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Reports of code's death are greatly exaggerated
241–250 of 486 posts
Re: Reports of code's death are greatly exaggerated
#242Earlier quoted context omitted.
It has been proven that recurrent neural networks are Turing complete [0]. So for every computable function, there is a neural network that computes it. That doesn't say anything about size or efficiency, but in principle this allows neural networks to simulate a wide range of intelligent and creative behavior, including the kind of extrapolation you're talking about. [0] https://www.sciencedirect.com/science/article…
Turing conpleteness is not associated with crativity or intelligence in any ateaightforward manner. One cannot unconditionally imply the other.
after all, CSS is Turing Complete ;)
https://stackoverflow.com/questions/2497146/is-css-turing-co...
Re: Reports of code's death are greatly exaggerated
#243Earlier quoted context omitted.
Like what?
New cryptography algorithms, particularly post-quantum cryptography New zero-knowledge proofs Video compression And so forth.
Those are all instances of reuse of existing techniques in new contexts. And when genuinely-new algorithms do arise from genuinely-new areas of study, it's easy enough to teach LLMs how to apply and deploy them.
Re: Reports of code's death are greatly exaggerated
#244Earlier quoted context omitted.
That’s factually untrue. I’m using models to work on frameworks with nearly zero preexisting examples to train on, doing things no one’s ever done with them, and I know this because I ecosystem around these young frameworks. Models can RTFM (and code) and do novel things, demonstrably so.
Yeah. I work with bleeding edge zig. If you just ask Claude to write you a working tcp server with the new Io api, it doesn’t have any idea what it’s doing and the code doesn’t compile. But if you give it some minimal code examples, point it to the recent blog posts about it, and paste in relevant points from std it does incredibly well and produce code that it has not been trained on.
It’s always been about context, then being able to communicate it.
Manager people or managing a hyper-knowledgeable intern (LLM). If you know what you need, actually want what you want (super difficult), and have the ability to provide context to someone else… management has always been easier for you than others.
I find one of the more interesting things about the current “AI debate” is that many programmers are autistic or at least close one side of an empathetic spectrum that they’ve always had trouble communicating what is needed for a task and why. So it’s hard for me to take the opinions going around.
Re: Reports of code's death are greatly exaggerated
#245should note that Mark Twain died 13 years after he announced reports of his death an exaggeration.
We may expect code to be killed off in AI's troublesome teen years.
Re: Reports of code's death are greatly exaggerated
#246Earlier quoted context omitted.
Most art forms do not have a wildly changing landscape of materials and mediums. In software we are seeing things slow down in terms of tooling changes because the value provided by computers is becoming more clear and less reliant on specific technologies. I figure that all this AI coding might free us from NIH syndrome and reinventing relational databases for the 10th time, etc.
The bar to create the new X framework has just been lowered so I expect the opposite, even more churn.
All frameworks make some assumptions and therefore have some constraints. There was always a well-understood trade-off when using frameworks of speeding up early development but slowing down later development as the system encountered the constraints.
LLMs remove the time problem (to an extent) and have more problems around understanding the constraints imposed by the framework. The trade-off is less worth it now.
I have stopped using frameworks completely when writing systems with an LLM. I always tell it to use the base language with as few dependencies as possible.
Re: Reports of code's death are greatly exaggerated
#247Chris Lattner, inventor of the Swift programming language recently took a look at a compiler entirely written by Claude AI. Lattner found nothing innovative in the code generated by AI [1]. And this is why humans will be needed to advance the state of the art. AI tends to accept conventional wisdom. Because of this, it struggles with genuine critical thinking and cannot independently advance the state of the art. AI…
I think the fact that AI can make a working compiler is crazy, especially compared to what most of us thought was possible in this space 4 years ago.
Lately, there have been a few examples of AI tackling what have traditionally been thought of as "hard" problems -- writing browsers and writing compilers. To Christ Lattner's point, these problems are only hard if you're doing it from scratch or doing something novel. But they're not particularly hard if you're just rewriting a reference implementation.
Writing a clean room implementation of a browser or a compiler is really hard. Writing a new compiler or browser referencing existing implementations, but doing something novel is also really hard.
But writing a new version of gcc or webkit by rephrasing their code isn't hard, it's just tedious. I'm sure many humans with zero compiler or browser programing experience could do it, but most people don't bother because what's the point?
Now we have LLMs that can act as reference implementation launderers, and do it for the cost of tokens, so why not?
Re: Reports of code's death are greatly exaggerated
#248Chris Lattner, inventor of the Swift programming language recently took a look at a compiler entirely written by Claude AI. Lattner found nothing innovative in the code generated by AI [1]. And this is why humans will be needed to advance the state of the art. AI tends to accept conventional wisdom. Because of this, it struggles with genuine critical thinking and cannot independently advance the state of the art. AI…
"The AI made a compiler, but it wasn't that novel, so AI is not novel" is a very poor rhetorical foundation Man - just think about what you said Two years ago that would have been beyond shocking. If 'AI is making compilers' - then that's 'beyond disruptive'. It's very true that AI has 'reversion to the mean' characteristics - kind of like everything in life .. ... but it's just unfair to imply that 'AI can't be crea…
The LLM didn’t make a compiler. It generated code that could plausibly implement one. Humans made the compilers it was trained on. It took many such examples and examples of other compilers and thousands of books and articles and blog posts to train the model. It took years of tweaking, fitting, aligning and other tricks to make the model respond to queries with better, more plausible output. It never made, invented, or reasoned about compilers. It’s an algorithm and system running on a bunch of computers.
The C compiler Anthropic got excited about was not a “working” compiler in the sense that you could replace GCC with it and compile the Linux kernel for all of the target platforms it supports. Their definition of, “works,” was that it passed some very basic tests.
Same with SQLite translation from C to Rust. Gaping, poorly specified English prose is insufficient. Even with a human in the loop iterating on it. The Rust version is orders of magnitude slower and uses tons more memory. It’s not a drop in Rust-native replacement for SQLite. It’s something else if you want to try that.
What mechanism in these systems is responsible for guessing the requirements and constraints missing in the prompts? If we improve that mechanism will we get it to generate a slightly more plausible C compiler or will it tell us that our specifications are insufficient and that we should learn more about compilers first?
I’m sure its possible that there are cases where these tools can be useful. I’m not sure this is it though. AGI is purely hypothetical. We don’t simulate a black hole inside a computer and expect gravity to come out of it. We don’t simulate the weather systems on Earth and expect hurricanes to manifest from the computer. Whatever bar the people selling AI system have for AGI is a moving goalpost, a gimmick, a dream of potential to keep us hooked on what they’re selling right now.
It’s unfortunate that the author nearly hits on why but just misses it. The quotes they chose to use nail it. The blog post they reference nearly gets it too. But they both end up giving AI too much credit.
Generating a whole React application is probably a breath of fresh air. I don’t doubt anyone would enjoy that and marvel at it. Writing React code is very tedious. There’s just no reason to believe that it is anything more than it is or that we will see anything more than incremental and small improvements from here. If we see any more at all. It’s possible we’re near the limits of what we can do with LLMs.
Re: Reports of code's death are greatly exaggerated
#249The biggest point everyone keeps missing is that a single code review makes your vibe coded code go from “terrifyingly dangerous” to “better than most people’s code” in one step.
We’re at a point where LLMs write great code, way better than my average coworkers used to anyway. Of course, not reviewing said code by an expert would be a silly as not reviewing a coworkers code, there might be security vulnerabilities in there, hardcoded api keys, etc. But once it’s been professionally reviewed, it’s just as safe as any code written by a human only probably if a higher quality than most people write.
On HN there’s an argument I keep seeing go back and forth which is like “vibe coding is the worst thing ever” and the other side will be like “AI is the second coming of christ and we don’t need programmers” - I think the reason we have what appears to be such opposing views is that those views are actually really close to one another, and proper review is all that separates one from the other.
If you’re already an expert and you don’t vibe code most things and then carefully test and review after, you’re wasting the benefits of these machines. If you’re not an expert then you shouldn’t be employed in the first place, as the main thing people are employed for is responsibility, not output.
This has always been the way in everything. A foreperson gets paid more than a worker on a building site not because they build more than the worker, but because they’re responsible for more than the worker. This is the real reason why programmer jobs won’t go away in my opinion.
Re: Reports of code's death are greatly exaggerated
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