[lordnacho]

By and large, I agree with the article. Claude is great and fast at doing low level dev work. Getting the syntax right in some complicated mechanism, executing an edit-execute-readlog loop, making multi file edits.

This is exactly why I love it. It's smart enough to do my donkey work.

I've revisited the idea that typing speed doesn't matter for programmers. I think it's still an odd thing to judge a candidate on, but appreciate it in another way now. Being able to type quickly and accurately reduces frustration, and people who foresee less frustration are more likely to try the thing they are thinking about.

With LLMs, I have been able to try so many things that I never tried before. I feel that I'm learning faster because I'm not tripping over silly little things.

[bossyTeacher]

> I feel that I'm learning faster

Yes, you are feeling that. But is that real? If I take all LLMs from you right now, is your current you still better than your pre-LLM you? When I dream I feel that I can fly and as long as I am dreaming, this feeling is true. But the subject of this feeling never was.

[sothatsit]

If you use coding agents as a black box, then yes you might learn less. But if you use them to experiment more, your intuition will get more contact with reality, and that will help you learn more.

For example, my brother recently was deciding how to structure some auth code. He told me he used coding agents to just try several ideas and then he could pick a winner and nail down that one. It's hard to think of a better way to learn the consequences of different design decisions.

Another example is that I've been using coding agents to write CUDA experiments to try to find ways to optimise our codegen. I need an understanding of GPU performance to do this well. Coding agents have let me run 5x the number of experiments I would be able to code, run, and analyse on my own. This helps me test my intuition, see where my understanding is wrong, and correct it.

In this whole process I will likely memorise fewer CUDA APIs and commands, that's true. But I'm happy with that tradeoff if it means I can learn more about bank conflicts, tradeoffs between L1 cache hit rates and shared memory, how to effectively use the TMA, warp specialisation, block swizzling to maximise L2 cache hit rates, how to reduce register usage without local spilling, how to profile kernels and read the PTX/SASS code, etc. I've never been able to put so much effort into actually testing things as I am learning them.

[frde_me]

I feel like my calculator improves my math solutions. If you take away my calculator, I'll probably be worse at math than I was before. That doesn't mean I'm not better off with my calculator however.

[embedding-shape]

That's a pretty interesting take on it, I hadn't considered it like that before when I was considering if my skills were atrophying or not from LLM usage with coding.

[ep103]

Your calculator doesn't charge per use

[baq]

If calculators were invented today, they’d only be sold with a monthly subscription

[frde_me]

If it did, would it change its usefulness in terms of the value it outputs? (through agreed, if I had to pay money it would increase the cost, and so the tradeoff)

[fastasucan]

One guy I work with has little formal training (and mid level experience), but do a lot with LLM's. But in every situation he has to do anything without an LLM he heavily struggles/are not able to anything (say a basic sql query). There is no way someone with his experience and position would still be at that level.

I guess people differ in thinking that is a good or a bad thing. I think it makes up for a huge risk, as he cant really judge good from bad code (or architecture), but his supervisors have put him in a position where he should.

[onemoresoop]

It’s a bit like the shift from film to digital in one very specific sense: the marginal cost of trying again virtually collapsed. When every take cost money and setup time, creators pre-optimized in their head and often never explored half their ideas. When takes became cheap, creators externalized thought as they could try, look, adjust, and discover things they wouldn’t otherwise. Creators could wander more. They could afford to be wrong because of not constantly paying a tax for being clumsy or incomplete, they became more willing to follow a hunch and that's valuable space to explore.

Digital didn’t magically improve art, but it let many more creatives enter the loop of idea, attempt and feedback. LLMs feel similar: they don’t give you better ideas by themselves, but they remove the friction that used to stop you from even finding out whether an idea was viable. That changes how often you learn, and how far you’re willing to push a thought before abandoning it. I've done so many little projects myself that I would have never had time for and feel that I learned something from it, of course not as much if I had all the pre LLM friction, but it should still count for something as I would never have attempted them without this assistance.

Edit: However, the danger isn’t that we’ll have too many ideas, it’s that we’ll confuse movement with progress.

When friction is high, we’re forced to pre-compress thought, to rehearse internally, to notice contradictions before externalizing them. That marination phase (when doing something slowly) does real work: it builds mental models, sharpens the taste and teaches us what not to bother to try. Some of that vanishes when the loop becomes cheap enough that we can just spray possibilities into the world and see what sticks.

A low-friction loop biases us toward breadth over depth. We can skim the surface of many directions without ever sitting long enough in one to feel its resistance. The skill of holding a half formed idea in our head, letting it collide with other thoughts, noticing where it feels weak, atrophies if every vague notion immediately becomes a prompt.

There’s also a cultural effect. When everyone can produce endlessly, the environment fills with half-baked or shallow artifacts. Discovery becomes harder as signal to noise drops.

And on a personal level, it can hollow out satisfaction. Friction used to give weight to output. Finishing something meant you had wrestled with it. If every idea can be instantiated in seconds, each one feels disposable. You can end up in a state of perpetual prototyping, never committing long enough for anything to become yours.

So the slippery slope is not laziness, it is shallowness, not that people won’t think, but people won’t sit with thoughts. The challenge here is to preserve deliberate slowness inside a world that no longer requires it: to use the cheap loop for exploration, while still cultivating the ability to pause, compress, and choose what deserves to exist at all.

[imiric]

> Being able to type quickly and accurately reduces

LLMs can generate code quickly. But there's no guarantee that it's syntactically, let alone semantically, accurate.

> I feel that I'm learning faster because I'm not tripping over silly little things.

I'm curious: what have you actually learned from using LLMs to generate code for you? My experience is completely the opposite. I learn nothing from running generated code, unless I dig in and try to understand it. Which happens more often than not, since I'm forced to review and fix it anyway. So in practice, it rarely saves me time and energy.

I do use LLMs for learning and understanding code, i.e. as an interactive documentation server, but this is not the use case you're describing. And even then, I have to confirm the information with the real API and usage documentation, since it's often hallucinated, outdated, or plain wrong.

[lordnacho]

> I'm curious: what have you actually learned from using LLMs to generate code for you?

I learn whether my design works. Some of the things I plan would take hours to type out and test. Now I can just ask the LLM, it throws out a working, compiling solution, and I can test that without spending my waking hours on silly things. I can just glance at the code and see that it's right or wrong.

If there are internal contradictions in the design, I find that out as well.

[baq]

> LLMs can generate code quickly. But there's no guarantee that it's syntactically, let alone semantically, accurate.

This has been a non-issue with self-correcting models and in-context learning capabilities for so long that saying it today highlights highly out of date priors.

[imiric]

You're referring to tools that fetch content from the web, read my data on disk, and feed it to the models?

I can see how that would lead to a better user experience, but those are copouts. The reality is that the LLM tech without it still has the same issues it has had all along.

Besides, I'll be damned if I allow this vibe coded software to download arbitrary data from the web on my behalf, scan my disk, and share it with companies I don't trust. So when, and if, I can do so safely and keep it under my control, I'll give it a try. Until then, I'll use the "dumb" versions of these models, feed them context manually myself, and judge them based purely on their actual performance.

[baq]

The 'copouts' are what the frontier models are designed to do. If you aren't using the tool as they're intended to, you'll get poor results, obviously.

[imiric]

If in order to use a product as intended I have to punch myself in the face, I'll take the poor results, obviously.

[baq]

You can trade punching yourself in the face for shaving with an angle grinder if you want these kinds of analogies.