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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 si

by japhyr 18d ago

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.

pluc 18d ago
> 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.
- DrewADesign 18d ago
  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.
  - busssard 18d ago
    >> If you’re trying to learn the language, fail. AI-coding tools are the deepl/gtranslate of coding. they might help you understand a foreign website/text better but you wont learn the language with it. and you will continue to be reliant on them until you learn the language. So when you dont have internet access etc. For programming, this was already true for many programmers before LLM. I wasnt able to do much without access to stackoverflow. especially with more complex tasks that i had no experience working with before. Its one thing to figure out an elegant solution to a concrete task, but often it was remembering integrations, libraries, adapters and packages i dindt often work with. So i agree fully, learning a language takes time. The central question is, why are you learning the language? for personal development? for understanding the process the LLM is solving for you? for deep optimization? i can do a fluent translation from german to english for my GF, but sometimes its too exhausting and i paste a text into a translator (or llm) and just read the english text. The same is true for coding. When nuance is important you might want to have a skilled programmer look over what you generated. BTW does anyone use the LLM to directly generate assember code :D
    - testerius 18d ago
      Yes, there is a funny PR in open source project about refactoring source code to assembler code or even machine code 0s and 1s. I do not remember exactly but I remember I LOLed hard when I saw it.
  - ahalay-mahalay 18d ago
    I’ve seen people here learn programming languages by building a compiler, but my go-to project is usually the ICFP 2006 contest. It has a well defined scope, it is entertaining, gets you into the advanced concepts pretty fast as you debug and optimize performance.
  - prajish 17d ago
    Completely agree with the failing part. It's in these moments of "failure" and "struggle" that learning actually happens. Productive failure is a well researched concept in education - https://pubmed.ncbi.nlm.nih.gov/31089856/ https://pubmed.ncbi.nlm.nih.gov/31089856/
senko 18d ago
> 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.
- skydhash 18d ago
  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).
  - bbmatryoshka 18d ago
    non deterministic abstraction are absolutely useful, outside of software sector they have been used since the start of civilization ("a worker" is a very very non deterministic abstraction, outside from the most basic tasks)
    - skydhash 18d ago
      I’m sure that in every case where there such non deterministic abstraction, it’s been always statistically or with a lot of hand waving. So with a heavy dose of expected errors. Pro LLM users don’t want to talk about the error margins of whatever practice or product they’re putting out.
rickydroll 18d ago
I suspect my opinion on this won't be very popular, but it's like driving a car. If you're going to learn how to drive four wheels, you should start by taking the motorcycle safety course and learn on two wheels first. We can discuss why later. Taking that concept to programming, I wouldn't start with a desktop or a web app. I would start with a little embedded system like an ESP32 or one of the small Raspberry Pi controllers (2350). I've come to this opinion because of the people I've mentored as they improved their code-writing skills. The ones who did best were the ones who started with embedded systems. I believe you have to get down and dirty with the machine to understand the code and what it's doing. If you're making a motor controller work, you can use an LLM to generate code using a library or some cut-and-paste MicroPython code. But when it doesn't work, you have to break out the cheapy mini scope you got from Amazon and look at the waveforms. Seriously, get your hands dirty at the controller level; understand queues as driven by hardware, not hidden in a library. Or, even simpler: you need to understand why the blinking lights are blinking, but not the way you thought they would. If I were writing a course on programming, I would give students deliberately and increasingly wrong cut-and-paste code and leave the solution as an exercise to the reader.
ltononro 18d ago
I think there might be a take on learning with AI vs the pressure to develop fast. AI could be an amazing tool to learn. But who wants to learn when you had to deliver 3 days ago? The corp culture is kind of what is killing it. Not sure how things are going in universities tho. If you stop and use it to learn, take time, ask questions. I am sure you can learn a shit ton out of it, most people were learning in the beginning when you had to copy/paste/debug in chatgpt. If I had to answer to this, I'd say that learning has become optional, helpful, but optional. Not in the sense that it is not good to learn it (or anything), but in the sense that you need to opt to learn it, and to do so you will have to sacrifice speed. Do what I say makes sense?
- beej71 18d ago
  I'd quip this summary, only half tongue-in-cheek: "the goal was never to learn things".
  - ltononro 18d ago
    well, does it all boil down to capitalism?
rgoulter 17d ago
> 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. Pre-LLM, I'd distinguish between e.g. "I know Python" and "I know this codebase". So if I wrote a codebase in Python I'd be familiar with it, if someone else wrote a codebase in Python I'd be familiar with the Python. -- An LLM coding agent can give a codebase in Python very quickly. With a newbie, they'd be familiar with neither; but an LLM coding agent can give them a full solution written in Python. I'd say that this power from LLM coding agents blurs the distinction, in some sense. But to an extent it's always been the case that abstractions allow programming without a full understanding of everything down to atoms. People 'can' learn faster than they used to. But I'd think those who are curious to learn will be able to have better results than those who only have a shallow understanding.

by japhyr 18d ago

pluc 18d ago

> 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.

DrewADesign 18d ago
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.
- busssard 18d ago
  >> If you’re trying to learn the language, fail. AI-coding tools are the deepl/gtranslate of coding. they might help you understand a foreign website/text better but you wont learn the language with it. and you will continue to be reliant on them until you learn the language. So when you dont have internet access etc. For programming, this was already true for many programmers before LLM. I wasnt able to do much without access to stackoverflow. especially with more complex tasks that i had no experience working with before. Its one thing to figure out an elegant solution to a concrete task, but often it was remembering integrations, libraries, adapters and packages i dindt often work with. So i agree fully, learning a language takes time. The central question is, why are you learning the language? for personal development? for understanding the process the LLM is solving for you? for deep optimization? i can do a fluent translation from german to english for my GF, but sometimes its too exhausting and i paste a text into a translator (or llm) and just read the english text. The same is true for coding. When nuance is important you might want to have a skilled programmer look over what you generated. BTW does anyone use the LLM to directly generate assember code :D
  - testerius 18d ago
    Yes, there is a funny PR in open source project about refactoring source code to assembler code or even machine code 0s and 1s. I do not remember exactly but I remember I LOLed hard when I saw it.
- ahalay-mahalay 18d ago
  I’ve seen people here learn programming languages by building a compiler, but my go-to project is usually the ICFP 2006 contest. It has a well defined scope, it is entertaining, gets you into the advanced concepts pretty fast as you debug and optimize performance.
- prajish 17d ago
  Completely agree with the failing part. It's in these moments of "failure" and "struggle" that learning actually happens. Productive failure is a well researched concept in education - https://pubmed.ncbi.nlm.nih.gov/31089856/ https://pubmed.ncbi.nlm.nih.gov/31089856/

senko 18d ago

> 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.

skydhash 18d ago
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).
- bbmatryoshka 18d ago
  non deterministic abstraction are absolutely useful, outside of software sector they have been used since the start of civilization ("a worker" is a very very non deterministic abstraction, outside from the most basic tasks)
  - skydhash 18d ago
    I’m sure that in every case where there such non deterministic abstraction, it’s been always statistically or with a lot of hand waving. So with a heavy dose of expected errors. Pro LLM users don’t want to talk about the error margins of whatever practice or product they’re putting out.

rickydroll 18d ago

I suspect my opinion on this won't be very popular, but it's like driving a car. If you're going to learn how to drive four wheels, you should start by taking the motorcycle safety course and learn on two wheels first. We can discuss why later. Taking that concept to programming, I wouldn't start with a desktop or a web app. I would start with a little embedded system like an ESP32 or one of the small Raspberry Pi controllers (2350). I've come to this opinion because of the people I've mentored as they improved their code-writing skills. The ones who did best were the ones who started with embedded systems. I believe you have to get down and dirty with the machine to understand the code and what it's doing. If you're making a motor controller work, you can use an LLM to generate code using a library or some cut-and-paste MicroPython code. But when it doesn't work, you have to break out the cheapy mini scope you got from Amazon and look at the waveforms. Seriously, get your hands dirty at the controller level; understand queues as driven by hardware, not hidden in a library. Or, even simpler: you need to understand why the blinking lights are blinking, but not the way you thought they would. If I were writing a course on programming, I would give students deliberately and increasingly wrong cut-and-paste code and leave the solution as an exercise to the reader.

ltononro 18d ago

I think there might be a take on learning with AI vs the pressure to develop fast. AI could be an amazing tool to learn. But who wants to learn when you had to deliver 3 days ago? The corp culture is kind of what is killing it. Not sure how things are going in universities tho. If you stop and use it to learn, take time, ask questions. I am sure you can learn a shit ton out of it, most people were learning in the beginning when you had to copy/paste/debug in chatgpt. If I had to answer to this, I'd say that learning has become optional, helpful, but optional. Not in the sense that it is not good to learn it (or anything), but in the sense that you need to opt to learn it, and to do so you will have to sacrifice speed. Do what I say makes sense?

beej71 18d ago
I'd quip this summary, only half tongue-in-cheek: "the goal was never to learn things".
- ltononro 18d ago
  well, does it all boil down to capitalism?

rgoulter 17d ago

> 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. Pre-LLM, I'd distinguish between e.g. "I know Python" and "I know this codebase". So if I wrote a codebase in Python I'd be familiar with it, if someone else wrote a codebase in Python I'd be familiar with the Python. -- An LLM coding agent can give a codebase in Python very quickly. With a newbie, they'd be familiar with neither; but an LLM coding agent can give them a full solution written in Python. I'd say that this power from LLM coding agents blurs the distinction, in some sense. But to an extent it's always been the case that abstractions allow programming without a full understanding of everything down to atoms. People 'can' learn faster than they used to. But I'd think those who are curious to learn will be able to have better results than those who only have a shallow understanding.