Using AI to “help” learn to program is replacing the person’s effort thinking through the problems fully and is ultimately going to stunt their learning.
I wouldn't be comfortable with an accountant who couldn't do practical arithmetic in their head, or a surveyor who didn't have a fluent grasp of trigonometry and the ratios or function values for common angles.
Of course -- I don't care about the people working for them as a bookkeeper or assistent. They can be button monkeys as much as their boss lets them, but I also wouldn't expect those folk to reach very high in their career. Not everyone's going to, and a disinterest in and lack of technical fluency is a darn good predictor.
Your teacher was giving good advice about building skills and internalizing knowledge, because those are what contribute to mastery of a craft; maybe you were just being too pedantic or cynical to hear it?
The core idea is no, calculating in your head doesn’t turn you into a pro. Being a pro makes calculating in your head safe enough to rely on until you get to your desk and double-check.
That said, I was an integrator half my life and seen some accountants, big and small. Everyone used a calculator, it’s right next to their mouse and there’s another one elsewhere. And no one ever tried to talk about numbers afk, except for ballparks. Most of the times they suggest to go and query a db together. I find it very professional.
I have no interest interacting with someone who is going to get into how great the slide ruler is and how kids these days need to learn the slide ruler. When I was younger I thought this type of person was highly admirable but now older and wiser I see how full of shit they are.
> Does it really matter though? It sounds awfully like when a school teacher said you're not going to have an calculator in your pocket.
Yes. A younger me had teachers say that to me, and that younger me thought they were wrong.
But it turns out they were right, and younger me was wrong. Calculator dependence created a ceiling built from "educational debt" that many years later severely limited my mathematical ability.
The problem is focusing too much on getting the "answer," and losing sight of building the abilities that allow you to get there with your own mind. Eventually the need to manipulate your automated crutch turns into a bottleneck.
I wonder if that would be an issue if nobody had any.
Can you explain how your problems with arithmetic affected your ability to reason and prove things? I struggle to see how the two are connected.
It didn't affect my ability to reason and prove things, just as long those things don't strongly require the knowledge and skills I should have gotten from the calculator shaped gap in my education. I lack a lot of the background knowledge and/or familiarity and comfort with many skills that I should have.
Forbidding calculators and requiring students to do mental math for absolutely everything is unnecessary. But requiring students to solve integrals by hand when they're learning about integrals? Entirely reasonable.
If your goal when teaching coding is to teach the mechanical process of writing code: sure, go ahead and use LLMs for that process. But if your goal is to develop a deeper understanding of how to code, then LLMs can very easily obscure that. The end goal is not always just the answer.
I remember having to write code on a piece of paper in university.
Curiously, this lead to a lot of other students not really learning or caring about indentations and attempting to make code formatted in a generally readable way once they actually got access to IDEs and such.
They'd write it much like you would regular text on a piece of paper and would get absolutely stumped with unbalanced brackets whenever some nested structure would be needed.
Not only that, but somehow a lot of them didn't quite get around to using the various refactoring features of the tools, since they treated them like editors for writing text.
Did it help me memorize the basic constructs of the languages in question? Absolutely not, at least in the long term, since nowadays I have to jump between a lot of different languages and none of the syntax quite sticks, rather a more high level approach to problem solving.
LLMs just make that easier for me, letting me offload more work around language constructs and boilerplate, albeit requiring occasional intervention.
Maybe my memory just sucks.
imagine if the calculator in your pocket gave you subtly wrong answers most of the time
The problem with AI-assisted coding is that, applied uncritically, it circumvents this understanding. And without it, you won't even be able to judge whether the code that CoPilot etc spit out is correct for the task.
For me personally, it’s a fantastic way to learn. I could definitely see people just using it and not actually learning, but to each their own.
Would it be better code if someone with 3 years of university and 5 years of coding practice did it? Yes, very probably, but the gap seems to be narrowing. Humorously I don't know enough about good code to tell you whether what I build with llms is good code. Sometime I build a function that feels magical, other times it seems like a fragile mess. But I don't know.
Do I know "javascript" or "python" or the theory of good coding practice? No, not currently. But I am building things. Things that I have personal, very specific requirements for. Where I don't have to liaise or berate someone else. Where I don't have to pay someone else. Where I don't share the recognition (if there is any ever) of the thing, I and only I have produced- (with chatGPT, Gemini and most recently llama3).
Folks have been feeling superior for 70 years and earning a good living because they spoke the intermediary language of compute engines. What makes them actually special NOW is computer science, the theory- the languages, we have very cheap (and in the case of open source local models free) translators for those now. And they can teach you some computer science as well, but that is still time and practice.
I'm the muggle. The blunt. And I'm loving this glowy lantern, this psi-rig.
When it comes to production quality code that should capture complex and/or business-critical functionality, you do want an experienced person to have architected the solution and to have written the code and for that code to have been tested and reviewed.
The risk right now is of many IT companies trying to win bids by throwing inexperienced devs at complex problems and committing to lower prices and timelines by procuring a USD 20 per month Github Co-Pilot subscription.
You individually may enjoy being able to put together solutions as a non-programmer. Good for you. I myself recently used ChatGPT to understand how to write a web-app using Rust and I was able to get things working with some trial and error so I understand your feeling of liberation and of accomplishment.
Many of us on this discussion thread work in teams and on projects where the code is written for professional reasons and for business outcomes. The discussion is therefore focused on the reliability of and the readability of AI-assisted coding.
What I really need is things like "show me the complete chain of code for this particular user activity in the app and highlight tokens used in authentication" ... - something senior engineers struggle to pull from our hundreds of services and huge pile of code. And so far sourcegraph and lightstep are incapable of doing that job. Maybe with better RAG or infinite context length or some other improvement there will be that tool. But currently the combined output of 1000's of engineers over years almost un-navigable. Some of that code might be crisp, some of it is definitely of llm-like quality (in a bad way)- I know this because I hear people's explanation of said code and how they misremembered it's function during post mortems. Folks copy and pasting outdated example code from the wiki etc. ie making things they don't understand. I presume that used to happen from stackoverflow too. Engineers moving to llm won't make too much difference IMO.
I agree, your points are valid, but I see "prompt engineering" as democratization of the ability to code. Previously this was all out of reach for me, behind a wall of memorization of language and syntax that I touched in the Pascal era and never crossed. 12 hours to build my first node.js app that did something in exactly the way I had wanted for 30 years. (including installing git and vscode on windows- see, now I am truly one to be reviled)
Copilot may work for simple scripts, but even for that where it mostly for things right, in my experience it still introduced subtle bugs and incorrect results more often than not.