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AI Researcher – An intelligent assistant that reads, analyzes, and summarizes research papers, extracts key insights, identifies trends, and suggests related studies, helping researchers accelerate literature reviews and stay updated with cutting-edge developments.

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🤖 AI Researcher Free

Autonomous AI Research Assistant - 100% Free Edition

License: Contact Sreeram Python 3.8+ Cost: $0.00

Discover research gaps, generate hypotheses, and run experiments using only free resources!

AI Researcher Free is a complete autonomous research assistant that can analyze literature, identify research gaps, generate novel hypotheses, and run machine learning experiments - all without any costs or API keys required.

✨ Features

🆓 100% Free Operation

  • ❌ No API keys required
  • ❌ No subscription fees
  • ❌ No usage limits
  • ✅ Complete offline capability
  • ✅ Free cloud integration ready

🔬 Autonomous Research Pipeline

  • 📚 Literature Analysis: Scrape and analyze papers from arXiv
  • 🔍 Gap Identification: Automatically find underexplored research areas
  • 💡 Hypothesis Generation: Create testable research hypotheses
  • 🧪 Experiment Runner: Design and execute ML experiments
  • 📊 Trend Analysis: Identify patterns in research literature

🛠️ Multiple Interfaces

  • 🌐 Web Interface: Beautiful Streamlit and Gradio apps
  • 📓 Jupyter Notebook: Interactive research environment
  • 🖥️ Command Line: Quick start demo script
  • 📱 API Ready: Programmatic access to all features

🚀 Free Compute Options

  • 💻 Local CPU/GPU: Run on your own hardware
  • ☁️ Google Colab: Free GPU access
  • 🦙 Ollama Integration: Local LLM support
  • 🤗 Hugging Face: Free model hosting

🚀 Quick Start

1. Installation

# Clone the repository
git clone https://github.com/Sreeram5678/AI-Researcher.git
cd AI-Researcher

# Run the setup script
chmod +x setup.sh
./setup.sh

2. Quick Demo

# Activate environment
source venv/bin/activate

# Run quick start demo
python demo/quick_start.py

3. Web Interface

# Streamlit (recommended)
streamlit run demo/streamlit_app.py

# Or Gradio
python demo/gradio_app.py

4. Jupyter Notebook

jupyter notebook demo/jupyter_demo.ipynb

🎯 Example Workflow

1. Literature Analysis

from core.free_knowledge_base import FreeKnowledgeBase

# Initialize knowledge base
kb = FreeKnowledgeBase()

# Analyze papers from arXiv
result = kb.ingest_literature(
    query="attention mechanisms computer vision",
    max_papers=50
)

# Get statistics
stats = kb.get_statistics()
print(f"Analyzed {stats['total_papers']} papers")

2. Research Gap Identification

# Find research gaps
gaps = kb.find_research_gaps("machine learning")

for gap in gaps[:5]:
    print(f"Gap: {gap['concept']}")
    print(f"Confidence: {gap['confidence_score']:.2f}")

3. Hypothesis Generation

from core.template_generator import TemplateHypothesisGenerator

# Generate hypotheses
generator = TemplateHypothesisGenerator()
hypotheses = generator.generate_hypotheses(gaps, num_hypotheses=5)

for hypothesis, confidence in hypotheses:
    print(f"Hypothesis: {hypothesis}")
    print(f"Confidence: {confidence:.2f}")

4. Run Experiments

from core.experiment_runner import FreeExperimentRunner

# Run ML experiment
runner = FreeExperimentRunner()
results = runner.run_vision_experiment(
    hypothesis=hypotheses[0][0],
    experiment_config={
        'dataset': 'cifar10',
        'epochs': 10,
        'batch_size': 32
    }
)

print(f"Test Accuracy: {results['test_results']['accuracy']:.2f}%")

📊 System Architecture

graph TD
    A[Literature Analysis] --> B[Knowledge Base]
    B --> C[Gap Identification]
    C --> D[Hypothesis Generation]
    D --> E[Experiment Design]
    E --> F[Model Training]
    F --> G[Results Analysis]
    G --> H[Research Insights]
    
    I[arXiv Papers] --> A
    J[Free LLMs] --> D
    K[Free Datasets] --> E
    L[Local/Cloud GPU] --> F
Loading

🏗️ Project Structure

ai_researcher_free/
├── 📁 core/                    # Core system modules
│   ├── free_knowledge_base.py  # Literature analysis & storage
│   ├── template_generator.py   # Template-based hypothesis generation
│   ├── free_llm_generator.py   # Free LLM integration
│   ├── experiment_runner.py    # ML experiment execution
│   └── paper_analyzer.py       # Research trend analysis
├── 📁 demo/                    # User interfaces & examples
│   ├── streamlit_app.py        # Web interface (Streamlit)
│   ├── gradio_app.py           # Alternative web interface
│   ├── jupyter_demo.ipynb      # Interactive notebook
│   └── quick_start.py          # Command-line demo
├── 📁 data/                    # Data storage
├── 📁 models/                  # Downloaded models
├── 📁 results/                 # Experiment outputs
├── requirements.txt            # Dependencies
├── setup.sh                    # Installation script
└── README.md                   # This file

💡 Use Cases

🎓 Academic Research

  • Identify unexplored research directions
  • Generate novel research hypotheses
  • Analyze literature trends in your field
  • Design preliminary experiments

🏢 Industry R&D

  • Explore new product opportunities
  • Analyze competitive landscape
  • Generate innovative solutions
  • Prototype new approaches

📚 Learning & Education

  • Understand research methodologies
  • Learn about AI/ML techniques
  • Practice experiment design
  • Explore scientific literature

🚀 Startup Innovation

  • Find market gaps and opportunities
  • Generate product ideas
  • Validate technical feasibility
  • Build proof-of-concepts

🆓 Free Resources & Alternatives

Free LLM Options

  • 🦙 Ollama: Local LLM hosting (recommended)
  • 🤗 Hugging Face: Free inference API (30k requests/month)
  • 🌐 Together AI: Free tier available
  • 📝 Template-based: Always works offline

Free Compute Resources

  • ☁️ Google Colab: 12+ hours free GPU daily
  • 📊 Kaggle Kernels: 30 hours/week free GPU
  • 🎯 Paperspace: Free tier available
  • 💻 Local CPU: Works great for smaller experiments

Free Datasets

  • 🖼️ Computer Vision: CIFAR-10, MNIST, Fashion-MNIST
  • 📝 NLP: IMDB, Reuters, AG News
  • 🔊 Audio: LibriSpeech, Common Voice
  • 📊 General: UCI ML Repository, Kaggle

🛠️ Advanced Setup

Local LLM with Ollama

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Download models
ollama pull llama2:7b
ollama pull mistral:7b
ollama pull codellama:7b

# Start server
ollama serve

Google Colab Integration

# In Colab notebook
!git clone https://github.com/Sreeram5678/AI-Researcher.git
%cd AI-Researcher
!pip install -r requirements.txt

# Use free GPU
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using: {device}")

Hugging Face Setup

# Get free token from https://huggingface.co/settings/tokens
export HUGGINGFACE_API_KEY="your_free_token_here"

# Or add to .env file
echo "HUGGINGFACE_API_KEY=your_free_token_here" >> .env

📊 Performance & Benchmarks

System Requirements

Component Minimum Recommended
RAM 4GB 8GB+
Storage 2GB 10GB+
CPU 2 cores 4+ cores
GPU None (CPU works) Any CUDA GPU

Speed Benchmarks

Task CPU (8GB RAM) GPU (RTX 3090)
Literature Analysis (50 papers) ~3 minutes ~1 minute
Hypothesis Generation (5 hyp.) ~30 seconds ~10 seconds
Quick Experiment (5 epochs) ~5 minutes ~1 minute
Full Experiment (50 epochs) ~45 minutes ~8 minutes

Accuracy Results

Dataset Model Type Accuracy Parameters
CIFAR-10 Simple CNN ~65-75% ~100K
CIFAR-10 Attention CNN ~70-80% ~150K
CIFAR-10 Simple ViT ~60-70% ~200K
MNIST Simple CNN ~95-98% ~50K

🔧 Customization

Add New Domains

# Extend domain keywords in template_generator.py
domain_keywords = {
    'your_domain': {
        'concept': ['your', 'domain', 'keywords'],
        'task': ['specific', 'tasks'],
        'metric': ['evaluation', 'metrics']
    }
}

Custom Datasets

# Add dataset support in experiment_runner.py
def load_custom_dataset(self):
    # Your dataset loading logic
    return train_loader, test_loader, num_classes

New Model Architectures

# Extend model creation in experiment_runner.py
def create_custom_model(self, num_classes):
    class CustomModel(nn.Module):
        # Your model architecture
        pass
    return CustomModel(num_classes)

🤝 Contributing

We welcome contributions! Here's how to get started:

Types of Contributions

  • 🐛 Bug Reports: Found an issue? Let us know!
  • 💡 Feature Requests: Have an idea? Share it!
  • 📝 Documentation: Help improve our docs
  • 🔧 Code: Submit pull requests
  • 🧪 Testing: Help us test new features

Development Setup

# Fork the repository
git clone https://github.com/Sreeram5678/AI-Researcher.git
cd AI-Researcher

# Create development environment
python -m venv dev_env
source dev_env/bin/activate
pip install -r requirements.txt
pip install -r requirements-dev.txt

# Run tests
pytest tests/

# Run linting
flake8 core/ demo/
black core/ demo/

Contribution Guidelines

  1. 🍴 Fork the repository
  2. 🌿 Create a feature branch
  3. ✅ Add tests for new features
  4. 📝 Update documentation
  5. 🔍 Run tests and linting
  6. 📤 Submit a pull request

📚 Documentation

Detailed Guides

API Reference

Tutorials

🐛 Troubleshooting

Common Issues

Installation Problems

# Fix permission issues
sudo chmod +x setup.sh

# Update pip
pip install --upgrade pip

# Install system dependencies (Ubuntu/Debian)
sudo apt-get update
sudo apt-get install python3-dev build-essential

Memory Issues

# Reduce batch size
config['batch_size'] = 16

# Use smaller datasets
config['train_subset_size'] = 200

# Clear GPU cache
torch.cuda.empty_cache()

Network Issues

# Test arXiv access
curl -I https://arxiv.org/

# Use proxy if needed
export https_proxy=your_proxy_here

# Reduce paper count
MAX_PAPERS = 10

Missing Dependencies

# Reinstall requirements
pip install -r requirements.txt --force-reinstall

# Check Python version
python --version  # Should be 3.8+

# Update PyTorch
pip install torch torchvision --upgrade

🔒 Privacy & Security

Data Privacy

  • ✅ Local Processing: All data stays on your machine
  • ✅ No Tracking: No analytics or telemetry
  • ✅ Open Source: Full transparency
  • ✅ Offline Capable: Works without internet

Security Features

  • 🔒 No API Keys: No sensitive credentials needed
  • 🛡️ Sandboxed: Runs in isolated environment
  • 🔍 Code Review: Open source for security audits
  • 📝 Audit Trail: All operations logged

📄 License Information

📄 License: Contact Sreeram for licensing terms and permissions.

For Licensing Information:

  • 📧 Contact Sreeram at [email protected]
  • 📋 Discuss Usage Rights and permissions
  • 🤝 Commercial Use - Contact for terms
  • 📚 Academic Use - Contact for terms
  • 🔒 All Usage requires permission from Sreeram

Project Files

🙏 Acknowledgments

Inspiration & Resources

  • 🤗 Hugging Face - For democratizing AI
  • 🦙 Ollama - For local LLM hosting
  • 📚 arXiv - For open access to research papers
  • ☁️ Google Colab - For free GPU access
  • 🎓 Academic Community - For open science

Special Thanks

  • Sreeram - Project creator and maintainer
  • All contributors and testers
  • Open source AI/ML community
  • Researchers sharing their work openly
  • Educators promoting AI literacy

🤝 Contributing

We welcome contributions from the community! Whether you're fixing bugs, adding features, or improving documentation, your help makes AI Researcher better for everyone.

Quick Start

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

Contribution Guidelines

Types of Contributions

  • 🐛 Bug Reports - Help identify and fix issues
  • 💡 Feature Requests - Suggest new capabilities
  • 📚 Documentation - Improve guides and examples
  • 🔧 Code - Submit pull requests with improvements
  • 🧪 Testing - Test features and report issues

📞 Support & Community

Get Help

Stay Updated

🎯 Roadmap

Version 2.0 (Coming Soon)

  • 🤖 Multi-Agent Research: Collaborative AI researchers
  • 📊 Advanced Analytics: Deep research insights
  • 🌍 Multi-Language: Support for non-English papers
  • 🔗 API Gateway: RESTful API for integrations

Version 3.0 (Future)

  • 🧠 Neural Architecture Search: Automated model design
  • 📱 Mobile App: Research on the go
  • 🌐 Federated Learning: Collaborative research networks
  • 🎨 Visual Programming: No-code research design

🤖 AI Researcher by Sreeram

Autonomous Research Assistant - 100% Free Forever

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💰 $0.00 Cost • 📄 License Required • 🚀 Contact Sreeram

🚀 Get Started • 📚 Documentation • 💬 Community

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AI Researcher – An intelligent assistant that reads, analyzes, and summarizes research papers, extracts key insights, identifies trends, and suggests related studies, helping researchers accelerate literature reviews and stay updated with cutting-edge developments.

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