I usually use GitHub CoPilot for all my work since I'm a developer, and I use it majorly in my development user stories. I do a lot of enhancements of features using GitHub CoPilot. Regarding GitHub CoPilot, the one main usage of it in my daily life was when I was committed to my 100-day AI journey. Most of the Python AI prototypes that I have done were mostly completed using GitHub CoPilot, assisted by all the ideas that I had. The main important use case is to use GitHub CoPilot for all the development and the enhancements that I do on a daily basis in terms of development. Mostly the projects that I was building in my 100-day journey, including Fast APIs and Streamlit APIs, were completely built by using GitHub CoPilot. Even debugging ML models, including the Hugging Face emotions and all those things, were easily done by GitHub CoPilot. The context-aware suggestions and the speed coding done by GitHub CoPilot has been helpful for me in many tasks that I do on a regular basis. On the company side, for the major development user stories, I cover it using GitHub CoPilot, and even the Agentic mode in GitHub CoPilot has been so helpful because earlier for about one year, it was just the chat model. I was using it in IntelliJ and Visual Studio most of the time. However, once that chat model has been enhanced into Agentic mode, that's when I actually got to know the actual power of GitHub CoPilot in building things faster. Earlier it was just a chat model, so I was chatting, getting to know the context, and then after that, I was actually using that code in developing tasks. But now, since the Agentic mode has come in IntelliJ with respect to GitHub CoPilot, it's an absolute beast in terms of all the development that I do on a daily basis.
There are multiple use cases for this product because it is tied in with LLMs altogether. I have been using it for coding purposes, for reviewing code, for any Git work that we do, such as push, pull, or committing. Additionally, I use it for developing test cases, and it has been really helpful in those terms altogether. It does assist with creating unit tests, and it totally depends on your prompt and whatever you give to it. One of the biggest impacts from the company's perspective is the turnaround time. If I had to do work that could take me about two days, I can finish that within half an hour altogether. This is where it is helping us by reducing one and a half days altogether. Another half day sometimes is needed to refresh or review the code that has been generated so that it does not fall anywhere, but it does help a great deal in those terms. Learning-wise, it also helps significantly, and there are many use cases. For example, we are developing one RAG system with it, a chatbot where it reads all the test cases and gives answers in a real text-like structure. It is a great help altogether.
I use GitHub CoPilot for cybersecurity practices. I am not a developer, but I use it to build MCP tools directly in the codebase. My goal was not to learn programming but to utilize the tools effectively.
My usual use cases for GitHub CoPilot include creating simple system architectures and getting input on using AI for building solution architectures. I also use it for a lot of test documentation and automation, and we have a lot of technical documentation that needs to be done based on the code. A little bit of code debugging is being done, but not extensively. To write some simple codes, simple feature functionality code, and to debug that code is what we are using it for.
Software Engineer at a tech vendor with 1,001-5,000 employees
Real User
Top 5
Jan 7, 2025
I use GitHub Copilot for various tasks, and its usage varies depending on the case and sometimes on the task itself. Occasionally, when I'm tasked with rebasing our entire codebase, I use Copilot to summarize the purpose of the file to gain an understanding of what the entire code is meant for. Afterward, I provide suggestions on how to minimize the codes and logic so that the code is refactored and redundant code is eliminated. This saves me a lot of time. Before using Copilot six months ago, the process was cumbersome as we used Chargebee, which required copying the code and pasting it into Chargebee. However, GitHub Copilot's extension is integrated into VS Code, making the process more efficient. By simply hitting a keyboard combination, I can communicate directly with the agent within the Code Editor. This integration makes the process seamless and easy to use, as it has the code context baked into the Code Editor.
Senior Solutions Architect at a computer software company with 51-200 employees
Real User
Top 5
Nov 13, 2024
I primarily use GitHub Copilot for coding and also utilize natural language prompts to get some code suggestions. Additionally, I use it after the development team completes their work to check for any vulnerabilities, as it provides a vulnerability report.
Most of the time, it is for code suggestions. It allows improving the efficiency and productivity of development. It is used by developers within the IDE, so the development environment.
Principal Consultant ( Test automation - Performance testing) at TTC Global (TTC) at TTC at a tech services company with 51-200 employees
Real User
Top 5
Sep 12, 2024
I'm using GitHub Copilot as a plugin for IntelliJ IDE. Specifically, I'm using the Community Edition of IntelliJ, not the commercial version. However, our organization provides us with a commercial license for GitHub Copilot.
I use the platform to assist with code suggestions and improve the efficiency of our software development teams. It helps with code assistance for novice programmers by providing them with coding best practices.
If I want to find things quickly and easily, I'll use it for content, but I don't cut and paste. It's like I'm checking my ideas. If you had a Microsoft Teams environment, we could use it to create a virtual agent or something. I know the general things I can do with AI and chat engines.
I just installed the product idea in my ID extensions, as it will be handy for auto-completions and suggestions. Sometimes, I use the tool to simplify the code and for documenting the code.
I use the solution in my company since, as a data engineer, I also work with Python. For writing a code or for reviewing the code, I'm using code on it.
For example, I participate in a radio show on IT topics. I use OpenAI to prepare the script I will use in the program. So, I need some queries and questions or topics for discussion. I use OpenAI to create AI business scripts tailored to how I want to address the topics. I have used OpenAI to improve my English skills. For example, I have asked for recommendations to enhance this text.
Whenever I need to write test cases for my development code, I just give the command to Copilot, and it automatically helps me write them. Then I just need to review them. It also really helps me write the skeleton of the basic design. It won't give me a 100% solution, but it gives me a 75% solution. Then I just need to review it, make some cosmetic changes, and it's ready. Overall, where I used to spend a whole day on test-related activities, now I just need to spend two to three hours, and I can complete my job.
Technical Program Manager at a healthcare company with 10,001+ employees
Real User
Nov 7, 2023
The primary focus has been on leveraging its capabilities for increased ease of use and productivity. The main goal is to accelerate application development, allowing us to build applications more efficiently.
GitHub CoPilot helps its users summarize documents. GitHub CoPilot has an AI capability that allows it to give its users a summary of documents that it has received from them. GitHub CoPilot also provides meeting summaries and suggestions on how and when you are supposed to send reports or emails. GitHub CoPilot establishes a total baseline of its users' activities so that it can later recommend to its users how to structure their day.
Apart from being a DevOps and solution architect, I am also a data analyst. I use GitHub CoPilot when I want to do some analysis and get some exact technical details. I don't have time to always go to my browser and figure out how to implement something, so once I am at work and I have the initial setup done already, I really know what to do and how to go about it. The product is simple to work on since if I have to work on a project, I can do some analysis before developing infographics, making it a really efficient product.
GitHub CoPilot accelerates developer productivity with code generation, test case creation, and code explanation. It provides context-aware suggestions, integrates with popular IDEs, and supports multiple languages.GitHub CoPilot significantly boosts development efficiency by reducing coding and debugging time. Its user-friendly auto-complete and variable detection features streamline complex tasks, serving as a learning tool for developers. Areas needing improvement include its accuracy,...
I usually use GitHub CoPilot for all my work since I'm a developer, and I use it majorly in my development user stories. I do a lot of enhancements of features using GitHub CoPilot. Regarding GitHub CoPilot, the one main usage of it in my daily life was when I was committed to my 100-day AI journey. Most of the Python AI prototypes that I have done were mostly completed using GitHub CoPilot, assisted by all the ideas that I had. The main important use case is to use GitHub CoPilot for all the development and the enhancements that I do on a daily basis in terms of development. Mostly the projects that I was building in my 100-day journey, including Fast APIs and Streamlit APIs, were completely built by using GitHub CoPilot. Even debugging ML models, including the Hugging Face emotions and all those things, were easily done by GitHub CoPilot. The context-aware suggestions and the speed coding done by GitHub CoPilot has been helpful for me in many tasks that I do on a regular basis. On the company side, for the major development user stories, I cover it using GitHub CoPilot, and even the Agentic mode in GitHub CoPilot has been so helpful because earlier for about one year, it was just the chat model. I was using it in IntelliJ and Visual Studio most of the time. However, once that chat model has been enhanced into Agentic mode, that's when I actually got to know the actual power of GitHub CoPilot in building things faster. Earlier it was just a chat model, so I was chatting, getting to know the context, and then after that, I was actually using that code in developing tasks. But now, since the Agentic mode has come in IntelliJ with respect to GitHub CoPilot, it's an absolute beast in terms of all the development that I do on a daily basis.
There are multiple use cases for this product because it is tied in with LLMs altogether. I have been using it for coding purposes, for reviewing code, for any Git work that we do, such as push, pull, or committing. Additionally, I use it for developing test cases, and it has been really helpful in those terms altogether. It does assist with creating unit tests, and it totally depends on your prompt and whatever you give to it. One of the biggest impacts from the company's perspective is the turnaround time. If I had to do work that could take me about two days, I can finish that within half an hour altogether. This is where it is helping us by reducing one and a half days altogether. Another half day sometimes is needed to refresh or review the code that has been generated so that it does not fall anywhere, but it does help a great deal in those terms. Learning-wise, it also helps significantly, and there are many use cases. For example, we are developing one RAG system with it, a chatbot where it reads all the test cases and gives answers in a real text-like structure. It is a great help altogether.
I use GitHub CoPilot for cybersecurity practices. I am not a developer, but I use it to build MCP tools directly in the codebase. My goal was not to learn programming but to utilize the tools effectively.
My usual use cases for GitHub CoPilot include creating simple system architectures and getting input on using AI for building solution architectures. I also use it for a lot of test documentation and automation, and we have a lot of technical documentation that needs to be done based on the code. A little bit of code debugging is being done, but not extensively. To write some simple codes, simple feature functionality code, and to debug that code is what we are using it for.
I use GitHub Copilot for various tasks, and its usage varies depending on the case and sometimes on the task itself. Occasionally, when I'm tasked with rebasing our entire codebase, I use Copilot to summarize the purpose of the file to gain an understanding of what the entire code is meant for. Afterward, I provide suggestions on how to minimize the codes and logic so that the code is refactored and redundant code is eliminated. This saves me a lot of time. Before using Copilot six months ago, the process was cumbersome as we used Chargebee, which required copying the code and pasting it into Chargebee. However, GitHub Copilot's extension is integrated into VS Code, making the process more efficient. By simply hitting a keyboard combination, I can communicate directly with the agent within the Code Editor. This integration makes the process seamless and easy to use, as it has the code context baked into the Code Editor.
I primarily use GitHub Copilot for coding and also utilize natural language prompts to get some code suggestions. Additionally, I use it after the development team completes their work to check for any vulnerabilities, as it provides a vulnerability report.
Most of the time, it is for code suggestions. It allows improving the efficiency and productivity of development. It is used by developers within the IDE, so the development environment.
I am maintaining my code as a code repository and using my student account with GitHub CoPilot.
I'm using GitHub Copilot as a plugin for IntelliJ IDE. Specifically, I'm using the Community Edition of IntelliJ, not the commercial version. However, our organization provides us with a commercial license for GitHub Copilot.
I use the platform to assist with code suggestions and improve the efficiency of our software development teams. It helps with code assistance for novice programmers by providing them with coding best practices.
If I want to find things quickly and easily, I'll use it for content, but I don't cut and paste. It's like I'm checking my ideas. If you had a Microsoft Teams environment, we could use it to create a virtual agent or something. I know the general things I can do with AI and chat engines.
I just installed the product idea in my ID extensions, as it will be handy for auto-completions and suggestions. Sometimes, I use the tool to simplify the code and for documenting the code.
We use the tool for code snippets that we integrate into our solutions.
I use the solution in my company since, as a data engineer, I also work with Python. For writing a code or for reviewing the code, I'm using code on it.
For example, I participate in a radio show on IT topics. I use OpenAI to prepare the script I will use in the program. So, I need some queries and questions or topics for discussion. I use OpenAI to create AI business scripts tailored to how I want to address the topics. I have used OpenAI to improve my English skills. For example, I have asked for recommendations to enhance this text.
Whenever I need to write test cases for my development code, I just give the command to Copilot, and it automatically helps me write them. Then I just need to review them. It also really helps me write the skeleton of the basic design. It won't give me a 100% solution, but it gives me a 75% solution. Then I just need to review it, make some cosmetic changes, and it's ready. Overall, where I used to spend a whole day on test-related activities, now I just need to spend two to three hours, and I can complete my job.
The primary focus has been on leveraging its capabilities for increased ease of use and productivity. The main goal is to accelerate application development, allowing us to build applications more efficiently.
GitHub CoPilot helps its users summarize documents. GitHub CoPilot has an AI capability that allows it to give its users a summary of documents that it has received from them. GitHub CoPilot also provides meeting summaries and suggestions on how and when you are supposed to send reports or emails. GitHub CoPilot establishes a total baseline of its users' activities so that it can later recommend to its users how to structure their day.
Apart from being a DevOps and solution architect, I am also a data analyst. I use GitHub CoPilot when I want to do some analysis and get some exact technical details. I don't have time to always go to my browser and figure out how to implement something, so once I am at work and I have the initial setup done already, I really know what to do and how to go about it. The product is simple to work on since if I have to work on a project, I can do some analysis before developing infographics, making it a really efficient product.