English is a terrible language for deterministic outcomes in complex/complicated systems. Vibe coders won't understand this until they are 2 years into building the thing.
LLMs have their merits and he sometimes aludes to them, although it almost feels accidental.
Also, you don't spend years studying computer science to learn the language/syntax, but rather the concepts and systems, which don't magically disappear with vibe coding.
This whole direction is a cheeky Trojan horse. A dramatic problem, hidden in a flashy solution, to which a fix will be upsold 3 years from now.
I'm excited to come back to this comment in 3 years.
I think that you seem to be under the impression that Karpathy somehow alluded to or hinted at that in his talk, which indicates you haven't actually watched the talk, which makes your first point kind of weird.
I feel like one of the stronger points he made, was that you cannot treat the LLMs as something they're explicitly not, so why would anyone expect deterministic outcomes from them?
He's making the case for coding with LLMs, not letting the LLMs go by themselves writing code ("vibe coding"), and understanding how they work before attempting to do so.
The disclaimer you mention was indeed mentioned, although it's "in one ear, out the other" with most of his audience.
If I give you a glazed donut with a brief asterisk about how sugar can cause diabetes will it stop you from eating the donut?
You also expect deterministic outcomes when making analogies with power plants and fabs.
> maybe you've seen a lot of GitHub code is not just like code anymore there's a bunch of like English interspersed with code and so I think kind of there's a growing category of new kind of code so not only is it a new programming paradigm it's also remarkable to me that it's in our native language of English and so when this blew my mind a few uh I guess years ago now I tweeted this and um I think it captured the attention of a lot of people and this is my currently pinned tweet uh is that remarkably we're now programming computers in English now
I agree that it's remarkable that you can tell a computer "What is the biggest city in Maresme?" and it tries to answer that question. I don't think he's saying "English is the best language to make complicated systems uncomplicated with", or anything to that effect. Just like I still think "Wow, this thing is fucking flying" every time I sit onboard a airplane, LLMs are kind of incredible in some ways, yet so "dumb" in some other ways. It sounds to me like he's sharing a similar sentiment but about LLMs.
> although it's "in one ear, out the other" with most of his audience.
Did you talk with them? Otherwise this is just creating an imaginary argument against some people you just assume they didn't listen.
> If I give you a glazed donut with a brief asterisk about how sugar can cause diabetes will it stop you from eating the donut?
If I wanted to eat a donut at that point, I guess I'd eat it anyways? But my aversion to risk (or rather the lack of it) tend to be non-typical.
What does my answer mean in the context of LLMs and non-determinism?
> You also expect deterministic outcomes when making analogies with power plants and fabs.
Are you saying that the analogy should be deterministic or that power plants and fabs are deterministic? Because I don't understand if the former, and the latter really isn't deterministic by any definition I recognize that word by.
Particularly not a 40min video.
Maybe it is tongue-in-cheek, maybe I am serious. I am not sure myself. But sometimes the interesting discussions comes from what is on top of the posters mind when viewing the title. Is that bad?
Someone here shared this ancient article by Dijkstra about this exact thing a few weeks ago: https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...
- training data - approximation of the desired outcome
Neither support a good direction for the complexity of some of the system around us, most of which require dedicated language. Imagine doing calculus or quantum physics in English. Novels of words would barely suffice.
So a context window as big as the training data itself?
What if the training data is faulty?
I'm confident you understand that working code or not doesn't matter in this analogy. Neither does LLMs reaching out for the right tool.
LLMs has its merits. Replacing concrete systems that require a formal language and grammar is not.
`1 + 1 = 2` because that's how maths works, not because of deja vú.
Context window will solve a class of problems, but will not solve all problems with AI.
I am a real user and I am on a general purpose e-commerce site and my ask is "I want a TV that is not that expensive", then by definition the user request is barely deterministic. User requests are normally like this for any application. High level and vague at best. Then developers spend all their time on edge cases, user QA, in the weeds junk that the User does not care about at all. People dont want to click filters and fill out forms for your app. They want it to be easy.
Same can't be applied when your supplier needs 300 68 x 34 mm gaskets by the BS10 standard, to give a random, more precise example.
I've been coming to the realization that working with LLMs offer a different set of considerations than working on your own. Notably i find that i often obsess about design, code location, etc because if i get it wrong then my precious after-work time and energy are wasted on refactoring. The larger the code base, the more crippling this becomes for me.
However refactoring is almost not an issue with LLMs. They do it very quickly and aggressively. So the areas i'm not vibing on is just reviewing, and ensuring it isn't committing any insane sins. .. because it definitely will. But the structure i'm accepting is far from what i'd make myself. We'll see how this pans out long term for me, but it's a strategy that i'm exploring.
On the downside, my biggest difficulty with LLMs is getting them to just.. not. To produce less. Choosing too large of tasks is very easy and the code can snowball before you have a chance to pump the breaks and course correct.
Still, it's been a positive experience so far. I still consider it vibing though because i'm accepting far less quality work than what i'd normally produce. In areas where it matters though, i enforce correctness, and have to review everything as a result.
Agreed. That's genuinely a good framing for clients.
First, instruct a friend/colleague of how to multiply two 2 digit numbers in plain English.
Secondly (ideally with a different friend, to not contaminate tests), explain the same but using only maths formulas.
Where does the prompting process start and where does it end? Is it a one-off? Is the prompt clear enough? Do all the parties involved communicate within same domain objects?
Hopefully my example is not too contrived.
This is what an agent can do with an LLM. LLMs can help take English and generate some sort of an algorithm. The agent stores algorithm not the prompt. I do not know what current commercially available agents do but this was always clear to me.
He explicitly says that both LLMs and traditional software have very important roles to play.
LLMs though are incredibly useful when encoding the behavior of the system deterministically is impossible. Previously this fell under the umbrella of problems solved with ML. This would take a giant time investment and a highly competent team to pull off.
Now anyone can solve many of these same problems with a single API call. It’s easy to wave this off, but this a total paradigm shift.
