Ask HN: Anyone else struggle with how to learn coding in the AI era?

I've been programming for 60 years. I started working with LLM in mid-2025 and have created a number of useful tools in my consulting business. For the programming part, and actually the planning part, I estimate that it increases my productivity by a factor of 5.

Additionally, I always am looking closely at the code and I think I am an incrementally better programmer.

My two cents as a university teacher:

In my view AI tools are a sort of super-advanced interactive documentation. You can learn factual information (excluding allucinations) by either asking or looking at the generated code and explanations of it. But in the same way documentation alone was not a sufficient learning tool before, AI is not now.

What AI cannot give you and I suggest you to learn through other resources:

- algorithmic proficiency, i.e. how to decompose your problems into smaller parts and compose a solution. You don’t necessarily need a full algorithms course (even though you find good ones online for free) but familiarising with at least some classical non-trivial algorithm (e.g. sorting or graph-related ones) is mind-changing.

- high-level design and architecture, i.e. how to design abstractions and use them to obtain a maintainable codebase when size grows. Here the best way is to look at the code of established codebases in your preferred programming language. A good writer is an avid reader. A good programmer reads a lot of other people’s code.

- how programming languages work, i.e. the different paradigms and way of thinking about programming. This lets you avoid fixing on a single one and lets you pick the right tool for each task. I suggest learning both strongly-typed and dynamic languages, to get the feeling of their pros and cons.

That’s an incomplete list from the top of my mind.

You can still use AI as a tool in learning these things, but good old books and online resources (like Coursera) worked really well for decades and are not obsolete at all.

And the last thing is the most important: curiosity about how things work and about how to make them better!

I learned all of my programming outside of university and textbooks. It’s one way to learn. Not the only way though - and it has its limits - but you can get pretty far.

But here is my advice. Learning by doing with AI seems akin to copying source from one location (I.e. view source, stackoverflow).

My tips:

- Understand all of the code in a commit before committing it (per feature/bug).

- Learn by asking AI for other ways or patterns to accomplish the something it suggests.

- Ask Claude Code to explain the code until you understand it.

- If code looks complex, ask if it can be simplified. Then ask why the simple solution is better.

- Tell AI that you’d like to use OOP, functional programming, etc.

One way to measure if you’re learning is to pay attention to how often you accept AI’s first suggestion versus how many times you steer it in a different direction,

It’s really endless if your mindset is to build AND learn. I don’t think you need to worry about it based on the fact you’re here asking this question.

I think it's nearly impossible to "learn" to the same depth when someone else writes the code: it doesn't matter if its your teacher, friend, coworker, or AI writing the code. There absolutely is a difference between having to toil to come up with an answer, fail, fail some more, work through design flaws, then eventually come up with the right answer. You learn a lot in the process.

Versus someone or something giving you the, or even several, correct answers and you picking one. You are given what works. But you don't know why, and you don't know what doesn't work.

Learning from AI coding probably is somewhere between traditional coding and just reading about coding. I'm not sure which one it's closer to though.

However, it may not be necessary to learn to that depth now that AI coding is here. I don't really know.

As someone who learned programming the hard way I hated it even when I am really good at it. It got on the way of me doing useful products and services that I wanted to create.

Because of that I learned Lisp so I could do metaprogramming, because manually it would take multiple humans lives to be able to create what I want before I die, even (or specially) controlling a small group of people.

We use Claude code and personally I love it. It is like the electric sawmill instead of humans cutting manually, sweating and being exhausted after half an hour of work.

After decades programming I know how to tell the AI what I want, and how to control/log/assert/test/atomize everything so it behaves.

You can use AI to teach you programming, the problem is that you need to tell her what you want, and if you are not experience you don't.

So do small projects and let the AI do 80% of the work, spend the remainder 20% finishing by hand. Usually LLMs are amazing for approaching valid solutions but are really bad at making something perfect. They fix something and destroy something else. You do that manually.

But "learning programming" in abstract is like "learning to drill", why do you want to drill? What do you want to drill? Where do you want to drill?

You need to make it specific into specific projects, with specific timelines. "I want to play the piano" is abstract. "I want to play this version of Feather theme in 3 months" is specific.

From your description it sounds like you have the most important stuff: variety, a willingness to do things and a willingness to seek advice. I doubt anyone on HN really knows what it takes to learn to be a coder in the new vibe world. It is really too soon to have seen people 'grow up' and the paths that lead to success or not. In general though if you want to learn something you need to do stuff related to what you want to learn, you need to do stuff in many different ways and you need to ask others what they are doing and have done to see if their paths can help you. Keep doing those things and you will likely be fine. The only other advice I can give you you probably already know, find a passion project. For me it was (initially) fractals. Then it was a thousand other things. One passion project will get you through a lot of learning.

Learn how to program without AI. Learn about software engineering, including algorithms, data structures, software architecture and design, the lot. Work on projects entirely without AI, and once you have completed some projects, work on some more, also without AI. Continue until you have mastered software engineering.

Only once you can engineer and develop whatever you want _well_ use AI, as you will have to direct said AI to follow your design, understand everything the AI spits out, and clean up after the mistakes it makes -- things that require you to be able to program _better_ than the AI in the first place. Never make the mistake of thinking that the AI can come up with a worthwhile design for you.

Always remember that what AI produces fundamentally cannot be trusted as is -- generative AI 'hallucinates' by its very nature -- and that unlike code you written before yourself you won't truly understand what it is 'trying' to do off the bat. Sure, you may have written natural language directions for the AI, but natural languages are often ambiguous and often do not fully signal intent, so the AI may not have done what you actually intended for it to do.

Never think that quantity equals quality -- while generative AI may easily spit out reams of code, those reams of code will lack the fundamental quality of code you have hand-written yourself. Think of what the AI generates as if it were the output of a beginning junior developer chronically high on meth, with everything that entails.

Just don’t use an LLM for learning for doing projects at first. I only use it for things I already know how to do or for research. I treat it like a teenage intern.

1. https://exercism.org/

2. disable copilote

3. only "talk" about concepts and patterns with AI

Don't be afraid to go deep in simple sounding topics. The modern world is so full of learning material that there's the temptation to ingest as much of it as possible, but true learning happens when you give yourself time with one topic at a time. And I'd say this is more important than even, because generative AI is becoming great at precisely generating things, not so in understanding complex topics.

That said, learning the fundamental topics can limit your thinking first if they feel difficult, so it's an interesting question how to keep the naïve creativity of the beginner that's something that can really help when building with AI because there are less limitations in your thinking based on how things used to be.

If you can’t code without an AI then you don’t really know how to code. It’s important to learn skills manually before automating them.

We're all ultimately just learning what we need to to get the job done. After 20 years programming, it is very clear that nobody knows everything. Everyone just knows their own little slice of the software world, and even then you have to 'use it or loose it'. If you're feeling imposter syndrome, keep a study side project going where you don't use any AI, something like NAND to Tetris that forces you to learn low level concepts, and then just stay productive using AI for the rest of your work.

I'd like to draw a parallel to carpentry:

A carpenter uses tools to shape wood into furniture. Each tool in the toolbox has different uses, but some are more efficient than others. For example, a table saw lets the carpenter cut more quickly and accurately than a hand saw. Nobody would say "that's not a real carpenter, he cheats by using a table saw".

A carpenter can also have an assistant (and I'm specifically not talking about an apprentice) who can help with certain tasks. The assistant might be trained by someone else and know how to perform complex tasks. When the carpenter builds something with the assistants help, is that considered a team effort? Does the carpenter need to take responsibility for the assistants mistakes, or the trainer? Who gets credit for the work?

I don't have answers for these questions, but I think the parallel to software is straightforward: we have a new tool (assistant) that's available, and we're trying to use it effectively. Perhaps it's going to replace some of our older tools, and that's a good thing! Some of us will be lazy and offload everything to it, and that's bad.

I do think that learning the fundamentals is as necessary as ever, and AI is a great tool for that as well.

(Disclaimer: I've been programming for about 15 years, and haven't integrated AI into my workflow yet.)

I don’t try to ship quickly. I started learning programming 2024, I’d say I’m pretty good with Python, proficient in vanilla web tech, ok with C, and I know basics of React/Fullstack. Starting from nothing I’d say I have progressed very fast, I follow a uni CS course. LLM’s have certainly helped in explaining concepts and to learn, but I don’t use them to code pretty much at all.

I recognised that my weaknesses are more in understanding the mathematical fundamentals of computation, so now I’m mostly studying maths rather than coding, currently linear algebra and propability theory. Coding is the easy part I’d say. Hopefully I get to concentrate on the study of my sworn enemy, algorithms, at some point.

I’d like to be able to do low-code and graphics/sound -programming some day. Or maybe use that knowledge some other cool stuff, if we are all replaced by robots anyway.

I recommend zachtronics games. I wouldn't go as far as to claim direct knowledge or skill transfer to "real" programming, but it sure feels like it's exercising the metaphorical muscles in a very different way.

Side note, I'm assuming you find joy in programming. If you don't, there's better ways to spend your time.

One can use AI to lead you to better sources. The issue I face is, whenever I search something I want to understand in a search engine, the first 10 links are always low-quality SEO links, or surface level AI generated tutorials. There is a treasure of high-quality blogs, books, interactive tutorials out there which don't show up when you search for it. For example, if you wanted to learn socket programming, you'd be better off following Beej's guide to socket programming instead of 100 g4g pages. Similarly, for Bash, you'd actually understand how every word you write works instead of just memorizing 20 commands if you followed TLDP's book or lhunath's guide. How do you find these resources? Use Perplexity or Reddit's AI to search for high-quality resources.

I truly believe, that even before Vibe Coding the amount of abstractions one is developing against has been in the way of learning programming and feeling good about it.

You do React + Redux or any other framework and feel like a lot of decision have been made for you without grasping the actual reasoning for these decisions.

The best learners I have encountered and for a year, I am trying to implement:

Learn the platform you develop for on a side project. You develop for the web and more on the programming side: Learn JS for Web and HTML. You will encounter state management, animations, events, DOM manipulation etc.. Solve them without libraries, first.

You never stop learning to code. Experimenting with LeetCode and ClaudeCode - revising the landscape of DS&A (again) enough to steer the which, when and what, and validate the how. Eg: I know what a SkipList does, when to use and fuzzily how it works. The hardest thing about leetcode: 1) knowing you will forget it 2) knowing you will never use it much 3) knowing you are forgoing time to learn other stuff 4) knowing that AI makes knowing it redundant

Drop AI, open a basic editor and write everything by hand without asking anything to AI. Do searches by yourself. That’s how world worked for decades pre 2022. Debug by your own, without asking anything to AI as well.

Is this like learning calligraphy in the typesetting era?

Before the AI era, I didn’t know much bash, but I was a reasonably OK programmer besides that I think. I found by getting AI to write me a lot of bash scripts and following along and then making edits myself when I needed small things changed I ended up with the ability to write bash now, and actually kind of appreciated as a language where as before I thought it was confusing. YMMV

Like anything with enough dedication you can achieve what you want.