Comment by badlibrarian
2 hours ago
It's August 9, 2026 and if you're a software engineer who hasn't had multiple "holy shit, I can't believe it just did that" moments, it's time to consider a new trade.
2 hours ago
It's August 9, 2026 and if you're a software engineer who hasn't had multiple "holy shit, I can't believe it just did that" moments, it's time to consider a new trade.
I find this to be an incredibly myopic viewpoint. Software engineering is not defined by a single tool or technology, but rather how we design, implement, and maintain processes of value we want to automate or scale.
LLMs will certainly be an aide, but assuming adoption of them is required across a whole _field_ ignores whole classes of problems, domains, and constraints the production of software covers.
Rather, we should be wary of allowing our skills and hard-earned knowledge to atrophy by over reliance on new technology that is far from perfect, reliable, or even universally available. These tools more than any before suffer from the junk-in-junk-out problem and I’d much rather work with someone who knows their fundamentals than someone who knows their way around a the LLM of the month.
On the contrary, taken at face value I find the GP comment to be spot on.
They aren't saying LLM adoption is required across 100% of the field. They are pointing out that LLMs have reached an impressively capable state, and someone insufficiently inclined to test new tooling that they still dismiss LLMs as "just fancy autocomplete" is exactly who gets left behind when technology advances.
Two things can be true at the same time, 1) that LLMs are not required across a whole field, and 2) that software engineers unwilling to acknowledge their utility have as much a place in the future as the software engineers of 50 years ago who refused to use a compiler.
Can you elaborate on how someone would get "left behind"? Isn't the whole point of LLMs that they use natural language and require less effort?
If they're so easy to use, wouldn't the opposite be true? i.e. people who over rely on LLMs become weaker at the core competency? Nobody gives a rat's ass if you did something in an hour or took all night. The deadline is still next week because of all the meetings. What's the point?
I recently asked it a series of technical physics questions about a field I used to be involved in to evaluate whether it'd be useful for doing research, and literally every single answer it gave was critically wrong - even for basic questions. Its certainly true that I went "holy shit", but not in a good way
Okay. Please generate using an AI model code for a safety-critical system which is able to be incorporated into an aircraft and that passes the coding standards and requirements in that domain and come back and tell us all about it. Surely, if AI was so good across the entire domain of software engineering, this would be trivial to do.
Edit: although you might be subject to an NDA... But this is pretty much my test for "AIs will take all the jobs": can it write truly safety-critical software yet?
Okay. Please write such a system yourself.
Most programmers are not experienced enough to write such code themselves off-hand, but more importantly, the safety-critical systems are made safe by following a strict process, not by skills of individuals, which makes it orthogonal to involvement of LLMs.
Also, weird choice of example. 99.9% of coding is not safety-critical, so whatever reservations this would imply (even if it actually doesn't imply any), don't apply anyway.
> Okay. Please write such a system yourself.
Why? This has already done via existing processes. My point was to illustrate that if the future is LLMs then surely these processes wouldn't be needed anymore? After all, the LLM would just... Do it itself.
> ... the safety-critical systems are made safe by following a strict process, not by skills of individuals, which makes it orthogonal to involvement of LLMs.
And an LLM that is going to take all the jobs wouldn't be able to execute that process independently and with little oversight?
If my example is, to you, not a good one, what would you rather I use? Most of the common ones can be overly trivialized/minimized (particularly by someone who is uninterested in admitting that LLMs can't do something). That is not to imply that the gp is this kind of individual, but far too many people who I ask to do this (or something similar) are exactly that kind of person: believing that LLMs are insanely great and can't admit (or see) the cons.
Long bet: the NTSB will require all transportation software to be submitted for independent review, and that review process will be heavily automated by AI, within 5 years. Take it?
If we assume the current administration (and the following one) continues to be bullish on AI, then this is a suckers bet. Assuming the next administration isn't too busy cleaning up the messes of the current one.
I did but it was followed by "holy shit now I gotta review this +3000 -500 LOC PR full of subtly wrong abstractions, needless diversions and incorrect assumptions".
People keep repeating your sentiment here but I simply can't follow, are we even on the same planet? Or did everyone switch to just not caring about maintainability and code quality anymore? Or are your work tasks simply so mindnumbingly, stupidly simple that even an AI can oneshot them properly?
I mean this honestly btw, not dismissively like some sister comments. The gap between the productivity increases people report on HN and what I experience myself is insane. In fact, if I factor in the procrastination I find myself doing on dotting the i's on a supposedly "one-shotted" AI implementation of a nontrivial feature, I think the AI actively slows me down.
The only way I've found that I can actually use AI productively and sustainably is in very small tight loops and, well, at that point it's not that much faster than just typing in the code (with the occasional "Cursor Tab" complete).
Am I doing something wrong?
You have to have a team of agents with different roles reviewing that for you. An Advocate, a Skeptic, an Architect, a Security Engineer, a Disgruntled User, a Single Mom, a Clueless CTO, etc. each provides their unique prospective on the change. Then you have a Remediator and a Consolidator to address the feedback. Finally a Liquidator in case of a catastrophic failure.
You forgot Boot the Dog who simply wakes up periodically to check if the crypto rugpull is ready
Ah, Poe’s law… my favorite
I prefer to have a Clippy agent that identifies code that should be copied and pasted into random locations for no good reason. You may think it's not necessary given how LLMs operate, but I have found it's critical for 10x gains.
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Very, very senior dev here who has never taken a shortcut in his life. Don't mean to sound like a prick but you're doing it wrong. You have to find your own path through this stuff. It works, promise. I was in your shoes six weeks ago.
Wait, only six weeks ago? That's not long enough to be reporting success, genuinely. Come back in a few months, bare minimum. I'm far from a skeptic, but I've also personally had a number of moments over the past 12 months where I've declared "this is the future!" about some new app I vibe-coded, or some new workflow I started following, only to abandon it a few weeks later. And it's not just me. Check back in on many of the vibe-coded projects or AI tools you see shared and you'll see the same. It's happened at my work too — people taking over entire meetings to discuss their new revolutionary AI workflow and then, a week later, nothing...
A big problem is that these codebases rot. Agents move incredibly fast at first, but then as you pay less attention (or perhaps no attention at all) to the architecture, they slowly fall to bits. So then you decide, I'll use AI to rewrite it! And it gets better for a while until, well, you get it.
That's not to say there isn't value here, there absolutely is, just — chill. A little.
To be fair, a "very very senior dev" wouldn't tell me that I'm doing it wrong without telling me what I'm doing wrong.
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I think to effectively use LLMs in a controlled way, they can help with three things: - Help you pump out boilerplate, or otherwise "obvious" code, that you can review at a glance - Help you gain understanding, either through rapid plan writing/revision or code quality/architectural/security analysis. The gotcha is that it can only help along one axis at a time, or at least can only communicate information sensibly to humans this way - Cross-cutting or fuzzy matching across a codebase. This is the hugest win, like "replace this pattern with the new approach everywhere". This used to be the scariest sort of task, and now it is one of the more stable things LLMs get right.
Where things go off the rail is when you want it to plan AND implement features. The blind spots of LLMs are not where they are for humans and way more work to anticipate. You have to stay on top of the bucking bronco, but you CAN move much faster if you can architect your system so more tasks fall in the "obvious" bucket - that is where the art of engineering still lives. Human understanding remains the goal.
I've had a similar experience on huge codebases written entirely by an aI. It works for very very specific cases (e.g., Opus 5 has helped me with SIMD optimizations) but I wouldn't trust it to do a 10000 LoC project even with agents just because of the complexity problem and the shear amount of code I have to review. Or I'll have to change a bunch of things because the LLM made assumpts I didn't specify and it didn't ask about (e.g.: I have had to repeatedly tell these models to use std::atomic_flag and not std::atomic<bool> for a project I maintain because for some reason I cannot fathom, they love, love using the generic std::atomic<T> template, and they love using std::atomic<bool> where an std::atomic_flag would be better). Just little things add up, and before you know it I'm spending more time fixing it's issues than I am making progress.
People review PRs? LGTM.
Mr lucky over here with an agent that removes code!
Fair, that was unrealistic. One day, maybe!
Oh, I have those moments daily. Like, "holy shit, I can't believe it just fucked up a rote task this badly".
I'm convinced a lot of people just haven't tried and are digging their heels in and refusing to do so.
Claude Opus 5 is insane. Still not able to do big complex projects well start to finish but it can, with guidance, implement large parts of them.
There are now open weights models approaching it in performance too.
I respect the people who still haven't tried. They held a firm line when the rest of us were drawn to the shiny new thing. They're not becoming dependent on yet another centralized service (let's be real, the big centralized models are still way better than the open-weights models that most of us can run, particularly for coding). They can still develop software with nothing but their own computer, and that computer doesn't even have to be particularly new.
Not everyone is a web developer bub
Might I recommend HVAC or plumbing, it's honorable work and pays well. Trim trees if you've got the body for it.
I don't recommend web work, the last ten years or the next ten. Wouldn't touch the stuff. I skipped the phone app era, too.
Why is there honor in HVAC? Family honor?
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I had DeepSeek 4 Pro do a very good job yesterday of loop-unrolling and SIMD-ifying (both SSE/AVX and ARM Neon versions) some very old scalar Col3f image processing and resampling/resizing C++ code I hadn't touched in 12 years or so.
It also wrote some unit tests that validated the kernel sampling weights, and wrote some Jupyter notebooks to go along with the kernel algorithms as comparisons.
It's not just web dev... It helps (a lot in some cases) if you ask very specific things rather than just "make this vague thing", but I'm more and more coming round to the conclusion it is now a useful dev tool (until two months ago I was a sceptic).
> image processing and resampling/resizing C++ code I hadn't touched in 12 years or so.
So an unimportant, personal project. People seem to extrapolate being able to do something cool into being able to do useful work, which is what this whole discussion is about.
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This is still rather basic stuff to be absolutely honest with you.
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Yeah I expect it to be helpful in some basic image processing stuff, but it will quickly fail once you try to implement research papers on the topic.
https://news.ycombinator.com/item?id=49226923
I have been doing this stupid shit for long enough that, frankly, I am only happy when it does exactly what I intended.
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Nah, for what was broadly avaiable, last year it was still in the area of better custom stackoverflow answers. A good search engine for code. Helpful, but I don't remember mindblown moments.
This year we have agents work with complex codebases. That was and still is mindblown to me.
Don't make new accounts just to add snarky responses. If you're gonna do it, use your real account so we know who to flag.
I'll worry when that stops happening. Probably means I'm getting in my own way or not thinking big enough.
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Learning is lifelong, in most trades. What are you on about? Especially in something vast as programming/SW/ops.
That's exactly my point — by now you should have learned enough not to be surprised.