A guide to Gen AI / LLM vibecoding for expert programmers

> I think the article could be more accurately titled "A Guide to Gen AI / LLM Vibecoding for Programmers who hate their job"

Given how “vibe coding” is all about explaining clearly the requirements and defining context, it’s for programmers who should have chosen middle management as a career.

To actual programmers that enjoy the craft, using an LLM means ruining the beautiful art of abstraction and mental visualisation of a complex piece of logic, by putting it through the very lossy process of explaining it into words. It would be a bit like a composer or a painter having to use words instead of leveraging their well-honed instinct.

> Which programmer wants that?!

Agreed.

My LLM usage has quickly become a more efficient way to solve problems that basically require copy/pasting from some documentation I have to look up where me doing it myself is more error prone.

I was recently doing a fairly complex set of data transformations and understanding what the data means remained essential. AI tends to fail spectacularly at really understanding the nuances of data (that often requires understanding the underlying business logic generating the data).

However it's very useful when I have to do a bunch of window functions, where I can describe the operation but don't want to look up the syntax. Or just writing SQL when I can explain exactly what I need it to do.

Similarly working with Pytorch involves a fair bit of what I consider pseudo-boilerplate where the code itself is quite boring, but contains just enough complexity that it can't be simply automated away. Hand rolling this stuff often leads to small errors/bugs, but LLMs can do a spectacular job of quickly generating this, provided you do know exactly what you're looking to build.

What's interesting is that this has still been a major speed boost, because looking up how to do some tedious thing you just forgot how to do can really break flow.

almost every job ive ever had has been to build stuff. frequently programming is used to build stuff. programming is not my job, building stuff is. it’s perfectly normal (and a sign of a more mature engineer, imho) to prefer building stuff to the annoying stuff that gets in the way of building stuff, especially since many languages are obnoxious to work with.

I think I enjoy programming. Vibe coding removes most of the parts that I like. It already looks like hell. I'm probably a minority, but I don't think I'm alone in this.

If someone enjoys building houses with a hammer and nail, they still can. Other like using tools.

You will always be able to write code by hand. But you will not be able to keep up with other engineers who master using AI tools. I am a software engineer, I like building things. And I don't use binary or assembly, or C or any other lower level languages for good reasons. AI is just another higher level abstraction. A tool that allows me to build things a little faster. I use it every day.

I like coding. I've been doing it for a couple decades. I disagree with the "managing scrum" metaphor. Sure, you can use LLMs that way. And there is some truth to the fact that it may feel more like writing detailed Jira tickets than actually programming at times if you are trying to have it make huge changes... BUT coding with LLMs is really just a higher level abstraction. And the good news of that is LLMs are more deterministic than they seem, which is a lot of what people are fearful of losing by giving LLMs "the reins".

One nice thing about programming is that the computer is a dumb funnel for your human brain to encoded actions. If the program doesn't work, you did something wrong, missed a semicolon etc. This still applies to LLMs. If the LLM gave you shit output, it is your fault. You gave it shit input. If the model you used was the wrong one, you can still get good results, you just have to put in more work or break the problem down more first. But it's still programming. If you treat using LLMs that way, and start to apply your normal programming approaches to your prompts, you can find a workflow that satisfies your demands.

But even if you only use LLMs to rubber duck a problem, by literally typing "The program is doing X,Y,Z but it's only supposed to do Z. I already looked at A,B,C and I have a theory that G is causing the issue but I'm not seeing anything wrong there." and just paste that in the chat you might be surprised what it can turn up. And that's a fine use case!

LLMs are broadly useful and there are certainly elements of programming that are the "shit-shoveling" parts for you, from debugging to writing tests to planning or even re-writing Jira tickets, LLMs can help at different levels and in different ways. I think prescriptive calls to "code this way with LLMs" are shortsighted. If you are determined to write each line yourself, go for it. But like refusing to learn IDE shortcuts or use a new tool or language, you are simply cutting yourself off from technological progress for short term comfort.

The best part of programming to me is that it is always changing and growing and you are always learning. Forget the "AI eating the world" nonsense and treat LLMs as just another tool in your toolkit.

That's kind of the whole point. Setting up a few MCPs etc. is really a minor thing in the grand scheme of things. Sure, I have setup context7, Sequential Thinking, etc. and my Claude.md has a lot of details in it, but that only improves the accuracy so much. My whole point is that if the mindset is "I need this to be very accurate for it to work", then you're already putting too much effort into it. I personally have not found any of that time worth it. For any PR I had to put effort into, I could have done it quicker myself. So I learned those things and they just didn't have a payoff for my work. They did really well in some things like front end development, devops, and stuff of that sort, but if it's the normal kind of problem that is my "here's my actual hard problem of the day", i.e. something where I would need to start white boarding a new algorithm and semi-prove some numerical stability result before throwing code, the LLMs just fail to ever come up with anything new enough to get a solution.

But, slamming down commands for it on only easy problems in order to get 90% of a PR done and just finishing it yourself? That can get you 6 PRs in instead of 3, where you did the hard 3 and just looked at the transcripts for another 15, and tossed all but the 3 that looked good. Using Claude like that takes about half an hour out of your day and you get a good benefit, and that is what I am pointing to as a very useful approach.

I personally don't think any amount of MCP servers will make "Claude, find me a novel algorithmic improvement in this space and code it up" work, but hey if that works for you that's great. But reviewing and checking the proofs would likely make it not worth it for what I'm doing.

The article clearly isn't directed at how expert programmers are 'supposed' to use AI, though we see at least one of those a day on the front page of HN now. The first paragraph makes it pretty obvious that the author is trying to convince people who have completely disregarded AI, for one reason or another, to give it another try in a more meaningful way. Telling the reader how the author configures his AI isn't going to convince those people, as they likely are already very aware of how these tools 'work' the problem is in the 'how to use them' department which has nothing to do with what MCP tool you're using.

Might make sense getting it to instead create a CST traversal that deletes feature flags by their id. Then you have a re-usable trustworthy tool that you can incrementally improve/verify.

If you have examples, can you provide git commit hashes that it can diff and use as a reference?

For repeating patterns I'll identify 1-3 commit hashes or PRs, reference them in a slash command, and keep the command up to date if/when edge cases occur.

Which LLM? How are you prompting it?

I've been using Cursor for the last few months and notice that for tasks like this, it helps to give examples of the code you're looking for, tell it more or less how the feature flags are implemented and also have it spit out a list of files it would modify first.

Not to be rude, but what about understanding the "transcendental nature" of LLMs allows people to build more, faster, or with better maintainability than a "hardened industry titan"?

> generic advice about how you should use the same tool and methodology (claude code, scrum) that everyone’s already using, lot of hand waving about being “senior,” This may be the most 2025 blog post ever written.

This part tracks. It's honestly rather generic.

> if anyone takes the art of software programming further using LLMs, it’s going to be young inexperienced people who grow up closely observing and learning the transcendental nature of LLMs and software, not hardened industry titans joyfully cracking their whip over an army of junior devs and LLMs.

This, I'm not sure applies either. TBH the biggest risk I'm seeing already right now is how quickly we're starting to see juniors trying to enter the job market who don't have the faintest idea how actually code. Let alone build software (but let's be honest, that's usually been the case). What decisions to make when writing something are based on factors outside just implementing the functionality: how maintainable is this? how extensible?

Giving a junior a sycophant that's reasonably competent at spitting out something functional (not necessarily functional in the sense you need it to be but apparently working) is a recipe for disaster IMO.

There will absolutely be some who "get it". But, how does that scale?

More worryingly, something I don't see discussed enough is "review fatigue". It's far more fatiguing reviewing the output of an LLM than writing the code yourself a lot of the times. Early on in your career, this might lead to the tendency to just say "eh, looks alright enough".

I totally agree. I've recently been using Cursor to help create a new react project with a lot of functionality. I realized I need to have it do more smaller steps that I get to have lots of input on rather than say "this is the big picture now go forth".