I use Cursor daily - here's how I avoid the garbage parts

I’ve been a paying cursor user for 4-5 months now and feeling the same. A lot more mistakes leaking into my PRs. I feel a lot faster but there’s been a noticeable decrease in the quality of my work.

Obviously I could just better review my own code, but that’s proving easier said than done to the point where I’m considering going back to vanilla Code.

> Personally, and quietly, I have a major concern about the conflict of interest of Cursor deciding which files to add to context then charging you for the size of the context.

> As with so many products, it's cheap to start with, you become dependent on it, then one day it's not cheap and you're fucked.

If it gets too expensive, then I guess the alternative becomes using something like Continue.dev or Cline with one of the providers like Scaleway that you can rent GPUs from or that have managed inference… either that, or having a pair of L4 cards in a closet somewhere (or a fancy Mac, or anything else with a decent amount of memory).

Whereas if there are no well priced options anywhere (e.g. the upfront investment for a company to buy their own GPUs to run with Ollama or something else), then that just means that running LLM based systems nowadays is economically infeasible for many.

What do you consider "heavy"? Is it optimising an algorithm or "rewrite this whole codebase in <a different language>"?

Then only use it for the small tasks? There's one button you have to click to turn it off.

I think it's weakening junior engineers' reasoning and coding abilities as they become reliant on it without having lived for long, or at all, in the before times.

As someone old enough to have built websites in Notepad.exe it's totally reasonable that I ask my teams to turn off syntax highlighting, brace matching, and refactoring tools in VSCode. I didn't have them when I started, so they shouldn't use them today. Modern IDE features are just making them lazy.

/s

>>but it falls down with anything heavy.

If you are using LLMs to write anything more than a if(else)/for block at a time you are doing it wrong.

>>I think it's weakening junior engineers' reasoning and coding abilities as they become reliant on it without having lived for long, or at all, in the before times.

When I first started work, my employer didn't provide internet access to employees, their argument would always be how would you code if there was no internet connection, out there in the real world? , As it turns out they were not only worried about the wrong problem, but the got the whole paradigm about this new world wrong.

In short it was not worth building anything at all in a world internet doesn't exist.

>>then one day it's not cheap ...

Again you are worried about the wrong thing, your worry should not be what happens when its no longer cheaper, but when it, as a matter of fact gets cheaper. Which it will.

> This seems like a perfectly reasonable strategy until you realize you cannot do the same thing with reasoning models, if you're limiting the reasoning to just the initial prompt.

Keep in mind that what we call "reasoning" models today are the first iteration. There's no fundamental reason why you can't do what you stated. It's not done now, but it can be done.

There's nothing stoping you from running "tinking" in "chunks" of 1-2 paragraphs, doing some search, and adding more context (maybe from pre-reasoned cache) and continuing the reasoning from there.

There's also work being done on think - summarise - think - summarise - etc. And on various "RAG"-like thinking.

>Cursor has been trying to do things to reduce the costs of inference, especially through context pruning. For instance, if you "attach" files to a conversation, Cursor no longer stuffs the code from those files into the prompt. Instead, it'll run function calls to open those files and read bits and pieces of the code until the model feels it has enough information. While that seems like a perfectly reasonable strategy, it starts to fall apart when integrating reasoning models.

In general I feel like this was always the reason automatic context detection could not be good in fixed fee subscription models - providers need to constrain the context to stay profitable. I also saw that things like Claude Code happily chew through your codebase, and bank account, since they are charging by token - so they have the opposite incentive.

This is only surface-level deep. Cursor already has Quotas for their paid plans and Usage-based Pricing for their larger models, which I run into and fall over to their usage based model every month.

Imo most of their incentive on context-pruning comes not just from reducing the token amount, but from the perception that you only have to find "the right way"tm to build that context window automatically, to get to coding panacea. They just aren't there yet.

> Instead, it'll run function calls to open those files and read bits and pieces of the code until the model feels it has enough information. This seems like a perfectly reasonable strategy until you realize you cannot do the same thing with reasoning models, if you're limiting the reasoning to just the initial prompt.

There's nothing about this that conflicts with reasoning models, I'm not sure what you mean here.

Reflecting on your comment I realized that using a huge amount of GPUs is akin to an Turing machine approaching infinite speed. So I think the promise of LLMs writing code is basically saying: if we add a huge number of reading/writing heads with unbounded number of rules, we can solve decideability. Because what is the ability to generate arbitrarily complex code if not solving the halting problem? Maybe there's a more elegant or logical way to postulate this, or maybe I'm just confused or plain wrong, but it seems to me that it is impossible to generate a program that is guaranteed to terminate unless you can solve decideability. And throwing GPUs at a huge tape is just saying that the tape approaches infinite size and the Turing machine approaches infinite speed...

Or put another way, isn't the promise of software that is capable to generate any software given a natural language description in finite time basically assuming P=NP? Because unless the time can be guaranteed to be finite, throwing GPU farms and memory at this most general problem (isn't the promise of using software to generating arbitrary software the same as the promise that any possible problem can be solved in polynomial time?) is not guaranteed to solve it in finite time.

> Cursor has been trying to do things to reduce the costs of inference, especially through context pruning.

You can also use cline with gemini-2.0-flash, which supports a huge context window. Cline will send it the full context and not prune via RAG, which helps.

I had the exact same experience, pretty sure this happens in most cases, people just don’t realize it

Just get a Mac Studio with 512GB RAM and run a local model when the internet is down.

> aider [...] It kept telling me to "add files" that are in the damn directory that I opened it in.

That's intentional, and I like it. It limits the context dynamically to what is necessary (of course it makes mistakes). You can also add files with placeholders and in a number of other ways. but most of the time I let Aider decide. It has a repomap (https://aider.chat/docs/repomap.html), gradually building up knowledge and makes proposals based on this and other information it gathered also with token costs and out-of-context-window in mind.

As for manual changes: aider is opinionated regarding the role of Git in your workflow. At first glance, this repels some people and some stick to this opinion. For others, it is exactly one of the advantages, especially in combination with the shell-like nature of the tool. But the standard Git handling can still be overridden. For me personally, the default behavior becomes more and more smooth and second nature. And the whole thing is scriptable, I only begin to use the possibilities.

In general: Tools have to be learned, impatient one-shot attempts are simply not enough anymore.

> Nvidia is rich because they have insane margins on their GPUs. They cost a fraction of what they sell them for. That means that price will crash over time. So, I'm optimistic that a lot of this stuff will improve rapidly.

OTOH currently the LLM companies are probably taking a financial loss with each token. Wouldn't be surprised if the price doesn't even cover the electricity used in some cases.

Also e.g. Gemini already runs on Google's custom hardware, skipping the Nvidia tax.

> Nvidia is rich because they have insane margins on their GPUs. They cost a fraction of what they sell them for. That means that price will crash over time. So, I'm optimistic that a lot of this stuff will improve rapidly.

That still leaves us with an ungodly amount of resources used both to build the GPUs and to run them for a few years before having to replace them with even more GPUs.

Its pretty amazing to me how quickly the big tech companies pivoted from making promises to "go green" to buying as many GPUs as possible to burn through entire powerplants worth of electricity.

Try Claude Code. It figures out context by itself. I’m having a lot of success with it for a few days now, whereas I never caught on with Cursor due to the context problem.

> why forking VS Code when all this functionality could just be an extension, same as Copilot does?

Have you programmed extensions for VSCode before? While it seems like a fairy extensible system overall, the editor component in particular is very restrictive. You can add text (that's what extensions like ErrorLens and GitLens are doing), inlay hints, and on-hover popup overlays (those can only trigger on words, and not on punctuation). What Cursor does: the automatic diff-like views of AI suggestions with graphic outlines, floating buttons, and whatnot right on top of the text editing view - is not possible in vanilla VSCode.

This was originally driven by necessity of tighter control over editor performance. In its early days VSCode was competing with Atom - another extensible JS-powered editor from GitHub, and while Atom had an early lead due to larger extensions catalog VSCode ultimately won the race because they manged to maintain lower latency of their text editor component. Nowadays they still don't want to introduce extra extension points to it, because newer faster editors pop out all the time, too.

> and the gain vs Copilot is not that high

I think that's (at least part of) your answer. More friction to move back from an entirely separate app rather than disabling an extension.

By trying things and seeing what it’s good and bad at. For example, I no longer let it make data modelling decisions (both for client local data and database schemas), because it had a habit of coding itself into holes it had trouble getting back out of, eg duplicating data that it then has difficulty keeping in sync, where a better model from the start might have been a more normalised structure.

But I came to this conclusion by first letting it try to do everything and observing where it fell down.

I had that same experience with Claude Code. I tried to do a 95% "Idle Development RPG" approach to developing a music release organization software. At the beginning, I was really impressed, but with more and more complexity, it becomes increasingly incoherent, forgetting about approaches and patterns used elsewhere and reinventing the wheel, often badly.

Or the context not being large enough for all the obscure functions and files to go into the context. I am too basic to have dug deep enough, but a simple (automatic) documentation context for the entire project would certainly improve things for me.

Agreed. One useful tip is to have Cursor break up large files into smaller files. For some reason, the model doesn't do this naturally. I've had several Cursor experiments grow into 3000+ line files because it just keeps adding.

Once the codebase is reasonably structured, it's much better at picking which files it needs to read in.

This is a huge issue that was already raised on their forums and it's very surprising they didn't address it yet.

[0] https://forum.cursor.com/t/environment-secrets-and-code-secu...

This is true of every single service provider outside of fully OSS solutions, which are a teeny tiny fraction of the world's service providers.

There's always Aider with local models!

Some additional thoughts on GPT-4.5: it provides BFK-9k experience - eats e̶n̶e̶r̶g̶y̶ ̶c̶e̶l̶l̶s̶ budget ($2 per call!) like there is no tomorrow, but removes bugs with a blast.

In my experience, the gap between Claude 3.7 and GPT-4.5 is substantial. Claude 3.7 behaves like an overzealous intern on stimulants. It delivers results but often includes unwanted code changes, resulting in spaghetti code with deeply nested conditionals and redundant null checks. Although initial results might appear functional, the resulting technical debt makes subsequent modifications increasingly difficult, often leaving the codebase in disarray. GPT-4.5 behaves more like a mid-level developer, thoughtfully applying good programming patterns.

Unfortunately, the cost difference is significant. For practical purposes, I typically combine models. GPT-4.5 is generally reserved for planning, complex bug fixes, and code refinement or refactoring.

In my experience, GPT-4.5 consistently outperforms thinking models like o1. Occasionally, I'll use o3-mini or DeepSeek R1, but GPT-4.5 tends to be noticeably superior (at least, on average). Of course, effectiveness depends heavily on prompts and specific problems. GPT-4.5 often possesses direct knowledge about particular libraries (even without web searching), whereas o3-mini frequently struggles without additional context.

Don’t they all do this? Surely they’re not just doing naive text, n-gram, regex, embeddings, etc, right?