A completely custom and private Copilot that MUST have code completions that include C++

Creating a completely custom and private code completion tool similar to Copilot, specifically focusing on C++, involves several components. Below are steps to consider when developing such a tool: ### Steps to Create a Custom C++ Code Completion Tool #### 1. Define Requirements - **Language Support**: Ensure support for C++ and any specific standards (e.g., C++11, C++14, C++17, etc.). - **Integration with IDEs**: Decide which IDEs you want to support (e.g., Visual Studio, CLion, or a custom IDE). - **Privacy**: Ensure no code samples are sent to external servers. #### 2. Choose a Model - **Language Models**: You can use open-source models like GPT-Neo, GPT-J, or fine-tune a transformer model on C++ code. - **Training Data**: Use datasets containing C++ code such as repositories from GitHub, open-source projects, and public C++ codebases. #### 3. Data Preparation - **Scraping**: Collect C++ code examples. You can use GitHub’s API to scrape code from public repositories while ensuring compliance with their usage policy. - **Cleaning Data**: Remove irrelevant parts of the code, such as comments that don't contribute to code understanding, or non-standard code styles. #### 4. Model Training/Fine-tuning - **Fine-tuning**: Utilize your dataset to fine-tune the chosen language model. You can use frameworks like Hugging Face’s Transformers or PyTorch for model integration. - **Performance Optimization**: Optimize the model by reducing size and ensuring it can run efficiently in the environment you choose. #### 5. Setup Local Environment - Run the model locally to maintain privacy. Set up your machine with enough GPU/CPU power depending on the model size. - Create a REST API or similar interface to communicate between the IDE and the model. #### 6. Integrate with IDE - **IDE Plugins**: Develop plugins for the chosen IDEs. You will need to interact with the IDE’s API to provide code suggestions. - **Key Bindings**: Implement key bindings for invoking code completions. #### 7. Implement Features - **Context-Aware Suggestions**: Ensure that your tool provides suggestions based on the context of the code being written. - **Documentation Integration**: Optionally, integrate documentation lookups into the suggestions, providing users with references to function definitions, libraries, etc. #### 8. Testing - Perform thorough testing with diverse C++ codebases to ensure it provides useful and relevant completions. - Gather feedback from users to improve the model and suggestions. #### 9. Continuous Improvement - Regularly update the model with new data to keep it relevant and improve performance. - Allow users to provide feedback directly through the IDE, adjusting the model based on common usage scenarios. #### 10. Deployment - Ensure that your tool can be easily installed and used by other developers. Consider providing a GUI for configuration if necessary. ### Tools & Technologies - **Machine Learning Frameworks**: TensorFlow, PyTorch - **IDE Integration**: Language Server Protocol (LSP) for broader compatibility - **Version Control**: Git for managing your codebase - **Containerization**: Docker to package your development environment ### Additional Considerations - **Privacy and Security**: Make sure that any data processed is kept secure and private. Avoid sending any sensitive information. - **User Interface**: Consider designing a simple user interface for easier interaction with your tool. - **Documentation**: Provide a user manual that explains how to install and use the tool, including troubleshooting steps. Creating a private code completion tool is a challenging but rewarding project that can significantly enhance your programming workflow in C++.