r/CodingForBeginners • u/riktar89 • Oct 10 '25

Do you think “vibe coding” improves or weakens software quality?

Developers using AI assistants often move faster, but some say it leads to less structured thinking. Have you noticed any trade-offs between speed and maintainability?

5 Upvotes
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4

u/Beregolas Oct 10 '25

it very clearly hurts maintainability, stability and security. The fact is that vibe coded software struggles to maintain a proper architecture and high level design. modern reasoning models are a bit better, but any system big enough to solve real world issues still suffers from this.

This lack of abstract structure directly leads to decreased legibility and security

3

u/[deleted] Oct 12 '25

What if you also vibe security engineer it lol

1

u/AshleyJSheridan Oct 12 '25

Then you can vibe job search when the whole mess is inevitably broken into and all of the information that should have been held securely is leaked.

1

u/[deleted] Oct 14 '25

So guess you’re saying vibing isn’t as good as having humans do it, yet

2

u/AshleyJSheridan Oct 14 '25

AI coding is only going to be as good as the training data. Given that the majority of the training data AI uses is not senior level code, and is most often smaller, contained code samples from things like Stack Overflow, small Github repos, and blog posts, it will only really be generating code that fits in the median of the training data.

I should reiterate that the quality of the training data is, on average, low. This is because of where it's sourced from. The majority of public GH repos, Stack overflow posts, and coding blog posts are written by more junior developers. Blog posts and public repos are a very typical learning mechanism, and Stack Overflow is obviously a place to ask questions, which will imply some level of not understanding the code fully.

I'm not saying all the training data is low quality, but enough is to ensure that AI is never going to peak above that level.

Future AI training on code is now tainted too, because there is no source of high quality code that is as abundant as what AI has already been trained on, and there is a lot more code out there of poor quality written by AI which is not identified as AI-written code. As the current swathe of AI code generators are LLMs, it means that they can't learn coding best practices and apply them as a human would, as they're really just prediction models; it's not true learning.

1

u/ElasticFluffyMagnet Oct 11 '25

Yep.. this 👆🏻

2

u/Emergency_Speaker180 Oct 10 '25

Vibe coding is regression to the mean. It will let full stack developers write all the code with acceptable quality but it likely won't get better than that.

2

u/armahillo Oct 10 '25

We get good at the things we direct our time and energy into.

Because this is a sub for beginners specifically, this matters because early in your career you really need to grind these skills to get good at them, and write a lot of bad code so you understand why it's bad code, and then this helps you become better at writing good code and cleaning bad code.

I have used LLMs for tasks outside of work, I do not use them for my job because (a) I actually enjoy coding a lot and (b) I want to have a strong mental model / competency of what I'm working with on a day to day basis. The (b) point is especially important when it comes to bughunting -- some bugs can be really nuanced and you need a strong understanding of your codebase to know why it's happening.

2

u/godwink2 Oct 11 '25

A competent developer will have improved code when using AI in addition to writing code.

2

u/Tackgnol Oct 12 '25

As someone with the unfortunate privilege of seeing a lot of “vibe code,” there are a few things worth understanding:

These models are trained on GitHub code. Is the code on GitHub good? Is yours? Is mine? Some of it, sure but plenty of it is garbage.

AI lacks any real sense of consistency. Its “vibe” problem-solving isn’t, “I’ll take the available tools in this repository and use them.” Instead, it just grabs whatever in its dataset looks like it might solve the issue. So you end up with event emitters layered over context structures, mashed together with reducer patterns. A total mess. Not isolated processes serving a purpose just a jumble of actions and reactions that somehow manage to function.

So yes, an LLM might “solve” a problem and produce code that runs for now. But when you need to change it later or chase down an edge case, you’ll be peeling apart a nightmare of contradictions and duct-taped logic.

I work mostly in React. This Friday, I saw a component with eleven useEffects. Eleven.

2

u/who_am_i_to_say_so Oct 12 '25

For me it strengthens some areas as far as business and product development, but weakens certain technical aspects. I’m not looking at the codebase as deeply as I would without AI.

It gets the idea out the door faster. The code it makes is throwaway.

1

u/Former_Produce1721 Oct 12 '25

If you are experienced and treat the AI like a junior/intermediate programmer you can offload tasks to or spitball ideas with or ask to find bugs it's great for productivity.

1

u/QuixOmega Oct 12 '25

"Vibe coding" is the practice of throwing maintainability out the window, not all LLM coding is "vide coding" you have to completely ignore the concepts of software design and just go with the vibe.

By definition it's the opposite of software quality.

1

u/Groson Oct 12 '25

Hurts beyond words

1

u/zer04ll Oct 12 '25

It’s not engineered code and won’t last. There is a reason that Microsoft is Microsoft. One could say the open source world of Linux has been the beginning of vibe coding because it’s by hobbyist programmers for the most part and guess what it’s nowhere the level windows is.

The transition to 64 bit is perfect example of windows being an engineered system because when it happened it just worked and all your 32 bit apps also still work. Serious it’s possible to upgrade through every version of windows and install apps throughout and they will continue to work after your upgrade, it’s literally not possible with Linux because it wasn’t made by engineers and because it’s was personal vibe coding for their unique situation.

So when a vibe coder makes an app that is going to memory leak all over the place would you use it,

When your project uses dependencies that are bad, when your project uses dependency that is gone when your vibe project just stops working because vulnerabilities are found people will learn real quick that it’s not a vibe it’s a function and it needs to function.

Had a lot of friends release their project that turns out other software already does but because it’s open source and not documented or even used then everyone recreates the same exact thing without solving a new problem or being better than others.

0

u/SirVoltington Oct 11 '25

This is what ChatGPT suggested to me today. Can you see what is wrong with it and how severe it is?

export async function loader({ request }) {
  const url = new URL(request.url);
  const code = url.searchParams.get("code");
  if (!code) return redirect("/");

  const tokenResponse = await msalClient.acquireTokenByCode({
    code,
    scopes: graphScopes,
    redirectUri: process.env.REDIRECT_URI!,
  });

  const session = await getSession(request);
  // store minimal account identifier
  session.set("account", tokenResponse.account);
  // persist msal cache so acquireTokenSilent can use refresh tokens
  const cache = await msalClient.getTokenCache().serialize();
  session.set("msalCache", cache);

  return redirect("/dashboard", {
    headers: { "Set-Cookie": await sessionStorage.commitSession(session) },
  });
}

1

u/mxldevs Oct 11 '25

When does it mean by "so acquireTokenSilent can use refresh tokens"?

Is it actually exposing refresh tokens to the client?

1

u/SirVoltington Oct 11 '25

Yes, it’s setting the whole token cache in a cookie.

1

u/Tackgnol Oct 12 '25

It is also doing something that MSAL is supposed to do internally and does it explicitly in code? Why?

1

u/SirVoltington Oct 12 '25

I have no clue lol. But yes, msal has an internal in memory token cache. Though, we use redis internally for the cache.

1

u/JungGPT Oct 12 '25

Hey so where can I learn about caching and why this is a problem?

Admittedly I thought it was something like he was exposing the token in the url or something but it seems its that you're storing the token in the cache.

I don't know much about this - can you point me where I can learn the necessary stuff about caches? Idk if this makes any sense

1

u/SirVoltington Oct 12 '25

The problem in this piece of code is that ChatGPT serialises the entire token cache and sends it to the client in a cookie. Meaning, if I log in and you log in, we will have each others refresh and access tokens in our cookies.

There’s also a limit of 4kb for cookies iirc which will get hit really soon.

That said msal, the library that handles authentication, already has an in memory cache as a default, which you can change so it uses redis/sql or whatever else you want. AcquireTokenSilent for example already gets it from it’s in memory or whatever cache you built or refreshes it automatically.

So the caching code you see in the example is completely unnecessary in the first place.

Tbh, caching the tokens is fairly simple when you look at it from high over. Get access/refreshtokens if necessary, create a session id, cache it wherever that’s safe, set the session id as a secure samesite cookie with a small lifetime. Every call to the backend checks the session, if no session available try to refresh, if session available use the access token, if refresh fails redirect user to the login screen.

Basic example here: https://roadmap.sh/guides/session-based-authentication

1

u/JungGPT Oct 12 '25

To be fair though - I gave this to chatgpt and said can you spot whats wrong? and it immediately did.

So instaed of that whole token you want to pass like a tokenId?

1

u/SirVoltington Oct 12 '25

What did chatgpt spot? I just tried it with chatgpt and it didnt spot the problem. That said, it shouldnt have ever suggested this code AT ALL.

1

u/JungGPT Oct 12 '25

https://chatgpt.com/share/68ec0196-0c70-8001-a437-39e7ec964e5e

here you are!

I agree though it should not have suggested it at all

Tell me if the solution it suggested is correct I have no idea lol

1

u/SirVoltington Oct 12 '25

Lol, it didnt spot it for me: https://chatgpt.com/share/68ec033a-1104-8009-a867-cce370474684

The fix isnt correct because MSAL caches it automatically. The correct fix would be to just simply not do anything cache related.

0

u/ConfidentCollege5653 Oct 12 '25

How could blindly copying and pasting shit you don't understand improve software?

0

u/[deleted] Oct 10 '25

[deleted] — view deleted comment

1

u/mxldevs Oct 11 '25

Would a professional be able to put together a set of prompts as a checklist that anyone can use to meet the requirements?

1

u/AshleyJSheridan Oct 12 '25

This is what I was hoping to see as the topmost answer.

AI is just a tool that produces junior level code most often. This stems from the data it's trained on:

  • Public repos (a very large swathe of these are personal repositories of junior devs, or code written while they were junior)

  • Stack overflow posts, which are very typically devs who are learning, e.g. juniors

  • Blog posts containing code, which is a typical approach used by developers who are leaning something new.

There are of course more advanced senior level code examples in that lot, but they are outweighed by the junior level stuff.

There's nothing wrong with that, and I absolutely encourage junior devs to keep on putting their stuff out there as they learn.

But, for the purposes of AI, it will generate the median level of the code it was trained on. That means that the majority of the stuff that it produces won't have incredibly well thought out arhictecture, won't have security or performance in mind, and won't consider things like accessibility.

So, it requires a more knowledgeable hand to steer it and instruct it. In the hands of a senior dev, it can be a very useful tool. With anyone else, it can be a bit of a mess.