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Comment by huijzer | Hacker News Reader

I’ve used Cursor a lot and the conclusion doesn’t surprise me. I feel like I’m the one *forcing* the system in a certain direction and sometimes an LLM gives a small snippet of useful code. Sometimes it goes in the wrong direction and I have to abort the suggestion and force it into another direction. For me, the main benefit is having a typing assistant which can save me from typing one line here and there. Especially refactorings is where Cursor shines. Things like moving argument order around or adding/removing a parameter at function callsites is great. Saved me a ton of typing and time already. I’m way more comfortable just quickly doing a refactoring when I see one.

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Weird. I have such a different experience with Cursor.

Most changes occur with a quick back and forth about top level choices in chat.

Followed with me grabbing appropriate interfaces and files for context so Sonnet doesn't hallucinate API, and then code that I'll glance over and around half the time suggest one or more further changes.

It's been successful enough I'm currently thinking of how to adjust best practices to make things even smoother for that workflow, like better aggregating package interfaces into a single file for context, as well as some notes around encouraging more verbose commenting in a file I can provide as context as well on each generation.

Human-centric best practices aren't always the best fit, and it's finally good enough to start rethinking those for myself.

Interesting. What project are you working on? For me it's writing a library in Rust.

This is what’s needed to get the most out of these tools. You understand deeply how the tool works and so you’re able to optimize its inputs in order to get good results.

This puts you in the top echelon of developers using AI assisted coding. Most developers don’t have this deep of an understanding and so they don’t get results as good as yours.

So there’s a big question here for AI tool vendors. Is AI assisted coding a power tool for experts, or is it a tool for the “Everyman” developer that’s easy to use?

Usage data shows that the most adopted AI coding tool is still ChatGPT, followed by Copilot (even if you’d think it’s Cursor from reading HN :-))

This! I've been using Cursor regularly since late 2023. It's all about building up effective resources to tactfully inject into prompts as needed. I'll even give it sample API responses in addition to API docs. Sometimes I'll have it first distill API docs down into a more tangible implementation guide and then save that as a file in the codebase.

I think I'm just a naturally verbose person by default, and I'm starting to think that has been very helpful in me getting a lot out of my use of LLMs and various LLM tools over the past 2+ years.

I treat them like the improv actors they are and always do the up front work to create (with their assistance) the necessary broader context and grounding necessary for them to do their "improv" as accurately as possible.

I honestly don't use them with the immediate assumption I'll save time (although that happens almost all the time), I use them because they help me tame my thoughts and focus my efforts. And that in and of itself saves me time.

I'll add few things at which Cursor with Claude is better than us (at least in time/effort):

- explaining code. Enter some legacy part of your code nobody understands, LLMs aren't limited to keeping few things in memory like us. Even if the code is very obfuscated and poorly written it can understand what it does and the purpose and suggest refactors to make it understandable

- explaining and fixing bugs. Just the other day Antirez posted a bug of him debugging a Redis segfault on some C code providing context and stack trace. This might be a hit or miss at times, but more often than not it saves you hours

- writing tests. It often comes up with many more examples and edge cases than I thought of. If it doesn't, you can always ask it to.

In any case I want to stress that LLMs are only as good as your data and prompts. They lack the nuance of understanding lots of context, yet I see people talking to them like humans that understand the business, best practices and others.

That first one has always felt super crazy to me, I've figured out what lots of "arcane magic, don't touch" type of functions genuinely do since LLMs have become a thing.

Even if it's slightly wrong it's usually at least in the right ballpark so it gives you a very good starting point to work from. Almost everything is explainable now.

I can relate, I have been genuinely amazed more than once by how it could "understand" some very complex code nobody dared to touch like you mention.

Kinda reminds me of that Glados quote, haha:

"These next tests require cooperation. Consequently, they have never been solved by a human. That's where you come in. You don't know pride, you don't know fear, you don't know anything. You'll be perfect."

It takes someone with no ego, no preconceptions, and infinite patience to delve in and come back alive.

Agreed, AI has been a godsend for trying to understand snippets of perl code in our codebase that were basically unreadable before unless you were an expert.

I think the .cursorrules and .cursorignore files might be useful here.

Especially the .cursorrules file, as you can include a brief overview of the project and ground rules for suggestions, which are applied to your chat sessions and Cmd/Ctrl K inline edits.

For moving argument order and removing parameter is already doable by a mature IDE and they are more predictable than AI (Jetbrains IDEs support them well. In VSCode it may need extensions.)

But adding parameter is not well supported by IDE as it requires knowing which value to pass. This is where Cursor can shine.

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