understanding what the machine spits out

This is exactly why people will still need to learn to code. It might write good code, but until it can write perfect code every time, people should still know enough to check and correct the mistakes.

After a certain point, learning to code (in the context of application development) becomes less about the lines of code themselves and more about structure and design. In my experience, LLMs can spit out well formatted and reasonably functional short code snippets, with the caveate that it sometimes misunderstands you or if you’re writing ui code, makes very strange decisions (since it has no special/visual reasoning).

Anyone a year or two of practice can write mostly clean code like an LLM. But most codebases are longer than 100 lines long, and your job is to structure that program and introduce patterns to make it maintainable. LLMs can’t do that, and only you can (and you can’t skip learning to code to just get on to architecture and patterns)

Great question.

is there any legit reason anyone should learn advanced coding techniques?

Don’t buy the hype. LLMs can produce all kinds of useful things but they don’t know anything at all.

No LLM has ever engineered anything. And there’s no sparse (concession to a good point made in response) current evidence that any AI ever will.

Current learning models are like trained animals in a circus. They can learn to do any impressive thing you an imagine, by sheer rote repetition.

That means they can engineer a solution to any problem that has already been solved millions of times already. As long as the work has very little new/novel value and requires no innovation whatsoever, learning models do great work.

Horses and LLMs that solve advanced algebra don’t understand algebra at all. It’s a clever trick.

Understanding the problem and understanding how to politely ask the computer to do the right thing has always been the core job of a computer programmer.

The bit about “politely asking the computer to do the right thing” makes massive strides in convenience every decade or so. Learning models are another such massive stride. This is great. Hooray!

The bit about “understanding the problem” isn’t within the capabilities of any current learning model or AI, and there’s no current evidence that it ever will be.

Someday they will call the job “prompt engineering” and on that day it will still be the same exact job it is today, just with different bullshit to wade through to get it done.

I worry for the future generations of people who can use chatgpt to write code but have absolutely no idea what said code is doing.

I use it to write code, but I know how to write code and it probably turns a week of work for me into a day or two. It’s cool, but not automagic.

LLMs are just computerized puppies that are really good at performing tricks for treats. They’ll still do incredibly stupid things pretty frequently.

I’m a software engineer, and I am not at all worried about my career in the long run.

In the short term… who fucking knows. The C-suite and MBA circlejerk seems to have decided they can fire all the engineers because wE CAn rEpLAcE tHeM WitH AI 🤡 and then the companies will have a couple absolutely catastrophic years because they got rid of all of their domain experts.

I’m my experience they do a decent job of whipping out mindless minutea and things that are well known patterns in very popular languages.

They do not solve problems.

I think for an “AI” product to be truly useful at writing code it would need to incorporate the LLM as a mere component, with something facilitating checks through static analysis and maybe some other technologies, maybe even mulling the result through a loop over the components until they’re all satisfied before finally delivering it to the user as a proposal.

No, because that would require it being trained on good code. Which is rather rare.

They can write good short bits of code. But they also often produce bad and even incorrect code. I find it more effort to read and debug its code then just writing it myself to begin with the vast majority of the time and find overall it just wastes more of my time overall.

Maybe in a couple of years they might be good enough. But it looks like their growth is starting to flatten off so it is up for debate as to if they will get there in that time.

No, a large part of what “good code” means is correctness. LLMs cannot properly understand a problem so while they can produce grunt code they can’t assemble a solution to a complex problem and, IMO, it is impossible for them to overtake humans unless we get really lazy about code expressiveness. And, on that point, I think most companies are underinvesting into code infrastructure right now and developers are wasting too much time on unexpressive code.

The majority of work that senior developers do is understanding a problem and crafting a solution appropriate to it - when I’m working my typing speed usually isn’t particularly high and the main bottleneck is my brain. LLMs will always require more brain time while delivering a savings on typing.

At the moment I’d also emphasize that they’re excellent at popping out algorithms I could write in my sleep but require me to spend enough time double checking their code that it’s cheaper for me to just write it by hand to begin with.

A broken clock is right twice a day.

For basic boiler plate like routes for an API, an etl script from sample data to DB tables, or other similar basics, yeah, it’s perfectly acceptable. You’ll need to swap out dummy addresses, and maybe change a choice or two, but it’s fine.

But when you’re trying to organize more complicated business logic or debug complicated dependencies it falls over

Of course it can. It can also spit out trash. AI, as it exists today, isn’t meant to be autonomous, simply ask it for something and it spits it out. They’re meant to work with a human on a task. Assuming you have an understanding of what you’re trying to do, an AI can probably provide you with a pretty decent starting point. It tends to be good at analyzing existing code, as well, so pasting your code into gpt and asking it why it’s doing a thing usually works pretty well.

AI is another tool. Professionals will get more use out of it than laymen. Professionals know enough to phrase requests that are within the scope of the AI. They tend to know how the language works, and thus can review what the AI outputs. A layman can use AI to great effect, but will run into problems as they start butting up against their own limited knowledge.

So yeah, I think AI can make some good code, supervised by a human who understands the code. As it exists now, AI requires human steering to be useful.

This question is basically the same as asking “Are 2d6 capable of rolling a 9?”

That all depends on where the data set comes from. The code you’ll get out of an LLM is the average code of the data set. If it’s scraped from the internet (which is very likely) the code you’ll get will be an amalgam of concise examples from one website, incorrect examples from another, bits from blogs with all the typos and all the gunk and garbage that’s out there.

Getting LLM code to work well takes an understanding of what the code it gives you actually does and why it’s bad. It will always be bad because it cannot be better than the dataset and in order for a dataset to be big enough to train an LLM it’ll have to have everything they can get including all the trash. But it can be good for providing you a framework to start with. It is however never going to replace actual programming and understanding of programming. The talk of LLMs completely replacing programers is mostly coming from people who do not understand coding or LLMs at all.