Ask HN: How will AI affect learning programming?

LLMs by their nature are good at word or concept association. Cohesiveness in length is where they begin to break down.

I tend to liken them to very drunken scholars. They know things, usually at about a Wikipedia level, and they’re cheery too. But they lack a capacity to doubt themselves; they often are confidently wrong.

Often the greatest help is understanding natural language, but given its hiccups… time using it is probably best spent using it as a drunken librarian – to teach how to phrase and fetch information

As one telling recent example, I tried using an LLM to help with some jq (with which I’m rusty); it got a few basics and then repetitively tripped over a syntax hiccup on loop, “correcting” itself to the same answer each time. A StackOverflow search or two, for comparison, answered my questions and taught some new syntax too. Probably took less time, but more critical thought.

That, coupled with the fact LLMs tend to give an answer and then also an unnecessary verbose step-by-step, means I tend to dislike them.

I also have a huge bugbear about “AI” as a term because it tells you very little. Plenty of applied statistics (markov chains, clustering algos, deep learning eg computer vidion; even SearchRank) are used heavily in research and other cases to do a lot of good. Even for the layman: the Seek app by iNaturalist is awesome for identifying common plant species; Stockfish is (now) a NN that dominates in chess.

But these are classifiers, not generators. By their very nature it is just statistics to evaluate a classifier on a test dataset. Generators, however, are far, far thornier to test, and seem a lot more prone to overfitting.

While I’m not familiar with a typical trained generator tensor, I imagine the optimal one will be surprisingly sparse, though not in a structured way - corresponding to a more clustered “small world” network, which IRL seem the most productive.

Very broadly speaking, I think it's going to have the same impact as search engines had where productivity increases but understanding decreases.

I very much doubt we're going to see a massive shift where everyone becomes a system analyst or service designer and we just punch in business requirements out comes a ready-to-release system.

I can see automated ui testing tools becoming truly amazing if AI Agents are even half of what they're hyped up to be. At the moment they kind of work, but also a bit of a headache.

Are we talking about "coding" or software engineering ? It's somewhat good at "coding", which makes up maybe 25% of my workload, and not so good at engineering imho.

"coding" is like learning cursive and how to spell words, no matter how good you are at that it won't make you a NYT best seller.

My wife is currently enrolled in a computer science program, and AI was absolutely vital for getting her over the 0 to 1 known programming languages chasm (Python, in her case). It has been less helpful (still very!) for C++ after that, but C++ after Python is much easier than C++ with no background at all. So I don't think ignoring the "just programming fundamentals" phase is the right call. I think that's where the greatest difference is going to be by far.

I think a great many more experienced people forget just how hard it is to learn your first, just how many new unintuitive concepts appear no matter what language you choose. Many, many more people are now able to learn that without getting frustrated to the point of giving up, because they all have a digital Aristotle in their pocket to guide them.