From my experience, just to say the thing that many such articles never mentioned, because the author has the innate ability anyway:
Seek mental stability, or whatever mental state that can push you to programming for 10 years without completely burning you out. If you can’t even do some programming after a relatively easy work day, you are burnt out.
These days, I'm not sure anymore. I learned through self-study, reading documents. I don't have a degree, and I don't have the exceptional talent to be involved in language standardization.
Everyone around me codes with AI, and most of them produce better results than I do. That's natural. I'm good at some things, but I'm bad at many others. I still hand-code occasionally, but now the main is AI coding, and hand-coding is the sub.
This document probably held true until about 3 or 4 years ago. But since last year, I'm not sure. I have nothing to say other than 'Just do it.'
People say vibe coding has security issues, but honestly, I have security issues in my own code too, from TTL and all that. In the end, with vibe coding, knowing the right security keywords and understanding the problems seems to come from experience anyway.
Reading 'So You Want to be a Programmer,' I feel like conversations with other programmers and the whole process of being a programmer feel distant to me now. Since AI came out, I barely talk about programming with other programmers.
It's not just me—most Korean programmers around me are the same. The reason is simple: those kinds of programming discussions make us realize how limited our knowledge is, since our understanding is often filtered through English sources and then translated into Korean. So we just end up not talking to each other about it.
When I saw this document about 4 or 5 years ago, it felt like it was absolutely true. The environment has changed drastically, and reading this again now. Back then I treated it like a Bible, but not anymore. I realize that if you don't have a degree or aren't a native English speaker, there was always a limit to how much you could participate. These days, everything is changing so fast. As AI breaks down the English barrier, the quality of materials I can read has gone up, and I'm starting to think that even programming mindsets might change depending on language background.
But if I were to advise someone starting programming from scratch today, I'd probably just tell them to learn through vibe coding and get familiar with programming keywords and black-box coding.
Vibe coding has become the baseline these days. The domains individuals need to handle have become so broad that it's gotten more complex.
In the past, being good at just one domain was enough to make a living. But these days, everyone wants to become a full-stack developer—something that used to be rare. Even then, full-stack developers had a clear front-end or back-end bias, with some areas being shallow and others deep. Now, people are expected to be good at everything.
If you want to become a true expert, I'd say keep hand-coding and focus on a single domain. But if not, I'd say just go all in on vibe coding. Use it a lot, and build your own worldview through it
The feeling of hand-coding and vibe coding is so different that I can't help but tell people to practice both. They complement each other, but they don't really feel like they're connected or intertwined. They rely on different kinds of intuition.
Still, I think I'll keep hand-coding. Not because it's more productive, but purely because I enjoy it as a hobby. AI writes better code than I do, but hand-coding is still enjoyable enough as a hobby
One of may favorite things to do was to spend some time tinkering around to learn a new programming language. I collected quite a few and it was fun to learn new paradigms and how to organize programs. But now since AI… idk I just can’t do it, it seems just completely useless in the age of AI. Like getting better at the technical aspects of programming just doesn’t feel important anymore. I don’t think I’ll ever be able to forgive LLMs for that. It really took away something I enjoyed.
When I decided to really learn how to program, after dabbling in very basic C++ from books and using BASIC this article and Eric S. Raymond’s ‘How to Become a Hacker’ [1] (CATB is struggling to load at all these days it seems) were two of the first articles I found. While HtBaH was probably more influential on me immediately, Teach Yourself was a great appetizer for the concept of pacing myself in my learning and in the choices I made.
With all the “AI” has made coding/learning to code a dead end and unnecessary crud being peddled currently I don’t know if people who want to actually learn to program are going to find resources that can be as motivating as this and other articles from `the before times`. It leaves me slightly bummed out, but in my less pessimistic moments I think motivated learners are going to see through the hype and find these types of resource anyway.
On a side note, the Norvig’s articles about implementing a Scheme in Python were my first foray into designing and implementing programming languages, compilers, and interpreters which really shaped my interest all the way through to today.
FWIW, Peter Norvig also gave an updated take on what programming will look like with LLMs around 2 years ago. It did give me some hope back then, but with the development of Fables and Kimis and Sols I think it's truly anyone's guess where the art of programming will go.
In ten years. The kids may not believe that, but you could give realistic timeframes without getting hanged for it. In the age of LLMs, if you weren't done yesterday, it takes too long.
Hm since the discourse is mostly on how AI changes this, I still want to make a point that it's worth learning programming (and more), even if I'm a huge proponent of AI being economically valuable.
Humans at the moment are still better at building on our own knowledge and compounding our effort:output ratio, due to our superior plasticity. See https://joyemang33.github.io/blog/2026/humans-dont-just-samp... for a small experiment. That's why for programmers experienced with a particular language/codebase/domain, it feels like minimal effort to know where and what to change. "Why use LLMs when I can do it in 10 minutes"?
Of course, relying on our long term memory instead of re-ingesting the code, is also a problem for having assumptions that don't get updated, e.g. revisiting a codebase that's been updated by many others. With the rate of change/churn of codebases I'm wondering if this is naturally forcing us to use AI anyways, since it's really hard to keep track of a lot of code changes by AI as a human. Once you start down the path of not reading code yourself, the friction to start reading code gets higher in a feedback loop. I know people (incl. myself) who are still reading code in their own coding/agent sessions, but that's unsustainable in an organizational setting - you won't be able to ingest N'0,000s of LoC of changes every week if there is no incentive to do so.
Is that a good thing? It's unlikely LLMs in their current state will be "neuroplastic" for a single person's use case (mostly market reasons - cost of owning, running, tuning models will likely be unattainable for the foreseeable future). In a few generations, this would be a total surrender of all foundational coding to LLMs (unsure about knowledge work in other domains). Will text files with superintelligent models be enough to encode long term and broad understanding of a domain? Is it necessary if a model is superintelligent? What if a person/nation/species loses access to LLMs through supernatural / political reasons at that point? Will it be like losing the last remaining COBOL programmers to your mainframe? I'm sure we're resilient enough to re-learn all of that, but it will be a huge crutch like never before.
All that to say, it's still worth learning programming, and doing some things by hand, even if it's not "economically valuable" (at least on the surface). Even with web frameworks, it's still worth manually plumbing between frontend/backend/database. Even with interpreters and compilers, it is still worth understanding and doing a bit of assembly. Even with assemblers, it's still worth understanding machine code, CPU and architecture quirks. It's worth looking at the historical artifacts (both senses of the word) and understanding intents, constraints, and creativity (as another comment [1] has more beautifully said).
Two "further than programming" courses that also embody the spirit of the article (get interested, hands-on, play) have been impactful on my appreciation of computing systems are nand2tetris [2] and OS in 1000 lines [3]. Both are "easy" in the sense that they make certain design decisions so you don't have to dig through thousands of pages of manuals. If you wish, during or after the process, you can explore specific concepts further. It's broken down in the right pieces and checkpoints, so you get quick feedback on whether something is working or not. It's still very much a puzzle you have to solve, but the right difficulty/complexity ladder (great instructional design).
I think what doesn't get surfaced enough is that the cost of AI tokens is likely to decrease as new computing hardware gets scaled, such as neuromorphic (or later, photonic) computing.
To assume the current cost of Claude Code and the like is largely fixed is not likely to be accurate. The economic incentive to lower the cost of ML compute at both training and inference is very strong -- and likely even stronger once the bubble bursts.
Once the compute cost is much lower, and the systems more advanced as well, then the future of programming changes. The role of the human in this may be more like: we need one senior experienced full-stack, and like 1-3 others, to build and maintain large complex apps reliably.
When I was 11, I pulled a book off the shelf at the nearby library called "Teach Yourself C++ in 21 Days". I remember thinking the title was clearly nonsense, but if I could even learn a fraction of it, that would be pretty cool. We didn't have a computer at home yet, so I wrote out the exercises by hand.
I think I got about a third of the way through. I knew (debatably) what inheritance was, that a Dog is an Animal, etc.
School, university, and 10 years of career later across multiple stacks and companies, there's still a heap I don't know, but you can drop me in a lot of codebases and I'll figure it out.
> So go ahead and buy that Java/Ruby/Javascript/PHP book; you'll probably get some use out of it. But you won't change your life, or your real overall expertise as a programmer in 24 hours or 21 days.
I went down a five hour rabbit hole learning about jwz as a result of this post. What an interesting guy - if I knew about the DNA lounge a week earlier, I would have visited when I was in the city
I totally agree with this when it comes to skill acquisition and deep expertise, which I care about more with skills than whatever rewards I might get for that expertise.
It's just important not to conflate this deep expertise with the milestone you need to get a job or something. Especially when combined with expertise in other fields, even very little effort can let you advance a lot.
26 comments
[ 3.2 ms ] story [ 23.8 ms ] threadTeach Yourself Programming in Ten Years (1998) - https://news.ycombinator.com/item?id=44697007 - July 2025 (102 comments)
Teach Yourself Programming in Ten Years (1998) - https://news.ycombinator.com/item?id=39001755 - Jan 2024 (302 comments)
Teach Yourself Programming in Ten Years (1998) - https://news.ycombinator.com/item?id=33287618 - Oct 2022 (112 comments)
Teach Yourself Programming in Ten Years (1998) - https://news.ycombinator.com/item?id=27411276 - June 2021 (115 comments)
Teach Yourself Programming in Ten Years (1998) - https://news.ycombinator.com/item?id=20543495 - July 2019 (87 comments)
Teach Yourself Programming in Ten Years (1998) - https://news.ycombinator.com/item?id=16574248 - March 2018 (51 comments)
Teach Yourself Programming in Ten Years (1998) - https://news.ycombinator.com/item?id=9395284 - April 2015 (61 comments)
Teach Yourself Programming in Ten Years (1998) - https://news.ycombinator.com/item?id=5519158 - April 2013 (86 comments)
Teach Yourself Programming in Ten Years by Peter Norvig (2001) - https://news.ycombinator.com/item?id=3439772 - Jan 2012 (29 comments)
Teach Yourself Programming in Ten Years - https://news.ycombinator.com/item?id=191235 - May 2008 (19 comments)
Norvig: Teach Yourself Programming in Ten Years - https://news.ycombinator.com/item?id=43243 - Aug 2007 (7 comments)
Seek mental stability, or whatever mental state that can push you to programming for 10 years without completely burning you out. If you can’t even do some programming after a relatively easy work day, you are burnt out.
BTW ^ is my state nowadays.
Everyone around me codes with AI, and most of them produce better results than I do. That's natural. I'm good at some things, but I'm bad at many others. I still hand-code occasionally, but now the main is AI coding, and hand-coding is the sub.
This document probably held true until about 3 or 4 years ago. But since last year, I'm not sure. I have nothing to say other than 'Just do it.'
People say vibe coding has security issues, but honestly, I have security issues in my own code too, from TTL and all that. In the end, with vibe coding, knowing the right security keywords and understanding the problems seems to come from experience anyway.
Reading 'So You Want to be a Programmer,' I feel like conversations with other programmers and the whole process of being a programmer feel distant to me now. Since AI came out, I barely talk about programming with other programmers.
It's not just me—most Korean programmers around me are the same. The reason is simple: those kinds of programming discussions make us realize how limited our knowledge is, since our understanding is often filtered through English sources and then translated into Korean. So we just end up not talking to each other about it.
When I saw this document about 4 or 5 years ago, it felt like it was absolutely true. The environment has changed drastically, and reading this again now. Back then I treated it like a Bible, but not anymore. I realize that if you don't have a degree or aren't a native English speaker, there was always a limit to how much you could participate. These days, everything is changing so fast. As AI breaks down the English barrier, the quality of materials I can read has gone up, and I'm starting to think that even programming mindsets might change depending on language background.
But if I were to advise someone starting programming from scratch today, I'd probably just tell them to learn through vibe coding and get familiar with programming keywords and black-box coding.
Vibe coding has become the baseline these days. The domains individuals need to handle have become so broad that it's gotten more complex.
In the past, being good at just one domain was enough to make a living. But these days, everyone wants to become a full-stack developer—something that used to be rare. Even then, full-stack developers had a clear front-end or back-end bias, with some areas being shallow and others deep. Now, people are expected to be good at everything.
If you want to become a true expert, I'd say keep hand-coding and focus on a single domain. But if not, I'd say just go all in on vibe coding. Use it a lot, and build your own worldview through it
The feeling of hand-coding and vibe coding is so different that I can't help but tell people to practice both. They complement each other, but they don't really feel like they're connected or intertwined. They rely on different kinds of intuition.
Still, I think I'll keep hand-coding. Not because it's more productive, but purely because I enjoy it as a hobby. AI writes better code than I do, but hand-coding is still enjoyable enough as a hobby
With all the “AI” has made coding/learning to code a dead end and unnecessary crud being peddled currently I don’t know if people who want to actually learn to program are going to find resources that can be as motivating as this and other articles from `the before times`. It leaves me slightly bummed out, but in my less pessimistic moments I think motivated learners are going to see through the hype and find these types of resource anyway.
On a side note, the Norvig’s articles about implementing a Scheme in Python were my first foray into designing and implementing programming languages, compilers, and interpreters which really shaped my interest all the way through to today.
1) http://www.catb.org/~esr/faqs/hacker-howto.html
Not sure if the savings add up enough to offset the investment in learning to program.
https://www.youtube.com/watch?v=ia6aJIplmtc
Humans at the moment are still better at building on our own knowledge and compounding our effort:output ratio, due to our superior plasticity. See https://joyemang33.github.io/blog/2026/humans-dont-just-samp... for a small experiment. That's why for programmers experienced with a particular language/codebase/domain, it feels like minimal effort to know where and what to change. "Why use LLMs when I can do it in 10 minutes"?
Of course, relying on our long term memory instead of re-ingesting the code, is also a problem for having assumptions that don't get updated, e.g. revisiting a codebase that's been updated by many others. With the rate of change/churn of codebases I'm wondering if this is naturally forcing us to use AI anyways, since it's really hard to keep track of a lot of code changes by AI as a human. Once you start down the path of not reading code yourself, the friction to start reading code gets higher in a feedback loop. I know people (incl. myself) who are still reading code in their own coding/agent sessions, but that's unsustainable in an organizational setting - you won't be able to ingest N'0,000s of LoC of changes every week if there is no incentive to do so.
Is that a good thing? It's unlikely LLMs in their current state will be "neuroplastic" for a single person's use case (mostly market reasons - cost of owning, running, tuning models will likely be unattainable for the foreseeable future). In a few generations, this would be a total surrender of all foundational coding to LLMs (unsure about knowledge work in other domains). Will text files with superintelligent models be enough to encode long term and broad understanding of a domain? Is it necessary if a model is superintelligent? What if a person/nation/species loses access to LLMs through supernatural / political reasons at that point? Will it be like losing the last remaining COBOL programmers to your mainframe? I'm sure we're resilient enough to re-learn all of that, but it will be a huge crutch like never before.
All that to say, it's still worth learning programming, and doing some things by hand, even if it's not "economically valuable" (at least on the surface). Even with web frameworks, it's still worth manually plumbing between frontend/backend/database. Even with interpreters and compilers, it is still worth understanding and doing a bit of assembly. Even with assemblers, it's still worth understanding machine code, CPU and architecture quirks. It's worth looking at the historical artifacts (both senses of the word) and understanding intents, constraints, and creativity (as another comment [1] has more beautifully said).
Two "further than programming" courses that also embody the spirit of the article (get interested, hands-on, play) have been impactful on my appreciation of computing systems are nand2tetris [2] and OS in 1000 lines [3]. Both are "easy" in the sense that they make certain design decisions so you don't have to dig through thousands of pages of manuals. If you wish, during or after the process, you can explore specific concepts further. It's broken down in the right pieces and checkpoints, so you get quick feedback on whether something is working or not. It's still very much a puzzle you have to solve, but the right difficulty/complexity ladder (great instructional design).
[1] https://news.ycombinator.com/item?id=49093468 [2] emmelaich ↗ The Abstruse Goose comic mentioned: https://archive.is/HEHmt hdivider ↗ I think what doesn't get surfaced enough is that the cost of AI tokens is likely to decrease as new computing hardware gets scaled, such as neuromorphic (or later, photonic) computing. dzonga ↗ good thing - these articles keep getting shared so often. DavidPiper ↗ When I was 11, I pulled a book off the shelf at the nearby library called "Teach Yourself C++ in 21 Days". I remember thinking the title was clearly nonsense, but if I could even learn a fraction of it, that would be pretty cool. We didn't have a computer at home yet, so I wrote out the exercises by hand. chux52 ↗ I had this same book, only made it to chapter 3 or so (making an OK button) is how I remember it. rushil_b_patel ↗ Great read rrvsh ↗ I went down a five hour rabbit hole learning about jwz as a result of this post. What an interesting guy - if I knew about the DNA lounge a week earlier, I would have visited when I was in the city FinnLobsien ↗ I totally agree with this when it comes to skill acquisition and deep expertise, which I care about more with skills than whatever rewards I might get for that expertise.
To assume the current cost of Claude Code and the like is largely fixed is not likely to be accurate. The economic incentive to lower the cost of ML compute at both training and inference is very strong -- and likely even stronger once the bubble bursts.
Once the compute cost is much lower, and the systems more advanced as well, then the future of programming changes. The role of the human in this may be more like: we need one senior experienced full-stack, and like 1-3 others, to build and maintain large complex apps reliably.
yeah people's attention might be shot. but it's still a blessing to those who read it the first time, and the ones that re-read it.
the unfortunate thing in the 'llm' era - we seem hell bent on destroying the foundations of knowledge.
I think I got about a third of the way through. I knew (debatably) what inheritance was, that a Dog is an Animal, etc.
School, university, and 10 years of career later across multiple stacks and companies, there's still a heap I don't know, but you can drop me in a lot of codebases and I'll figure it out.
> So go ahead and buy that Java/Ruby/Javascript/PHP book; you'll probably get some use out of it. But you won't change your life, or your real overall expertise as a programmer in 24 hours or 21 days.
True, but it DID change my life.
Ended up in Accounting, dabbled with Python and now back to programming with Cursor/Claude.
It did get me just far enough so I could see that computers were complicated but not actually magic and like you did change my life.
It's just important not to conflate this deep expertise with the milestone you need to get a job or something. Especially when combined with expertise in other fields, even very little effort can let you advance a lot.