Introduction Github code pilot and how to run code pilot locally

Overview: What is GitHub Copilot?

GitHub Copilot is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI. It acts as an intelligent assistant for developers, providing real-time code suggestions and completions directly within your Integrated Development Environment (IDE). By leveraging machine learning models trained on a vast amount of public code, Copilot can help you write code faster, reduce errors, and even suggest entire functions based on natural language comments.

Why We Need to Use Copilot

Copilot offers several features that can significantly enhance your coding experience:

Generate Code

Copilot can auto-generate code snippets, reducing the time spent on writing boilerplate code and helping you focus on more complex tasks.

Enhance Productivity

By automating repetitive coding tasks, Copilot allows you to focus on more creative aspects of development, leading to a more streamlined workflow.

Test Code by Comment

You can write comments describing the functionality you need, and Copilot will generate the corresponding code. This is particularly useful for quickly prototyping and testing new ideas.

Debug and Learn

Copilot provides clear and concise code suggestions that can help you debug your code and learn new programming techniques.

Tips for Using GitHub Copilot Effectively

To maximize the benefits of GitHub Copilot, consider these tips:

Experiment with your prompts

Providing clear and descriptive comments helps Copilot understand the context better and generate more accurate code suggestions. For example, instead of writing "sort list," you could write "sort a list of integers in ascending order."

Keep a couple of relevant tabs open

GitHub Copilot uses a technique called neighboring tabs that allows the AI pair programmer to contextualize your code by processing all of the files open in your IDE instead of just the single file you’re working on

Provide specific details

When you provide specific details, GitHub Copilot will be able to generate more accurate code suggestions. For example, if you want GitHub Copilot to retrieve data from an API, you need to tell it what type of data you want to retrieve, how to process the data

Reducing Costs by Setting Up Copilot Locally

While GitHub Copilot is a powerful tool, extensive use can lead to significant costs. To manage these costs, consider setting up Copilot locally.

What is Ollama? 

Ollama ( is a local setup solution that allows you to run machine learning models on your own hardware. This reduces dependency on cloud services and helps manage costs effectively. By using local resources, you can achieve similar functionality without the recurring expenses associated with cloud-based AI tools.

How Ollama Works with Open Models

Ollama integrates seamlessly with open-source machine learning models, allowing them to run locally on your hardware. This setup leverages your computational resources, enabling efficient code generation and other AI functionalities without relying on external cloud services.

Using Ollama with VS Code and the Continue Extension

To use Ollama with Visual Studio Code (VS Code), you can integrate it with the Continue extension, which facilitates AI-powered code completions in your local development environment.

Demo using copilot locally with Ollama and Startercoder model


GitHub Copilot is a transformative tool for developers, offering intelligent code suggestions, automated testing capabilities, and significant productivity enhancements. By following best practices for prompt writing and utilizing local setups like Ollama, developers can maximize Copilot's benefits while managing costs effectively. Integrating Ollama with VS Code through the Continue extension provides a robust, cost-efficient solution for AI-powered code generation in a local environment. For these reasons, incorporating tools like Ollama is highly recommended for developers looking to optimize their coding workflows.

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