GitHub CoPilot Reviews

The product's most valuable feature is vulnerability management features.

GitHub CoPilot's alerting features need improvement.

We have been using GitHub CoPilot for 3 years.

The product has high stability.

We haven't encountered scalability issues as we are still in the process of onboarding more applications. However, it might be beneficial once we transition to the cloud.

Around 50 developers from our organization use GitHub CoPilot daily. However, most developers still use Azure DevOps.

We used Azure DevOps. We decided to switch to CoPilot for advanced security features.

The initial setup is easy. It takes a few days to complete. I rate the process a 9 out of 10.

Our in-house team of developers implemented the platform. At the moment, a team of 30 developers is maintaining it.

GitHub CoPilot has greatly enhanced coding efficiency for new projects, particularly through its advanced security features.

The platform offers a comprehensive package, including firewall capabilities. You don't need to download additional applications or purchase separate solutions for security testing and vulnerability management.

I rate it a 9 out of 10.

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.

I have used OpenAI to learn how to engage with my clients, daughters, and nephews. For example, they gave me a challenge where we discussed a topic and started asking OpenAI questions to see what responses it generated. On the professional side, I have primarily used it for improving emails, resumes, or other situations. Also, in coding, I remember using it when encountering issues implementing code for an application. I usually start by asking OpenAI before turning to Google or other platforms because it helps me identify the problem faster in specific situations.

In certain instances, OpenAI didn't respond in the expected way. It was in the Power Automate. The responses were more general and didn't address the specific point. There were significant limitations. Typically, the queries I've used it for are relatively small in scope.

I prefer OpenAI over Copilot, which is integrated with coding tools like GitHub, because Copilot seems difficult to use. Copilot's responses are different from what I need to understand the problem. OpenAI has helped me a lot. It hasn't provided direct solutions, but it aids in pinpointing the issue.

Initially, OpenAI is free, but you'll need to pay for it later.

Overall, I rate the solution a 9 out of 10.

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.

Previously, when using other AI agents, I had to provide the code without the context of its usage or ultimate goals. These agents only checked the code for logic without understanding the bigger picture of the project. However, Copilot is integrated into my environment, providing the context and the bigger picture of how the code is used throughout the project. This makes the suggestions more helpful since the agent works better with more context. Copilot's integration saves a lot of time, which previously required cumbersome processes involving Chargebee. 

With Copilot, a keyboard combination brings up a chat interface, allowing direct communication with the agent within the Code Editor. This seamless integration has made it very easy to use.

In terms of improvements for Copilot, I haven't considered much since it offers many useful interactions. It integrates well with GitHub repositories, tracks changes on PRs, and provides valuable suggestions where applicable. There is excellent support across various code editors like JetBrains, VS Code, and NeoGen. I also run many automations within GitHub. When an NPR is raised, it automatically provides suggestions, which is part of the enterprise edition without limitations. 

While I don't see immediate room for improvement, one suggestion is for Copilot to provide specific suggestions for certain lines of code instead of rewriting entire sections. If the tool could focus on specific lines where changes are suggested, it would save time and reduce server load.

I have been using it for five to six months.

I would rate the stability a solid eight. As we are in the early phase of the AI boom, there are continuous improvements and modifications. While there is significant room for improvement and AI's fundamental nature sometimes leads to diverging topics, in most cases, it does not generate irrelevant code.

Scalability depends on the plan. With an enterprise plan, there are no limitations, so scalability is not an issue. As it is entirely cloud-based, scalability is inherent to the cloud infrastructure. I would rate it a solid nine out of ten.

GitHub's forum support and community are excellent. With a large user base, it covers a wide range of questions, from simple to complex, ensuring that answers are available. This community support proves highly useful whenever I have a doubt regarding anything. Even the smallest questions receive helpful responses.

The setup is straightforward. I mostly use VS Code and sometimes JetBrains IDE. Once logged into my GitHub account in VS Code, installing the Copilot extension is simple. I search for GitHub Copilot in the extensions, add it, and then sign in. It redirects me to the browser to authenticate with the GitHub token, confirming a successful login. The process is similar in JetBrains IDE, requiring extension installation and verification through GitHub login.

They recently made Copilot free to use up to a certain limit, which is a positive change. The pricing aligns with other AI agents like Chargebee and Anthropic Cloud. The ability to switch between models, such as GPT-4 or Cloudera, enhances its intelligence.

For Copilot's competitors, several exist, but they lack Copilot's flexibility and accuracy. Since GitHub hosts a vast portion of global code, its models are trained for better suggestions. Another solution, Cursor, offers an IDE entirely AI-based, which provides more context and control over files but differs from GitHub's concept and isn't a direct competitor.

Based on my current experience, I would give Copilot a solid nine out of ten due to its automation capabilities and integration into workflows. 

It streamlined and robustly automated processes that previously took days and encountered many errors. Copilot is highly recommended for everyone. Some perceive it as a job-threatening tool, however, I see it as a time-saving and convenient aid, similar to modern technologies that replaced older methods. The tool is now free for everyone, and I urge others to take advantage of its benefits. 

I use the solution in my company for information about the user story. It also helps to know how many tests or flows we need to test.

The most valuable skill and characteristic of GitHub CoPilot is that you can find any information in seconds.

The technology offered by the product in the retail industry and banking processes has certain shortcomings, making them areas that can be improved.

I have been using GitHub CoPilot for five months.

It is a scalable solution. Scalability-wise, I rate the solution a seven out of ten.

I think the use, setup, or maybe how we can use the tool depends on how much of your tasks can be done with it. For example, if you can use the tool every day for less than an hour, you can improve your tasks and skills with the tool on a daily basis. In my case, for example, I use Postman every day so I can improve my job and my tasks every day.

I rate the technical support a nine out of ten.

Positive

I have experience with ChatGPT and Vertex AI from Google. I think they are all the same tools coming from different providers. The principal difference between all the tools is the updates. Vertex AI has information from the last year, but GitHub CoPilot may have information for the current year. It all depends on how many updates one can have on a product.

The product's initial setup phase is easy.

The solution can be deployed in a matter of minutes.

The tool saves 25 percent of the time for me.

Speaking about the tool's ability to handle complex coding tasks compared to simpler ones, I would say that you can take care of the flows in Postman and translate them using Selenium or Python, and you can add it to your automation test.

GitHub CoPilot has adapted to the specific languages and frameworks I use, and it offers great assistance. I can say this because if I have one question, I can go to GitHub CoPilot to ask my question, and I find the information I need. I think GitHub Copilot is of great assistance as it can add more information about your work, your job, your task, and your flows, and you can find better answers and better examples day by day.

I rate the tool a nine out of ten.

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.

GitHub CoPilot's stability is an area of concern where improvements are required.

I have been using GitHub CoPilot for three or four months. I have experience with the preview version of the product. Though I am not sure about it, I think that GitHub CoPilot is not yet released in the market.

Stability-wise, I rate the solution a seven out of ten.

I will not be able to comment on the scalability part of the solution since my company has not undertaken a full-fledged deployment process across our organization's users.

All the people in my company use GitHub CoPilot in one form or the other.

The product's initial setup is straightforward because it's pushed through the updates. The product's updates are allowed automatically, so the setup process is not something one needs to get involved in deeply.

The solution can deployed in less than a minute since it is done through the automatic updates provided by GitHub.

No technical people are required to take care of the deployment and maintenance of the solution since it gets automatically pushed as an update from GitHub CoPilot.

The solution's deployment part was managed with the help of one of our company's in-house teams.

GitHub CoPilot comes readily available for enterprise customers, so it's a free add-on if you already have GitHub's enterprise license.

I recommend the solution to those who plan to use it.

As it is a new product in the market, I rate the overall tool an eight to nine out of ten.

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.

Some of the suggestions provided by GitHub CoPilot are not accurate, making it an area of concern where improvements are required.

I have been using GitHub CoPilot for two years. I use the solution's latest version. I am a customer of GitHub.

Stability-wise, I rate the solution an eight out of ten.

The stability issues in GitHub CoPilot are the same as the ones in GitHub Codespaces. Even with a high-speed internet connection, the product hangs and has some glitches.

Scalability-wise, I rate the solution an eight out of ten since the product's scalability still has a lot of room for improvement.

I haven't contacted the technical support team of GitHub CoPilot since if I face any stability-related issues with the product, I stop or disable it and then enable it, after which it starts working again.

The initial setup of the product is easy.

For deployment, you just need to log in to your GitHub account through your ID, after which GitHub Codespaces automatically connect to GitHub CoPilot to your GitHub account.

The solution is deployed on the cloud.

GitHub CoPilot's deployment process did not take a long time.GitHub CoPilot is a service that is not complex. The deployment process was very fast.

The product offers a free version and a paid version. Whether to choose the product's free version or paid version depends on the size of the company where it will be used. If your company size is going to increase and many people plan to use the product, then GitHub will charge you a certain amount. The account on GitHub I use is free for a year, after which I need to start paying if I want to use the solution. In my company, for some of the users, I have seen that certain payments need to be made to GitHub CoPilot.

Before using GitHub CoPilot, you need to consider what you are working on so that you don't miss what you are trying to achieve. When you are working on professional projects or very important projects for which, it may not be a good idea to use GitHub CoPilot. You should have good knowledge of what you are doing.

I rate the overall product an eight out of ten.

We use GitHub CoPilot for syntax purposes. It helps me a lot when I forget my mistakes.

The solution's most valuable features are context awareness, multi language support, integration with popular IDs like Visual Studio Code or JetBrains, and reduced coding time.

The solution provides good code completion and code generation. We have a learning aid in the framework for learning purposes. For prototyping, the tool has prototyping ideas in the core structures, which is very helpful. The solution helps me with testing and debugging.

Multi-language support is also available in GitHub CoPilot. Visual Studio Code or JetBrains can be seamlessly integrated to work with CoPilot, which is helpful. The code quality of the solution will help me reduce my coding time when we do complex things. Overall reliance on GitHub CoPilot reduced the manual coding skills. All those things will help me a lot while using GitHub CoPilot.

The solution's accuracy and relevance could be improved. My application may not be very accurate, but that is one reason we should carefully review and modify the code. If you take the code generated by the tool bluntly, it will give you a different kind of error. The solution's dependency on AI reduces manual coding skills.

Regarding licensing and copyright issues, we should be cautious about the solution's potential to embed code snippets that are very similar to our copyrighted code. We cannot take any chances with copyright issues.

I have been using GitHub CoPilot for the last one year.

I rate the solution’s stability a seven out of ten.

I rate the solution’s scalability an eight out of ten.

The solution’s initial setup is easy.

The solution's installation may take around 20 minutes.

GitHub CoPilot is less expensive than other solutions.

GitHub CoPilot does not require much maintenance. The solution has reduced our coding time and manual work by 10% to 30%.

Overall, I rate the solution a seven out of ten.

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 can upload code snippets or class files and ask for solutions to improve them, which works well. It's a great productivity tool that saves me time searching websites. While there are alternatives like ChatGPT and other AI tools, the advantage of the tool is that it is built into the IDE.

One drawback is that the solution sometimes suggests unwanted code, especially if I accidentally press the tab. This doesn't happen often. Sometimes it seems to understand my code, but other times it doesn't. This inconsistency is confusing.

I suggest adding a feature to write code directly in the IDE from chat responses instead of copying and pasting. GitHub Copilot can generate code based on method comments in the IDE, but chat responses require manual copying. A feature flag to allow direct writing from chat to IDE would streamline the process.

The product should also reduce its price so that it can improve the community engagement. 

I have been using the product for three months. 

As for performance and usage, I've only encountered connectivity issues once or twice. It responds well about 99 percent of the time. 

Regarding stability, there have been a couple of instances where the chat plugin got stuck. However, I don't see this as a major concern. It might improve over time, but it's not a significant issue.

The tool's deployment is mostly straightforward. The plugin installation is simple. However, there's one confusing part during the login process. When you log into your GitHub Copilot account, it redirects you to a website that asks for a device ID or something similar. I can't remember exactly what it asks for, but this ID is shown in the IDE popup. It's a bit confusing and takes a few minutes to understand this workflow. It would be helpful if they added a message saying you can find the device ID in your IDE popup. Adding more instructions to that screen could improve the login flow since it redirects you to a website to log in.

The ROI decreases if questions are too long. If it automatically accessed the content or context, questions could be asked easily with a few lines of code or statements. Currently, providing all details takes a long time, reducing productivity. Since it resides in the IDE, it should ideally have access to the codebase and know more about the context where questions are asked rather than starting from scratch.

Each user needs their license, whether it's a big team or a small team.

It's a helpful tool, but you need strong programming knowledge to use it effectively. If you don't understand Java or your objectives, GitHub Copilot might give wrong answers.

It is good at what it's designed for when handling complex coding scenarios or languages. However, there are limitations. The main issue is similar to other AI agents - requiring skilful prompting to get relevant answers. If GitHub Copilot had access to the entire codebase we're working on, it could provide more relevant and productive outcomes. But I'm not sure how security and privacy aspects would be handled in that situation.

The tool tends to give atomic answers rather than addressing very complex, multi-class-spanning questions. If it had access to more context, it might be able to provide better answers for complex scenarios involving multiple classes or large code structures.

I rate the overall solution an eight out of ten. I can recommend it, given it's affordable, because there are many free alternatives. They may need to introduce a community edition or similar option to compete and remain relevant.