GitHub Copilot is presented as an AI pair programmer, that plugs into the user's editor. It then turns natural language prompts into code, offers multi-line function suggestions, speeds up test generation, filters out common vulnerable coding patterns, and blocks suggestions matching public code.
I have also used Anthropic's Claude code Its amazing, and I would say it is even better than GitHub Copilot. However, the only issue with claude code is its subscription price, which is very very high as compared to GitHub Copilot.
I used Cursor AI as well, along with CoPilot. Curson has its own AI editor, but Copilot works with almost every code editor. So I don't need to depend on just one editor, and I get the flexibility to choose my own editors. The billing is also good and doesn't require many …
In terms of AI and developing tasks, GitHub Copilot is the only tool I have used so far. Copilot Work, Copilot Web, Copilot Teams, Copilot Excel, Copilot Word, Copilot Outlook, Copilot Power Point are other agents of Copilot that I use daily, but are all complementary of GitHub …
ChatGPT, Perplexity, and Grok are AI tools that developers use to boost productivity. However, GitHub Copilot outperforms them due to its tight integration with Visual Studio. GitHub Copilot can analyze all the code in your workspace and provide contextual assistance within …
It has historically worked much better. However, as all of this is relatively new technology it is hard to really judge something since most of the time you are kind of using a beta version of a product. I believe things will get better over time. That said, Microsoft copilot …
It is useful that copilot integrates so well with vscode, which is a very common IDE. I used Tabnine for a little while but it was not that intuitive, and did not seem as helpful as GitHub copilot was. I have enjoyed GitHub copilot a lot, especially the ease of hitting the tab …
Copilit is fantastic at the following: 1. Solving simple, well-defined problems, such as implementing an algorithm, manipulating a data structure, or string manipulation and regex. 2. Implementing simple APIs that are mainly CRUD in nature, with moderate business logic inside them, which may involve some processing or passing the data through an algorithm. 3. Implementation of well-defined activities, such as implementing a connection to an Oracle DB using Hibernate or JDBC, or implementing boilerplate code for a backend service to listen to Kafka events. It is not that great when it comes to understanding and implementing code in a proprietary DSL. It struggles when implementing a major feature across a complex codebase. I believe developers should also adopt the trust-but-verify paradigm when expecting highly secure or regulated code from GitHub Copilot.
I feel that GitHub Copilot's overall usability is good due to its tight integration with Visual Studio and the workspace. However, developers expect greater ease of use, as there is a learning curve to realize productivity gains with the tool fully. I think there is room for improvement in GitHub Copilot's UI integration within Visual Studio.
It is useful that copilot integrates so well with vscode, which is a very common IDE. I used Tabnine for a little while but it was not that intuitive, and did not seem as helpful as GitHub copilot was. I have enjoyed GitHub copilot a lot, especially the ease of hitting the tab key and seeing quick progress in my tasks.
