I'm the author of Python Crash Course, and I got this exact same email this week. I was thinking of writing a public response as well, because any attempt to sincerely answer these questions takes something along the lines of a full post. It's also worth a public response because many people who are getting into programming for the first time right now are asking variations of these same questions.
> Do I think that AI enables people to develop faster than they can keep up?
Absolutely. That's the core of this person's email, and everyone else who asks similar questions. Just five years ago, the only way to build a working project of moderate complexity was to learn the basic to intermediate concepts required to make an MVP. Now, if you can steer an LLM reasonably well, you can quickly build an MVP that goes well beyond your own understanding of the implementation.
I don't think anyone has clear answers to all the questions brought up in this email. I think people can learn faster than they used to, because they can make connections between different areas faster than they used to. But it requires skill and discipline in how you learn, and how you work. You have to intentionally build your understanding as you build your projects.
> Just five years ago, the only way to build a working project of moderate complexity was to learn the basic to intermediate concepts required to make an MVP.
> Now, if you can steer an LLM reasonably well, you can quickly build an MVP that goes well beyond your own understanding of the implementation.
Somewhat agree. Five years ago you could build an MVP without understanding how to open TCP sockets or how to parse HTTP headers. You didn't need to understand relational databases, let alone B-trees or cache locality. You didn't need to know how to install Linux.
Now you don't need to understand the details of connecting to Stripe or Auth0 or setting up a Kubernetes cluster.
> You have to intentionally build your understanding as you build your projects.
Some things you need to understand-others, not so much. Depends on what you're doing, the scale, risks, etc, but that's always been the case.
That’s the power of abstraction when there’s a good API around something to hide the internal that doesn’t matter much at an higher level. You only need ‘open’ and ‘read’ instead of dealing with disk access and file system trasversal.
But those abstraction are deterministic in nature, so there’s a very good guarantee of their behavior. Someone using LLM and not caring about the generated code is just asking for trouble. The code may work, but there’s no guarantee about its behavior (including error handling and edge cases).
> I think people can learn faster than they used to
Agree, but that only applies for people who were experienced developers before AI took over. Let's see in 5-10 years what our caliber looks like when you skip the foundations.
If you’re just talking about learning a programming language I think you need to be quite judicious in your AI usage.
In my experience, people learn programming languages best by overcoming frustrating roadblocks. You often end up learning something important, even if it’s just about your mindset or approach, that landed you there. This is the difference between someone with a wet signature on their comp sci diploma and someone with a few years under their belt.
A lot of people start with tutorials and cargo-cult their way through solving their first problems, but eventually need to learn how to do things the tutorial code can’t. It seems like the AI coding tools can could perpetually make things that could be bashed together well enough to sorta solve a problem and think “oh I’ll just learn about that later,” and then never learn about it at all. If your goal is to make some quick tool to help you with something at work in a different field, well, touchdown. If you’re trying to learn the language, fail.
I'm a software engineer, I do software development but also system maintenance, and I do handle networking and telephony systems, and work with some juniors. Working with AI is problematic. It can speed up you but at the same time delay you. For the system maintenance part sometimes you need to do a lot of stuff fast and in various machines and you can't just count on a cloud based AI oracle (that takes time) to do your job for you. And the same time, the more you use it as a oracle, the less competent you get. If you are an expert, I would say in any area, you do benefit from using AI as a tool but it easily can become a double edged sword and make you less proficient. For juniors, it can make them rapidly produce stuff that is impressive and works ok for sites and some visual stuff, but it's impossible for a junior to become an expert if they get stuck in the AI using loop. For AI to cause a clean impact, I would say that we would have to live in a world where software engineering didn't matter. That is, the choice of databases, high availability systems, the programming languages themselves.
Recently I start to do some hobby project by learning Common Lisp to understand more about the libraries I used on app
I read the document and sometimes use LLM as a quick search engine because I am tired of every query on google that use AI to summarize
The project goes slowly but seems the basics I grasped over the years help a lot
So perhaps it still worth to learn by hand with trial and fail
I agreed with the author that one must learn deep above the abstraction and I truely think programming still a thing even the agentic coding is getting powerful
I'm doing this. After getting started with LLM coding, I became super interested in learning to code, just out of passion. I walked out of engineering thinking physics was elite, but now I understand how passionate I am about building things, and how boring quantum mechanics was. Better late than never.
I still differentiate between code monkeys, coders, programmers, hackers and software developers/engineers. Software development is not coding alone, you need to follow best practices and principles to create a stable, maintainable and trustworthy product, one that _you_ or your company owns. Maybe "code monkeys" (which is a minority) are replaceable. But for now, LLM cannot have a wider vision for your products future. The willingness of building something durable is totally human. To make this possible professional software developers are still mandatory and they will be for a long time. And yes, I think is it possible to learn those best practice and principle without coding. But I think this is very hard and boring.
Building software for me has always been about creatin a set of concepts (data structures, basic behaviors) out of the primitives of the platform (language, libraries,…) and then coordinate their behavior according to the requirements.
Based on comments here, LLM users belong in two categories: Those that don’t understand the previous paragraph and those that believe they can get the concepts and coordination out of prompts and specs.
But for both of them, there’s a common trait, which is not caring about maintenance. And you can observe this today where most AI projects either don’t survive the public release or have to revert to more traditional methods.
I am starting to see how many developers actually need to re-learn programming in the age of LLMs.
A while back Claude went down in the middle of a somewhat frantic initial deployment of a product to production at a company where a friend of mine works. And suddenly nobody was able to do anything. Because nobody had actually read the code and had no idea how it worked.
So essentially: much of their day to day work now depends entirely on the availability of a couple of frontier LLMs.
"The same kind of argument was used when China was admitted to the World Trade Organization. And indeed, lots of new jobs were created, just not in the Western world."
China's entry into the WTO is really not a good evidentiary example for AI causing mass unemployment. Unemployment in the U.S. had already been increasing at the time, peaked soon after, decreased to well below the point it had been at China's entry, and only went up again during the Great Financial Crisis, which had nothing -- or at least very little -- to do with competition from China. That's not to say that jobs weren't lost, even en masse, but they were replaced, and U.S. unemployment has been near record lows in recent years. China's WTO entry is a supporting point, not a counterpoint, to the idea that jobs lost to AI will be replaced by new ones.
I agree. Possibly more fun than ever. Bugs are being caught earlier through AI review, higher quality and quantity of tests, features are going out faster, bad decisions can be easily course corrected and bike shedding is dropping dramatically.
I miss the romanticism of trad coding but shipping better solutions to my customers was the goal. Hard to argue things aren’t better when AI is used intelligently by experienced people.
Because for a lot of millenials and older developers coding/making thing by hand was the fun part. The endless meetings, scrum rituals, code reviews etc were the annoying parts of the job.
Now they took away coding by hand, so what's there to enjoy? In a field that was already sensitive to burn out and churn taking the joy from the daily routine doesn't help with that.
Only thing you can do is get in build something for 2 years hope you got bought by big tech before the pile of slop code collapses on itself.
My biggest issue with halting AI progress right now is we are in a dangerous place where AI is only just good enough to be dangerous. So I see an argument to continue development until its competent to depend on.
IMHO, I think that it could be better if the question about how to learn programming in the age of LLMs were asked to someone who is learning now by using LLMs. Someone who learned programming thirty years ago can perhaps give you only one side of the coin, whereas someone learning today from scratch using LLMs could give you good advice on what the real difficulties are and where the main drawbacks lie. Combining both views would give a better idea of the landscape.
But I think at least in front-end engineering, the bigger question is what a new dev values.
LLMs make the browser itself easier to understand in depth, if that's something you want. Building in vanilla-everything, no deps, is now doable at speed and scale for beginners too.
Once you understand the web stack and the principles, you can migrate to any framework and understand their "magic" fast. I think that's not a bad learning path at all, if you use it well, and results in a more competent web-dev than the previous pre-LLM cycle produced.
But if the beginner values output aesthetics and speed more than building their mental model, it is possible to spend years building things without developing any understanding.
For me, at least, the more interesting question is how can we make beginners more interested in the former path than the latter.
As a programming languages researcher I disagree. HTML is not just a programming language (a declarative one), it literally has the word “language” in the name. The world of programming languages is much larger than general purpose languages.
Also, whether JavaScript is interesting is a matter of opinion. Given that for a long time it was the only web language, I’d say that makes it interesting. It’s also in my opinion a poorly designed language but that also makes it “interesting” because its behavior is sometimes hard to predict.
Javascript running in the browser is an amazing tool to learn programming as everything you need is right there in the program that's already installed anyway.
No True Scotsmanning someone over that (especially someone who wants to learn something new) is a bit weird, IMHO
Would you prefer starting with compiling a program or running a python script? Imho for beginners it is too heavy, even a python that is so hyped recently.
>whereas someone learning today from scratch using LLMs could give you good advice on what the real difficulties are
if we're talking about the LLM usage as described in the article, they can't do that by definition because they're not learning. You can replace the word "model" in the article with "guy I hired on fiver" and there would be no difference. They outsourced the building of a product.
If you're having robots lift weights for you at the gym it's a moot point to ask what the real problem with your form is, you don't even have one.
You can use an LLM as a kind of tutor to ask it questions the same way you'd google, but you can't actually substitute the programming if you want to learn programming
For example he could tell you about how the LLM destroyed the main database (source of data for him) and so you should prompt the LLMs for how to avoid losing all your data. Real experiences help but not painless.
>I may have built a system that is above my own level of understanding.
I feel like that about a lot of code i did myself; If you don't structure things very logically and really think about your comments; A few months or years will leave you with a hell of a learning curve to understand what you created.
AI actually helps with this, if you have the right prompt injections. I feel like the correct way to handle AI is to take a step back in abstracting problems.
I'm very use to collapsing subroutines to make things readable, maybe even further back from this though, the issue is words become too vague to be useful at these scales.
AI LLM systems, i.e. perplexity.ai, are very good at tutoring someone about how something works, i.e. advanced math, and when done in a loop can be very useful at tutoring, better than youtube videos I've seen on the same subject. The one thing I will usually request in (in the case of math), is to suffix the prompt with "explain this in terms a 9th grader would understand", and this is good enough to explain something in simpler terms with various breakdowns that can be understood by anyone to tutor yourself in alot of subjects using this method. This can be applied to programming, auto repair, construction, almost any subject at this point.
Programming education in the LLM era will be different from what it is now. Many of the learning methods emerging now are the practices of a "good senior programmer."
But realistically speaking, choosing LLM programming ultimately means pouring out an enormous amount of code, and it's difficult to verify all of it. Common sense says that if you produce 10,000 lines in an hour, you can't read all of it, and even if you do read it, you'd have to rewrite it. The problem is that LLM code differs from human abstraction. Or more precisely, it lacks a programmer's habits, so it's hard for me to maintain.
Clearly, programming in the LLM era will be different. The problem is that I can't get a sense of what that way of doing things actually is.
I think that low-priority frontend work will probably be handled by LLMs, while only complex animation work will be handled by humans, and humans will end up working only on things like payment modules, which are hard to fix if something actually goes wrong.
LLMs are now better at optimization than most people.
I think bill gates summarized it pretty well in a recent letter [1]. There are pro's and con's to every new technology. Learning can be greatly accelerated with the use of llm's but you have to use them the right way. Just like calculators help further down the line, they do not help you when you are still trying to learn the basic concepts of arithmetic. I personally think I have found a way of working with llms that really accelerates getting stuff done while i am still able to learn. It means reading more, and (although I hate this in part) reading generated text. What is infuriating is when I suspect people writing to me with generated text, it is insulting and should be banned. Makes me want to spend more time offline (probably a good thing in my case).
I think it's still important for young people to learn coding without the LLM. they need to see the little pieces before they can build big structures. It will be like calculators, just on a bigger scale: you learn how arithmetic works, and then you rely on the calculator when you are multiplying large numbers.
My guess is it will probably take some time to incorporate LLM use into education. People who are graduating right now have a problem, being between two worlds. Those graduating in a few years might have chance to figure out what to do.
> I may have built a system that is above my own level of understanding
If I venture into an unknown area, I end up where the letter-writer ends up when he is visiting programming. Suppose I am curious about an advanced math topic, like Navier-Stokes. The LLM's answer to the news about the new advance last week is strewn with words I don't understand. Asking about anything produces another essay with more things, a loop that never closes. If it were my specialist area, I imagine I would eventually hit some point where the explanation connects to something familiar.
I think this is the wall people run into when they don't have the fundamentals. You eventually get to a point where the machine is asking you for decisions that you won't know the consequences of, and when you are trying to clarify, you end up in a massive rabbit hole. It's not that different from asking a real expert about their area, they will eventually ask you to clarify something that means something to them, but not to you.
I learned programming the slow way. I would run into phrases like "memory barrier" or "green thread" and find an article using the keywords, which led to more searches, which led to more...
There are also many false dawns. Early on, after some success writing some trading strategies, I thought I had it, in the sense that I would be able to write any program required. But it wasn't true, I would run into an iceberg from time to time. Huge areas of knowledge that I hadn't come across.
Obviously I'm not claiming I finally know everything, but LLMs have arrived at a very convenient time for me. For the things I build, there is rarely anything that I don't understand at a fundamental level. When it asks me something, it's an incidental question: what decision should we make? What are the superficial changes that are needed to fit the architecture to the desired product?
I am essentially using LLM as a very quick junior, who knows how the OS works well enough to compile things and analyze logs. These are things that would take a lot of attention in the old days because they can break on very small errors, but the direction was known from the start, and thus for me (having paid the learning cost already) it is just a matter of waiting for the AI to get the code into the desired state.
I have a somewhat usable experience. I was asked to build a trading system a few years ago, which would connect to certain exchanges and show an orderbook. This kind of thing is bread and butter, but writing it up at a new firm would still take weeks.
In recent engagements, I've simply declaratively told the LLM what properties I wanted to see in the solution, waited, and answered a few questions. Since the architecture is the same, there weren't a lot of real decisions. The time difference is immense.
I said this in different ways before and got shoveled because of the way I said it:
None of us know how to farm, not even the chefs who cook for us at a restaurant or fast food joint, but we eat every day and nobody's going around making people feel guilty about not knowing how to till soil and sow seeds..
In programming and other creativity, most people's skills will [have to] change/evolve into managing, directing, dictating, knowing what you want, describing it, and focusing on the end product and iterating,
instead of wrestling with why the f is a string a pointer to a pointer to a character
just like we don't track the phases of the moon and seasonal rainfall before we can have a nice salad to eat.
Yeah but should programming issues become "how can I be more friendly to the waiter so they give me what I want?"
That's not what I want programming to become. I'd choose a different profession then. Maybe I'd become a surgeon because the AI labs haven't cracked robotics yet. And the people in general look up to surgeons and down to IT people, but that's another story.
> I said this in different ways before and got shoveled because of the way I said it
Are you sure that was the problem? Because to me it seems like it’s the argument which doesn’t hold. You’re engaging in what is called a False Equivalence.
Those things are not the same. Crucially, the food you buy at the store or restaurant is heavily regulated, provenance established, safety checks in place. When there’s a widespread issue, we have systems in place for recalls. The differences don’t stop there, and you can’t ignore them just because the processes so ingrained and well established that you forget they’re there.
> the food you buy at the store or restaurant is heavily regulated, provenance established, safety checks in place. When there’s a widespread issue, we have systems in place for recalls
I live in a country where those things are not regulated and you have to be really careful to not buy something that would send you to the hospital.
>> "About a year ago I became fascinated by AI-assisted programming. Despite having no formal CS background, with LLMs I managed to build a fairly large TypeScript/JavaScript system [...] At first it felt almost magical: [...]
It's comical how these people claim first person: "I built". Look: having a LLM shit you some code is in no way different than paying some third world country dude on Upwork 5 bucks to build you "a Facebook clone" or whatever preposterous claim of grand software. In fact at this point it's cheaper to pay that third world country team than a LLM.
And yet before the advent of LLMs noone ordering a job on Upwork was delusional enough to claim "I built it". Although it's the same magical process, like the magic ring in fairy stories. You put the ring on your finger, rotate it and make a wish and the ring makes it appear. Well, for 5 bucks or something.
But nowadays every half witted retard with 50 bucks to spend goes to a LLM and has some "Facebook clone" spitted out and claims "I BUILT THIS!". You haven't built shit, and you know nothing!
Fortunately, reality strikes sooner or later but boy am I tired of Lord of The rings claims.
I'm the author of Python Crash Course, and I got this exact same email this week. I was thinking of writing a public response as well, because any attempt to sincerely answer these questions takes something along the lines of a full post. It's also worth a public response because many people who are getting into programming for the first time right now are asking variations of these same questions.
> Do I think that AI enables people to develop faster than they can keep up?
Absolutely. That's the core of this person's email, and everyone else who asks similar questions. Just five years ago, the only way to build a working project of moderate complexity was to learn the basic to intermediate concepts required to make an MVP. Now, if you can steer an LLM reasonably well, you can quickly build an MVP that goes well beyond your own understanding of the implementation.
I don't think anyone has clear answers to all the questions brought up in this email. I think people can learn faster than they used to, because they can make connections between different areas faster than they used to. But it requires skill and discipline in how you learn, and how you work. You have to intentionally build your understanding as you build your projects.
> Just five years ago, the only way to build a working project of moderate complexity was to learn the basic to intermediate concepts required to make an MVP.
> Now, if you can steer an LLM reasonably well, you can quickly build an MVP that goes well beyond your own understanding of the implementation.
Somewhat agree. Five years ago you could build an MVP without understanding how to open TCP sockets or how to parse HTTP headers. You didn't need to understand relational databases, let alone B-trees or cache locality. You didn't need to know how to install Linux.
Now you don't need to understand the details of connecting to Stripe or Auth0 or setting up a Kubernetes cluster.
> You have to intentionally build your understanding as you build your projects.
Some things you need to understand-others, not so much. Depends on what you're doing, the scale, risks, etc, but that's always been the case.
That’s the power of abstraction when there’s a good API around something to hide the internal that doesn’t matter much at an higher level. You only need ‘open’ and ‘read’ instead of dealing with disk access and file system trasversal.
But those abstraction are deterministic in nature, so there’s a very good guarantee of their behavior. Someone using LLM and not caring about the generated code is just asking for trouble. The code may work, but there’s no guarantee about its behavior (including error handling and edge cases).
> I think people can learn faster than they used to
Agree, but that only applies for people who were experienced developers before AI took over. Let's see in 5-10 years what our caliber looks like when you skip the foundations.
If you’re just talking about learning a programming language I think you need to be quite judicious in your AI usage.
In my experience, people learn programming languages best by overcoming frustrating roadblocks. You often end up learning something important, even if it’s just about your mindset or approach, that landed you there. This is the difference between someone with a wet signature on their comp sci diploma and someone with a few years under their belt.
A lot of people start with tutorials and cargo-cult their way through solving their first problems, but eventually need to learn how to do things the tutorial code can’t. It seems like the AI coding tools can could perpetually make things that could be bashed together well enough to sorta solve a problem and think “oh I’ll just learn about that later,” and then never learn about it at all. If your goal is to make some quick tool to help you with something at work in a different field, well, touchdown. If you’re trying to learn the language, fail.
I'm a software engineer, I do software development but also system maintenance, and I do handle networking and telephony systems, and work with some juniors. Working with AI is problematic. It can speed up you but at the same time delay you. For the system maintenance part sometimes you need to do a lot of stuff fast and in various machines and you can't just count on a cloud based AI oracle (that takes time) to do your job for you. And the same time, the more you use it as a oracle, the less competent you get. If you are an expert, I would say in any area, you do benefit from using AI as a tool but it easily can become a double edged sword and make you less proficient. For juniors, it can make them rapidly produce stuff that is impressive and works ok for sites and some visual stuff, but it's impossible for a junior to become an expert if they get stuck in the AI using loop. For AI to cause a clean impact, I would say that we would have to live in a world where software engineering didn't matter. That is, the choice of databases, high availability systems, the programming languages themselves.
Recently I start to do some hobby project by learning Common Lisp to understand more about the libraries I used on app
I read the document and sometimes use LLM as a quick search engine because I am tired of every query on google that use AI to summarize
The project goes slowly but seems the basics I grasped over the years help a lot
So perhaps it still worth to learn by hand with trial and fail
I agreed with the author that one must learn deep above the abstraction and I truely think programming still a thing even the agentic coding is getting powerful
I'm doing this. After getting started with LLM coding, I became super interested in learning to code, just out of passion. I walked out of engineering thinking physics was elite, but now I understand how passionate I am about building things, and how boring quantum mechanics was. Better late than never.
I still differentiate between code monkeys, coders, programmers, hackers and software developers/engineers. Software development is not coding alone, you need to follow best practices and principles to create a stable, maintainable and trustworthy product, one that _you_ or your company owns. Maybe "code monkeys" (which is a minority) are replaceable. But for now, LLM cannot have a wider vision for your products future. The willingness of building something durable is totally human. To make this possible professional software developers are still mandatory and they will be for a long time. And yes, I think is it possible to learn those best practice and principle without coding. But I think this is very hard and boring.
Building software for me has always been about creatin a set of concepts (data structures, basic behaviors) out of the primitives of the platform (language, libraries,…) and then coordinate their behavior according to the requirements.
Based on comments here, LLM users belong in two categories: Those that don’t understand the previous paragraph and those that believe they can get the concepts and coordination out of prompts and specs.
But for both of them, there’s a common trait, which is not caring about maintenance. And you can observe this today where most AI projects either don’t survive the public release or have to revert to more traditional methods.
I am starting to see how many developers actually need to re-learn programming in the age of LLMs.
A while back Claude went down in the middle of a somewhat frantic initial deployment of a product to production at a company where a friend of mine works. And suddenly nobody was able to do anything. Because nobody had actually read the code and had no idea how it worked.
So essentially: much of their day to day work now depends entirely on the availability of a couple of frontier LLMs.
"The same kind of argument was used when China was admitted to the World Trade Organization. And indeed, lots of new jobs were created, just not in the Western world."
China's entry into the WTO is really not a good evidentiary example for AI causing mass unemployment. Unemployment in the U.S. had already been increasing at the time, peaked soon after, decreased to well below the point it had been at China's entry, and only went up again during the Great Financial Crisis, which had nothing -- or at least very little -- to do with competition from China. That's not to say that jobs weren't lost, even en masse, but they were replaced, and U.S. unemployment has been near record lows in recent years. China's WTO entry is a supporting point, not a counterpoint, to the idea that jobs lost to AI will be replaced by new ones.
https://fred.stlouisfed.org/series/UNRATE
As long as you have critical thinking, it's fine.
So I would say it accentuates the gap between good developers and bad ones.
A sharp sense for logic and causality etc is what differentiates.
Sorry, programming is still fun. LLMs can't change that.
I agree. Possibly more fun than ever. Bugs are being caught earlier through AI review, higher quality and quantity of tests, features are going out faster, bad decisions can be easily course corrected and bike shedding is dropping dramatically.
I miss the romanticism of trad coding but shipping better solutions to my customers was the goal. Hard to argue things aren’t better when AI is used intelligently by experienced people.
I'm curious - how do you explain the fact that as you look all around you, lesser number of devs are having fun?
Because their job stopped, to a large degree, involving programming.
Because for a lot of millenials and older developers coding/making thing by hand was the fun part. The endless meetings, scrum rituals, code reviews etc were the annoying parts of the job.
Now they took away coding by hand, so what's there to enjoy? In a field that was already sensitive to burn out and churn taking the joy from the daily routine doesn't help with that.
Only thing you can do is get in build something for 2 years hope you got bought by big tech before the pile of slop code collapses on itself.
> The endless meetings, scrum rituals, code reviews etc were the annoying parts of the job.
Those are all self inflicted and not a necessary part of the job.
My biggest issue with halting AI progress right now is we are in a dangerous place where AI is only just good enough to be dangerous. So I see an argument to continue development until its competent to depend on.
IMHO, I think that it could be better if the question about how to learn programming in the age of LLMs were asked to someone who is learning now by using LLMs. Someone who learned programming thirty years ago can perhaps give you only one side of the coin, whereas someone learning today from scratch using LLMs could give you good advice on what the real difficulties are and where the main drawbacks lie. Combining both views would give a better idea of the landscape.
Difficulty is too many technologies available. So a beginner needs something/someone that teaches principles.
I have realized that simplier (boring) is better. E.g. simple html5 css combo is better instead of trying to navigate in JS frameworks.
There is something to this.
But I think at least in front-end engineering, the bigger question is what a new dev values.
LLMs make the browser itself easier to understand in depth, if that's something you want. Building in vanilla-everything, no deps, is now doable at speed and scale for beginners too.
Once you understand the web stack and the principles, you can migrate to any framework and understand their "magic" fast. I think that's not a bad learning path at all, if you use it well, and results in a more competent web-dev than the previous pre-LLM cycle produced.
But if the beginner values output aesthetics and speed more than building their mental model, it is possible to spend years building things without developing any understanding.
For me, at least, the more interesting question is how can we make beginners more interested in the former path than the latter.
>simple html5 css combo
HTML is not a programming language IMHO. Javascript running in browser is not very interesting either.
As a programming languages researcher I disagree. HTML is not just a programming language (a declarative one), it literally has the word “language” in the name. The world of programming languages is much larger than general purpose languages.
Also, whether JavaScript is interesting is a matter of opinion. Given that for a long time it was the only web language, I’d say that makes it interesting. It’s also in my opinion a poorly designed language but that also makes it “interesting” because its behavior is sometimes hard to predict.
Javascript running in the browser is an amazing tool to learn programming as everything you need is right there in the program that's already installed anyway.
No True Scotsmanning someone over that (especially someone who wants to learn something new) is a bit weird, IMHO
Don’t catch me by word :)
Would you prefer starting with compiling a program or running a python script? Imho for beginners it is too heavy, even a python that is so hyped recently.
>whereas someone learning today from scratch using LLMs could give you good advice on what the real difficulties are
if we're talking about the LLM usage as described in the article, they can't do that by definition because they're not learning. You can replace the word "model" in the article with "guy I hired on fiver" and there would be no difference. They outsourced the building of a product.
If you're having robots lift weights for you at the gym it's a moot point to ask what the real problem with your form is, you don't even have one.
You can use an LLM as a kind of tutor to ask it questions the same way you'd google, but you can't actually substitute the programming if you want to learn programming
For example he could tell you about how the LLM destroyed the main database (source of data for him) and so you should prompt the LLMs for how to avoid losing all your data. Real experiences help but not painless.
>I may have built a system that is above my own level of understanding.
I feel like that about a lot of code i did myself; If you don't structure things very logically and really think about your comments; A few months or years will leave you with a hell of a learning curve to understand what you created.
AI actually helps with this, if you have the right prompt injections. I feel like the correct way to handle AI is to take a step back in abstracting problems.
I'm very use to collapsing subroutines to make things readable, maybe even further back from this though, the issue is words become too vague to be useful at these scales.
[delayed]
AI LLM systems, i.e. perplexity.ai, are very good at tutoring someone about how something works, i.e. advanced math, and when done in a loop can be very useful at tutoring, better than youtube videos I've seen on the same subject. The one thing I will usually request in (in the case of math), is to suffix the prompt with "explain this in terms a 9th grader would understand", and this is good enough to explain something in simpler terms with various breakdowns that can be understood by anyone to tutor yourself in alot of subjects using this method. This can be applied to programming, auto repair, construction, almost any subject at this point.
It was much easier in the Age of Empires II Expansion Edition. Miss those times, too... :-/
Programming education in the LLM era will be different from what it is now. Many of the learning methods emerging now are the practices of a "good senior programmer."
But realistically speaking, choosing LLM programming ultimately means pouring out an enormous amount of code, and it's difficult to verify all of it. Common sense says that if you produce 10,000 lines in an hour, you can't read all of it, and even if you do read it, you'd have to rewrite it. The problem is that LLM code differs from human abstraction. Or more precisely, it lacks a programmer's habits, so it's hard for me to maintain.
Clearly, programming in the LLM era will be different. The problem is that I can't get a sense of what that way of doing things actually is.
I think that low-priority frontend work will probably be handled by LLMs, while only complex animation work will be handled by humans, and humans will end up working only on things like payment modules, which are hard to fix if something actually goes wrong.
LLMs are now better at optimization than most people.
I think bill gates summarized it pretty well in a recent letter [1]. There are pro's and con's to every new technology. Learning can be greatly accelerated with the use of llm's but you have to use them the right way. Just like calculators help further down the line, they do not help you when you are still trying to learn the basic concepts of arithmetic. I personally think I have found a way of working with llms that really accelerates getting stuff done while i am still able to learn. It means reading more, and (although I hate this in part) reading generated text. What is infuriating is when I suspect people writing to me with generated text, it is insulting and should be banned. Makes me want to spend more time offline (probably a good thing in my case).
[1]https://www.gatesnotes.com/home/home-page-topic/reader/a-tur...
I'm sure people said the same thing about fire, some 100s of 1000s of years ago.
ai will mitigate but not fully remove coders
"...I'd seriously consider learning carpentry, metalworking, gun-smithing..."
Those sound like hobbies? Outside of apocalyptic/utopian scenarios that is.
I think it's still important for young people to learn coding without the LLM. they need to see the little pieces before they can build big structures. It will be like calculators, just on a bigger scale: you learn how arithmetic works, and then you rely on the calculator when you are multiplying large numbers.
My guess is it will probably take some time to incorporate LLM use into education. People who are graduating right now have a problem, being between two worlds. Those graduating in a few years might have chance to figure out what to do.
> I may have built a system that is above my own level of understanding
If I venture into an unknown area, I end up where the letter-writer ends up when he is visiting programming. Suppose I am curious about an advanced math topic, like Navier-Stokes. The LLM's answer to the news about the new advance last week is strewn with words I don't understand. Asking about anything produces another essay with more things, a loop that never closes. If it were my specialist area, I imagine I would eventually hit some point where the explanation connects to something familiar.
I think this is the wall people run into when they don't have the fundamentals. You eventually get to a point where the machine is asking you for decisions that you won't know the consequences of, and when you are trying to clarify, you end up in a massive rabbit hole. It's not that different from asking a real expert about their area, they will eventually ask you to clarify something that means something to them, but not to you.
I learned programming the slow way. I would run into phrases like "memory barrier" or "green thread" and find an article using the keywords, which led to more searches, which led to more...
There are also many false dawns. Early on, after some success writing some trading strategies, I thought I had it, in the sense that I would be able to write any program required. But it wasn't true, I would run into an iceberg from time to time. Huge areas of knowledge that I hadn't come across.
Obviously I'm not claiming I finally know everything, but LLMs have arrived at a very convenient time for me. For the things I build, there is rarely anything that I don't understand at a fundamental level. When it asks me something, it's an incidental question: what decision should we make? What are the superficial changes that are needed to fit the architecture to the desired product?
I am essentially using LLM as a very quick junior, who knows how the OS works well enough to compile things and analyze logs. These are things that would take a lot of attention in the old days because they can break on very small errors, but the direction was known from the start, and thus for me (having paid the learning cost already) it is just a matter of waiting for the AI to get the code into the desired state.
I have a somewhat usable experience. I was asked to build a trading system a few years ago, which would connect to certain exchanges and show an orderbook. This kind of thing is bread and butter, but writing it up at a new firm would still take weeks.
In recent engagements, I've simply declaratively told the LLM what properties I wanted to see in the solution, waited, and answered a few questions. Since the architecture is the same, there weren't a lot of real decisions. The time difference is immense.
I said this in different ways before and got shoveled because of the way I said it:
None of us know how to farm, not even the chefs who cook for us at a restaurant or fast food joint, but we eat every day and nobody's going around making people feel guilty about not knowing how to till soil and sow seeds..
In programming and other creativity, most people's skills will [have to] change/evolve into managing, directing, dictating, knowing what you want, describing it, and focusing on the end product and iterating,
instead of wrestling with why the f is a string a pointer to a pointer to a character
just like we don't track the phases of the moon and seasonal rainfall before we can have a nice salad to eat.
Yeah but should programming issues become "how can I be more friendly to the waiter so they give me what I want?"
That's not what I want programming to become. I'd choose a different profession then. Maybe I'd become a surgeon because the AI labs haven't cracked robotics yet. And the people in general look up to surgeons and down to IT people, but that's another story.
Sadly, you will have to choose another profession, and as your example shows, the better paying ones need 10 years of gruntwork to get there.
In which language a string would be a pointer to a pointer to a character?
Even in C, string literals are simply a sequence of null-terminated char, no pointer involved.
That’s one of the worst analogies I’ve seen lately
> I said this in different ways before and got shoveled because of the way I said it
Are you sure that was the problem? Because to me it seems like it’s the argument which doesn’t hold. You’re engaging in what is called a False Equivalence.
https://en.wikipedia.org/wiki/False_equivalence
Those things are not the same. Crucially, the food you buy at the store or restaurant is heavily regulated, provenance established, safety checks in place. When there’s a widespread issue, we have systems in place for recalls. The differences don’t stop there, and you can’t ignore them just because the processes so ingrained and well established that you forget they’re there.
> the food you buy at the store or restaurant is heavily regulated, provenance established, safety checks in place. When there’s a widespread issue, we have systems in place for recalls
I live in a country where those things are not regulated and you have to be really careful to not buy something that would send you to the hospital.
>> "About a year ago I became fascinated by AI-assisted programming. Despite having no formal CS background, with LLMs I managed to build a fairly large TypeScript/JavaScript system [...] At first it felt almost magical: [...]
It's comical how these people claim first person: "I built". Look: having a LLM shit you some code is in no way different than paying some third world country dude on Upwork 5 bucks to build you "a Facebook clone" or whatever preposterous claim of grand software. In fact at this point it's cheaper to pay that third world country team than a LLM.
And yet before the advent of LLMs noone ordering a job on Upwork was delusional enough to claim "I built it". Although it's the same magical process, like the magic ring in fairy stories. You put the ring on your finger, rotate it and make a wish and the ring makes it appear. Well, for 5 bucks or something.
But nowadays every half witted retard with 50 bucks to spend goes to a LLM and has some "Facebook clone" spitted out and claims "I BUILT THIS!". You haven't built shit, and you know nothing!
Fortunately, reality strikes sooner or later but boy am I tired of Lord of The rings claims.