What Cursor is actually doing when it rewrites your code (and why it feels uncomfortably accurate)

The first time Cursor correctly guesses what you're about to write before you've typed more than three characters, it feels helpful. The fifth time, it feels a bit invasive. By the tenth time, you're wondering if perhaps you've become predictable, or worse, if your coding patterns are so obvious that a large language model trained on GitHub's collective output has you completely figured out.

AI pair programming tools like Cursor don't just autocomplete your semicolons. They read your entire codebase, work out what you're probably trying to accomplish, and suggest whole functions before you've properly thought through the implementation yourself. It's genuinely useful. It's also occasionally unnerving in a way that's hard to articulate without sounding like you're worried about becoming obsolete.

How it actually works (the technical bit)

Cursor is essentially VS Code with an AI assistant that's had access to rather a lot of code. When you're working, it's scanning your project files, reading your recent changes, and maintaining a running understanding of what your codebase does. Every time you start typing, it's not just looking at the current line — it's considering your file structure, your import statements, your naming conventions, and probably that oddly specific utility function you wrote last Tuesday.

The underlying model (usually GPT-4 or Claude, depending on your settings) receives what's called "context" — carefully selected chunks of your code that are relevant to what you're doing right now. This context window is finite, so Cursor has to be strategic about what it includes. Your current file, obviously. Recently edited files, quite likely. That random config file you haven't touched in months, probably not.

When you trigger a completion or rewrite, Cursor sends this context along with your cursor position and any highlighted code to the AI model, which then generates suggestions based on patterns it's learned from millions of other repositories. It's not magic; it's statistical pattern matching at an enormous scale. Which doesn't make it any less effective, just slightly less mysterious.

The context problem (or why it sometimes suggests nonsense)

Here's where it gets interesting. The quality of Cursor's suggestions depends entirely on how much relevant context it can provide. If you're working in a file that's relatively self-contained, it does brilliantly. If you're trying to fix a bug that involves interactions between five different services, three database tables, and that caching layer someone implemented before you joined the company, it's essentially guessing.

The model can't actually understand your code in the way you do. It doesn't know that the authentication middleware is behaving oddly because of a deployment three weeks ago. It just knows that statistically speaking, when someone writes a function that starts like this, it usually ends like that. Sometimes that's exactly what you need. Sometimes it confidently suggests something that would technically run but would also charge your company's AWS account approximately nine thousand pounds by teatime.

You still need to read what it suggests. This should be obvious, but the speed and confidence with which Cursor produces code can create a dangerous sense that it's probably fine. It probably is fine. But "probably" is doing some heavy lifting in that sentence.

When it's uncannily good

There are moments when Cursor seems to read your mind, and those moments are genuinely useful. You're halfway through writing a test, and it suggests the exact assertions you were about to write. You start typing a new function, and it generates not just the implementation but the error handling you'd forgotten to consider. You highlight a messy block of code, ask it to refactor, and it comes back with something cleaner that you'd have eventually written yourself but in about twenty minutes instead of twenty seconds.

These moments happen because you're working in patterns. Good code tends to follow conventions — consistent naming, predictable structures, standard approaches to common problems. AI models are exceptionally good at recognising and reproducing patterns. When your codebase is well-structured and your intent is clear from context, Cursor can be remarkably helpful.

It's particularly good at the boring bits. Converting a callback-based function to async/await. Writing TypeScript interfaces based on example data. Generating repetitive CRUD operations. The stuff that needs doing but doesn't require creative problem-solving. This is arguably where it provides the most value — not replacing the thinking parts of programming, but handling the mechanical bits so you can focus on the thinking.

When it gets weird

Then there are the moments that feel slightly off. You're working on a new feature, and Cursor suggests a function name for something you haven't implemented yet but were planning to. It completes a comment describing your intent before you've finished typing it. It refactors your code in exactly the way you'd been thinking about but hadn't mentioned anywhere.

This isn't telepathy; it's pattern recognition. Your code already indicates your intentions through structure, naming, and context. If you've created a new service class with certain imports and a constructor, the methods you're likely to add are fairly predictable. But knowing this intellectually doesn't make it feel less strange when it happens.

There's also the issue of style adoption. Cursor learns from your codebase, which means it starts writing code that sounds like you. This is useful for consistency, but it also means it might replicate your bad habits. If you have a tendency to write overly clever one-liners or give variables unhelpful names, Cursor will cheerfully continue the tradition.

The actual workflow changes

Using Cursor changes how you write code, often in ways you don't immediately notice. You start writing comments first because you've learned that describing what you want often produces better suggestions than trying to write the code directly. You structure your code more conventionally because unusual patterns confuse the AI. You split large files more frequently because it helps Cursor maintain relevant context.

Some of these changes are good practices anyway. Some of them are optimising for an AI assistant rather than for human readability or maintainability. It's worth being conscious of which is which.

There's also a subtle shift in the nature of programming work. Less time typing, more time reviewing. Less starting at a blank file wondering how to begin, more evaluating whether what the AI suggested is actually what you want. It doesn't necessarily make you faster — sometimes reviewing and correcting AI-generated code takes longer than writing it yourself — but it does change the cognitive load.

What this means for actually writing software

Cursor and tools like it are genuinely useful, particularly for experienced developers who can quickly evaluate whether a suggestion is sensible. If you already know what good code looks like, having an AI generate a first draft can save time. If you're still learning, it's less clear whether having solutions handed to you helps or hinders understanding.

The uncomfortable accuracy isn't really about the AI being too good. It's about realising how much of programming is pattern-based work that can be automated. The creative parts — system design, debugging complex interactions, making architectural decisions — still require human judgment. But a surprising amount of day-to-day coding is applying known patterns to specific situations, which is exactly what large language models excel at.

This doesn't make programming easier in any fundamental sense. It shifts the difficulty from "writing code" to "knowing what code to write and whether the code that's been written is correct." Which, when you think about it, was always the hard part anyway.

Why we write about this sort of thing

We write about technology trends because it's rather central to what we do. Keeping up with how tools like Cursor are changing development workflows isn't optional curiosity — it's part of staying competent at our actual jobs. Understanding what's possible with current AI tooling informs how we build things, what we recommend to clients, and whether we're solving problems efficiently or just doing things the way we've always done them because we haven't noticed there's a better option now.

If this sort of practical analysis of emerging tech is useful to you, have a look at our other articles on current technology trends. They represent the kind of informed perspective that only comes from actually working with these tools rather than just reading press releases about them. None of which would be remotely credible if we didn't know what we were doing in the first place.

Read More Tech Trend Articles

Agentic Engineering Explained

No Internet, No Problem: The New Software That Works Perfectly Offline

The App Store Shortcut: How "Hybrid" Websites are Replacing Phone Apps

See How We Use Tech

TrailTrack - The Best Ever Outdoor Hiking Website

RiOrganise - Get Organised!

RiNet - Our Very Own Intranet