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.

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

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)

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

A broken clock is right twice a day.

Writing code is probably one of the few things LLMs actually excell at. Few people want to program something nobody has ever done before. Most people are just reimplimenting the same things over and over with small modifications for their use case. If imports of generic code someone else wrote make up 90% of your project, what’s the difference in getting an LLM to write 90% of your code?

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.

Yes and no. GPT usually gives me clever solutions I wouldn’t have thought of. Very often GPT also screws up, and I need to fine tune variable names, function parameters and such.

I think the best thing about GPTis that it knows the documentation of every function, so I can ask technical questions. For example, can this function really handle dataframes, or will it internally convert the variable into a matrix and then spit out a dataframe as if nothing happened? Such conversions tend to screw up the data, which explains some strange errors I bump into. You could read all of the documentation to find out, or you could just ask GPT about it. Alternatively, you could show how badly the data got screwed up after a particular function, and GPT would tell that it’s because this function uses matrices internally, even though it looks like it works with dataframes.

I think of GPT as an assistant painter some famous artists had. The artist tells the assistant to paint the boring trees in the background and the rough shape of the main subject. Once that’s done, the artist can work on the fine details, sign the painting, send it to the local king and charge a thousand gold coins.

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.

I’ve tried Copilot and to be honest, most of the time it’s a coin toss, even for short snippets. In one scenario it might try to autocomplete a unit test I’m writing and get it pretty much spot on, but it’s also equally likely to spit out complete garbage that won’t even compile, never mind being semantically correct.

To have any chance of producing decent output, even for quite simple tasks, you will need to give an LLM an extremely specific prompt, detailing the precise behaviour you want and what the code should do in each scenario, including failure cases (hmm…there used to be a term for this…)

Even then, there are no guarantees it won’t just spit out hallucinated nonsense. And for larger, enterprise scale applications? Forget it.

The LLM can type the Code, but you need to know what you want / how you want to solve it.

In my experience, not at all. But sometimes they help with creativity when you hit a wall or challenge you can’t resolve.

They have been trained off internet examples where everyone has a different style/method of coding, like writing style. It’s all very messy and very unreliable. It will be years for LLMs to code “good” and will require a lot of training that isn’t scraping.

AI can only really complete tasks that are both simple and routine. I’d compare the output skill to that of a late-first-year University student, but with the added risk of halucination. Anything too unique or too compex tends to result in significant mistakes.

In terms of replacing programmers, I’d put it more in the ballpark of predictive text and/or autocorrect for a writer. It can help speed up the process a little bit, and point out simple mistakes but if you want to make a career out of it, you’ll need to actually learn the skill.

my dad uses this LLM python code generation quite routinely, he says the output’s mostly fine.

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.