There's also a huge difference between liking to program and liking to work as a programmer. I despise the latter as business programming takes the joy out of everything. Trying to educate management about the current boundaries of the product or having to work extra hard because a product manager promised features that dont yet exist is exhausting. Not being allowed to work on fixing tech debt while having to build on top of it is pain. Doesn't help being a solo dev in a start up either so maybe that's the issue.

> it’s (a) going to be harder for you than for people who like it and (b) maybe you should consider a field that you do like?

This shit is so infantile. It's like people have never heard of grinding. Do you think big law attorneys "love" their jobs? How about public accountants? How about dentists (famously known for having high suicide rates).

> Maybe you hate programming, but you want the money enough.

Yes welcome to the grand epiphany that drives 99% of people. There's nothing special about programmers.

> I suspected that main topic of discussion here would be "is it worth learning CS at all in 2026?"

Considering the current state of the job market I don't think it is good idea to go into CS in 2026 expecting a lucrative career. People who just love to program will find a job eventually, of course.

> Does anyone suspect that some HN posts have a lot of astroturfing from AI-adjacent organisations?

Why does it have to be AI? I don't work for OpenAI/Anthropic/etc. and am an "AI-skeptic" overall. I don't believe that the current job market conditions are caused by AI. I think the issue is that the field has become saturated with all your regular "fullstack web ninjas" while higher education institutions are still pumping hordes of CS(-adjacent) grads. Things will get worse (people that went into CS before the downturn are still graduating) before they get better (smaller number of people who are truly interested are choosing the field these days which will result less people of higher average quality graduating in a few years).

I actually feel like the discussion here has trended relatively negative on AI for the most part.

Things change quickly though, and it makes sense for opinions to, too.

I'm sure there are bots on here as much as astroturfing.

Partly is seems to be how quickly an article has comments leaning to one side or another. Once a few of these comments get off the ground, it's hard for voting on HN to reflect the discussion these days.

It does appear that more users from Reddit etc. are not just using HN these days, but commenting. The quality of posts and comments has definitely decreased, in line with the quality of content on blog posts decreasing.

My impression is that there is definitely astroturfing around AI, yes

HN has become per-thread view-enforced. It's pretty obvious now what the "correct" views are for any given thread, any dissenting comments are downvoted to death. When the next thread comes along, the opposite view might be the "allowed one". There's a particularly egregious amount of veiled partisanship behind a lot of posting too.

This could be a group think phenomenon, or it could be botting. Hard to say. I'd say in at least a few cases, it's someone with access and interest into bot downvotes landing on a thread and using that to suppress dissenting views.

Looking at the article headings these don't feel like computer science, and rather how to approach coding problems. This is useful, but not Computer Science. I think that we should refer to this as Programming Engineering or Software Engineering.

It is important to call these distinctions out in my mind because the Computer Science is often the concepts or foundations, whereas the Software Engineering is about how to convert those concepts and use them in a situation such that the concepts are well implemented, tested, and will stand the tests of time (including changing it). They're different skills and concepts.

Beej has been a stable reference for me for decades, also. By applying the knowledge gained in his networking guide, I've won many contracts for work that others considered too difficult (or "impossible" in the budget constraints) .. but which I managed to deliver, on time and under budget, because Beej had led the way.

Easily one of my top 5 favourite people on the Internet, alongside Linus Torvalds and so on.. I would say Beejs' impact on technology has been understated but definitely an order of magnitude or two greater than most.

Not my kids, but I also passed my Networking course thanks to his guide (early 10s), and I used it as reference material for teaching about sockets in an Operating Systems course this year.

Sometimes I just love the Internet, man.

I'd recommend going on Thriftbooks and ordering a textbook. I can't remember the exact copy I had years ago when I was self-learning CS, but it was like $4.00 for a really incredible textbook.

Now, I don't have a degree, so take my advice with a grain of salt, but the book was really really good, and honestly, if you've been programming for awhile, I think most of the concepts didn't require a heavy math background (of course, it would probably help). The chapters were like: Symbolic Logic, Set Theory, Proofs, Algorithms, Cryptography, and other things which I can't remember.

Edit: The book is free to read online

EDIT EDIT: Removed link as I don't know if that was a "legal" link.

It's out of stock on Thriftbooks, but looks to go for $6-8 on there.

https://www.mathacademy.com/ is a great combination of structured learning across an incremental skill tree with practise problems to prove to yourself that you understand. It’s a big commitment but helped me go from “hasn’t done any math for a while and probably missed some basics” to much more comfortable. You can do the self-test to pick a starting level and work up from there.

As with many things you basically have to sit down and do the work, though, you’re not going to get better just by inhaling books and videos. MA isn’t a fun/gamified learning platform like Duolingo, the ‘fun’ comes from putting the work in and seeing yourself improve. For me it went from a grind initially to something I enjoyed doing.

https://www.geogebra.org/ is also worth exploring for its novel visual approach, but is much more rudimentary, less challenging, and less deep than MA.

https://betterexplained.com is a goldmine

Khan Academy https://www.khanacademy.org/

My advice is to (re-)learn elementary algebra to a proficient level before attempting any other branch of math. That is a core prerequisite for absolutely everything. By elementary algebra I mean roughly everything in classes called “Algebra” or “pre-calculus” that you learn in an American high school before calculus. Geometry and trigonometry can’t hurt either but algebra is more central.

> one or two math subjects

Only one or two? :)

It's not easy as an adult but it's definitely doable, don't get discouraged. The main hurdle isn't knowledge of specific topics, most undergraduate courses assume little to no previous baggage, i'd say it's more the lack of "mathematical maturity"[0], or the ability to "bridge" between the formal language of math and the intuitive "what we're doing here".

When you're writing code, you probably don't stop to think "I need to do this operation for each element of this vector, a for loop is what I need", you instead have a high level idea of what you're trying to accomplish and "make the code happen", filling in the formalities as needed. Trying to go line by line is how beginners operate, and that's why they never get anything done. I'd never get anything done either if I had to work like that!

The reason why many people get stuck in math is similar. You read a definition that goes "for all ε>0 there exists δ such that for all ..." and you immediately get confused, trying to keep the entire "abstract syntax tree" of what you just read in memory. Like in the code example, the "mature" way to see it is that we're trying to capture an idea, and the formalism is instrumental in that. What are the variables "morally" doing? (At a certain point you'll realize the formalism is actually working for you rather than against you, but that's a rant for another time...)

The conceptually easier but more time-consuming thing to do is to practice symbol pushing if you lost that since high school. For example: is it immediately obvious to you what (a+b)^n is if you expand it? Do you remember how to factor (a^3 + b^3)? Do not despair if you don't. Many more people than you think can't do that off the top of their heads, but it's the kind of "mechanical" skills that's probably blocking you at this point.

Another important aspect to learn is a bit of notation, the "standard library" of math, as it were. Understand "for all" and "exists" as quantifiers, and how they interact with negation and logical operators. It should be eventually obvious to you that negation "inverts" quantifiers. Learn at least a little bit how to work with naive set theory: union, intersection, etc. Look up what the "common" sets (integers, rationals, reals, complex) are and how they relate with each other.

And finally, try to get a feel for how proofs work. That's going to be important, even for the type of math you need for computer science.

Good luck!

--- [0] https://en.wikipedia.org/wiki/Mathematical_maturity

Must have been nearly 25 years ago now, this was one of my first dips into network programming after very briefly dabbling in systems programming based on nothing but random man page exploration and K&R. Think I still have it printed somewhere.

Beej has really done the world a solid, it's also making me happy that they're still around and being kept up to date (as much as these things change).

Since I had to find the link: https://beej.us/guide/bgnet/

I was lucky to work with him for a while. He’s a great guy IRL too.

Yea agree, free educational content is worth so much. Especially in a world where everyone wants to make a quick coin by selling courses or whatever. Would never have looked into C if it wasn't for beejs guide, as that other book that's often recommended is as dry as math books...

I recently read his networking guide as part of a class and it was invaluable. It gets you up to speed without overwhelming you with detail. It's a lightweight read.

> 7.3 No Copy-Paste Coding

> [...] But nowadays they tend to punt to some AI. Beginning developers should not do this.

I don't know how I feel about this. I get the sentiment, I really do. But it almost reads like a chess Grandmaster in the 90s telling up-and-coming players to not practice against Deep Blue because it will teach you bad habits.

> 1 Foreword

> Are you getting into Computer Science, or thinking about it? Or maybe you’re in it already. This super-high-level guide is for you!

> I’m not going to talk about how to write code (much). I’ll I’m going to talk about in these roughly 40 pages is more about how to learn when you’re a nascent software developer.

Page 1

What about operating systems, architecture, compilers, networking, and the like? I have seen people argue that computer science is the more theoretical side of things, but many university CS programs cover both systems and theory (or sometimes skew to one side).

I see it as "learning how to learn", not directly learning itself like a curriculum. It's more for those who want to build proper study habits.

Reading the chapter of AI seems to support that feeling. It was about tips on where to use it, where to not use it as a shortcut, how to be critical of any output, and some personal speculation.