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Claude is good at assembling blocks, but still falls apart at creating them

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Re: Claude is good at assembling blocks, but still falls apart at creating them

#91

Post by ep103 »

frde_me wrote: post #86
bossyTeacher wrote: post #78

Earlier quoted context omitted.

> 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.

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.

Your calculator doesn't charge per use

Re: Claude is good at assembling blocks, but still falls apart at creating them

#92

Post by frde_me »

ep103 wrote: post #91
frde_me wrote: post #86

Earlier quoted context omitted.

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.

Your calculator doesn't charge per use

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)

Re: Claude is good at assembling blocks, but still falls apart at creating them

#93

Post by sothatsit »

bossyTeacher wrote: post #78
lordnacho wrote: post #64

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…

> 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.

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.

Re: Claude is good at assembling blocks, but still falls apart at creating them

#94

Post by disconcision »

I've yet to be convinced by any article, including this one, that attempts to draw boxes around what coding agents are and aren't good at in a way that is robust on a 6 to 12 month horizon.

I agree that the examples listed here are relatable, and I've seen similar in my uses of various coding harnesses, including, to some degree, ones driven by opus 4.5. But my general experience with using LLMs for development over the last few years has been that:

1. Initially models could at best assemble a simple procedural or compositional sequences of commands or functions to accomplish a basic goal, perhaps meeting tests or type checking, but with no overall coherence,

2. To being able to structure small functions reasonably,

3. To being able to structure large functions reasonably,

4. To being able to structure medium-sized files reasonably,

5. To being able to structure large files, and small multi-file subsystems, somewhat reasonably.

So the idea that they are now falling down on the multi-module or multi-file or multi-microservice level is both not particularly surprising to me and also both not particularly indicative of future performance. There is a hierarchy of scales at which abstraction can be applied, and it seems plausible to me that the march of capability improvement is a continuous push upwards in the scale at which agents can reasonably abstract code.

Alternatively, there could be that there is a legitimate discontinuity here, at which anything resembling current approaches will max out, but I don't see strong evidence for it here.

Re: Claude is good at assembling blocks, but still falls apart at creating them

#95

Post by frde_me »

imiric wrote: post #77
mikece wrote: post #2

In my experience Claude is like a "good junior developer" -- can do some things really well, FUBARS other things, but on the whole something to which tasks can be delegated if things are well explained. If/when it gets to the ability level of a mid-level engineer it will be revolutionary. Typically a mid-level engineer can be relied upon to do the right thing with no/minimal oversight, can figure out incomplete instr…

> In my experience Claude is like a "good junior developer" We've been saying this for years at this point. I don't disagree with you[1], but when will these tools graduate to "great senior developer", at the very least? Where are the "superhuman coders by end of 2025" that Sam Altman has promised us? Why is there such a large disconnect between the benchmarks these companies keep promoting, and the actual real world…

I mean, I'm shipping a vast majority of my code nowadays with Opus 4.5 (and this isn't throwaway personal code, it's real products making real money for a real company). It only fails on certain types of tasks (which by now I kind of have a sense of).

I still determine the architecture in a broad manner, and guide it towards how I want to organize the codebase, but it definitely solves most problems faster and better than I would expect for even a good junior.

Something I've started doing is feeding it errors we see in datadog and having it generate PRs. That alone has fixed a bunch of bugs we wouldn't have had time to address / that were low volume. The quality of the product is most probably net better right now than it would have been without AI. And velocity / latency of changes is much better than it was a year ago (working at the same company, with the same people)

Re: Claude is good at assembling blocks, but still falls apart at creating them

#97

Post by groby_b »

disconcision wrote: post #94

I've yet to be convinced by any article, including this one, that attempts to draw boxes around what coding agents are and aren't good at in a way that is robust on a 6 to 12 month horizon. I agree that the examples listed here are relatable, and I've seen similar in my uses of various coding harnesses, including, to some degree, ones driven by opus 4.5. But my general experience with using LLMs for development over…

LLMs are bad at creating abstraction boundaries since inception. People have been calling it out since inception. (Heck, even I got a twitter post somewhere >12 months old calling that out, and I'm not exactly a leading light of the effort)

It is in no way size-related. The technology cannot create new concepts/abstractions, and so fails at abstraction. Reliably.

Re: Claude is good at assembling blocks, but still falls apart at creating them

#98

Post by irishcoffee »

soulofmischief wrote: post #75
adrian_b wrote: post #71

Earlier quoted context omitted.

I agree that after discussions with a LLM you may be led to novel insights. However, such novel insights are not novel due to the LLM, but due to you. The "novel" insights are either novel only to you, because they belong to something that you have not studied before, or they are novel ideas that were generated by yourself as a consequence of your attempts to explain what you want to the LLM. It is very frequent for…

The point is that the combined knowledge/process of the LLM and a user (which could be another LLM!) led to it walking the manifold in a way that produced a novel distribution for a given domain. I talk with LLMs for hours out of the day, every single day. I'm deeply familiar with their strengths and shortcomings on both a technical and intuitive level. I push them to their limits and have definitely witnessed novel…

> I push them to their limits and have definitely witnessed novel output.

I’m quite curious what these novel outputs are. I imagine the entire world would like to know of an LLM producing completely, never-before-created outputs which no human has ever thought before.

Here is where I get completely hung up. Take 2+2. An LLM has never had 2 groups of two items and reached the enlightenment of 2+2=4

It only knows that because it was told that. If enough people start putting 2+2=3 on the internet who knows what the LLM will spit out. There was that example a ways back where an LLM would happily suggest all humans should eat 1 rock a day. Amusingly, even _that_ wasn’t a novel idea for the LLM, it simply regurgitated what it scraped from a website about humans eating rocks. Which leads to the crux: how much patently false information have LLMs scraped that is completely incorrect?

Re: Claude is good at assembling blocks, but still falls apart at creating them

#99

Post by MrOrelliOReilly »

onemoresoop wrote: post #66
fennecbutt wrote: post #52

Earlier quoted context omitted.

I agree somewhat, but more when it comes to its use of logic - it only gleans logic from human language which as we know is a fucking mess. I've commented before on my belief that the majority of human activity is derivative. If you ask someone to think of a new kind of animal, alien or random object they will always base it off things that they have seen before. Truly original thoughts and things in this world are a…

LLMs lack agency in the sense that they have no goals, preferences, or commitments. Humans do, even when our ideas are derivative. We can decide that this is the right choice and move forward, subjectively and imperfectly. That capacity to commit under uncertainty is part of what agency actually is.

But they do have utility functions, which one can interpret as nearly equivalent

Re: Claude is good at assembling blocks, but still falls apart at creating them

#100

Post by kylecazar »

XenophileJKO wrote: post #56
andy99 wrote: post #48

Earlier quoted context omitted.

No, this describes the common understanding of LLMs and adds little to just calling it AI. The search is the more accurate model when considering their actual capabilities and understanding weaknesses. “Lossy compression of human knowledge” is marketing.

It is fundamentally and provably different than search because it captures things on two dimensions that can be used combinatorially to infer desired behavior for unobserved examples. 1. Conceptual Distillation - Proven by research work that we can find weights that capture/influence outputs that align with higher level concepts. 2. Conceptual Relations - The internal relationships capture how these concepts are rela…

Aren't the conceptual relations you describe still, at their core, just search (even if that's extremely reductive)? We know models can interpolate well, but it's still the same probabilistic pattern matching. They identify conceptual relationships based on associations seen in vast training data. It's my understanding that models are still not at all good at extrapolation, handling data "way outside" of their training set.

Also, I was under the impression LLM's can replicate irony and humor simply because that text has specific stylistic properties, and they've been trained on it.

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