What I like the most is that it saves me time on repetitive tasks. With FastAPI or Django, it suggests almost complete endpoints, models, and CRUD logic, which speeds things up quite a bit.

It also helps when I integrate APIs or write SQL queries: it's not always perfect, but it gives me a quick foundation to work from. Review collected by and hosted on G2.com.

What I don't like is that sometimes it suggests things that "seem correct," but aren't entirely so. With FastAPI, for example, it has suggested poorly constructed validations or dependencies that pass at first glance, but then fail in logic or security. Review collected by and hosted on G2.com.

It automatically corrected code ,installs requirements .txt file ,run python commands parallely as well as sequentially .also run commands in a virtual env if possible.it is very beneficial for coders have tons of files made and they donnot want to dig in for errors finding.just ask to GitHub copilot chat and it will make that sorted for you.Also it helps in running the total project from scratch from installing dependencies to testing project on corner test cases. Review collected by and hosted on G2.com.

Many a times when it don't find solution it not stops ,instead it go on a infinite loop checking the same condition again and again and hence wastage of cpu cycles and resources happen.also manyatimes it do not take user responses as they are said for.it automatically starts its computation if the user is simply asking to show file . Review collected by and hosted on G2.com.

The best part is how familiar the platform feels, especially for beginners, and the free credits are a nice bonus. You can use both online and offline models on it, and there are plenty of configuration options. Overall, it has really made writing code and building solutions much easier for me. Review collected by and hosted on G2.com.

There are very few plugins and MCPs available, and the options become even more limited when I try to use tools while working on code that’s on another server. This is especially noticeable when I’m connected to that server in VS Code through an SSH tunnel. Review collected by and hosted on G2.com.

I like how easy it is to just provide a prompt and have the work done in seconds. When needed, it can take my input and make changes directly in my file, instead of me having to go back and forth copying and pasting specific pieces of code. Review collected by and hosted on G2.com.

At times your intended task may not be done correctly until you look deep into the logic and find out that what it was supposed to do it didn't do instead it found a simpler hard coded way out. Review collected by and hosted on G2.com.

What I like most about GitHub Copilot is that it helps me stay focused while coding. Instead of spending time looking up syntax or writing repetitive code, I can concentrate on solving the actual problem. It's especially useful for generating boilerplate code, suggesting functions, and helping me learn new libraries or frameworks more quickly. I always review and understand the suggestions before using them, but overall it saves time and makes development much more efficient. Review collected by and hosted on G2.com.

One thing I dislike about GitHub Copilot is that its suggestions are not always accurate or optimized, especially for complex logic or unfamiliar codebases. Sometimes it confidently generates code that looks correct but contains subtle bugs or doesn't follow the project's coding style. It can also make it tempting to accept suggestions without fully understanding them, so I think it's important to review and test everything before using it. Overall, it's a great productivity tool, but it still requires human judgment. Review collected by and hosted on G2.com.

I use GitHub Copilot on a daily base, it imporves my productivity by generating code suggestions when I work with VS code, and provide useful solutions for creating a frameworks. Saved me from repetitive coding tasks especailly recently on a work with building Data quality framework with Python. Review collected by and hosted on G2.com.

Not always provide the answers correctly and directly, need to review carefully and test the code using different data. Review collected by and hosted on G2.com.

What I love most about GitHub Copilot is how much it speeds up my daily workflow. It’s incredibly good at predicting repetitive boilerplate code and standard function structures. Instead of constantly looking up minor syntax details or typing out tedious setups, I can just write a comment or start typing a function name, and Copilot fills in the rest. It keeps me in the flow state much longer. Review collected by and hosted on G2.com.

Sometimes it generates code that looks perfectly correct at first glance but contains subtle bugs, deprecated syntax, or references to non-existent library methods. If you aren't paying close attention, it's easy to accept a suggestion that breaks the build, meaning you still have to spend time carefully reviewing and debugging its output. Review collected by and hosted on G2.com.

Contextual Autocomplete: It suggests entire blocks of code, functions, and tests by analyzing your current file and open tabs.

Boilerplate Reduction: It handles repetitive tasks like writing unit tests, regex, or standard API calls, allowing you to focus on logic.

Natural Language to Code: You can write a comment describing what you want (e.g., // function to validate email using regex), and it will generate the implementation.

Multi-language Support: It works across dozens of languages including Python, JavaScript, TypeScript, Ruby, Go, and Java.

IDE Integration: It lives directly inside popular editors like VS Code, JetBrains, and Neovim, so there is no need to switch windows. Review collected by and hosted on G2.com.

1. Inaccuracy and "Hallucinations"

Code Quality: It often suggests code that is inefficient, outdated, or uses libraries that don't actually exist (hallucinations).

Bugs: It can generate syntactically correct code that contains subtle logical errors, requiring you to spend more time debugging than if you had written it yourself.

2. Context Limitations

Large Projects: It sometimes "forgets" logic established earlier in a file or fails to understand the broader architecture of a complex project.

Proprietary Logic: It struggles with custom frameworks or internal business logic that wasn't part of its public training data.

3. Privacy and Security

Data Training: Many users are concerned about their code being sent to GitHub's servers to train future models. As of early 2026, some users have expressed frustration over "automatic opt-in" policies for data collection.

Vulnerabilities: There is a risk that the AI might suggest patterns that include known security vulnerabilities (like SQL injection) if they were prevalent in its training set. Review collected by and hosted on G2.com.

Copilot fits seamlessly into VS Code with fast, reliable suggestions that keep my flow uninterrupted, even on larger tasks. It saves time on repetitive work, making it worth the cost. Setup is quick, and getting started feels effortless with minimal learning curve.

I also like that it gives access to multiple AI models. Review collected by and hosted on G2.com.

One downside of GitHub Copilot is that it sometimes feels a bit slower as compared to Cursor, especially when working on larger or multi-file changes.

But Copilot is much cheaper (around $10/month vs $20 for Cursor), and for day-to-day coding, it still covers most needs really well. Review collected by and hosted on G2.com.

What I like best about GitHub Copilot is how it significantly speeds up development by providing real-time code suggestions and auto-completions. It helps reduce repetitive coding tasks, generates boilerplate code quickly, and even suggests entire functions based on context. This allows me to focus more on problem-solving and logic rather than syntax. I also appreciate how it adapts to my coding style over time and supports multiple languages, making it a valuable productivity booster in day-to-day development. Review collected by and hosted on G2.com.

One downside of GitHub Copilot is that its suggestions are not always accurate or optimal, especially for complex logic or edge cases, so you still need to carefully review and validate the code. It can sometimes generate outdated or inefficient patterns, which may lead to technical debt if used blindly. Another limitation is that it may not fully understand the broader context of a large application, resulting in irrelevant or incomplete suggestions. Additionally, over-reliance on it can reduce hands-on problem-solving and learning for developers, particularly for those still building strong fundamentals. Review collected by and hosted on G2.com.