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!
this post was submitted on 27 Jun 2026
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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.
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I suspect it's the same people that blindly copied stackoverflow code without understanding it. Which is likely where the LLMs are getting most of its answers from in the first place.
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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.
The lazy part is not questioning the bullshit they noticed and did nothing about - not using the tool
I disagree on that; we lose the muscles we don't use and I've already seen that happening. It's also making people want to jump straight to implementation without proper design and I think that's a recipe for trouble.
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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 are 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.
Good engineers will produce more good code, because they ask the right questions, know what good looks like and check the output.
Best code is the code that is never written in the first place. But that concept is difficult to explain and measure for management purposes. Just because "AI" can automate well-structured, tested boilerplate for "good" developers does not mean all that boilerplate should be there.
Amen. And LLMs have a bias for producing text. And the more questions I ask it, for example to solve more edge cases, it quickly escalates on the existing suggestion rather than find a new approach.
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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.
Wow, that was a screed, (a worthy one) and yeah, an engineering degree should be special IMO, as perhaps a (pure) Math one. should also be. We have a tendency to regard you lesser, in self defense,, but that professional responsibility is significant, a more elegant weapon from a more civilized age. I do apologize,, the steam age thing was out of line (but meant with heart, trains rock, and IMO is where 'engineering' started) has it's roots.
As a software developer who also has a background in civil engineering and an EIT, I rue the fact that NCEES got rid of the software engineering PE exam before I had a chance to take it.
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ultimately about critical thought and solving problems/achieving functionality with limited resources
I find in software engineering that the resources tend to be ample, it's the capacity for critical thought that's constrained. Predicting the users, predicting the future, predicting what users will do and want to do in the future... get that right and you've got the requirements for good software. Without good requirements, your software can build all kinds of fancy bridges to nowhere.
At one point, about 10 years after graduating with my Masters' in Computer Engineering, I looked around about getting a PE license and the reality was: it had (and still has as far as I can tell) no value in the software field. I have a BS in EE, and if I wanted to start signing drawings for building electrical systems, a PE is just the thing for that. Around here, you need to apprentice under a PE for a time to get them to certify you as a PE, and the PEs we have aren't in software, they wouldn't know how to evaluate your software skills or professionalism. Reminds me of high school where they recognized that about 6 of the students knew far more about computer programming than the best (and only) teacher we had for it, so we were put in an "independent study" class to learn what we could from the equipment that various local businesses had donated to the school. Even as a practicing EE in the biomedical device design field, there was really no value to the PE license - for similar cultural reasons: the best biomedical engineers are in a different world from our existing PEs.
you will also be held accountable if something goes wrong.
I graduated before the internet. I had researched local companies the old fashioned way, and the first one I drove to to ask about a job, when I got there the parking lot was empty and there was a padlock and chain on the door. Guess I need to go to #2 on the list... turns out they (a pacemaker company) had been reprocessing faulty devices and shipping them with inadequate testing after rework, leading to the devices going bad shortly after surgical implant, requiring additional surgery for replacement. A whole chain of technicians, engineers, and executive management were culpable for the expense and risk they were causing for the users of their pacemakers. Other than the FDA shutting them down, there was a bunch of blow-hard talk about accountability, fines and jail time for management, but neither even came to a court hearing. Our system is, frankly, still very wild-west in terms of accountability for engineers in emerging fields.
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.
Agreed, but software has already had life-safety-critical and massively financially impactful roles in society for 50+ years now, and I see precious little progress toward formal accountability.
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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. 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.
empirically derived principles and best practices exist and that software engineering is a thing.
The thing I find most vexing about "software engineering" is that the majority of it comes down to sociology/psychology more than it does science. People make mistakes. They mis-communicate, under-specify, assume, overlook, forget, and screw up.
Programmers practice somewhere between lawyers, authors and graphic artists, and other than the graphic art side of their endeavors, most people never "read" their product. The most valuable principles of software engineering have nothing to do with the complexity of sort algorithms, logic trees or other abstract concepts they were teaching in "computer science" back in the 1980s. The most valuable principles come down to: how do you manage the problems inherent in the situation of human beings writing a bunch of code that almost nobody ever sees which can be fraught with problems that almost nobody will detect until years after the original authors have all but forgotten what they did?
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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.
Finance software is astoundlingly sloppy.
I was working in a university town, happened to get hired in at decent market rates by a biomed startup that didn't mind paying for me. When they left town, I stayed, and a precious few other companies would pay "my rate" - so I ended up cold calling on quite a few places just to see what they were about. I stumbled into the software development manager's office of a company that did ATM and POS software - they used the same tech stack I was using for biomed. After a few minutes the manager stopped the conversation and said "sure, fine, I want you, but: can I afford you? What's your current salary?" I told him, he laughed and said: "Well, I'm the highest paid software guy in the building and I don't make half that. Mostly we hire kids from the Uni who think they want the experience, they turn over every few months on average. I've told management how bad that is for the quality of our software, they don't care."
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Insane stuff.
Hopefully, those are the sorts of companies that should 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.
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It can work as you say, but some companies are pushing 10x (or whatever) with fewer people, so quality is guaranteed to go to shit.
AI is an amplifier.
So very much this ^^^.
If you put in the same time and effort creating software using AI that you would have put in coding by hand, in my experience you get better software, much more thorough documentation and automated testing, and fewer "oops" moments down the line. Not perfection, but better.
If you just give a loosely specified prompt and take the first functional looking thing that comes out, you can get code 10x faster than ever before, and it's going to be a 100x bigger mess to maintain.
A rule of thumb (aka useless constant applied to imaginary metrics) that my colleagues and I have found is: 80%. Work on an assumption that what you get back from each AI pass is about 80% good or right. Work to identify the 20% that needs more refinement, do another pass, now you're up to 96% good - and honestly probably already better than most first pass ready for a pull request code we used to submit 2 years back. Do a third pass on that and you've probably got something that's not going to give any trouble in all but some really rare cases, and you got it in about half the time you would have spent on lower quality output.
I have been trying, with limited success, to get our junior engineers to use AI to review their own code before submitting pull requests. Some do a single pass and their PRs are pretty good, one says he "doesn't believe in AI" and his code typically needs 3-4 review passes before it's even acceptable, even though he's clearly using AI to write the documentation. AI review is how they're finding all these zero day exploits in widely used products, it works, it finds maybe 80% of things you're looking for (if you keep the scope focused inside its context window capacity.) We are having slightly more success with all the junior engineers by having them submit 5-10 small pull requests per 2 week sprint instead of one big one. This not only helps human reviewers understand the bite sized chunks, it also means the AI reviews are more thorough. It also means the architectural definition steps are much more critical because review of tiny chunks misses more of the architectural level picture.
The biggest ethical question I have about using AI centers on management of management expectations. If management really thinks the human contribution value in software creation has disappeared overnight - I'd look for different management, because that ship just steered straight into an iceberg field. Some of them may pull off the Kessel run in less than 12 parsecs, but most won't.
In my experience the time spent getting AI to do what I want can just be used to write good code in the first place.
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I've read that because they have less and less experience actually writing code junior developers are having trouble analyzing the code produced.
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You only have to snorkel through the mile of shit if you let the shit-pipe in in the first place.
I mean, the pull requests will arrive.
Especially if they are security related, even if only 5% are anything, that's still quite a few actual bugs. 😞
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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.
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?
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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.
There's a lot of existential dread out there... try talking with an out of work actress about AI and find out how she really feels...
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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.
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Yep... I'm leaving the industry after 20+ years because of this industry hellscape
If you want to burn the world to see how hot it gets, you'll have to compete with AI for that as well. It's already doing that pretty well.
Meanwhile in infrastructure land, we got fired cuz "AI replaces us". Have fun with your unpatched servers and firewalls, dickheads
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.
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