- Professional Developers: Speed up coding with intelligent suggestions based on your own code context.
- Beginner Programmers: Get guided help through code completions that follow best practices.
- Enterprise Teams: Standardize coding practices and improve code quality with shared team models.
- Data Scientists & ML Engineers: Use IntelliCode in Python and Jupyter environments for smart suggestions.
- Cross-Platform Developers: Use it across languages and frameworks including C#, Python, JavaScript, and more.
How to Use IntelliCode?
- Install Visual Studio or VS Code: IntelliCode is built into these environments or available as an extension.
- Enable IntelliCode Features: Activate AI-assisted IntelliSense and suggestions.
- Start Coding: Write code as usual and watch IntelliCode recommend context-aware completions.
- Use Team Models: Train IntelliCode on your team’s codebase to personalize suggestions.
- Refine with Feedback: Accept, ignore, or adapt IntelliCode’s suggestions as needed to improve accuracy.
- Trained on High-Quality Repos: Models are trained on over 500,000 GitHub repos for accuracy.
- Personalized Team Models: Fine-tune IntelliCode to match your team’s coding style and preferences.
- Multi-Language Support: Works across popular languages like C#, Python, JavaScript, TypeScript, and more.
- Deep IDE Integration: Fully integrated into Visual Studio and VS Code for seamless workflows.
- AI-Assisted Refactoring: Offers suggestions beyond just completions, including smarter refactorings.
- Free for Individuals: IntelliCode is included at no cost for individual developers using Visual Studio.
- Context-Aware Code Suggestions: Learns from code context for smarter, accurate completions.
- Team Model Training: Customizes suggestions based on your organization’s codebase.
- Multi-Language Support: Works with C#, Python, JavaScript, Java, and more.
- Deep IDE Integration: Native support in Visual Studio and Visual Studio Code.
- Only Works in Microsoft IDEs: Limited to Visual Studio and VS Code environments.
- Team Training Setup Needed: Requires configuration and training for optimal team model usage.
- Dependent on Code Quality: Suggestions depend heavily on the training data’s quality and patterns.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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