Hypothetically my ISP will sell me unmetered 10 Gb service but I wonder if they would actually make good on their word ...

The difference is that 3D printing still requires someone, somewhere to do the mechanical design work. It democratises printing but it doesn't democratise invention. I can't use words to ask a 3d printer to make something. You can't really do that with claude code yet either. But every few months it gets better at this.

The question is: How good will claude get at turning open-ended problem statements into useful software? Right now a skilled human + computer combo is the most efficient way to write a lot of software. Left on its own, claude will make mistakes and suffer from a slow accumulation of bad architectural decisions. But, will that remain the case indefinitely? I'm not convinced.

This pattern has already played out in chess and go. For a few years, a skilled Go player working in collaboration with a go AI could outcompete both computers and humans at go. But that era didn't last. Now computers can play Go at superhuman levels. Our skills are no longer required. I predict programming will follow the same trajectory.

There are already some companies using fine tuned AI models for "red team" infosec audits. Apparently they're already pretty good at finding a lot of creative bugs that humans miss. (And apparently they find an extraordinary number of security bugs in code written by AI models). It seems like a pretty obvious leap to imagine claude code implementing something similar before long. Then claude will be able to do security audits on its own output. Throw that in a reinforcement learning loop, and claude will probably become better at producing secure code than I am.

Both of those are fixed, unchanging, closed, full information games. The real world is very much not that.

Though geeks absolutely like raving about go and especially chess.

Yeah but, does that actually matter? Is that actually a reason to think LLMs won't be able to outpace humans at software development?

LLMs already deal with imperfect information in a stochastic world. They seem to keep getting better every year anyway.

I don't buy the whole "LLMs will be magic in 6 months, look at how much they've progressed in the past 6 months". Maybe they will progress as fast, maybe they won't.

If this trend continues, the models will be better than me in less than a decade. Unless progress stops, but I don’t see any reason to think that would happen.

Setting aside any implications for your analogy. This is now possible.

Workflow can be text-to-model, image-to-model, or text-to-image to model.

I’m not a fan of analogies, but here goes: Apple don’t make iPhones. But they employ an enormous number of people working on iPhone hardware, which they do not make.

If you think AI can replace everyone at Apple, then I think you’re arguing for AGI/superintelligence, and that’s the end of capitalism. So far we don’t have that.

The first part is making sure you built to your specification, the second thing is making sure you built specification was correct.

The second part is going to be the hard part for complex software and systems.

I don't know about you, but I'd much rather be shown a demo made by our end users (with claude) than get sent a 100 page spec. Especially since most specs - if you build to them - don't solve anyone's real problems.

Demo, don't memo.

Demo for the main flow is easy. The hard part is thinking through all the corner cases and their interactions, so your system robustly works in real world, interacting with the everyday chaos in a non-brittle fashion.

Lol I've been programming for 30 years.

> The complexity of these systems is crazy. Unless he meant ah HTML text area with "save" button - then sure, why not.

What do you see as the difference between an LLM making an HTML text area and a save button, and an LLM making MS word? It just sounds like a scaling problem to me. We've been scaling computers since long before I was born. My first computer was a 386 with 4mb of ram. You needed a special add-in chip to enable floating point calculations. Now look at what we have.

As far as I can tell, the only difference between opus 4.6 and some future AI model that could code up MS word is a difference in scale. Are you betting against the entire computing (software and hardware) industry being unable to scale LLMs past their current point? That seems like a really bad bet to me. Especially seeing how far they've come in the last few years. Claude code can already do some quite complex tasks. I got it to write a simple web based email client for me yesterday. It took about an hour in total. It has some bugs, but the email client works.

We scaled hard drives. We scaled down silicon chips. We scaled digital camera sensors. And display resolutions. And networking bandwidth. We went from the palm pilot to the first iphone to modern phones. Do you really think we'll be unable to scale AI models?

100% bet - no way any "AI" will be able to generate you anything close to a complex piece of software like Ms Word within reasonable time and budget. Given infinite time and money - sure, anything is possible, just like a trilling monkeys randomly printing "War and Peace" once in a trillion years in some remote galaxy. I don't even understand your confidence given how much guidance and hand holding LLMs need at the moment to produce anything useful.

There are clearly two camps - one points to existing deficiencies, another - to trends, and getting wildly different predictions.

I'm looking at the trend line. A few years ago it couldn't make a simple webpage. Now it can make a bad C compiler in thousands of dollars of tokens. What does it look like in another few years? Or another 2 decades?

I'd much rather have a conversation with them to discuss their current problems and workflow, then offer my ideas and solutions.

Not going to. Is. Actually, always has been; it isn’t that coding solutions wasn’t hard before, but verification and validation cannot be made arbitrarily cheap. This is the new moat - if your solutions require time consuming and expensive in dollar terms qa (in the widest sense), it becomes the single barrier to entry.

You can: the words are in the G-code language.

I mean: you are used to learn foreign languages in school, so you are already used to formulate your request in a different language to make yourself understood. In this case, this language is G-code.

I spent years writing a geometry and gcode generator in grasshopper. I wasn’t generating every line of gcode (my typical programs are about 500k lines), but I write the entire generator to go from curves to movements and extrusions.

I used opus to rewrite the entire thing, more cleanly, with fewer bugs and more features, in an afternoon. Admittedly it would have taken a lot longer without the domain expertise from years of staring at geometry and gcode side by side.

Produce the 3D images of xxx from various angles.xxx should be able to do yyy.

This is the tricky part. Do you know anything about mechanical engineering?

Not saying adding few novel ideas (perhaps working world models) to the current AI toolbox won't make a breakthrough, but LLMs have their limits.

https://en.wikipedia.org/wiki/Ninety%E2%80%93ninety_rule

Except that the either side of it is immensely cheaper now.

How long before those lines cross? Intuitively it feels like we have about 2-3 years before claude is better at writing code than most - or all - humans.

I don't see it in practice though.

The fundamental problem hasn't changed: these things are not reasoning. They aren't problem solving.

They're pattern matching. That gives the illusion of usefulness for coding when your problem is very similar to others, but falls apart as soon as you need any sort of depth or novelty.

I haven't seen any research or theories on how to address this fundamental limitation.

The pattern matching thing turns out to be very useful for many classes of problems, such as translating speech to a structured JSON format, or OCR, etc... but isn't particularly useful for reasoning problems like math or coding (non-trivial problems, of course).

I'm pretty excited about the applications for AI overall and it's potential to reduce human drudgery across many fields, I just think generating code in response to prompts is a poor choice of a LLM application.

Have you actually tried the latest agentic coding models?

Yesterday I asked claude to implement a working web based email client from scratch in rust which can interact with a JMAP based mail server. It did. It took about 20 minutes. The first version had a few bugs - like it was polling for mail instead of streaming emails in. But after prompting it to fix some obvious bugs, I now have a working email client.

Its missing lots of important features - like, it doesn't render HTML emails correctly. And the UI looks incredibly basic. But it wrote the whole thing in 2.5k lines of rust from scratch and it works.

This wasn't possible at all a couple of years ago. A couple of years ago I couldn't get chatgpt to port a single source file from rust to typescript without it running out of context space and introducing subtle bugs in my code. And it was rubbish at rust - it would introduce borrow checker problems and then get stuck, trying and failing to get it to compile. Now claude can write a whole web based email client in rust from scratch, no worries. I did need to manually point out some bugs in the program - claude didn't test its email client on its own. There's room for improvement for sure. But the progress is shocking.

I don't know how anyone who's actually pushed these models can claim they haven't improved much. They're lightyears ahead of where they were a few years ago. Have you actually tried them?

I've been disappointed every time.

I do use the LLMs for summarization and "a better google" and am constantly confronted with how inaccurate they are.

I haven't tried with code in the past couple months because to be completely honest, I just don't care.

I enjoy my craft, I enjoy puzzling and thinking through better ways of doing things, I like being confronted with a tedious task because it pushes me towards finding more optimal approaches.

I haven't seen any research that justifies the use of LLMs for code generation, even in the short term, and plenty that supports my concerns about mid to long term impact on quality and skills.

So the TL;DR version is: nah.

i told my boss (not fully serious) we should ban anyone with less than 5 years experience from using the ai so they learn to write and recognize good code.

The LLM is a stochastic parrot. It will never be anything else unless we develop entirely new theories.

And, pray tell, how people are going to come up with such design?

The other day I tested an AI by giving it a folder of images, each named to describe the content/use/proportions (e.g., drone-overview-hero-landscape.jpg), told it the site it was redesigning, and it did a very serviceable job that would match at least a cheap designer. On the first run, in a few seconds and with a very basic prompt. Obviously with a different AI, it could understand the image contents and skip that step easily enough.

It's kind of telling that the number of apps on Apple's app store has been decreasing in recent years. Same thing on the Android store too. Where are the successful insta-apps? I really don't believe it's happening.

https://www.appbrain.com/stats/number-of-android-apps

I've recently tried using all of the popular LLMs to generate DSP code in C++ and it's utterly terrible at it, to the point that it almost never even makes it through compilation and linking.

Can you show me the library of apps you've launched in the last few years? Surely you've made at least a few million in revenue with the ease with which you are able to launch products.

There's a really painful Dunning-Kruger process with LLMs, coupled with brutal confirmation bias that seems to have the industry and many intelligent developers totally hoodwinked.

I went through it too. I'm pretty embarrassed at the AI slop I dumped on my team, thinking the whole time how amazingly productive I was being.

I'm back to writing code by hand now. Of course I use tools to accelerate development, but it's classic stuff like macros and good code completion.

Sure, a LLM can vomit up a form faster than I can type (well, sometimes, the devil is always the details), but it completely falls apart when trying to do something the least bit interesting or novel.

They wouldn’t even know where to begin!

Even if all sandboxing is done right, programs will be depended on to store data correctly and to show correct outputs.

I'm in a similar domain, the AI is like a very energetic intern. For me to get a good result requires a clear and detailed enough prompt I could probably write expression to turn it into code. Even still, after a little back and forth it loses the plot and starts producing gibberish.

But in simpler domains or ones with lots of examples online (for instance, I had an image recognition problem that looked a lot like a typical machine learning contest) it really can rattle stuff off in seconds that would take weeks/months for a mid level engineer to do and often be higher quality.

Right in the chat, from a vague prompt.

I think exceptional work, AI tools or not, still takes exceptional people with experience and skill. But I do feel like a certain level of access to technology has been unlocked for people smart enough, but without the time or tools to dive into the real industry's tools (figma, code, data tools etc).

I think the idea that LLM's will usher in some new era where everyone and their mom are building software is a fantasy.

I am usually a bit of an AI skeptic but I can already see that this is within the realm of possibility, even if models stopped improving today. I think we underestimate how technical things like WIX or Squarespace are, to a non-technical person, but many are skilled business people who could probably work with an LLM agent to get a simple product together.

People keep saying code was never the real skill of an engineer, but rather solving business logic issues and codifying them. Well people running a business can probably do that too, and it would be interesting to see them work with an LLM to produce a product.

In the same vein, I think you underestimate how much "hidden" technical knowledge must be there to actually build a software that works most of the time (not asking for a bug-free program). To design such a program with current LLM coding agents you need to be at very least a power user, probably a very powerful one, in the domain of the program you want to build and also in the domain of general software. Maybe things will improve with LLM and agents and "make it work" will be enough for the agent to create tests, try extensively the program, finding bugs and squashing them and do all the extra work needed, who know. But we are definitely not there today.

The walls and plateaus that have been consistently pulled out from "comments of reassurance" have not materialized. If this pace holds for another year and a half, things are going to be very different. And the pipeline is absolutely overflowing with specialized compute coming online by the gigawatt for the foreseeable future.

So far the most accurate predictions in the AI space have been from the most optimistic forecasters.

No such thing as trajectory when it comes to mass behavior because it can turn on a dime if people find reason to. Thats what makes civilization so fun.

Im really tired, and exhausted of reading simple takes.

Grok is a very capable LLM that can produce decent videos. Why are most garbage? Because NOT EVERYONE HAS THE SKILL NOR THE WILL TO DO IT WELL!

I don't know if they will ever get there, but LLMs are a long ways away from having decent creative taste.

Which means they are just another tool in the artist's toolbox, not a tool that will replace the artist. Same as every other tool before it: amazing in capable hands, boring in the hands of the average person.

This matters less for text (including code) because you can always directly edit what the AI outputs. I think it's a lot harder for video.

I wonder if it would be possible to fine train an AI model on my own code. I've probably got about 100k lines of code on github. If I fed all that code into a model, it would probably get much better at programming like me. Including matching my commenting style and all of my little obsessions.

Talking about a "taste gap" sounds good. But LLMs seem like they'd be spectacularly good at learning to mimic someone's "taste" in a fine train.

True. But quantity has a quality of its own.

I'm personally delighted at the idea of outsourcing all the boring cookie cutter programming work to an AI. Things like writing CSS, plumbing between my database, backend server and web UI. Writing and maintaining tests. All the stuff that I've done 100 times before and I just hate doing by hand over and over again.

There's lots of areas where it doesn't really matter that the code it produces isn't beautifully terse and performant. Sometimes you just need to get something working. AIs can do weeks of work in an afternoon. The quality isn't as good. But for some tasks, that's an excellent trade.

It's driven by it in the sense that better tools and the democratization of them changes people's baseline expectations.

It's independent of it in that doing the baseline will not stand out. Jurassic Park's VFX stood out in 1993. They wouldn't have in 2003. They largely would've looked amateurish and derivative in 2013 (though many aspects of shot framing/tracking and such held up, the effects themselves are noticeably primitive).

Art will survive AI tools for that reason.

But commerce and "productivity" could be quite different because those are rarely about taste.

How can I proclaim what I said in the comment above? Because Ive spent the past week producing something very high quality with Grok. Has it been easy? Hell no. Could anyone just pick up and do what Ive done? Hell no. It requires things like patience, artistry, taste etc etc.

The current tech is soul-less in most people hands and it should remain used in a narrow range in this context. The last thing I want to see is low quality slop infesting the web. But hey that is not what the model producers want - they want to maximize tokens.

With Opus 4.6 I'm seeing that it copies my code style, which makes code review incredibly easy, too.

At this point, I've come around to seeing that writing code is really just for education so that you can learn the gotchas of architecture and support. And maybe just to set up the beginnings of an app, so that the LLM can mimic something that makes sense to you, for easy reading.

And all that does mean fewer jobs, to me. Two guys instead of six or more.

All that said, there's still plenty to do in infrastructure and distributed systems, optimizations, network engineering, etc. For now, anyway.

HN is a echo chamber of a very small sub group. The majority of people can’t utilize it and needs to have this further dumbed down and specialized.

That’s why marketing and conversion rate optimization works, its not all about the technical stuff, its about knowing what people need.

For funded VC companies often the game was not much different, it was just part of the expenses, sometimes a lot sometimes a smaller part. But eventually you could just buy the software you need, but that didn’t guarantee success. Their were dramatic failures and outstanding successes, and I wish it wouldn’t but most of the time the codebase was not the deciding factor. (Sometimes it was, airtable, twitch etc, bless the engineers, but I don’t believe AI would have solved these problems)

Tbh, depending on the field, even this crowd will need further dumbing down. Just look at the blog illustration slops - 99% of them are just terrible, even when the text is actually valuable. That's because people's judgement of value, outside their field of expertise, is typically really bad. A trained cook can look at some chatgpt recipe and go "this is stupid and it will taste horrible", whereas the average HN techbro/nerd (like yours truly) will think it's great -- until they actually taste it, that is.

This is the schtick though, most people wouldn't even be able to tell when they taste it. This is typically how it works, the average person simply lacks the knowledge so they don't even know what is possible.

Agreed. Honestly, and I hate to use the tired phrase, but some people are literally just built different. Those who'd be entrepreneurs would have been so in any time period with any technology.

1) I don’t disagree with the spirit of your argument

2) 3D printing has higher startup costs than code (you need to buy the damn printer)

3) YOU are making a distinction when it comes to vibe coding from non-tech people. The way these tools are being sold, the way investments are being made, is based on non-domain people developing domain specific taste.

This last part “reasonable” argument ends up serving as a bait and switch, shielding these investments. I might be wrong, but your comment doesn’t indicate that you believe the hype.

Low quality music made in bulk seems much less useful than low quality code made in bulk.

The amount of work the author puts in is correlated with the value of the piece (insight/novelty/etc). AI-written text is a signal that there’s less less effort and therefore less value there.

It’s not a perfect correlation and there are lots of exceptions like foreign language speakers, but it is a signal.

As it is, it just adds noise. Much more so than AI-written comments themselves, at least here on HN.

Here is the response from Gpt-5.2 using my default custom instructions in the mac desktop app.

OBJECTIVE: Decide whether to drive or walk to a car wash ~50 meters from home, given typical constraints (car must be present for wash).

APPROACH: Use common car-wash workflows + short-distance driving considerations (warm engine, time, parking/queue). No web sources needed (stable, local decision).

OUTPUT: • Default choice: Drive. A car wash generally requires the car on-site, and 50 meters of driving is trivial. • When walking makes sense: • You’re going to drop the car off (hand-wash/detail) and pick it up later. • You want to avoid moving the car now (ice, tight parking, you’ve been drinking, etc.) and someone else will handle the car. • Micro-guidance: • If it’s an automatic wash you stay in, drive. • If it’s detail/hand wash with a wait, drive there, then walk home (or just wait).

CONFIDENCE: 92/100 Assumptions: It’s a normal car wash that requires your car to be there; conditions are safe to drive 50 meters.

I did get it, and in my view my point still stands. If I need to use special prompts to ask such a simple question, then what are we doing here? The LLMs should be able to figure out a simple contradiction in the question the same way we (humans) do.

Which is exactly how you're supposed to prompt an LLM, is the fact that giving a vague prompt gives poor results really suprising?

The whole idea of this question is to show that pretty often implicit assumptions are not discovered by the LLM.

"It's very good at thinking if you do the thinking for it"