GitHub CoPilot Reviews

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.

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.

The most valuable aspects in terms of features, revolve around its code generation capabilities. Firstly, the functionality for generating test cases based on existing code and application logic has been invaluable. Secondly, the code explanation feature has emerged as another essential tool. Beyond these, the primary function of facilitating the development of new code or additions to existing code remains a key focus.

There's room for improvement to ensure that suggestions align more precisely with the context of what I'm seeking, minimizing instances of unrelated or inaccurate code suggestions. It could benefit from enhancements in several key areas. Firstly, there's a need for improved integration across different Integrated Development Environments to ensure a consistent user experience. Secondly, providing users with more granular control over when Copilot is active, preferably on a per-project basis, would enhance flexibility. Also, broadening Copilot's contextual awareness beyond open files to consider the entire project or codebase context could lead to more accurate and relevant suggestions, particularly in scenarios where opening all files is impractical. The current state lacks maturity, and reporting is not readily available out of the box. Users have to request specific reports, involving collaboration with the data team, indicating a need for more streamlined and accessible reporting features.

I experimented with the technical preview version when it was initially launched last year, taking advantage of the free access provided at that time. However, in a professional context, I have been utilizing it for real-time database work over the past three and a half months.

The stability is generally satisfactory. The tool itself operates smoothly, but potential challenges may arise, especially in larger enterprises utilizing proxies. It's crucial to adhere to the documentation on operating with proxies to avoid glitches.

The scalability is impressive. It accommodates growth seamlessly; as you acquire more licenses, the tool scales accordingly to meet the increasing demand. As of now, we have successfully onboarded and currently have approximately three hundred and fifty active users.

We have engaged with the support team for feature requests, particularly in terms of porting, and have found the response to be somewhat delayed. It's worth noting that GitHub and Microsoft teams have communicated that they are actively addressing these concerns in private.

The initial setup is straightforward.

The initial setup operates on a license-based model, providing flexibility as you can allocate licenses to a GitHub team. While the implementation itself is straightforward and requires minimal ongoing maintenance, a significant challenge has emerged in terms of documentation, especially concerning security documentation. The current lack of comprehensive documentation raises concerns, particularly for larger enterprises. When activating Copilot, you are prompted to provide essential information, such as the location of your Azure instance where suggestions will be processed. Once you input this information, the installation proceeds seamlessly.

While it's premature for a comprehensive ROI analysis, we have observed a positive trend with users expressing satisfaction.

A personal license is priced at ten dollars per month, while a professional or enterprise license costs nineteen dollars per user, and these rates are consistent for all users.

While we have explored various options within the Microsoft ecosystem, such as the solution offered, we haven't actively employed any other AI tools at the enterprise level.

It's essential to perceive it as a supplementary tool, not the primary driver. Caution should be exercised in blindly accepting suggestions to avoid unexpected challenges. The time invested in reviewing suggestions is crucial, and the tool's effectiveness depends on the user's domain knowledge. While it can be ideal for bridging domain and technical aspects for those with expertise, users with less experience may overlook potential issues, necessitating careful consideration. Overall, I would rate it seven 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.

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.

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.

We use GitHub CoPilot to have ideas on how to code things and then bring them back to context.

GitHub CoPilot has good stability and performance.

GitHub CoPilot’s integration with other solutions could be improved.

I have been using GitHub CoPilot for a long time.

I rate GitHub CoPilot ten out of ten for stability.

GitHub CoPilot is a scalable solution.

We have seen a return on investment with GitHub CoPilot.

I am using the latest version of GitHub CoPilot. GitHub CoPilot is deployed on-cloud in our organization. I would recommend GitHub CoPilot to other users.

Overall, I rate GitHub CoPilot ten out of ten.