[godelski]

What concerns me is that learning depth is more discouraged than ever. For a long time it's been discouraged, which is natural as we have a preference for simple things rather than difficult/complex things. But we're pushing much harder than ever before. From the way we have influencer education videos to the way people push LLMs ("you can just vibe code, no thinking required"). We've advanced enough that it's easy to make things look good enough but looks can be deceiving. It's impossible to know what's good enough without depth of knowledge, without mastery.

No machine will ever be sufficient to overcome the fundamental problem: a novice is incapable of properly evaluating a system. No human is capable of doing this either, nor can they (despite many believing they can). It's a fundamental information problem. The best we can do is match our human system, where we trust the experts, who have depth. But we even see the limits of that and how frequently they get ignored by those woefully unqualified to evaluate. Maybe it'll be better as people tend to trust machines more. But for the same reason it could be significantly worse. It's near impossible to fix a problem you can't identify.

[ontouchstart]

Ironically, when machine learning is getting “deeper & deeper”, human learning is getting more and more impatient and shallow.

I have been searching “Vibe coding” videos on YouTube that are not promoting something. And I found this one and sat down and watched the whole three hours. It does take a lot of real effort.

https://www.youtube.com/watch?v=EL7Au1tzNxE

[godelski]

I'm a machine learning researcher myself and one of the things that frustrates me is that many of my peers really embrace the "black box" notion of ML models. There are plenty of deep thinkers too, but like with any topic the masters are a much smaller proportion. Just a bit of a shame given that I'm talking about a bunch of people with PhDs...

As for my experience vibe coding is that it is not so much vibing but needing to do a lot of actual work too. I haven't watched that video you linked but that sounds to reflect my actual experience and that of people I know offline.

[ontouchstart]

Since the JavaScript and Python worlds are heavily polluted by LLMs, I start to look into Rust and Cargo ecosystem. Surprisingly it picked up the pace as quickly as possible.

Once Rust can be agentic coded, there will be millions of mines hidden in our critical infrastructure. Then we are doomed.

Someone needs to see the problem coming and start to work on the paths to solution.

[godelski]

The mines are already being placed. There are plenty of people vibe coding C programs. Despite C documentation and examples being more prolific than rust, well... C vulnerabilities are quite easy to create and are even in those examples. You can probably even get the LLMs to find these mines, but it'll require you to know about them.

That's the real scary part to me. It really ramps up the botnets. Those that know what to look for have better automation tools to attack and at the same time we're producing more vulnerable places. It's like we're creating as much kindling as possible and producing more easy strike matches. It's a fire waiting to happen.

[ontouchstart]

I did a toy experiment on a pretty low level crate (serde) in Rust ecosystem, to run a simple demonstration from their website pulling in 42M of dependencies.

https://wtfm-rs.github.io/wtfm-serde/doc/wtfm_serde/

I know this is orders of magnitude smaller than npm or pip, but if this is the best we can get 50 years since 70s UNIX on PDP-11, we are doomed.

[godelski]

It amazes me how much we've embraced dependency hell. Surely we need some dependencies but certainly we're going overboard.

On a side note, I wonder how much of this is due to the avoidance of abstraction. I hear so many say that the biggest use they get from LLMs is avoiding repetition. But I don't quite understand this, as repetition implies poor coding. I also don't understand why there's such a strong reaction against abstraction. Of course, there is such a thing as too much abstraction and this should be avoided, but code, by its very nature, is abstraction. It feels much like how people turned Knuth's "premature optimization is the root of all evil" from "grab a profiler before you optimize you idiot" to "optimization is to be avoided at all costs".

Part of my questioning here is that as the barriers to entry are lowered do these kinds of gross mischaracterizations become more prevalent? Seems like there is a real dark side to lowering the barrier to entry. Just as we see in any social setting (like any subreddit or even HN) that as the population grows the culture changes significantly, and almost always to be towards the novice. For example, it seems that on HN we can't even make the assumption that a given user is a programmer. I'm glad we're opening up (as I'm glad we make barriers to entry lower), but "why are you here if you don't want to learn the details?" How do we lower barriers and increase openness without killing the wizards and letting the novices rule?

[markus_zhang]

It is from my observation that more and more companies, even FAANG, not only encourage fast iteration with LLM tools, but discourage true study and thinking through. The later is inevitably slow, which is not favourable for today’s fast iteration world. This makes me think that it is so important to get into the right team, otherwise one runs the risk of not properly thinking and experimenting again.

[ontouchstart]

So how do we find or make the right teams?

The incentive and loss function are pointing to short term attention and long term amnesia. We are fighting the algorithms.

[markus_zhang]

I think low level programming or anything critical is still relatively safe. That’s where I wish I could be, but still very far away from.