Can someone explain what Cursor AI actually does

General
1

I keep hearing about Cursor AI as a smarter code editor, but I’m still not clear on what it really is, how it works, or why developers prefer it over tools like VS Code with extensions. Can someone break down its main features, real-world use cases, and any downsides before I invest time switching my current setup

2

Cursor is basically “VS Code, but AI is wired into everything and is allowed to touch more of your code at once.”

Here is what it does and why devs like it.

  1. What Cursor is
  • It is a fork of VS Code, so it feels the same.
  • Same UI style, same shortcuts, most extensions work.
  • The difference is the AI stuff is first class, not bolted on.
  1. How it works in practice
    Main features you will notice:

• AI chat that knows your repo

  • You select files or folders.
  • Ask “Why does this request hang in staging” or “Where is this function used”.
  • It reads the actual code and answers with context.
  • It often adds file links in the answer, you can jump straight to code.

• AI edits inside the editor

  • You highlight code.
  • Hit a shortcut, write “Refactor to remove duplication and add tests”.
  • It edits the code block for you, instead of only generating new code in a side panel.
  • You see a diff and accept or revert.

• Whole project reasoning

  • Cursor streams the relevant files to the model.
  • It keeps an index of your repo to know what files matter.
  • This helps with bigger refactors, not only small snippets.

• Autocomplete

  • Like Copilot, but tuned for multi file context.
  • Picks up your project patterns faster in many cases.
  1. Why some devs prefer it over VS Code + extensions

You can do some of this in VS Code with:

  • GitHub Copilot
  • Copilot Chat
  • Codeium
  • Various random AI extensions

The problem is friction.

With Cursor:

  • AI chat, inline edits, refactors, test generation, all share the same context system.
  • The product is engineered around AI workflows, not as an afterthought plugin.
  • Shortcuts are consistent.
  • Messages and edits share recent history and selected files.

A real example from my use:

Task: Migrate a Node API from Express to Fastify.
Flow in Cursor:

  • Select /src/api folder.
  • Ask the chat: “Create a plan for migrating these routes to Fastify, keep behavior identical.”
  • It inspects the files, lists steps.
  • Then I go file by file: select route file, run “Refactor to Fastify, keep the same validation and responses.”
  • It edits in place, I review the diff, run tests.
  • If something fails, I paste failing test output into the chat, it patches the exact spots.

Doing the same in VS Code with Copilot + Chat took more juggling:

  • Manually pasting chunks.
  • It often lost context.
  • More back and forth to get consistent changes.
  1. Pricing and limits
  • There is a free tier with usage caps per day.
  • Paid tiers give more requests, bigger context, faster responses.
  • Under the hood, it uses OpenAI and some custom models, plus its own indexing.
  1. When Cursor makes sense for you

Good fit if:

  • You spend lots of time reading legacy code.
  • You do frequent refactors across multiple files.
  • You want an “AI pair programmer” that edits your existing code, not only suggests snippets.

Less useful if:

  • You write small scripts sometimes.
  • You already like VS Code + Copilot and feel productive.
  • Company policy blocks sending code to cloud models.
  1. Quick comparison with VS Code + Copilot

VS Code + Copilot:

  • Strong inline autocomplete.
  • Chat can see some context, but often needs copy paste.
  • AI lives more in the side bar.

Cursor:

  • Whole UX orbits around AI help.
  • Better repo level operations like “find where this behavior comes from” or “update all usages of this type”.
  • Editing flow feels like AI is a smart intern sitting in your editor, not a chatbot on the side.

If you want to test it without committing, a good experiment:

  • Take a boring refactor or bugfix in a medium size repo.
  • Try to solve it in VS Code with whatever AI tools you use now.
  • Then do the same in Cursor for the next similar task.
    You will see fast if it fits your style or if it gets in your way.
3

Cursor is basically what happens if you take “AI coding helper” and make that the main product, not just a VS Code plugin.

@hoshikuzu already covered the “what it does” pretty well, so I’ll just add a different angle and push back on a couple points.

1. What it actually is (in practice)
Under the hood it’s just a VS Code fork, yeah. But the important bit is how aggressively it tries to own your workflow:

  • Every coding action can be an AI action if you want: write, refactor, comment, test, docstrings, etc.
  • It remembers context across those actions better than a random stack of extensions usually does.
  • It treats “chatting with your repo” like a first class workflow, not an “oh also we have chat” button.

Where I slightly disagree with @hoshikuzu: it’s not just about “less friction.” It’s opinionated. Cursor kind of nudges you into an AI-first style of working. Some devs love that. Some hate it.

2. How it feels different from VS Code + Copilot

The big differences for me:

  • Unified brain vs. separate plugins
    In VS Code, Copilot autocomplete, Copilot Chat, random refactor extension… each has its own idea of context. Cursor glues all of that together with one context system. Your chat, your inline edits, your selected files, recent operations, all feed the same “session brain.” That’s actually the main value, not any single feature.

  • Repo scale is default, not special
    In Copilot Chat you can point to files and say “use this,” but half the time you’re copy pasting or fiddling with commands. Cursor treats “whole repo / folder / selection” as the normal unit of work. So you start to think in “change this subsystem” instead of “fix this function.”

  • Inline edits are the center of gravity
    Copilot autocomplete is strongest at suggesting the next few lines. Cursor is more about “rewrite this 80 line block” or “update all these handlers to new API.” You highlight, you describe the goal, you get a diff. It’s subtle, but it changes how you approach tasks.

3. Why some people actually prefer it

A few non-obvious reasons:

  • It helps with reading code, not just writing it
    Most AI tools are optimized for code gen. Cursor is secretly a code reader tool. Stuff like “explain this file,” “summarize this folder,” “trace what calls this thing across the codebase” is where it really shines. Great when you’re thrown into a huge legacy repo.

  • It rewards vague instructions a bit more
    This is controversial, but Cursor tolerates hand-wavy prompts like “clean this up and make it more idiomatic” a bit better in multi file context. In VS Code extensions, you often have to babysit the prompt: “here is file A, here is file B, do exactly X.” Cursor’s design makes it a bit more forgiving.

  • Less mode switching
    With VS Code + extensions you keep mentally switching:
    “Type code” mode → “Open chat side panel” mode → “Refactor plugin” mode.
    Cursor makes those feel like variations of one thing: “tell the AI what you want, then review the diff.”

4. Where it’s not magical

Stuff that gets hyped too much:

  • It does not magically understand a million-line monolith and perfectly refactor it. Context is bigger, not infinite. You still hit context limits.
  • It still hallucinates. It will invent functions, imports, and config at times. You still review every diff.
  • It can absolutely trash your code at scale if you just accept everything blindly. The blast radius is bigger because it touches more files.

Honestly if you’re already happy with VS Code + Copilot and you like copy/paste chat flows, Cursor may feel like overkill or just “same stuff, different skin.” The real win is for people who:

  • Constantly dive into unfamiliar repos
  • Do repetitive, cross-file refactors
  • Don’t mind leaning pretty hard on AI as a collaborator

5. Quick way to see if it’s for you

Concrete test that isn’t just “play with it for 10 mins”:

Pick a boring, real task like:

  • “Update all API calls to use new client”
  • “Replace this logging library across the service”

Do it once in your current VS Code + ext setup, then in Cursor:

  • In Cursor, try: select folder → describe change → accept/reject diffs.
  • Compare how often you had to copy/paste context, restate things, or fight the tool.

If the Cursor run feels like “ok that was noticeably less juggling,” it’s probably worth switching. If it feels basically the same, you’re not really in its target pain zone and your current setup is fine.

Tl;dr: Cursor is not some mystical “smarter editor,” it’s a VS Code fork that’s explicitly designed around AI doing multi file, repo-aware work with less context juggling. Whether that’s a huge upgrade or just “meh” depends entirely on how much time you spend reading / refactoring vs banging out greenfield code.

4

Think of Cursor AI as what happens when your editor assumes “AI is part of every action,” not “AI is an extra panel.” @codecrafter and @hoshikuzu nailed the core behavior, so I’ll zoom in on where it actually changes your day-to-day, and where it really does not.

What Cursor AI changes in your mental model

You stop thinking “open file, type code” and start thinking:

  • “Describe the change, then curate the diff”
  • “Ask questions about behavior, not just APIs”
  • “Treat the repo as something the tool can navigate for you”

Instead of “I need to remember where this is used,” you lean on:
“Find everywhere this shape is used and align it to the new type” and then review what Cursor AI proposes.

That mindset shift is the real product, more than any individual feature.

Where Cursor AI is genuinely strong

Pros:

  • Onboarding to big codebases
    It is actually useful for “I just joined this project, what is going on.” Asking things like “outline the request path from this controller to the DB” across multiple files is where it shines more than a Copilot-style tool.

  • Mid-size refactors
    Renaming types, adjusting APIs, migrating from one library to another across a few dozen files is a realistic sweet spot. It can keep enough context that you don’t spend all day copy pasting into a chat box.

  • Unified history
    When you chain queries like “explain this,” “ok refactor it,” “now fix tests,” the tool remembers recent work in a single context. That continuity is a step up over juggling separate extensions.

  • Good for people who think in goals, not snippets
    If you often phrase work as “make this module more idiomatic and documented” rather than “write this function line by line,” Cursor AI feels natural.

Where the hype outpaces reality

Here I slightly disagree with the “AI intern in your editor” framing.

  • It is not great for huge, systemic changes across thousands of files in one go. You still hit context limits. You will end up doing the refactor in waves and guiding it chunk by chunk.

  • It does not remove the need for you to understand the architecture. It just speeds up reading and editing once you already have a mental model. If you expect it to “design” a system from your vague idea, you will get a mess.

  • It can make you lazy about structure. When edits are cheap, you are tempted to keep patching instead of stepping back and doing one clear redesign.

Where it differs from VS Code + AI in a more subtle way

Both @codecrafter and @hoshikuzu call out friction and unified context, which I agree with, but one more angle:

Cursor AI nudges you toward a review-first habit. Because every serious change comes back as a diff, the natural loop becomes: describe goal → get diff → review like a PR. That is healthier than raw autocomplete, which quietly sneaks complexity in line by line.

With Copilot-style tools, it is easy to accept 30 tiny suggestions and realize later you do not quite know what half of them do.

When Cursor AI is probably not worth switching to

  • If your work is mostly small scripts, one-off tools, or coding interviews, VS Code plus a good autocomplete plugin is enough. The repo-aware stuff will be underused.

  • If you are very prompt-averse and prefer explicit, manual edits, Cursor AI will feel like overhead. You will fight the tool rather than collaborate with it.

  • If your team has strict compliance rules about code leaving your environment, then cloud-backed editors in general are a nonstarter, Cursor included.

Rough pros and cons summary

Pros for Cursor AI

  • Strong multi file and repo-aware assistance
  • Good for learning, exploring, and refactoring existing code
  • Diff-based editing encourages code review habits
  • Minimal context juggling versus stacking random AI extensions

Cons for Cursor AI

  • Still bounded by model context, cannot magically handle true monolith scale in one shot
  • Requires trust in cloud AI processing, which some orgs cannot allow
  • Can encourage overreliance on generated refactors instead of deliberate design
  • Not a dramatic upgrade if your work is simple or you are already very happy with a tuned VS Code + Copilot setup

How to decide without wasting a week

Take a real, slightly annoying task like “replace this logging library” or “update this API client across the service,” then:

  • Do it once with your current setup (Copilot, Codeium, whatever)
  • Do a similar task in Cursor AI, trying its repo-aware chat and inline diffs

If the second run feels like “less copy paste, fewer context gymnastics, reviewable diffs,” then its model of working probably fits you. If it feels basically like what you have now, you are not in the pain zone Cursor AI is actually designed to solve.