What Cursor actually is

Cursor is a fork of VS Code with an AI agent deeply integrated into the editor experience. Unlike GitHub Copilot, which primarily suggests completions as you type, Cursor can read your entire codebase, make multi-file edits, run terminal commands, and explain decisions — all from within the editor. The interaction model is closer to a pair programmer than an autocomplete tool.

The patterns that change most

Three workflows changed most noticeably after switching to Cursor for daily web development work:

Understanding unfamiliar code

Opening a file and asking "what does this component do and what does it depend on?" gets a useful answer in seconds. For codebases you have inherited or projects you come back to after months away, this is genuinely faster than reading through the code manually. The quality of the explanation scales with the clarity of the code — well-named functions produce better explanations than abstracted or compressed code.

Boilerplate generation

Scaffolding — creating a new API route that follows the same pattern as existing routes, writing a new component that matches the project's conventions, generating a type from an existing interface — is faster with Cursor than doing it manually. The model picks up patterns from the surrounding codebase rather than defaulting to generic examples.

Refactoring across files

Single-file refactoring tools in VS Code were already good. Multi-file refactoring — renaming a type and updating all the places that use it, restructuring a utility function and adjusting every caller — is where Cursor adds meaningful value. It does not always get every change right, but it reduces the mechanical part of the work.

Where it still requires you

Architecture decisions remain yours. Cursor will scaffold what you describe, but whether the structure you described is the right one for the problem is not something it evaluates critically. It is good at "how" and less useful at "whether."

Code review is not replaced. Cursor-generated code is plausible-looking code — it compiles, it follows the patterns it observed, and it does not obviously fail. That is different from correct code. The suggestions need the same level of review as code from a junior developer who knows the patterns but not the domain.

Performance and edge cases are frequently missed in generated code. Happy-path logic is handled well. Null states, rate limits, race conditions, and error handling are consistently under-specified unless you specifically ask for them.

The practical setup

A few configuration details make a meaningful difference in quality:

  • Add a .cursorrules file to your project with context about the tech stack, naming conventions, and any patterns specific to the codebase. This improves suggestion relevance significantly.
  • Use Claude Sonnet or Opus as the model for complex tasks — the speed difference versus the cheaper models is worth it for multi-file work.
  • Be explicit about constraints in prompts. "Add error handling, include a loading state, follow the existing pattern from AuthButton.tsx" produces much better output than "add a login button."

The honest comparison to Copilot

GitHub Copilot is better at inline completions as you type — it integrates more naturally into the flow of writing code without switching to a chat interface. Cursor is better at larger tasks that require understanding the broader codebase, making changes across multiple files, or explaining something complex.

Both tools are worth using. They solve slightly different problems in the same workflow.

Net assessment

Cursor makes mechanical development work faster. It does not make architecture decisions, it does not replace careful code review, and it does not know your domain. Used with that understanding, it is a significant productivity tool. Used as a replacement for thinking, it produces technically plausible but subtly wrong code at speed.