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It seems I’m the only software engineer on Lemmy who loves having AI. It’s not perfect, but it’s so much better than doing everything from scratch and it’s far more reliable for solving obscure runtime errors than chasing down all the typically dead-end results on a search engine for the stack trace. Or maybe I’m just the only one willing to endure the down votes. Either way, AI has been an exceptional boon in my daily workload.
Yeah, I use it to spar with when deciding on solving something. I ask it what are the domain-or-language-typical, idiomatic or best-practice ways of solving something. I trace errors by using AI. I even take whole-sale code it produces if it isn’t “serious” like just wanting the HTML or CSS lines that will do the trick.
It will definitely suggest things that I don’t think are correct and then I ignore it. It definitely wastes my time sometimes. I definitely don’t learn things well enough when using it. But I only have so much time and too many problems I need to solve.
I have coworkers with varying degrees of proficiency with AI. The ones that are better with it rein it in when it goes haywire. I have less of an issue with this. It still does awkward stuff here and there. The ones that are bad with it just commit the code for review and annoy me. When 60-80% or so of your PR can be refactored away then it’s a crap PR and honestly never should have been one. Don’t make me read through 2000 lines that should have been 400. It’s a waste of time. This mostly is it doing brain dead things like instead of passing a parameter into a method it makes 4 nearly identical methods.
I mostly use AI to implement a solution, not come up with the solution. I don’t care how fast you can type…AI can type faster. That also means I understand what it’s trying to build and how it should be going about it. That does mean you still have to use your brain and not offload your critical thinking to the machine which is what I see a lot of people doing with it.
In that regard I like AI but it’s still a love/hate relationship for sure. It both saves and costs me time just in different ways.
When 60-80% or so of your PR can be refactored away then it’s a crap PR and honestly never should have been one.
So their time savings in getting AI help to write their code means that you spend more of your presumably more expensive time doing reviews and educating them about slop removal instead of some higher-value activity. Sounds like you’re veering into negative ROI for the AI use if that’s true.
Luckily, I don’t review PRs very often, I have people to do that. But the general principle is that the content of a PR is the responsibility of the submitter, regardless of its source. Wrong algorithm? Their fault. No-good UTs? Their fault. Inappropriate or unsafe dependencies? Their fault. Slop? Their fault.
Luckily, with the work we do, there’s often nothing someone could train an LLM on, so we don’t see all that many PRs with AI-generated content, unless we’re using some well-known commodity library or framework in a common way. And that was always the easy stuff anyway.
I see an LLM as my good-but-not-perfect assistant, there to code up boring bits “loop thru this data and extract…”, “improve this bit of code please”, and to help with errors “why does this code give that error”.
I never let it do big slabs of code, and always run and check its code incrementally.
It is my code and the LLM just makes it easier to do. Thanks LLM.
This is probably a fine and responsible way of using LLM, but sadly the loudest voices are those crowing about coding being a “solved” problem and bragging about being 10x more productive by doing very little and certainly not reviewing refactoring and understanding the generated code.
Only gotcha for this is LLM is being offered well below cost, will you still want yo use it at 5x or 10x the cost?
No. I stopped using Copilot after the price increase. Now I do everything locally using Qwen. There’s a significant decrease in quality, not because Qwen is inherently worse, but because I only have 12GB of VRAM, but at least it’s affordable, and still better than no AI.
I recently helped evaluate it for company use. My test considered of vibecoding an App that takes a Pipewire screencast and does some basic image processing on each frame. In C#, which doesn’t have great options for talking to Pipewire. I did several runs with various iterations of Claude.
On the upside, it had few problems navigating DBus to negotiate a screencast handle. So that’s one annoying API out of the way.
On the downside, all attempts at taking to libpipewire through P/Invoke failed, usually because Claude hallucinated parts of the API or set constants to incorrect values. I only got a working program when I allowed the use of a prerelease Pipewire NuGet package.
The generated code was of acceptable quality but I wouldn’t allow it into a codebase without a refactor. The code has zero consistency and one time the whole solution didn’t have any namespaces. The fact that the LLM writes and rewrites the code during a single prompt means that you can get mild spaghetti as an initial state.
Honestly, I can see it for something like rapid prototyping or implementing basic scaffolding for an annoying API. But damn is Claude bad at detail work.
In my experience, AI is an amplifier.
Good engineers will produce more good code, because they ask the right questions, know what good looks like and check the output.
Bad engineers will produce reams more bad code. The mistakes they make will be amplified. They will give wrong and incomplete instructions, won’t see what the problems is with the result and will ship it anyway.
This amplification also means people will spend a larger proportion of time reviewing than coding, which I think is less interesting.
All of this is stuff that can, to some extent, be addressed with policy. You help and instruct juniors, encourage people to better understand and own their code, or at worst reprimand them if they don’t.
You can adjust expectations of product managers and explain to them that more is not better, as it always has been. Faster development can often come with bugs and tech debt and this is more of the same.
All I’ve said above is puts aside the ethical arguments of using or not using AI of course. That’s a separate can of worms entirely.
Nah, good engineers are retiring, bad engineers are running rampant. You give yourself away calling us engineers, we were never, except for some yearly title increase instead of money. Just programmers, and that is fine. Engineer is a whole other thing from the steam age, my BSc was in Math, worked fine to get me in.
Engineer is a whole other thing from the steam age, my BSc was in Math, worked fine to get me in.
As a mechanical engineer, I would beg to differ. When you strip away all the fancy math, engineering is ultimately about critical thought and solving problems/achieving functionality with limited resources. As one of my professors liked to say, “Anyone can build a bridge to support a load, but only an engineer can design a bridge that just barely holds that load.”
Engineering is an ancient domain that goes back to the very beginnings of civilization and continues to grow with our needs as we progress. Where once it was just mechanical, we now have domains like electrical, materials, and biomedical engineering. If we’ve hit a point where we need engineers who specialize in software, why shouldn’t we welcome in a new domain?
While it does feel weird calling software developers ‘engineers’, that is arguably what they do. It’s no less reductionist to suggest they are just programmers than it is to suggest that mechanical engineers are simply CAD and Excel jockeys. There’s a layer of comprehension about the systems in play and how they can be manipulated that gets lost in the reduction.
My only real sticking point about software engineers where I tend to push back is that Professional Engineer is a legally protected title and indicates licensure, at least in the US. It requires the right degree(s) and several years of work supervised by a PE to qualify for that licensure. The importance of the PE license is that you are recognized as an authority in your field- for good or ill. You can make big decisions, but you will also be held accountable if something goes wrong.
In my experience, many software engineers brush aside the importance of those types of qualifications because their field wasn’t quite as rigorous to enter. As we continue to develop a society where software mistakes can absolutely kill people (e.g. self-driving vehicles, robots, automated decision tools in medicine and insurance, etc) or cause massive economic damage, it’s critical that we decide how software engineers play a role in preventing those things and how we hold them accountable when they don’t.
Any shaved ape can code. One thing that distinguishes worthwhile coding from crap is adherence to engineering principles. Nitpicking about the semantics of the word “engineer” avoids the incontrovertible fact that empirically derived principles and best practices exist and that software engineering is a thing.
Coincidentally, my MSc is in mathematics and statistics, after a dual BSc in math and physics, so we’re from similar starting points. My education as a software engineer and later as a systems architect only came once I began coding. There’s a considerable body of empirical knowledge in the literature (along with too much irreproducible fluffy bullshit), but in my experience, the general awareness of that knowledge is worse among the newer generation of coders than older ones. I suspect that’s because they generally assume that the toolchain and processes do it for them.
The widespread adoption of Scrum has been another source of knowledge loss: it’s used in a number of situations where it does more harm than good, and even where it could succeed, it’s often misapplied (partially because some agile principles are impossible to implement in most real-life organizations, so misapplication is the only posssible kind of application). There are times when architecture and design matter greatly, and some agile practicioners seem to actually believe that they can be done on the fly or major shortcuts can be taken. “We’re not doing waterfall!” You know what? I’ve been in the business since before some of those fools were born, and I’ve never done a waterfall project. It was already an anti-pattern in Fred Brooks’s 1970 magnum opus. But agile vs waterfall was always a false dichotomy. It’s just that some of the OG agile people were too ignorant to know that, or too self-interested to admit it.
Bad & meh engineers get praised because they “waste” less time directing ai and reviewing output - barely working is good enough in the race to market.
I’ve seen things as serious as a privileged user for one customer having admin access to all customers being discovered during the last minute pentest literally days before the planned product launch. That product is supposed(and likely will) to move 250M USD for customers in the second half of this year. Under the current policy at my day job, coming all the way from the top, reviewing ai generated code at all should be an exception reserved for 0.1% most critical code. Yes, in finance.
Insane stuff.
Hopefully, those are the sorts of companies that will fail or get sued, so they learn their lesson. Not holding my breath though.
Companies have been doing insane shit for the sake of saving a buck or getting to market fast for decades, it’s nothing new. AI may or may not just make it worse.
im pretty sure this isn the reason, its the dread of being laid off, and unable to find another job, because these companies think AI replaced them. i had one person i know that was laid off since 2023, and another had been put on hold indefinitely.
There can be different reasons for different people. I personally have zero fear of being laid off (strong worker protection laws in Germany) and finding a new job this year was surprisingly easy (50-ish applications with no cover letters -> 3 interviews -> 3 offers) and I still feel the exact dread that is described in the article. The industry sucks in multiple different ways rn.
Yep… I’m leaving the industry after 20+ years because of this industry hellscape
I was doing a code review this week. There was nothing wrong with the code in terms of structure or performance, but it was doing this really weird operation with an ID after DB insert. I asked about it and the author was like “yeah, that’s weird; I don’t know why the AI did that. I’ll remove it.” My dude, I know you can write good code. Don’t be lazy!
I worked with a guy that 100% used AI to dev everything. didn’t even check to see if it would work before submitting a MR.
It got to the point that I stopped reviewing them and just rejected them outright with a simple comment, “doesn’t work”.
eventually he was fired. the evidence? the four months of shitty MRs he opened. the best part was, when I said “doesn’t work”, I was never wrong. none of his changes worked.
i dont understand that. i use ai for help reading through old stuff or to help me remember how tondo a thing i havent done in two years but blindly copy pasting blows my mind.
Same. I also code up about 50% of stuff so all the structure is there, effectively as guardrails, before using AI. Then prompting it instructions that are effectively the solution, so it doesn’t come up with its own.
Then, read through it all, replace things that could’ve been done better, and test.
On average it’s maybe 15-20% quicker than manually coding the whole lot. Try skip any of those steps and the chances of it blowing out increase to the point I just end up doing it all anyway and it’s taken twice as long because of it.
It’s alarming when people don’t even check.
On average it’s maybe 15-20% quicker than manually coding the whole lot.
Out of interest, how much is this 15-20% increase in productivity costing in tokens?
You’d really need to know the fully burdened cost of an hour of the person’s time who’d be doing the work, versus the cost of the tokens plus all the overheads involved in its administration and use of the AI solution (tokens, support, training). Same goes with the downsides-- you’d need to know how the rate of serious bugs changes when you incorporate the slop. Some of the defects will make it through reviews and testing and into prod.
If I got an AI-written codebase hard to navigate, I’d bill the AI usage it took to clean it up and, on top of it, the hours I have to put to actually do the job. We can definitely use AI to write good code, but it takes the kind of professional criteria that vibe coding lacks in order to do that.
We can definitely use AI to write good code
Get a different job. Now!
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