10 Must-Have AI Tools to Supercharge Your Software Development

1. examples

   1 171 8,063 7.3 Jupyter Notebook

   TensorFlow examples (by tensorflow)

   

   8. TensorFlow and PyTorch: These frameworks support AI and machine learning integrations, allowing developers to build and deploy intelligent models and workflows. TensorFlow is widely used for deep learning applications, offering pre-trained models and extensive documentation. PyTorch provides flexibility and ease of use, making it ideal for research and experimentation. Both frameworks support neural network design, training, and deployment in production environments. Download TensorFlow here and Download PyTorch here.

2. CodeRabbit

   coderabbit.ai featured

   CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.

   

3. cli

   2 79 5,058 9.7 TypeScript

   Snyk CLI scans and monitors your projects for security vulnerabilities. (by snyk)

   

   3. Snyk: Snyk automatically detects vulnerabilities and suggests fixes for code security, ensuring safer and more robust applications. It integrates with CI/CD pipelines, Git repositories, and cloud platforms to scan dependencies, container images, and infrastructure as code for vulnerabilities. Snyk also provides actionable recommendations to fix security issues, enabling teams to maintain compliance with security standards. Download Snyk here.

4. Pytorch

   3 384 88,230 10.0 Python

   Tensors and Dynamic neural networks in Python with strong GPU acceleration

   

   8. TensorFlow and PyTorch: These frameworks support AI and machine learning integrations, allowing developers to build and deploy intelligent models and workflows. TensorFlow is widely used for deep learning applications, offering pre-trained models and extensive documentation. PyTorch provides flexibility and ease of use, making it ideal for research and experimentation. Both frameworks support neural network design, training, and deployment in production environments. Download TensorFlow here and Download PyTorch here.

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