GitHub CoPilot Reviews

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 use it almost like a search engine, but it goes a step beyond. A search engine shows you many results, and you have to read everything to put it together. But with CoPilot, you ask a question, and it gives you a summary of all the websites out there. So it's faster learning.

It handle complex tasks as well. It is able to generate results that I want. 

The problem arises when a bot is not well-designed, which frustrates customers.

For example, with our local Comcast cable company, if you need to repair something, you go on the app. They ask, "What's wrong?" You say, "My power cord melted. I need a new power cord." But the bot responds, "Oh, your Internet's not working?" 

So, it doesn't recognize unusual issues. Then, they make you go through troubleshooting, saying, "Your Internet looks good, but your device isn't plugged in." It's like, "Yes, I know. I want to speak to an agent." 

And it takes another ten minutes of trying different things before you finally get to an agent.

This is an example of a poorly designed bot.

I have been using this solution for about a year.

It's the self-service type. We just use it very simply.

We just use it for the same reasons I do. We do work with other providers and program virtual agents or bots. Those are all the rage now. You put them on your website. You used to have simple ones like "I can check your deposits or cancel a check for you," but they're limited to what you program. 

But if you expand a bot like Swifter Robot with artificial intelligence and natural language processing, then it can almost be like a person. Then, you can do some really cool things with it. 

I haven't encountered any issues. When I do, I just call our IT group. I haven't had any problems with it, unlike OneDrive.

I believe it's just standard with Microsoft, so you download it with their installer.

Increased productivity and improved customer experience can count as an ROI. If customers need to know something, they'll either wait five minutes for an agent or they can just go ahead and find the answer themselves. Most will choose the latter. The problem is poorly designed bots that frustrate customers. 

We have a team that decides on the best tools, like Google Digital or other products.

I would recommend it because it's part of the Microsoft package, it's easy to use, and I haven't found any of the answers to be outrageous.

Overall, I would rate it a seven out of ten because I haven't used it enough.

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 like how GitHub CoPilot analyzes individual docstrings and keeps the method context relevant. Instead of throwing random codes, GitHub CoPilot sticks to a class of more context-relevant code generation, which I like.

Speaking about the code suggestion capabilities of the product, I would say that though it is good, there is a need to develop it further. In our company, when we have more dependent classes opened, we realize that GitHub CoPilot needs to be improved. GitHub CoPilot needs to analyze the whole package and try to give more relevant codes.

If the product plans to provide more extensions, then adding Eclipse would be helpful. There are no extensions provided by the product for Eclipse.

I wish the context window gets expanded, as it would be good for the product, especially since I am currently using some custom packages and custom classes. GitHub CoPilot needs to give some generic code generation instead of analyzing my classes and giving relevant codes. Apart from the aforementioned area, there are no major challenges in the tool.

I have been using GitHub CoPilot for ten months. I am a customer of the tool.

There are no stability issues with the product.

I don't know how to comment about the scalability feature of the product since it is just used as an extension.

I have not worked with other solutions, and I believe that GitHub CoPilot has a monopoly in the market because there is no other free tier version of other products in the market variable that meets my expectations.

The product's initial setup phase is easy.

The solution can be deployed in five minutes if you have activated a GitHub CoPilot account.

I have definitely experienced an ROI from using the product. It saved my company 30 percent on costs and time.

The pricing is reasonable. I think it is 10 USD per user.

The AI integration in the product is good.

If you are new to coding and you want to save some time by not writing a course from scratch, then you can definitely use GitHub CoPilot, as it saves a lot of time in the initial stages of development but not in the more advanced production-ready stages.

I rate the tool an eight out of ten.

GitHub CoPilot can be used for prompt engineering, delivering information to create a presentation, and comparing results from other AI tools, like ChatGPT or Gemini. You can use the CoPilot app while creating presentations, but we need to subscribe and pay an additional fee to use it. I'm truly considering talking to the management team to contact Microsoft to see if we can use that feature. It's a very useful feature.

The CoPilot app that you can use while creating presentations is very useful.

It would be useful if we could use the solution to create documents, not only prompts. It would also help if we could insert documents and create results in accordance with the documents inserted.

The tool has a feature that allows you to upload a voice document or a recording, and it can export the transcription for that recording. This transcription is not available in all languages. I would like to know whether the solution plans to expand the number of languages because I want to know if there's also a plan for Hebrew transcription.

There are limitations because you have to subscribe and pay more money to use the GitHub CoPilot tool.

I have been using GitHub CoPilot for a few months.

I rate the solution an eight out of ten for stability.

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

I've tried to contact Microsoft, but I haven't received a response. I'm not impressed with the technical support.

The solution's initial setup is pretty straightforward, but I'm not as impressed as ChatGPT and Gemini.

With GitHub CoPilot, we have more opportunities and can approach more people in less time. The solution shortens the time it takes to finish a certain task.

We have a license but need another one for the GitHub CoPilot tool.

Overall, I rate GitHub CoPilot an eight out of ten.

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.

In terms of functionality, the code suggestions in GitHub Copilot are particularly helpful for project development. It provides valuable insights into errors during compilation, allowing me to ask within Copilot itself for solutions without needing to search the internet or use platforms like Stack Overflow. This capability makes problem-solving more efficient.

In terms of understanding user queries and providing code that aligns with my expectations, there is room for improvement. Sometimes it requires multiple iterations to get the code I need, like for creating a login page with specific buttons and logic. 

A UI feature for drag-and-drop functionality, along the lines of services provided by Bubble or Wix, integrated with coding prompts, would be more beneficial. This would streamline creating code snippets for common tasks, increasing user satisfaction.

I have been using GitHub Copilot for more than six to eight months.

I would rate the stability of Copilot at eight or nine out of ten. Initially, there were some setup problems, but after that, it has been stable.

GitHub Copilot's support could improve by having a community and more detailed onboarding to encourage users to adopt the solution.

Neutral

I frequently use OpenAI's paid version, which offers excellent features, such as analyzing entire zip folders and providing insights. This helps with tasks like comparing different developers' outputs based on high-level requirements, which meet my expectations well.

The initial setup was straightforward. It required activating my account and connecting it, after which everything was seamless. A restart of the IDE ensured the model detection was resolved.

The pricing for GitHub Copilot is around $10 per month. While it offers significant productivity gains, lowering the price to $5 could encourage more users. If the price were lower, it might be more worth it, even if only used a few days a month.

I compared GitHub Copilot to OpenAI, which provides great features like analyzing zip folders and ranking inputs, meeting my requirements better.

On a scale of one to ten, I rate GitHub Copilot around seven to eight. GitHub should offer more competitive pricing and could benefit from incorporating functionalities like providing code analysis for entire zip folders to improve user experience.

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. 

The platform's most valuable feature is its code suggestion capability, which leverages AI to provide context-aware suggestions. It improves the coding skills of less experienced developers and speeds up the development process by reducing the time spent debugging and rewriting code.

They could improve the product in terms of integration with other tools.  Additionally, they should offer more robust support for highly regulated environments like finance or healthcare to enhance its applicability.

I have been using GitHub Copilot for a significant period. 

The solution is stable.

The product is highly scalable, as adding more licenses can be done easily without additional infrastructure costs.

The support team is knowledgeable and responsive.

Previously, I used Appian BPM, but the focus has shifted towards more modern technologies like DevOps and AI in DevOps. We switched to GitHub Copilot for its advanced AI-driven code assistance and seamless integration into the cloud-based development environment.

The initial setup was straightforward, involving license acquisition and basic configuration.

We implemented the product in-house. 

The return on investment is substantial, with an estimated ROI of 500%.

The product has a tiered pricing model that starts with a free version for individual developers but requires a separate license fee for enterprise use.

I rate GitHub CoPilot an eight out of ten. 

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.

The most valuable features of the solution stem from the fact that it gives you suggestions when you start typing.

I have not used the product much to be able to comment on what needs improvement in it. With the passage of time, I believe that there will be improvements in its prompt response, like ChatGPT, considering that GitHub will get more data. With time, the product will mature.

The product's price is an area of concern where improvements are required. ChatGPT is free, and people would rather prefer it over GitHub CoPilot.

Considering the way GitHub CoPilot works, it can improve its connection with technologies like SQL. Right now, I can't write an AWS Glue job in GitHub CoPilot. In the future, if the tool can improve and include multiple technologies and languages, I see that they have a scope to offer better scalability.

I have been using GitHub CoPilot for less than a month. I am just a consumer of the tool.

Sometimes, when the product doesn't function properly because of some network issue, I prefer to use ChatGPT. In ChatGPT, you can ask any general question.

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

Before GitHub CoPilot, I worked on ChatGPT, so I had to give the prompt to get what I wanted. ChatGPT is also a very helpful tool.

I currently work with GitHub CoPilot and ChatGPT.

ChatGPT gives the wrong output at times, after which you need to correct it. GitHub CoPilot gives correct codes since it always works on the existing codes in GitHub. GitHub CoPilot gives a correct output.

The product's initial setup phase is easy, but sometimes, if your company has firewall issues, it gives errors.

There is a need to pay around 10 USD to be able to use the solution.

I am planning to use this AWS Bedrock and another tool from AWS for its AI-based features. I have not decided on the tool I plan to use. I want to learn and see what AWS is doing with its products.

GitHub CoPilot improved my code efficiency by helping me save time. The tool gives me ready-made codes, which I review and then implement.

Speaking about GitHub CoPilot's ability in terms of context and syntax suggestions, the tool is helpful.

The product is easy to integrate.

I recommend the product to developers who plan to use it. It is not a free tool forever, meaning it is only free for a month.

I rate the tool a six to seven out of ten.

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.

Not that much, as I need to wait until the ongoing pilot ends to have final feedback. However, it is expected to improve coding productivity and the capacity to produce quickly and more efficiently.

The most known and important features from a development point of view are the code suggestions. It enhances efficiency and productivity in development. 

The merge request features and workflow are available. We don't use them much currently. GitHub Copilot enables the production of business projects quickly and efficiently.

I cannot really answer this one as I don't have enough experience to share this kind of feedback. Someone who uses it daily might better understand these questions.

I have used it for around six months.

Very high stability. I didn't notice any issues regarding stability.

The AI model is really scalable, so I would rate it at least nine out of ten. I am optimistic about the next releases being developed and designed.

I do not have experience contacting technical support, so I cannot provide feedback on this aspect.

Positive

Before GitHub Copilot, we evaluated GitLab with its Duo feature. Developers chose Copilot because of stronger code suggestions.

The initial setup was easy, particularly within the IDE. Minimal complexity was involved.

We use GitHub Copilot internally with the client's company. It is an internal project, and as an architect, I was involved in the discussions to decide between Copilot and other solutions.

I need to wait until the pilot we are undergoing ends to get a clear understanding of the ROI and productivity improvements.

I am not really included in the cost management part, so I don't have insights into the pricing or setup costs.

The other option considered was GitLab Duo. Based on presentations and evaluations, we chose GitHub Copilot due to its stronger code suggestion capabilities.

I would initially rate it nine out of ten based on what I know and the small experience I have had. For it to be ten out of ten, it needs more advancements in the AI part, though it is already strong. Future updates are anticipated.

I'd rate the solution nine out of ten.

I am evaluating the tool to understand how useful it is for development. I'm evaluating how smart it is.

The solution makes our work easier. If we use a particular coding style, the solution can identify our coding style. The solution increases coding efficiency. It tries to judge our coding and logging style and imitates it. It automatically suggests copy-paste functions. It saves us time.

If we try to do something that involves two to three files and functions, the tool gets confused. It gives us advice that does not work. The suggestions are not well evaluated. We need to put more effort into debugging. The tool miserably fails when multiple files are involved. The tool must improve its ability to work with multiple files.

The first suggestion provided by the product must work. Whenever the product provides a suggestion, it must validate it. It will be helpful for the users. If the first suggestion is good, we can use it. If the first suggestion is wrong, the product will not solve the problem even if we ask it multiple times.

I have been using the solution for the last one and a half months.

The tool is stable. It gives us an answer, whether accurate or not.

I rate the product’s scalability a five out of ten. It does not work well in complex coding scenarios.

The initial setup is easy. I am using the latest enterprise version.

The solution is costly. It is not reasonably priced.

I have also used ChatGPT. GitHub CoPilot is built into coding. If we are working on coding, GitHub CoPilot is a better choice. ChatGPT is a better choice if we want to understand the concepts.

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