \ --params '{"max_depth": 6, "learning_rate": 0.1}' \ --n_trials 100

3. Model Inference

Use the trained model for predictions:

from xgboost_model import predict # Example input features input_features = [0.5, 0.3, 0.8, 1.2, 0.7, 0.9, 1.1, 0.6, 0.4, 1.0] # Get prediction with confidence score result = predict(input_features, return_confidence=True) print(f"Prediction: {result.prediction}") print(f"Confidence: {result.confidence:.2f}")

4. Deployment Options

AWS Lambda
Flask
FastAPI
Docker
Kubernetes
Azure ML

Live Training Metrics

Real-time monitoring of XGBoost model training with Bleu.js v1.1.3:

🎯
(Epoch 45/100)
Training Loss
0.0234
Validation Loss
0.0289
📊
(Top 3: 0.45, 0.32, 0.23)
Learning Rate
0.01
GPU Memory
4.2GB
(Tree 12/50)
Bleu.js v1.1.3
Training Mode

LLaMA Model Integration

Harness the power of state-of-the-art language models with Bleu.js's seamless LLaMA integration for advanced AI capabilities.

Core Capabilities

Our LLaMA integration provides powerful features for advanced language processing:

Model Features

  • 70B parameter model with optimized inference
  • Multi-turn conversation support
  • Context-aware responses

Performance Optimization

  • Quantized model variants for efficiency
  • Dynamic batching and caching
  • GPU acceleration support

Advanced Features

  • Fine-tuning capabilities
  • Custom prompt engineering
  • Multi-modal input support
Response Time
25ms
Average
Context Length
32K
Tokens
Memory Usage
16GB
GPU RAM
Accuracy
98.5%
Benchmark

Implementation Guide

1. Model Setup

Initialize the LLaMA model with Bleu.js:

import { BleuJS } from 'bleujs'; const bleu = new BleuJS({ apiKey: process.env.BLEU_API_KEY, modelConfig: { type: 'llama', version: '2-70b', parameters: { temperature: 0.7, topP: 0.95, maxTokens: 2048 } } });

2. Basic Usage

Generate text with the LLaMA model:

// Single-turn generation const response = await bleu.generate({ prompt: 'Explain quantum computing', maxTokens: 200 }); // Multi-turn conversation const chat = await bleu.chat.create(); await chat.send('What is machine learning?'); const reply = await chat.send('Can you elaborate on that?');

3. Advanced Features

Utilize advanced LLaMA capabilities:

// Fine-tuning await bleu.models.fineTune({ model: 'llama-2-70b', trainingData: trainingData, epochs: 3 }); // Custom prompt engineering const prompt = bleu.prompts.create({ template: 'Answer as a {role}: {question}', variables: { role: 'expert', question: 'How does LLaMA work?' } });

4. Deployment Options

Docker
Kubernetes
AWS SageMaker
Azure ML
GCP Vertex AI
Local GPU

Live Performance Monitoring

Real-time metrics and performance data for LLaMA model with Bleu.js v1.1.3:

🤖
(Running)
Inference Speed
25ms
Memory Usage
16GB
📊
(3 tasks)
Context Length
32K
Accuracy
98.5%
(Processing)
Bleu.js v1.1.3
Live Mode

Code Generation

Transform your ideas into production-ready code with Bleu.js's advanced AI-powered code generation capabilities.

Core Capabilities

Our code generation engine provides powerful features for modern development:

Code Synthesis

  • Full-stack application generation
  • API endpoint creation
  • Database schema design

Code Optimization

  • Performance improvements
  • Security best practices
  • Code refactoring

Advanced Features

  • Multi-language support
  • Framework integration
  • Custom templates
Generation Speed
50ms
Average
Languages
12+
Supported
Accuracy
99.2%
Benchmark
Templates
100+
Available

Implementation Guide

1. Basic Generation

Generate code from natural language descriptions:

|

2. Advanced Generation

Generate complete applications with multiple components:

|
|
|

3. Code Optimization

Optimize existing code for better performance:

(2.3s)
(-45% RAM)
(+60% faster)

4. Integration Options

REST API
CLI Tool
VS Code
Web IDE
GitHub Action
CI/CD