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I work with outsourced code all the time and it is a tyre fire without exception. I just spent a week scrubbing a codebase where some dev “did the needful” and committed an on-by-default flag to bypass authentication checks because he didn’t known how to set up his local work environment.

People report the same “took a shortcut” issue with AI vibe coding, and I can confirm that I’ve had to rewrite practically everything the AI generated for me, despite using a frontier model dialed up to 11 thinking levels.

Having said that, AI is very useful for other activities like PR review, security vulnerability analysis, typo hunting, reverse engineering, etc.

I’m probably going to have to increase my subscription to the next tier but at the same time I still can’t use any of the code it generates.

If even one person can simultaneously experience "very useful, need to pay more for it" and "useless output code quality" then of course you'd expect a variety of opinions amongst the general user base.

> I work with outsourced code all the time and it is a tyre fire without exception. I just spent a week scrubbing a codebase where some dev “did the needful” and committed an on-by-default flag to bypass authentication checks because he didn’t known how to set up his local work environment.

OP knows this but finds himself in the strange position of having to defend India slop in order to defend AI slop, totally unnecessarily and unprompted. It's baffling to you and me.

I was similarly an AI skeptic 3 years ago. When GPT-4 was the state of the art, I thought we're going to plateau soon because of context size limits (remember back when you had to pay insane money just to get 32K)?

Last year was the first time I saw an AI agent actually debug and fix a non-trivial bug in a satisfactory way. Even then, trying to use it on larger tasks made it clear that it wasn't something I could just hand over the issue tracker to.

Now? I've been using Codex for the past several months to work on a nontrivial project. Which was prototyped in C++ (for library reasons mostly), then had the initial version written in Haskell, and more recently I got it ported to Rust to keep memory use in check on mobile.

These things are not trouble-free, but the sheer amount of progress made in just the last year alone is astounding. Skepticism is well and good, but healthy skepticism ought to yield to tangible evidence.

Good code is a living document that shows intent, and allows ease of maintainability.

Most people feel more productive with chat bots, but often end up wasting more time chasing self-inflicted issues. Same clown-car of Dev-ops proponents no doubt billing by the hour. =3

I've always noticed, within any subject involving tools, there are people who like the tools, and some people who like to use the tools to do something else.

With programming, I've always been in the later: it's a tool that allows me to do what I actually love, which is problem solving, system level thinking, and providing some nice solution to that problem, that happens to be through software.

So, I have an absolute blast with AI, because it helps do the more boring bits. And, seeing my non-programming colleagues get excited to see their vibe coded ideas become reality has been so much fun.

I'm genuinely curious to hear the perspective of someone anti-AI, who works in software. Perhaps the impending doom/skill shift of our profession?

Personally, it would still bother me if some lazy bro hit a code-generator and people end up dead.

For context search, I find LLM quite useful... still wrong 20% of the time... but it has some utility.

Here is a thought experiment: If "AI" will eventually generate your work, than what actual value do you bring to the table? =3

I am anti-vibe coding if that meets your criteria?

Reviewing vibe-coded PRs and features has been utterly exhausting over the past few months.

I work on critical, mature software - a small change in behaviour can mean data loss or non-compliance with regulations for our customers. The biggest problem with AI PRs is the sheer amount of churn, extra code and lack of intent with the PRs it generates.

The only way I can describe the latter is that an AI-only PR feels to me like a painting where everything is high detail - and you have to comb over each part before you understand why it's there because so much is superfluous. A well written human PR on the other hand, is painted such that your eye naturally follows the thought process of the author so you can just nod along during the review, as if the solution was obvious.

Also when I'm _using_ the agent; at least 50 percent of my time is spent telling it to stop with it's approach so it doesn't go down a useless rabbit hole and waste tokens.

I started similarly with it. I'm of the opinion that its a tool that behaves like a tool - how well it works depends on who is using it and how.

I don't have a good analogy but the immediate one that comes to mind is treating AI like a junior developer that you're mentoring. If you know what you're doing you can iterate quickly; if you don't then its a whole other story.

Claude built me a Markdown editor - I designed it, set coding standards, etc. It coded it to my spec. The output is in my opinion not bad and is very usable (for me - I use it daily now). Probably would have cost me north of $50k to get a team of seasoned devs to build it to the current level of polish. https://github.com/emrul/md

> The biggest problem with AI PRs is the sheer amount of churn, extra code and lack of intent with the PRs it generates.

But this isn't an LLM problem; this is a problem of undisciplined engineers who feel they need to cram extra stuff in a PR. If an engineer doesn't look at the output of the LLM and generate extra work then it's still on them, right?

> The only way I can describe the latter is that an AI-only PR feels to me like a painting where everything is high detail - and you have to comb over each part before you understand why it's there because so much is superfluous

This just indicates that the engineer doesn't know how to use the tool. Hell they can ask the LLM to split the work into focused PRs and Claude will be happy to do it and the results might no even be half bad.

> Also when I'm _using_ the agent; at least 50 percent of my time is spent telling it to stop with it's approach so it doesn't go down a useless rabbit hole and waste tokens.

If this is happening often then the tool is probably not fit for the job.

I don't believe so - it's not as though the original prompts asked for extra code churn (note that as soon as you look and edit the LLM code output extensively it ceases to be vibe coding, which I was talking about in my OP).

I'm not talking about extra feature s; I'm talking about for the same single feature the code is either convoulted because the algorithm is overly complicated or the abstractions are just wrong for the domain.

The PRs typically are already focused in that they address a single feature; or at least a single "usable" feature in a complex system which necessarily has a lot of connected parts and behaviors.

> then the tool is probably not fit for the job.

Perhaps; but with an LLM I haven't found which jobs it _does_ work for and which it doesn't. I already use planning mode extensively; and capture the major points, but then it makes a stupid decision mid implementation and just starts churning.

I'm not anti-AI but something I've been thinking about is the discipline it requires. As you said, it's a tool that allows you to rename a variable name on one end and do complete vibe coding on the other end. Developers may say that we should stay somewhere left on that spectrum, because that's where human's are more involved.

But developers also say good practices should be followed when talking to each other, and while some may do, reality is often very different.

It requires discipline, which varies a lot between developers, between projects, current mood, and so on.

In the beginning you might be careful doing small changes, but after a while you might get more tempted to accept the output for what it is, because ultimately that's much easier.

So the way I see it; the left side is harder work and potentially bigger but delayed dopamine hits, the right side is quick dopamine hits. How do we (at least those who struggle with discipline) resist just slipping to the right?

I started out carefully myself and slipped more into vibe coding, but I don't feel particularly proud of it for some reason.

> It requires discipline, which varies a lot between developers, between projects, current mood, and so on.

In the beginning you might be careful doing small changes, but after a while you might get more tempted to accept the output for what it is, because ultimately that's much easier.

Counterpoint: how is this any different from how things were pre-LLMs? I have seen, in the same codebase, some throughly well-written and tested PRs that read like Shakespeare and some of the laziest slop that even no LLM would ever write because humans have an unlimited capacity for laziness.

You catch the bad stuff through oversight, process, automated and manual checks, and the ultimate threat that your job depends on your ability to deliver so you better allocate at least enough energy into this so that you can ship moderately working code.

I just really hate talking to the computer in human language.

> So, I have an absolute blast with AI, because it helps do the more boring bits.

So... you're vibing? Not looking at the code at all?

> It reminds me a lot of when offshore outsources started getting more software development work from the mid-90s with all the derogatory remarks made towards 'Indian developers'.

What was the impetus of the derogatory remarks?

Some of it was indeed driven by sub-par work from the outsourcing firms, as the style of work was new and people on both sides hadn't developed the right skill set and processes to do the work well.

Some of it was genuine cultural differences. It's hard to work with people and get the results you want when you don't understand their culture, and how they communicate. (For example, people from some cultures just can't say "no" or "I don't know"; you need to learn how to communicate with them in a different way to get the understanding you need.)

Some of it was certainly a form of jingoistic or xenophobic protectionism.

LLM are good for context search, and template output.

However, you also get the lowest common salient answer guaranteed, uncopyrightable work (differs from public domain), and potential legal peril from copyright bleed-through.

We are in the golden Napster age of isomorphic plagiarism. =3