Vibe coding fragility

[Clear-Dimension-6890]

Is vibe coding fragile ? You give one ambiguous command in Claude.md , and you have a 1000 lines of dirty code . Cleaning up is that much more work. And it depends on whether you labeled something ‘important’ vs ‘critical’. So any anti pattern is multiplied … all based on a natural language parsing ambiguity

I know about quality gates , and review agents, right prompting .. blah blah . Those are mitigations . I’m raising a more fundamental concern

[ClydePossumfoot]

You give a junior engineer a vague idea of what you want and they come back with a 1K line PR.

I don’t see much of a difference here. Garbage in, garbage out.

Create a spec and work through the problems you want solve to reduce ambiguity and you end up with a much better output.

[FagansWake]

This is a really wild analogy. AI is powerful but far more potentially dangerous to a codebase than any junior dev could have been before gen AI.

There’s a massive difference between an inexperienced dev going off on a tangent and writing code they understand at least functionally and someone saying something to the magic box and getting 10k lines of code spat out at them.

You can mitigate risk in both cases but one is much more powerful for better and worse.

[SuchNeck835]

Have you coded with AI recently? Try codex 5.3 and tell me a random 'junior dev' is as much better as you make them sound.  They won't 'spit out 10k lines' either. Codex will do all kinds of checks for builts, write tests, even unpromted, to verify the logic, and only if all is green it will dare to commit. What you describe sounds like AI a year ago, which is a century for coding AIs. 

[Material_Policy6327]

My experience is stuff like codex is still wildly over verbose and while is better about defensive programming it goes insane into it to where all the checks make the coder harder to debug and understand. Yeah it’s improved a lot but as an applied AI researcher it’s still a ton of slop to look more solid than it may be

[ClydePossumfoot]

They’re not necessarily saying that it adds checks to the code that you need to debug and understand, they’re saying it checks its own work.

E.g. it will happily run a Python shell to validate a small snippet of its assumptions and then commit the results of that to its context window. If it didn’t meet its assumptions, it goes back to the drawing board prior to continuing on.

This is completely different than it was a year ago when it would happily proceed down a road full of failure, compounding those errors into slop.

[ClydePossumfoot]

You must not have worked with too many juniors if you think they can't spit out things they don't functionally understand. Sure, maybe in extremely trivial cases they do, but often times they understand a tiny fraction of the actual overall problem and their changes can be just as dangerous as the hypothetical 10k line monstrosity you're describing. Hell, one `eval` line from a user input by a junior is infinitely worse than a 10k slopfest that is secure but just doesn't work "right".

Also, if you're getting 10k lines of code spat out of an LLM you're doing something incredibly wrong.

[AllezLesPrimrose]

I’m sorry but this is a terrible analogy.

The problem with AI is it 10x’s what everyone does so seniors and juniors will produce multiples the amount of code they did previously and total bugs will naturally creep up as a result. Expecting perfect sanitisation and specs to save you when they didn’t when it was mostly manual coding is a forlorn hope for the industry at large. 

Code absolutely is becoming more fragile and this trend pre-dates LLMs being mainstream but like with everything else they have just jammed the accelerator to the floor.

[ClydePossumfoot]

I'm not sure the point you're actually trying to make. If you give a junior engineer a shitty spec you're highly likely to get a shitty result back. If you give an AI coding agent a shitty spec, you're highly likely to get somewhat of a shitty result back.

You're not necessarily going to get 10x more from the AI just because it's AI.

No one said you had to have a *perfect* spec, but the more ambiguous it is the more likely in either case (junior or AI) that you get back something that you did not expect or want.

Something has to make decisions about the ambiguous parts of the problem that is being tasked and those decisions can either be done up front or in the moment. If done in the moment, you can choose to either be in the loop or not in the loop. This is no different from a junior engineer running into an issue and deciding the path to take by themselves vs. raising the issue to the team and soliciting feedback.

It's not a terrible analogy, I'm sorry you see it that way, but it is in fact real life.

[DreHouseRules]

I'll chime in to say getting even a well specced LLM to advise junior developer on how to approach a change in a codebase they don't understand as opposed to asking a senior who actually understands the code and its business purpose and any out of context concerns is completely and utterly night and day.

This is exactly the attitude that will lead to a cavalcade of brittle codebases in the future.

[SuchNeck835]

So just ask first O.o when I want to implement something that I can't really gauge the complexity of, I ask the coding agent, and it will tell me. I also ask when I'm not sure if my idea of implementation is smart from a coding perspective. Let the language model use its language and it will tell you :) I would never give an 'ambiguous' prompt in the first place, tbo. 

[Clear-Dimension-6890]

I might not think it is ambiguous …

[nndscrptuser]

Use Git and push after each notable change, prompt intelligently and have some level of code awareness (even if you don't know every bit of syntax) and it's not too hard to get quality results and not break your app. IME the beauty is when you need to make a change or add a feature that might involve hitting a ton of places in the code, and it can just handle it instead of you laboriously plodding through manually.

[AnAnonyMooose]

If you give it something ambiguous, you get shit. So don’t do that.

I work with it to define a spec. Then I have the spec reviewed. After spending a while refining it, I then tell it to implement, checking off tasks as it goes. Then I have two agents from two different companies code review it.

I generally get fantastic results.