[bnfcl]

Quote of the main point in the article:

    In other words, the hard part moves from recall (“How do I write this?”) to judgment (“Does this actually make sense?”)

This is very true. But to evaluate if it makes sense, you first need experience writing the code. I am glad I learned software development over 15 years ago, and not today. AI is a super power, but without the experience to guide it, it can go horribly wrong really quickly.

[jorgeleo]

Yup!. Over 30 years experience here. Landed on the same place with the added feeling of "We were always intended to develop the judgement of what makes sense and maintainable, where is the surprise here?"

People want the "Fluent Text Generator" to think for them because its marketing calls it AI. At the end is just a tensor collection, probabilistically backtracked adjusted, text generator. It does produces fluency in the answer, but the fact that it sounds correct, to the point of passing a compiler approval, has nothing to do with been well thought out.

[reinitctxoffset]

I think it's easier to make your point without the baggage of the mechanism assertions.

The code is just wrong a lot. By denying the possibility of emergent phenomemona or model interiority you just hand the hypsters an easy point to score.

The code is just wrong, all the time. You're right about the part that matters and that we can measure.

[gatio]

Except... it does produce solid code in many scenarios. A lot of the time we are just applying the same patterns and tools to a specific problem. If you understand the problem, agents can and are producing quality code.

I do a lot with orchestrating cloud infra, provisioning and deprovisioning workflows, lifecycle management, updating CMDBs, access management, deployment automation, auditing configurations, and some APIs in front. The code is boring, mostly glue. The agents nail it quite consistently, especially with some guidance.

I do know what I want out of it, and that is deterministic quality code to solve specific business problems, so that we don't have people (or their agents) running amok.

[reinitctxoffset]

it does do good code sometimes. it does both. we're trying to figure out how to net that out to good overall. right now it's a bit tricky because we didn't build our world for the code to be really good and then disasterously bad all the sudden and then good for a while again and it seems like someone is programming a slot machine.

这波

[gofreddygo]

"How can we dig a well here" and "should we dig a well here" are neither mutually exclusive nor sufficient in their own regard.

Cant answer should we without having done a lot of it before. Cant learn unless you actually do it and see the consequences through.

[order-matters]

and yet it's also incredibly effective to be able to recognize "oh this plot is incredibly similar to these 100 other plots and there appears to be a pretty consistent pattern to what is driving where the well is built, and they are all constructed in a very similar fashion, i am just going to copy what they did"

AI is very useful when you dont need to invent anything new

[cratermoon]

Right up until you cut that fiber cable or gas line.

[agumonkey]

Yes a LLM is mostly an information memory de vice, but as a worker you still have to vet it.

I believe the human constraint made us find ways to layer and modularize systems so it is easier to check. Now the LLM produces a lot of code and abstraction have to be enforced. Something is missing.. (resonates with the latest Pocoo blog)

[zombot]

What is "de vice" supposed to mean?

[agumonkey]

smartphone typo, i meant 'device'

[leke]

Yep, I feel fortunate that programmers that made it well before AI are going to be very sort after in a world where software development is going to explode and be in very high demand.

[NuclearPM]

Sort?

[mondrian]

Sought

[gjvc]

"sought"

[prophesi]

They need to improve their spec on writing responses on Hacker News. Or maybe typos are an improvement by design to look human / increase engagement.

Not suggesting anything, but I'm wondering where the culture of having obvious misspellings without editing them out became the norm on HN. It's become the new em dash warning flag for me on forums.

[gjvc]

nobody cares about good spelling or expression any more, and haven't done for a long time, "as long as you understood then it must be ok, right?"

depressing directions.

[guywithahat]

I wonder about this. One would think new programmers, like old, will learn through experience to care most about the things that matter, and ignore things that don’t. In 10 years my pre-AI experience may condition me to waste time thinking about struct packing, while younger developers simply won’t know or care

[cdkmoose]

I think the problem will be that there was only so much we could do wrong while we were learning 15+ years ago and our mentors had a reasonable scope to watch around us as we learned. People learning now can get a coding agent to build the entire system for them and it is much harder for the mentor to review that work.

My concern is how much damage could be done by someone learning 15+ years ago vs someone learning now.

[asveikau]

> thinking about struct packing, while younger developers simply won’t know or care

That particular ship sailed more than 15 years ago. Back then if you asked people trained on high level GC-based languages about low level details or memory allocation, they wouldn't know.

[guywithahat]

I do a lot of embedded C++ work and byte stream work where packing matters, but I agree with you. For most development it’s irrelevant, and in the future I expect it to be even less relevant.

[contextfree]

But I feel like over the last ~10 years there's been a trend back towards learning more about them (e.g., with Rust and new C# features, or with C++ Win32 programming becoming a cool hipster thing somehow)? Or maybe that's just my own personal bubble.

[devin]

If you have to care about this stuff, then you do. I think it’s that simple.

[mostlysimilar]

That's where I've landed too. Maybe I'm just delusional and telling myself a comfortable story, but I think my ~15 years of experience building software by hand lets me utilize these robots in a way that people lacking it simply can't.

I still absolutely loathe talking to them and using them. But I feel less scared about losing my career these days. Work wants me to use it, I'm using it at work. In my hobby projects I still code by hand.

[asdfman123]

Yep, that's definitely true... for now.

The thing that concerns me, however, is that just a year ago it was only useful for doing things like reminding me how to read in a file. Now it's at the level of a competent junior, who can almost do the whole task from start to finish but needs a little supervision.

All the AI companies are retaining the messages we're sending to their agents to train the next models. I wouldn't be surprised if the agents will be able to do what I'm doing in a year or two.

UPDATE: I'm getting a lot of replies to this thread and while I'm not trying to be argumentative here, I have to ask if a lot of them aren't rooted in denial... it's been a hard adjustment process for me too.

[runarberg]

People were saying the exact same thing exactly a year ago. “Last years models were no good, but this year we can do anything; so what about next years models?” the people said in July of 2025.

Well... Now we have next years models, and people are still saying: “Last years models were no good, but this year we can do anything; so what about next years models?” And I am pretty sure in July 2027 (if this whole thing hasn’t collapsed in on it self by that point [very likely]) you all will be saying: “Last years models were no good, but this year we can do anything; so what about next years models?”

[orangedog]

Who is talking like this? Presumably the people looking forward to next year's models find some value out of this year's.

You seem to want it to collapse, but that's not the argument you person you're responding to is making.

[runarberg]

It is in fact very common:

https://hn.algolia.com/?dateEnd=1753056000&dateRange=custom&...

In particular those of us rooting for the collapse of AI were complaining about this speech a year ago as well:

https://news.ycombinator.com/item?id=44525074

[CuriouslyC]

We had O1 and Gemini 2.5 Pro last year, which were both very good models, just not for long horizon agentic tasks. They were surprisingly capable, just not without a lot of hand holding.

Current models are very good for long horizon agentic tasks, but they lack taste and higher level organizational principles. They can get a tremendous amount done with limited hand holding, but they still tend to build messy slop unsupervised/without intervention.

By this time next year hand writing code will be dead outside the rarest domains, and agents will be able to build larger projects coherently, and in two years human software taste and architectural guidance will be basically redundant. I predicted the complete automation (3+ 9s) of software engineering in 3 years back in mid 2025, if anything we're slightly ahead of schedule.

[webdood90]

You must really have your head in the sand if you truly believe AI will not soon be very close to, if not better than, humans at writing code.

[runarberg]

That is simply not technologically possible. You are speaking in science fiction here. You cannot train a neural network on existing things and then by some magic have the model be better then the training data. The laws of statistics prohibit it.

[CuriouslyC]

For code you can do better than humans on a lot of metrics pretty easily bro. This is because you can RL on objective verified outputs. Kind of like how RL was used to make agents wipe the floor with humans in Dota2, chess and go.

RLing taste and discernment are harder, but don't doubt that researchers can get a model that produces a more performant solution than you, while using less code, and being more secure/robust/etc, in a fraction of the time. Your moat for the moment is alignment with stakeholders and high level taste, enjoy it while you can.

[pydry]

For anything that isnt generic I find the effectiveness drops off very quickly.

It's already 100x better at writing todo apps than the average senior developer but when I get it to try and achieve something unusual using a relatively obscure library it flails.

[flyinglizard]

I find myself just jumping up the abstraction ladder, aiming for more and more ambitious projects. It's like electric mountain bikes; at first there was a lot of objection across the purists (and there still is), but eventually people understood you put in the same effort just go longer and faster. I work the same hours and strain my thought just as much but my output is significantly more ambitious. Left to their own devices even SOTA models like Fable would produce a complete mess over time, fine locally but awful globally. Admittedly, I don't know if it's the nature of the beast or if I'm just not thorough enough with my prompting and let the LLM guess what I want to end up with; but clearly some directional expertise is required.

[georgemcbay]

> UPDATE: I'm getting a lot of replies to this thread and while I'm not trying to be argumentative here, I have to ask if a lot of them aren't rooted in denial...

I do think there is a very common denialism that is rampant causing many people to believe on what is essentially faith that AI is going to get just good enough to be a perfect assistant to them, but then magically halt on progress right before it entirely replaces them.

And to be clear I believe LLMs are likely to plateau before they replace literally everyone working in software development, but I also think that a lot more than n% of developers believe they will be part of the n% who will survive unscathed, where n ends up being quite a small number.

[gofreddygo]

in a decade, we're all going to say how developers job is so different and yet still the same as it was 10 years ago.

[georgemcbay]

If in a decade we are discussing that online as opposed to tips on how to survive the most recent wet bulb event, then... wonderful, I'll take it.

I am less optimistic than you, but would love to be wrong.

[skydhash]

> Now it's at the level of a competent junior, who can almost do the whole task from start to finish but needs a little supervision.

Define tasks!

The most important aspects is that they’ve always been good at applying patterns, but have no discernment into why and the specific adjustments that needs to be done for a particular contexts. It’s rare that I can’t find discrepancy at the edges if several patterns have been used.

We already have good tools for patterns. It’s called code reusability. So frameworks, libraries, code generators, paradigms, snippets, and the mighty copy and paste (there’s a reason vim has like 26 registers).

Software development is all about managing complexity, which LLM are notoriously bad at. A little supervision won’t cut it.

[LinuxAmbulance]

> All the AI companies are retaining the messages we're sending to their agents to train the next models. I wouldn't be surprised if the agents will be able to do what I'm doing in a year or two.

Some things are very easy to solve via code (see https://xkcd.com/1425/), and others are extremely difficult.

For LLM based AI, it can be trained to write functionally correct code easily - there's tons of pre-existing code and documentation that can let AI learn patterns (LAMP stack, messaging queue, etc) and syntax.

Judgement is much more difficult. For some things "Should I go with a message queue for this problem?", there's a known set of solutions as to what works best in a specific situation, sure, LLMs can answer that easily.

But when it comes to turning human goals into code that does the job correctly and is maintainable and stable?

That's a heck of a lot different. Without human guidance and having tons of context gained by years of experience dealing with the very nuanced and situational judgement calls needed, LLMs are going to fail at doing a decent job.

LLMs (and junior people) don't know what they don't know. I think it will be really difficult to get a LLM to work well at a senior / staff level where it's more about coordination, working with human beings, handling situations based on legacy code, knowing what will and won't be fragile, etc.

[bnfcl]

Exactly, tacit knowledge is not easily added as AI training data.

[asdfman123]

> Some things are very easy to solve via code (see https://xkcd.com/1425/), and others are extremely difficult.

Note that comic is about a task that AI has already conclusively solved...

[LinuxAmbulance]

The irony is not lost on me!

[bnfcl]

Understanding the fundamentals of anything will always be a valuable skill, and that only comes from hard work and experience. And much of the experience lies in tacit knowledge, not easily added as AI training data.

That, combined with knowing what to build, and more importantly what NOT to build, I don’t think AI ever will take away from us.

[gofreddygo]

> hobby projects I still code by hand

So true. I was too blinded to not notice the joy of my hobby getting sucked away by not doing it by hand.

[techteach00]

This is called the bargaining phase.

[typon]

I am using a model that runs on a 100 GB300s, near AGI, all bets are off, $10B training run on a 1GW cluster, and it can't realize that when I told it to "please implement v2 of feature X" that I mean delete v1, not support v1 and v2 together, in a weird Frankenstein's monster of the two. Sorry, but I think my job is quite secure for the forseeable future.

[dudeinhawaii]

This is a perpetual pet peeve of mine with LLMs. They will always opt to ensure "backwards compatibility" with a codebase built 30 seconds ago.

I always have to explicitly state, "we are making a clean break to v1.0, do not implement any compatibility layers".

[sph]

This is hilarious and blindingly obvious in hindsight that an LLM would fall for that