AI code assistants are the first AI tools that became genuinely indispensable for a professional skill set. They sit inside your editor, understand your codebase, and complete code the way a senior engineer who's read all your files would - not just line by line, but in full logical blocks. If you write code and aren't using one, you're at a disadvantage.
Autocomplete quality
Does it predict what you're about to write, or does it offer generic suggestions? Good assistants understand intent from context; great ones anticipate the function you're building before you've written the name.
Chat and explanation features
Can you highlight a function and ask "what does this do?" or "refactor this for readability"? The most useful code assistants are conversational, not just completion engines.
Repository indexing
Does it understand your whole codebase or just the current file? Whole-repo context is the difference between a generic suggestion and one that matches your actual architecture.
Test generation
The drudgery of writing unit tests is a perfect job for AI. Check whether the tool generates meaningful tests or just boilerplate that passes trivially.
Cursor offers deeper codebase context and a more powerful chat interface. Copilot has broader IDE support and tighter GitHub integration. Most developers who switch to Cursor don't go back, but Copilot is the safer enterprise choice for teams standardizing on GitHub.
This varies by tool. GitHub Copilot Business and Copilot Enterprise explicitly block training on your private code. Cursor uses your code for completions but offers a privacy mode. Always read the data retention policy before using any tool on proprietary codebases.
Most handle Python, JavaScript, TypeScript, Go, Java, and C++ well. Niche languages (Elixir, Nim, Zig) have spottier support. Test with your specific stack - language support quality varies significantly between tools.
