Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Andrej Karpathy: Software in the era of AI [video]

youtube.com

Post reply on HN
Print view View original

431–440 of 827 posts

Re: Andrej Karpathy: Software in the era of AI [video]

#431

Post by jamessinghal »

dist-epoch wrote: post #393
NoOn3 wrote: post #310

Earlier quoted context omitted.

It's cheap now. But if you take into account all the training costs, then at such prices they cannot make a profit in any way. This is called dumping to capture the market.

There is no "capture" here, it's trivial to switch LLM/providers, they all use OpenAI API. It's literally a URL change.

This is changing; OpenAI's newer API (Responses) is required to include reasoning tokens in the context while using the API, to get the reasoning summaries, and to use some of the OpenAI provided tools. Google's OpenAI compatibility supports Chat Completions, not Responses.

As the LLM developers continue to add unique features to their APIs, the shared API which is now OpenAI will only support the minimal common subset and many will probably deprecate the compatibility API. Devs will have to rely on SDKs to offer comptibility.

Re: Andrej Karpathy: Software in the era of AI [video]

#432

Post by practal »

gylterud wrote: post #340
practal wrote: post #142

Earlier quoted context omitted.

> Being so similar to programming languages I think it is more important to be close to English than to programming languages, because that is the critical part: "As close to a programming language as necessary, as close to English as possible" is the goal, in my opinion, without sacrificing constraints such as simplicity.

Why? Why would the language used to express proof of correctness have anything to do with English? English was not developed to facilitate exact and formal reasoning. In natural language ambiguity is a feature, in formal languages it is unwanted. Just look at maths. The reasons for all the symbols is not only brevity but also precision. (I dont think the symbolism of mathematics is something to strive for though, we…

My answer is already in my previous comment: if you have two formal languages to choose from, you want the one closer to natural language, because it will be easier to see if informal and formal statements match. Once you are in formal land, you can do transformations to other formal systems as you like, as these can be machine-verified. Does that make sense?

Re: Andrej Karpathy: Software in the era of AI [video]

#433

Post by norir »

kordlessagain wrote: post #414
practal wrote: post #50

Great talk, thanks for putting it online so quickly. I liked the idea of making the generation / verification loop go brrr, and one way to do this is to make verification not just a human task, but a machine task, where possible. Yes, I am talking about formal verification, of course! That also goes nicely together with "keeping the AI on a tight leash". It seems to clash though with "English is the new programming l…

This thread perfectly captures what Karpathy was getting at. We're witnessing a fundamental shift where the interface to computing is changing from formal syntax to natural language. But you can see people struggling to let go of the formal foundations they've built their careers on.

Have you thought through the downsides of letting go of these formal foundations that have nothing to do with job preservation? This comes across as a rather cynical interpretation of the motivations of those who have concerns.

Re: Andrej Karpathy: Software in the era of AI [video]

#434

Post by jes5199 »

okay I’m practicing my new spiel:

this focus on coding is the wrong level of abstraction

coding is no longer the problem. the problem is getting the right context to the coding agent. this is much, much harder

“vibe coding” is the new “horseless carriage”

the job of the human engineer is “context wrangling”

Re: Andrej Karpathy: Software in the era of AI [video]

#435

Post by yusina »

shakna wrote: post #143
melvinmelih wrote: post #104

Earlier quoted context omitted.

> Driving is not a general problem, though. But what's driving a car? A generalist human brain that has been trained for ~30 hours to drive a car.

Human brain's aren't generalist! We have multiple parts of the brain that interact in vastly different ways! Your cerebellum won't be running the role of the pons. Most parts of the brain cannot take over for others. Self-healing is the exception, not the rule. Yes, we have a degree of neuroplasticity, but there are many limits. (Sidenote: Driver's license here is 240 hours.)

240 hours sounds excessive. Where is "here"?

Re: Andrej Karpathy: Software in the era of AI [video]

#436

Post by Eggpants »

NitpickLawyer wrote: post #349
guappa wrote: post #301

Earlier quoted context omitted.

Yeah, because they're bleeding money like crazy now. You should consider how much it actually costs, not how much they charge. How do people fail to consider this?

No, there are 3rd party providers that run open-weights models and they are (most likely) not bleeding money. Their prices are kind of similar, and make sense in a napkin-math kind of way (we looked into this when ordering hardware). You are correct that some providers might reduce prices for market capture, but the alternatives are still cheap, and some are close to being competitive in quality to the API providers.

Starts with “No” then follows that up with “most likely”.

So in other words you don’t know the real answer but posted anyways.

Re: Andrej Karpathy: Software in the era of AI [video]

#437

Post by danny_codes »

kypro wrote: post #425

I know we've had thought leaders in tech before, but am I the only one who is getting a bit fed up by practically anything a handful of people in the AI space say being circulated everywhere in tech spaces at the moment?

No it’s incredibly annoying I agree.

The hype hysteria is ridiculous.

Re: Andrej Karpathy: Software in the era of AI [video]

#438

Post by xpe »

abdullin wrote: post #291
bandoti wrote: post #285

Earlier quoted context omitted.

Here’s a few problems I foresee: 1. People get lazy when presented with four choices they had no hand in creating, and they don’t look over the four and just click one, ignoring the others. Why? Because they have ten more of these on the go at once, diminishing their overall focus. 2. Automated tests, end-to-end sim., linting, etc—tools already exist and work at scale. They should be robust and THOROUGHLY reviewed by…

A simple rule applies: "No matter what tool created the code, you are still responsible for what you merge into main". As such, task of verification, still falls on hands of engineers. Given that and proper processes, modern tooling works nicely with codebases ranging from 10k LOC (mixed embedded device code with golang backends and python DS/ML) to 700k LOC (legacy enterprise applications from the mainframe era)

> A simple rule applies: "No matter what tool created the code, you are still responsible for what you merge into main".

Beware of claims of simple rules.

Take one subset of the problem: code reviews in an organizational environment. How well does they simple rule above work?

The idea of “Person P will take responsibility” is far from clear and often not a good solution. (1) P is fallible. (2) Some consequences are too great to allow one person to trigger them, which is why we have systems and checks. (3) P cannot necessarily right the wrong. (4) No-fault analyses are often better when it comes to long-term solutions which require a fear free culture to reduce cover-ups.

But this is bigger than one organization. The effects of software quickly escape organizational boundaries. So when we think about giving more power to AI tooling, we have to be really smart. This means understanding human nature, decision theory, political economy [1], societal norms, and law. And building smart systems (technical and organizational)

Recommending good strategies for making AI generated code safe is hard problem. I’d bet it is a much harder than even “elite” software developers people have contemplated, much less implemented. Training in software helps but is insufficient. I personally have some optimism for formal methods, defense in depth, and carefully implemented human-in-the-loop systems.

[1] Political economy uses many of the tools of economics to study the incentives of human decision making

Re: Andrej Karpathy: Software in the era of AI [video]

#439

Post by fifilura »

tudorizer wrote: post #423
diggan wrote: post #410

Earlier quoted context omitted.

> English is a terrible language for deterministic outcomes in complex/complicated systems 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 no…

I watched the entire talk, quite carefully. He explicitly states how excited he was about his tweet mentioning English. 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 analo…

Either way, I am not sure it is a requirement on HN to read/view the source.

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?

Re: Andrej Karpathy: Software in the era of AI [video]

#440

Post by oc1 »

tudorizer wrote: post #405

95% terrible expression of the landscape, 5% neatly dumbed down analogies. 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/synt…

AI is all about context window. If you figured out the context problem, you will see that all these "AI is bullshit, it doesn't work and can't produce working code" goes away. Same for everything else.
Post reply on HN