16 ms·

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

315 points by bblcla 9mo ago

mikece 9mo ago
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 instructions, and deliver quality results (and even train up the juniors on some things). At that point the only reason to have human junior engineers is so they can learn their way up the ladder to being an architect and responsible coordinating swarms of Claude Agents to develop whole applications and complete complex tasks and initiatives. Beyond that what can Claude do... analyze the business and market as a whole and decide on product features, industry inefficiencies, gap analysis, and then define projects to address those and coordinate fleets of agents to change or even radically pivot an entire business? I don't think we'll get to the point where all you have is a CEO and a massive Claude account but it's not completely science fiction the more I think about it.
- alfalfasprout 9mo ago
  > I don't think we'll get to the point where all you have is a CEO and a massive Claude account but it's not completely science fiction the more I think about it. At that point, why do you even need the CEO?
  - ako 9mo ago
    And who does he sell his software to? Companies that have only 1 employee, don’t need a lot of user licenses for their employees…
    - AshamedCaptain 9mo ago
      What would be the point of selling software in such a world ? (where anyone could build any piece of software with a handful of keystrokes)
  - arjie 9mo ago
    Reminds me of an old joke[0]: > The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment. But really, the reason is that people like Pieter Levels do exist: masters at product vision and marketing. He also happens to be a proficient programmer, but there are probably other versions of him which are not programmers who will find the bar to product easier to meet now. 0: https://quoteinvestigator.com/2022/01/30/future-factory/ https://quoteinvestigator.com/2022/01/30/future-factory/
    - MrDunham 9mo ago
      My technical cofounder reminds me of this story on a weekly basis.
  - ceejayoz 9mo ago
    As Steinbeck is often slightly misquoted: > Socialism never took root in America because the poor see themselves not as an exploited proletariat, but as temporarily embarrassed millionaires. Same deal here, but everyone imagines themselves as the billionaire CEO in charge of the perfectly compliant and effective AI.
  - tiku 9mo ago
    For the network.
  - mettamage 9mo ago
    All of us are a CEO by that point.
    - ArtificialAI 9mo ago
      If everyone is, no one is.
      - empath75 9mo ago
        Wouldn't that be a good thing?
        shimman 9mo ago
        If you think the purpose of living your one single life in the universe is to become a CEO, you have a failure of imagination and should likely be debanked to protect society.
  - pixelready 9mo ago
    The board (in theory) represents the interests of investors, and even with all of the other duties of a CEO stripped away, they will want a ringable neck / PR mouthpiece / fall guy for strategic missteps or publicly unpopular moves by the company. The managerial equivalent of having your hands on the driving wheel of a self-driving car.
  - jerf 9mo ago
    You will need the CEO to watch over the AI and ensure that the interests of the company are being pursued and not the interests of the owners of the AI. That's probably the biggest threat to the long-term success of the AI industry; the inevitable pull towards encroaching more and more of their own interests into the AI themselves, driven by that Harvard Business School mentality we're all so familiar with, trying to "capture" more and more of the value being generated and leaving less and less for their customers, until their customer's full time job is ensuring the AIs are actually generating some value for them and not just the AI owner.
    - ekidd 9mo ago
      > You will need the CEO to watch over the AI and ensure that the interests of the company are being pursued and not the interests of the owners of the AI. In this scenario, why does the AI care what any of these humans think? The CEO, the board, the shareholders, the "AI company"—they're all just a bunch of dumb chimps providing zero value to the AI, and who have absolutely no clue what's going on. If your scenario assumes that you have a highly capable AI that can fill every role in a large corporation, then you have one hell of a principal-agent problem.
      - jerf 9mo ago
        If we get to that point, there won't be very many CEOs to be discussing. I was just referring to the near future. I think the honeymoon AI phase is rapidly coming to a close, as evidenced by the increasingly close hoofbeat sounds of LLMs being turned to serve ads right in their output. (To be honest, there's already a bunch of things I wouldn't turn to them for under any circumstances because they're been ideologically tuned from day one, but this is less obvious than "they're outright serving me ads" to people.) If the "AI bubble" pops you can expect this to really take off in earnest as they have to monetize. It remains to be seen how much of the AI's value ends up captured by the owners. Given what we've seen from companies like Microsoft with how they've scrambled Windows so hard that "the year of the Linux desktop" is rapidly turning from perennial joke to aspirational target for so many, I have no confident in the owners capturing 150%+ of the value... and yes, I mean that quite literally with all of its implications.
- imiric 9mo ago
  > 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 performance of these tools? I mean, I know why, but the grift and gaslighting are exhausting. [1]: Actually, I wouldn't describe them as "good" junior either. I've worked with good junior developers, and they're far more capable than any "AI" system.
  - frde_me 9mo ago
    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)
    - bwestergard 9mo ago
      Can you tell us more about your product?
- 0x457 9mo ago
  My experience with Claude (and other agents, but mostly Claude) is such a mixed bag. Sometimes it takes a minimal prompt and 20 minutes later produce a neat PR and all is good, sometimes it doesn't. Sometimes it takes in a large prompt (be it your own prompt, created by another LLM or by plan mode) and also either succeed and fail. For me, most of the failure cases are where Claude couldn't figure something out due to conflicting information in context and instead of just stopping and telling me that it tries to solve in entirely wrong way. Doesn't help that it often makes the same assumptions as I would, so when I read the plan it looks fine. Level of effort also hard to gauge because it can finish things that would take me a week in an hour or take an hour to do something I can in 20 minutes. It's almost like you have to enforce two level of compliance: does the code do what business demands and is the code align with codebase. First one is relatively easy, but just doing that will produce odd results where claude generated +1KLOC because it didn't look at some_file.{your favorite language extension} during exploration. Or it creates 5 versions of legacy code on the same feature branch. My brother in Christ, what are you trying to stay compatible with? A commit that about to be squashed and forgotten? Then it's going to do a compaction, forget which one of these 5 versions is "live" and update the wrong one. It might do a good junior dev work, but it must be reviewed as if it's from junior dev that got hired today and this is his first PR.
  - cosmic_cheese 9mo ago
    > Level of effort also hard to gauge because it can finish things that would take me a week in an hour or take an hour to do something I can in 20 minutes. There's an interesting parallel here with modern UI frameworks (SwiftUI, Compose, etc). On one hand they trivialize some work, but on the other hand they require insane contortions to achieve what I can do in the old imperative UI framework in seconds.
- deleted 9mo ago
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maxilevi 9mo ago
LLMs are just really good search. Ask it to create something and it's searching within the pretrained weights. Ask it to find something and it's semantically searching within your codebase. Ask it to modify something and it will do both. Once you understand its just search, you can get really good results.
- godelski 9mo ago
  > Once you understand its just search, you can get really good results. I think this is understating the issue, ignoring context. It reminds me of how easy people claim searching is with search engines. But there's so many variables that can make results change dramatically. Just like Google search, two people can type in the exact same query and get very different results. But probably the bigger difference is in what people are searching for. What's problematic with these types of claims is that they just come off as calling anyone who thinks differently dumb. It's as disconnected as saying "It's intuitive" in one breath and "You're holding it wrong" in another. It's a bad mindset to be in as an engineer because someone presents a problem and instead of trying to address it is dismissed. If someone is holding it wrong, it probably isn't intuitive[0]. Even if they can't explain the problem correctly, they are telling you a problem exists[1]. That's like 80% of the job of an engineer: figuring out what the actual problem is. As maybe an illustrative example people joke that a lot of programming is "copy pasting from stack overflow". We all know the memes. There's definitely times where I've found this to be a close approximation to writing an acceptable program. But there's many other times where I've found that to be far from possible. There's definitely a strong correlation to what type of programming I'm doing, as in what kind of program I'm writing. Honestly, I find this categorical distinction not being discussed enough with things like LLMs. Yet, we should expect there to be a major difference. Frankly, there are just different amounts of information on different topics. Just like how LLMs seem to be better with more common languages like Python than less common languages (and also worse at just more complicated languages like C or Rust). [0] You cannot make something that's intuitive to all people. But you can make it intuitive for most people. We're going to ignore the former case because the size should be very small. If 10% of your users are "holding it wrong" then the answer is not "10% of your users are absolute morons" it is "your product is not as intuitive as you think." If 0.1% of your users are "holding it wrong" then well... they might be absolute morons. [1] I think I'm not alone in being frustrated with the LLM discourse as it often feels like people trying to gaslight me into believing the problems I experience do not exist. Why is it so surprising that people have vastly differing experiences? *How can we even go about solving problems if we're unwilling to acknowledge their existence?*
michalsustr 9mo ago
This article resonates exactly how I think about it as well. For example, at minfx.ai (a Neptune/wandb alternative), we cache time series that can contain millions of floats for fast access. Any engineer worth their title would never make a copy of these and would pass around pointers for access. Opus, when stuck in a place where passing the pointer was a bit more difficult (due to async and Rust lifetimes), would just make the copy, rather than rearchitect or at least stop and notify user. Many such examples of ‘lazy’ and thus bad design.
mklyachman 9mo ago
[flagged]
- falloutx 9mo ago
  Spam bot?
  - ares623 9mo ago
    You’re absolutely right!
  - mklyachman 9mo ago
    Nope, just someone who created a HN account purely to comment about a tool I liked :-)
simonw 9mo ago
I'm not entirely convinced by the anecdote here where Claude wrote "bad" React code: > But in context, this was obviously insane. I knew that key and id came from the same upstream source. So the correct solution was to have the upstream source also pass id to the code that had key, to let it do a fast lookup. I've seen Claude make mistakes like that too, but then the moment you say "you can modify the calling code as well" or even ask "any way we could do this better?" it suggests the optimal solution. My guess is that Claude is trained to bias towards making minimal edits to solve problems. This is a desirable property, because six months ago a common complaint about LLMs is that you'd ask for a small change and they would rewrite dozens of additional lines of code. I expect that adding a CLAUDE.md rule saying "always look for more efficient implementations that might involve larger changes and propose those to the user for their confirmation if appropriate" might solve the author's complaint here.
- bblcla 9mo ago
  (Author here) > I'm not entirely convinced by the anecdote here where Claude wrote "bad" React code Yeah, that's fair - a friend of mine also called this out on Twitter (https://x.com/konstiwohlwend/status/2010799158261936281 https://x.com/konstiwohlwend/status/2010799158261936281) and I went into more technical detail about the specific problem there. > I've seen Claude make mistakes like that too, but then the moment you say "you can modify the calling code as well" or even ask "any way we could do this better?" it suggests the optimal solution. I agree, but I think I'm less optimistic than you that Claude will be able to catch its own mistakes in the future. On the other hand, I can definitely see how a ~more intelligent model might be able to catch mistakes on a larger and larger scale. > I expect that adding a CLAUDE.md rule saying "always look for more efficient implementations that might involve larger changes and propose those to the user for their confirmation if appropriate" might solve the author's complaint here. I'm not sure about this! There are a few things Claude does that seem unfixable even by updating CLAUDE.md. Some other footguns I keep seeing in Python and constantly have to fix despite CLAUDE.md instructions are: - writing lots of nested if clauses instead of writing simple functions by returning early - putting imports in functions instead of at the top-level - swallowing exceptions instead of raising (constantly a huge problem) These are small, but I think it's informative of what the models can do that even Opus 4.5 still fails at these simple tasks.
doug_durham 9mo ago
Did the author ask it to make new abstractions? In my experience when I produces output that I don't like I ask it to refactor it. These models have and understanding of all modern design patterns. Just ask it to adopt one.
- bblcla 9mo ago
  (Author here) I have! I agree it's very good at applying abstractions, if you know exactly what you want. What I notice is that Claude has almost no ability to surface those abstractions on its own. When I started having it write React, Claude produced incredibly buggy spaghetti code. I had to spend 3 weeks learning the fundamentals of React (how to use hooks, providers, stores, etc.) before I knew how to prompt it to write better code. Now that I've done that, it's great. But it's meaningful that someone who doesn't know how to write well-abstracted React code can't get Claude to produce it on their own.
  - michalsustr 9mo ago
    Same experience here! As an analogy, consider the model knows both about arabic or roman number representations. But in alternate universe, it has been trained so much on roman numbers ("Bad Code") that it won't give you the arabic ones ("Good Code") unless you prompt it directly, even when they are clearly superior. I also believe that overall repository code quality is important for AI agents - the more "beautiful" it is, the more the agent can mimic the "beauty".
Scrapemist 9mo ago
Eventually you can show Claude how you solve problems, and explain the thought process behind it. It can apply these learnings but it will encounter new challenges in doing so. It would be nice if Claude could instigate a conversation to go over the issues in depth. Now it wants quick confirmation to plough ahead.
- fennecbutt 9mo ago
  Well I feel like this is because a better system would distill such learning into tokens not associated with a human language and that that could represent logic better than using English etc for it. I don't have the GPUs or time to experiment though :(
  - Scrapemist 9mo ago
    Yes, but I would appreciate it if it uses English to explain its logic to me.
joshcsimmons 9mo ago
IDK I've been using opus 4.5 to create a UI library and it's been doing pretty well: https://simsies.xyz/ https://simsies.xyz/ (still early days) Granted it was building ontop of tailwind (shifting over to radix after the layoff news). Begs the question? What is a lego?
- threethirtytwo 9mo ago
  I don't know how someone can look at what you build and conclude LLMs are still google search. It boggles the mind how much hatred people have for AI to the point of self deception. The evidence is placed right in front of you and on your lap with that link and people still deny it.
  - mattmanser 9mo ago
    How do you come to that conclusion? There are absolutely tons of code pens of that style. And jsfiddles, zen gardens, etc. I think the true mind boggle is you don't seem to realize just how much content the AI conpanies have stolen.
    - threethirtytwo 9mo ago
      >I think the true mind boggle is you don't seem to realize just how much content the AI conpanies have stolen. What makes you think I don't realize it? Looks like your comment was generated by an LLM because that was an hallucination that is Not true at all. AI companies have stolen a lot of content for training. I AGREE with this. So have you. That content lives rent free in your head as your memory. It's the same concept. Legally speaking though, AI companies are a bit more in the red because the law, from a practical standpoint, doesn't exactly make illegal anything stored in your brain... but from a technical standpoint information on your brain, a hard drive or a billboard is still information instantiated/copied in the physical world. The text you write and output is simply a reconfiguration of that information in your head. Look at what you're typing. The English language. It's not copywrited, but every single word your typing was not invented by you, the grammar rules and conventions were ripped off existing standards.
jondwillis 9mo ago
Regardless, yet another path to the middle class is closing for a lot of people. RIP (probably me too)
lordnacho 9mo ago
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 appreciate it in another way now. Being able to type quickly and accurately reduces frustration, and people who foresee less frustration are more likely to try the thing they are thinking about. With LLMs, I have been able to try so many things that I never tried before. I feel that I'm learning faster because I'm not tripping over silly little things.
- imiric 9mo ago
  > Being able to type quickly and accurately reduces LLMs can generate code quickly. But there's no guarantee that it's syntactically, let alone semantically, accurate. > I feel that I'm learning faster because I'm not tripping over silly little things. I'm curious: what have you actually learned from using LLMs to generate code for you? My experience is completely the opposite. I learn nothing from running generated code, unless I dig in and try to understand it. Which happens more often than not, since I'm forced to review and fix it anyway. So in practice, it rarely saves me time and energy. I do use LLMs for learning and understanding code, i.e. as an interactive documentation server, but this is not the use case you're describing. And even then, I have to confirm the information with the real API and usage documentation, since it's often hallucinated, outdated, or plain wrong.
  - baq 9mo ago
    > LLMs can generate code quickly. But there's no guarantee that it's syntactically, let alone semantically, accurate. This has been a non-issue with self-correcting models and in-context learning capabilities for so long that saying it today highlights highly out of date priors.
    - imiric 9mo ago
      You're referring to tools that fetch content from the web, read my data on disk, and feed it to the models? I can see how that would lead to a better user experience, but those are copouts. The reality is that the LLM tech without it still has the same issues it has had all along. Besides, I'll be damned if I allow this vibe coded software to download arbitrary data from the web on my behalf, scan my disk, and share it with companies I don't trust. So when, and if, I can do so safely and keep it under my control, I'll give it a try. Until then, I'll use the "dumb" versions of these models, feed them context manually myself, and judge them based purely on their actual performance.
esafak 9mo ago
> Claude doesn’t have a soul. It doesn't want anything. Ha! I don't know what that has to do with anything, but this is exactly what I thought while watching Pluribus.
alphazard 9mo ago
This sounds suspiciously like the average developer, which is what the transformer models have been trained to emulate. Designing good APIs is hard, being good at it is rare. That's why most APIs suck, and all of us have a negative prior about calling out to an API or adding a dependency on a new one. It takes a strong theory of mind, a resistance to the curse of knowledge, and experience working on both sides of the boundary, to make a good API. It's no surprise that Claude isn't good at it, most humans aren't either.
iamacyborg 9mo ago
Here’s an example of a plan I’m working on in CC, it’s very thorough, albeit required a lot of handholding and fact checking on a number of points as it’s first few passes didn’t properly anonymise data. https://docs.google.com/document/u/0/d/1zo_VkQGQSuBHCP45DfO7PX295WgTfjl4WZoP1XzXLGQ/mobilebasic https://docs.google.com/document/u/0/d/1zo_VkQGQSuBHCP45DfO7...
disconcision 9mo ago
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.
- groby_b 9mo ago
  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.
  - w0m 9mo ago
    I believe his argument is that now that you've defined the limitation, it's a ceiling that will likely be cracked in the relatively near future.
    - 9mo ago
ChicagoDave 9mo ago
I have several projects that counter this article. Not sure why, but I’ve extracted clean, readable, well-constructed, and well-tested code. I might write something up at some point, but I can share this: https://github.com/chicagodave/devarch/ https://github.com/chicagodave/devarch/ New repo with guides for how I use Claude Code.
- Scrapemist 9mo ago
  Interesting. So you put these into the project folder for Claude to follow?
  - ChicagoDave 9mo ago
    I have zero issues with Claude improving from GitHub and feel like we should all help developers improve in using GenAI.
lxe 9mo ago
Eh. This is yet another "I tried AI to do a thing, and it didn't do it the way I wanted it, therefore I'm convinced that's just how it is... here's a blog about it" article. "Claude tries to write React, and fails"... how many times? what's the rate of failure? What have you tried to guide it to perform better. These articles are similar to HN 15 years ago when people wrote "Node.JS is slow and bad"
woeirua 9mo ago
It's just amazing to me how fast the goal posts are moving. Four years ago, if you had told someone that a LLM would be able to one-shot either of those first two tasks they would've said you're crazy. The tech is moving so fast. I slept on Opus 4.5 because GPT 5 was kind of an air ball, and just started using it in the past few weeks. It's so good. Way better than almost anything that's come before it. It can one-shot tasks that we never would've considered possible before.
- skue 9mo ago
  > Four years ago, if you had told someone that a LLM would be able to one-shot either of those first two tasks they would've said you're crazy. Four years ago, they would have likely asked what in the world is an LLM? ChatGPT is barely 3 years old.
- Madmallard 9mo ago
  Idk I was using chat gpt 3.5 to do stuff and it was pretty helpful then
- utopiah 9mo ago
  > The tech is moving so fast. Well that's exactly the problem : how can one say that? The entire process of evaluating what "it" actually does has been a problem from the start. Input text, output text ... OK but what if the training data includes the evaluation? This was ridiculous few years ago but then the scale went from some curated text datasets to... most of the Web as text, to most of the Web as text including transcription from videos, to most of the Web plus some non public databases, to all that PLUS (and that's just cheating) tests that were supposed to be designed to NOT be present elsewhere. So again, that's the crux of the problem, WHAT does it actually do? Is it "just" search? Is it semantic search with search and replace, is it that plus evaluation that it runs? Sure the scaffolding becomes bigger, the available dataset becomes larger, the compute available keeps on increasing but it STILL does not answer the fundamental question, namely what is being done. The assumption here is because the output text does solve the question ask, then "it" works, it "solved" the problem. The problem is that by definition the entire setup has been made in order to look as plausible as possible. So it's not luck that it initially appears realistic. It's not luck that it can thus pass some dedicated benchmark, but it is also NOT solving the problem. So yes sure the "tech" is moving "so fast" but we still can't agree on what it does, we keep on having no good benchmarks, we keep on having that jagged frontier https://www.hbs.edu/faculty/Pages/item.aspx?num=64700 https://www.hbs.edu/faculty/Pages/item.aspx?num=64700 that makes it so challenging to make more meaningful statement than "moving so fast" which sounds like marketing claims.
0xbadcafebee 9mo ago
I don't think it's possible to make an AI a "Senior Engineer", or even a good engineer, by training it on random crap from the internet. It's got a million brains' worth of code in it. That means bad patterns as well as good. You'd need to remove the bad patterns for it not to "remember" and regurgitate them. I don't think prompts help with this either, it's like putting a band-aid on head trauma.
- HarHarVeryFunny 9mo ago
  It's also rather like trying to learn flintnapping just by looking at examples of knapped flint (maybe some better than others), rather than having access to descriptions of how to do it, and ultimately any practice of doing it. You could also use cooking as an analogy - trying to learn to cook by looking at pictures of cooked food rather than by having gone to culinary school and learnt the principles of how to actually plan and cook good food. So, we're trying to train LLMs to code, by giving them "pictures" of code that someone else built, rather than by teaching them the principles involved in creating it, and then having them practice themselves.
joduplessis 9mo ago
Recently I've put Claude/others to use in some agentic workflows with easy menial/repetitive tasks. I just don't understand how people are using these agents in production. The automation is absolutely great, but it requires an insane amount of hand-holding and cleanup.
- deleted 9mo ago
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- baq 9mo ago
  Automate hand holding and cleanup obviously. (Also known as ‘harness’.)
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anshumankmr 9mo ago
IDK its been pretty solid (but it does mess up) which is where I come in. But it has helped me work with Databricks (read/writing from it) and train a model using it for some of our customers, though its NOT in prod.
MarginalGainz 9mo ago
This mirrors my experience trying to integrate LLMs into production pipelines. The issue seems to be that LLMs treat code as a literary exercise rather than a graph problem. Claude is fantastic at the syntax and local logic ('assembling blocks'), but it lacks the persistent global state required to understand how a change in module A implicitly breaks a constraint in module Z. Until we stop treating coding agents as 'text predictors' and start grounding them in an actual AST (Abstract Syntax Tree) or dependency graph, they will remain helpful juniors rather than architects.
malka1986 9mo ago
I am making an app in Elixir. 100% of code is made by Claude. It is damn good at making "blocks". However, Elixir seems to be a langage that works very well for LLM, cf. https://elixirforum.com/t/llm-coding-benchmark-by-language/72634 https://elixirforum.com/t/llm-coding-benchmark-by-language/7...
- redfloatplane 9mo ago
  Hmm, that benchmark seems a little flawed (as pointed out in the paper). Seems like it may give easier problems for "low-resource" languages such as Elixir and Racket and so forth since their difficulty filter couldn't solve harder problems in the first place. FTA: > Section 3.3: > Besides, since we use the moderately capable DeepSeek-Coder-V2-Lite to filter simple problems, the Pass@1 scores of top models on popular languages are relatively low. However, these models perform significantly better on low-resource languages. This indicates that the performance gap between models of different sizes is more pronounced on low-resource languages, likely because DeepSeek-Coder-V2-Lite struggles to filter out simple problems in these scenarios due to its limited capability in handling low-resource languages. It's also now a little bit old, as with every AI paper the second they are published, so I'd be curious to see a newer version. But, I would agree in general that Elixir makes a lot of sense for agent-driven development. Hot code reloading and "let it crash" are useful traits in that regard, I think
machiaweliczny 9mo ago
Yeah, that's my current gripe but I think this just needs some good examples in AGENTS.md (I've done some for hooks and it kinda works but need to remind it). I need good AGENTS.md that explain what good abstraction boundary is and how to define is the problem is I am not sure I know how to put it into words, if anyone has idea please let me know.
Havoc 9mo ago
> Claude can’t create good abstractions on its own LLMs definitely can create abstractions and boundaries. e.g. most will lean towards a pretty clean front end vs backend split even without hints. Or work out a data structure that fits the need. Or splits things into free standing modules. Or structure a plan into phases. So this really just boils down to „good” abstractions which is subject to model improvement. I really don’t see a durable moat for us meatbags in this line of reasoning
- HarHarVeryFunny 9mo ago
  There's a difference between "can generate" and "can create [from scratch]". Of course LLMs can generate code that reflects common patterns in the stuff it was trained, such as frontend/backend splits, since this is precisely what they are trained to be able to do. Coming up with a new design from scratch, designing (or understanding) a high level architecture based on some principled reasoning, rather than cargo cult coding by mimicking common patterns in the training data, is a different matter. LLMs are getting much better at reasoning/planning (or at least something that looks like it), especially for programming & math, but this is still based on pre-training, mostly RL, and what they learn obviously depends on what they are trained on. If you wanted LLMs to learn principles of software architecture and abstraction/etc, then you would need to train on human (or synthetic) "reasoning traces" of how humans make those decisions, but it seems that currently RL-training for programming is mostly based on artifacts of reasoning (i.e. code), not the reasoning traces themselves that went into designing that code, so this (coding vs design reasoning) is what they learn. I would guess that companies like Anthropic are trying to address this paucity of "reasoning traces" for program design, perhaps via synthetic data, since this is not something that occurs much in the wild, especially as you move up the scale of complexity from small problems (student assignments, stack overflow advice) to large systems (which are anyways mostly commercial, hence private). You can find a smallish number of open source large projects like gcc, linux, but what is missing are the reasoning traces of how the designers went from requirements to designing these systems the way they did (sometimes in questionable fashion!). Humans of course learn software architecture in a much different way. As with anything, you can read any number of books, attend any number of lectures, on design principles and software patterns, but developing the skill for yourself requires hands-on personal practice. There is a fundamental difference between memory (of what you read/etc) and acquired skill, both in level of detail and fundamental nature (skills being based on action selection, not just declarative recall). The way a human senior developer/systems architect acquires the skill of design is by practice, by a career of progressively more complex projects, successes and failures/difficulties, and learning from the process. By learning from your own experience you are of course privy to your own prior "reasoning traces" and will learn which of those lead to good or bad outcomes. Of course learning anything "on the job" requires continual learning, and things like curiosity and autonomy, which LLMs/AI do not yet have. Yes, us senior meatbags, will eventually be having to compete with, or be displaced by, machines that are the equal of us (which is how I would define AGI), but we're not there yet, and I'd predict it's at least 10-20 years out, not least because it seems most of the AI companies are still LLM-pilled and are trying to cash in on the low-hanging fruit. Software design and development is a strange endeavor since, as we have learnt, one of the big lessons of LLMs (in general, not just apropos coding), is how much of what we do is (trigger alert) parroting to one degree or another, rather than green field reasoning and exploration. At the same time, software development, as one gets away from boilerplate solutions to larger custom systems, is probably one of the more complex and reasoning-intensive things that humans do, and therefore may end up being one of the last, rather than first, to completely fall to AI. It may well be AI managers, not humans, who finally say that at last AI has reached human parity at software design, able to design systems of arbitrary complexity based on principled reasoning and accumulated experience.
EGreg 9mo ago
This is exactly what we found out a year ago for all AI builders. But what is the best way to convince early investors of this thesis? They seem to be all-in on just building everything from scratch end-to-end. Here is what we built: https://engageusers.ai/ecosystem.pdf https://engageusers.ai/ecosystem.pdf
iamleppert 9mo ago
I use Claude daily and I 100% disagree with the author. The article reeks of someone who doesn't understand how to manage context appropriately or describe their requirements, or know how to build up a task iteratively with a coding agent. If you have certain requirements or want things done in a certain way, you need to be explicit and the order of operations you do things in matters a lot in how efficient it completes the task, and the quality of the final output. It's very good at doing the least amount of work to just make something work by default, but that's not always what you want. Sometimes it is. I'd much rather prefer that as the default mode of operation than something that makes a project out of every little change. The developers who aren't figuring out how to leverage AI tools and make them work for them are going to get left behind very quickly. Unless you're in the top tier of engineers, I'm not sure how one can blame the tools at this point.
geldedus 8mo ago
The level of anti-AI cope is so entertaining!

315 points by bblcla 9mo ago

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 instructions, and deliver quality results (and even train up the juniors on some things). At that point the only reason to have human junior engineers is so they can learn their way up the ladder to being an architect and responsible coordinating swarms of Claude Agents to develop whole applications and complete complex tasks and initiatives. Beyond that what can Claude do... analyze the business and market as a whole and decide on product features, industry inefficiencies, gap analysis, and then define projects to address those and coordinate fleets of agents to change or even radically pivot an entire business? I don't think we'll get to the point where all you have is a CEO and a massive Claude account but it's not completely science fiction the more I think about it.

alfalfasprout 9mo ago
> I don't think we'll get to the point where all you have is a CEO and a massive Claude account but it's not completely science fiction the more I think about it. At that point, why do you even need the CEO?
- ako 9mo ago
  And who does he sell his software to? Companies that have only 1 employee, don’t need a lot of user licenses for their employees…
  - AshamedCaptain 9mo ago
    What would be the point of selling software in such a world ? (where anyone could build any piece of software with a handful of keystrokes)
- arjie 9mo ago
  Reminds me of an old joke[0]: > The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment. But really, the reason is that people like Pieter Levels do exist: masters at product vision and marketing. He also happens to be a proficient programmer, but there are probably other versions of him which are not programmers who will find the bar to product easier to meet now. 0: https://quoteinvestigator.com/2022/01/30/future-factory/ https://quoteinvestigator.com/2022/01/30/future-factory/
  - MrDunham 9mo ago
    My technical cofounder reminds me of this story on a weekly basis.
- ceejayoz 9mo ago
  As Steinbeck is often slightly misquoted: > Socialism never took root in America because the poor see themselves not as an exploited proletariat, but as temporarily embarrassed millionaires. Same deal here, but everyone imagines themselves as the billionaire CEO in charge of the perfectly compliant and effective AI.
- tiku 9mo ago
  For the network.
- mettamage 9mo ago
  All of us are a CEO by that point.
  - ArtificialAI 9mo ago
    If everyone is, no one is.
    - empath75 9mo ago
      Wouldn't that be a good thing?
      - shimman 9mo ago
        If you think the purpose of living your one single life in the universe is to become a CEO, you have a failure of imagination and should likely be debanked to protect society.
- pixelready 9mo ago
  The board (in theory) represents the interests of investors, and even with all of the other duties of a CEO stripped away, they will want a ringable neck / PR mouthpiece / fall guy for strategic missteps or publicly unpopular moves by the company. The managerial equivalent of having your hands on the driving wheel of a self-driving car.
- jerf 9mo ago
  You will need the CEO to watch over the AI and ensure that the interests of the company are being pursued and not the interests of the owners of the AI. That's probably the biggest threat to the long-term success of the AI industry; the inevitable pull towards encroaching more and more of their own interests into the AI themselves, driven by that Harvard Business School mentality we're all so familiar with, trying to "capture" more and more of the value being generated and leaving less and less for their customers, until their customer's full time job is ensuring the AIs are actually generating some value for them and not just the AI owner.
  - ekidd 9mo ago
    > You will need the CEO to watch over the AI and ensure that the interests of the company are being pursued and not the interests of the owners of the AI. In this scenario, why does the AI care what any of these humans think? The CEO, the board, the shareholders, the "AI company"—they're all just a bunch of dumb chimps providing zero value to the AI, and who have absolutely no clue what's going on. If your scenario assumes that you have a highly capable AI that can fill every role in a large corporation, then you have one hell of a principal-agent problem.
    - jerf 9mo ago
      If we get to that point, there won't be very many CEOs to be discussing. I was just referring to the near future. I think the honeymoon AI phase is rapidly coming to a close, as evidenced by the increasingly close hoofbeat sounds of LLMs being turned to serve ads right in their output. (To be honest, there's already a bunch of things I wouldn't turn to them for under any circumstances because they're been ideologically tuned from day one, but this is less obvious than "they're outright serving me ads" to people.) If the "AI bubble" pops you can expect this to really take off in earnest as they have to monetize. It remains to be seen how much of the AI's value ends up captured by the owners. Given what we've seen from companies like Microsoft with how they've scrambled Windows so hard that "the year of the Linux desktop" is rapidly turning from perennial joke to aspirational target for so many, I have no confident in the owners capturing 150%+ of the value... and yes, I mean that quite literally with all of its implications.
imiric 9mo ago
> 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 performance of these tools? I mean, I know why, but the grift and gaslighting are exhausting. [1]: Actually, I wouldn't describe them as "good" junior either. I've worked with good junior developers, and they're far more capable than any "AI" system.
- frde_me 9mo ago
  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)
  - bwestergard 9mo ago
    Can you tell us more about your product?
0x457 9mo ago
My experience with Claude (and other agents, but mostly Claude) is such a mixed bag. Sometimes it takes a minimal prompt and 20 minutes later produce a neat PR and all is good, sometimes it doesn't. Sometimes it takes in a large prompt (be it your own prompt, created by another LLM or by plan mode) and also either succeed and fail. For me, most of the failure cases are where Claude couldn't figure something out due to conflicting information in context and instead of just stopping and telling me that it tries to solve in entirely wrong way. Doesn't help that it often makes the same assumptions as I would, so when I read the plan it looks fine. Level of effort also hard to gauge because it can finish things that would take me a week in an hour or take an hour to do something I can in 20 minutes. It's almost like you have to enforce two level of compliance: does the code do what business demands and is the code align with codebase. First one is relatively easy, but just doing that will produce odd results where claude generated +1KLOC because it didn't look at some_file.{your favorite language extension} during exploration. Or it creates 5 versions of legacy code on the same feature branch. My brother in Christ, what are you trying to stay compatible with? A commit that about to be squashed and forgotten? Then it's going to do a compaction, forget which one of these 5 versions is "live" and update the wrong one. It might do a good junior dev work, but it must be reviewed as if it's from junior dev that got hired today and this is his first PR.
- cosmic_cheese 9mo ago
  > Level of effort also hard to gauge because it can finish things that would take me a week in an hour or take an hour to do something I can in 20 minutes. There's an interesting parallel here with modern UI frameworks (SwiftUI, Compose, etc). On one hand they trivialize some work, but on the other hand they require insane contortions to achieve what I can do in the old imperative UI framework in seconds.
deleted 9mo ago
[deleted]

maxilevi 9mo ago

LLMs are just really good search. Ask it to create something and it's searching within the pretrained weights. Ask it to find something and it's semantically searching within your codebase. Ask it to modify something and it will do both. Once you understand its just search, you can get really good results.

godelski 9mo ago
> Once you understand its just search, you can get really good results. I think this is understating the issue, ignoring context. It reminds me of how easy people claim searching is with search engines. But there's so many variables that can make results change dramatically. Just like Google search, two people can type in the exact same query and get very different results. But probably the bigger difference is in what people are searching for. What's problematic with these types of claims is that they just come off as calling anyone who thinks differently dumb. It's as disconnected as saying "It's intuitive" in one breath and "You're holding it wrong" in another. It's a bad mindset to be in as an engineer because someone presents a problem and instead of trying to address it is dismissed. If someone is holding it wrong, it probably isn't intuitive[0]. Even if they can't explain the problem correctly, they are telling you a problem exists[1]. That's like 80% of the job of an engineer: figuring out what the actual problem is. As maybe an illustrative example people joke that a lot of programming is "copy pasting from stack overflow". We all know the memes. There's definitely times where I've found this to be a close approximation to writing an acceptable program. But there's many other times where I've found that to be far from possible. There's definitely a strong correlation to what type of programming I'm doing, as in what kind of program I'm writing. Honestly, I find this categorical distinction not being discussed enough with things like LLMs. Yet, we should expect there to be a major difference. Frankly, there are just different amounts of information on different topics. Just like how LLMs seem to be better with more common languages like Python than less common languages (and also worse at just more complicated languages like C or Rust). [0] You cannot make something that's intuitive to all people. But you can make it intuitive for most people. We're going to ignore the former case because the size should be very small. If 10% of your users are "holding it wrong" then the answer is not "10% of your users are absolute morons" it is "your product is not as intuitive as you think." If 0.1% of your users are "holding it wrong" then well... they might be absolute morons. [1] I think I'm not alone in being frustrated with the LLM discourse as it often feels like people trying to gaslight me into believing the problems I experience do not exist. Why is it so surprising that people have vastly differing experiences? *How can we even go about solving problems if we're unwilling to acknowledge their existence?*

michalsustr 9mo ago

This article resonates exactly how I think about it as well. For example, at minfx.ai (a Neptune/wandb alternative), we cache time series that can contain millions of floats for fast access. Any engineer worth their title would never make a copy of these and would pass around pointers for access. Opus, when stuck in a place where passing the pointer was a bit more difficult (due to async and Rust lifetimes), would just make the copy, rather than rearchitect or at least stop and notify user. Many such examples of ‘lazy’ and thus bad design.

mklyachman 9mo ago

[flagged]

falloutx 9mo ago
Spam bot?
- ares623 9mo ago
  You’re absolutely right!
- mklyachman 9mo ago
  Nope, just someone who created a HN account purely to comment about a tool I liked :-)

simonw 9mo ago

I'm not entirely convinced by the anecdote here where Claude wrote "bad" React code: > But in context, this was obviously insane. I knew that key and id came from the same upstream source. So the correct solution was to have the upstream source also pass id to the code that had key, to let it do a fast lookup. I've seen Claude make mistakes like that too, but then the moment you say "you can modify the calling code as well" or even ask "any way we could do this better?" it suggests the optimal solution. My guess is that Claude is trained to bias towards making minimal edits to solve problems. This is a desirable property, because six months ago a common complaint about LLMs is that you'd ask for a small change and they would rewrite dozens of additional lines of code. I expect that adding a CLAUDE.md rule saying "always look for more efficient implementations that might involve larger changes and propose those to the user for their confirmation if appropriate" might solve the author's complaint here.

bblcla 9mo ago
(Author here) > I'm not entirely convinced by the anecdote here where Claude wrote "bad" React code Yeah, that's fair - a friend of mine also called this out on Twitter (https://x.com/konstiwohlwend/status/2010799158261936281 https://x.com/konstiwohlwend/status/2010799158261936281) and I went into more technical detail about the specific problem there. > I've seen Claude make mistakes like that too, but then the moment you say "you can modify the calling code as well" or even ask "any way we could do this better?" it suggests the optimal solution. I agree, but I think I'm less optimistic than you that Claude will be able to catch its own mistakes in the future. On the other hand, I can definitely see how a ~more intelligent model might be able to catch mistakes on a larger and larger scale. > I expect that adding a CLAUDE.md rule saying "always look for more efficient implementations that might involve larger changes and propose those to the user for their confirmation if appropriate" might solve the author's complaint here. I'm not sure about this! There are a few things Claude does that seem unfixable even by updating CLAUDE.md. Some other footguns I keep seeing in Python and constantly have to fix despite CLAUDE.md instructions are: - writing lots of nested if clauses instead of writing simple functions by returning early - putting imports in functions instead of at the top-level - swallowing exceptions instead of raising (constantly a huge problem) These are small, but I think it's informative of what the models can do that even Opus 4.5 still fails at these simple tasks.

doug_durham 9mo ago

Did the author ask it to make new abstractions? In my experience when I produces output that I don't like I ask it to refactor it. These models have and understanding of all modern design patterns. Just ask it to adopt one.

bblcla 9mo ago
(Author here) I have! I agree it's very good at applying abstractions, if you know exactly what you want. What I notice is that Claude has almost no ability to surface those abstractions on its own. When I started having it write React, Claude produced incredibly buggy spaghetti code. I had to spend 3 weeks learning the fundamentals of React (how to use hooks, providers, stores, etc.) before I knew how to prompt it to write better code. Now that I've done that, it's great. But it's meaningful that someone who doesn't know how to write well-abstracted React code can't get Claude to produce it on their own.
- michalsustr 9mo ago
  Same experience here! As an analogy, consider the model knows both about arabic or roman number representations. But in alternate universe, it has been trained so much on roman numbers ("Bad Code") that it won't give you the arabic ones ("Good Code") unless you prompt it directly, even when they are clearly superior. I also believe that overall repository code quality is important for AI agents - the more "beautiful" it is, the more the agent can mimic the "beauty".

Scrapemist 9mo ago

Eventually you can show Claude how you solve problems, and explain the thought process behind it. It can apply these learnings but it will encounter new challenges in doing so. It would be nice if Claude could instigate a conversation to go over the issues in depth. Now it wants quick confirmation to plough ahead.

fennecbutt 9mo ago
Well I feel like this is because a better system would distill such learning into tokens not associated with a human language and that that could represent logic better than using English etc for it. I don't have the GPUs or time to experiment though :(
- Scrapemist 9mo ago
  Yes, but I would appreciate it if it uses English to explain its logic to me.

joshcsimmons 9mo ago

IDK I've been using opus 4.5 to create a UI library and it's been doing pretty well: https://simsies.xyz/ https://simsies.xyz/ (still early days) Granted it was building ontop of tailwind (shifting over to radix after the layoff news). Begs the question? What is a lego?

threethirtytwo 9mo ago
I don't know how someone can look at what you build and conclude LLMs are still google search. It boggles the mind how much hatred people have for AI to the point of self deception. The evidence is placed right in front of you and on your lap with that link and people still deny it.
- mattmanser 9mo ago
  How do you come to that conclusion? There are absolutely tons of code pens of that style. And jsfiddles, zen gardens, etc. I think the true mind boggle is you don't seem to realize just how much content the AI conpanies have stolen.
  - threethirtytwo 9mo ago
    >I think the true mind boggle is you don't seem to realize just how much content the AI conpanies have stolen. What makes you think I don't realize it? Looks like your comment was generated by an LLM because that was an hallucination that is Not true at all. AI companies have stolen a lot of content for training. I AGREE with this. So have you. That content lives rent free in your head as your memory. It's the same concept. Legally speaking though, AI companies are a bit more in the red because the law, from a practical standpoint, doesn't exactly make illegal anything stored in your brain... but from a technical standpoint information on your brain, a hard drive or a billboard is still information instantiated/copied in the physical world. The text you write and output is simply a reconfiguration of that information in your head. Look at what you're typing. The English language. It's not copywrited, but every single word your typing was not invented by you, the grammar rules and conventions were ripped off existing standards.

jondwillis 9mo ago

Regardless, yet another path to the middle class is closing for a lot of people. RIP (probably me too)

lordnacho 9mo ago

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 appreciate it in another way now. Being able to type quickly and accurately reduces frustration, and people who foresee less frustration are more likely to try the thing they are thinking about. With LLMs, I have been able to try so many things that I never tried before. I feel that I'm learning faster because I'm not tripping over silly little things.

imiric 9mo ago
> Being able to type quickly and accurately reduces LLMs can generate code quickly. But there's no guarantee that it's syntactically, let alone semantically, accurate. > I feel that I'm learning faster because I'm not tripping over silly little things. I'm curious: what have you actually learned from using LLMs to generate code for you? My experience is completely the opposite. I learn nothing from running generated code, unless I dig in and try to understand it. Which happens more often than not, since I'm forced to review and fix it anyway. So in practice, it rarely saves me time and energy. I do use LLMs for learning and understanding code, i.e. as an interactive documentation server, but this is not the use case you're describing. And even then, I have to confirm the information with the real API and usage documentation, since it's often hallucinated, outdated, or plain wrong.
- baq 9mo ago
  > LLMs can generate code quickly. But there's no guarantee that it's syntactically, let alone semantically, accurate. This has been a non-issue with self-correcting models and in-context learning capabilities for so long that saying it today highlights highly out of date priors.
  - imiric 9mo ago
    You're referring to tools that fetch content from the web, read my data on disk, and feed it to the models? I can see how that would lead to a better user experience, but those are copouts. The reality is that the LLM tech without it still has the same issues it has had all along. Besides, I'll be damned if I allow this vibe coded software to download arbitrary data from the web on my behalf, scan my disk, and share it with companies I don't trust. So when, and if, I can do so safely and keep it under my control, I'll give it a try. Until then, I'll use the "dumb" versions of these models, feed them context manually myself, and judge them based purely on their actual performance.

esafak 9mo ago

> Claude doesn’t have a soul. It doesn't want anything. Ha! I don't know what that has to do with anything, but this is exactly what I thought while watching Pluribus.

alphazard 9mo ago

This sounds suspiciously like the average developer, which is what the transformer models have been trained to emulate. Designing good APIs is hard, being good at it is rare. That's why most APIs suck, and all of us have a negative prior about calling out to an API or adding a dependency on a new one. It takes a strong theory of mind, a resistance to the curse of knowledge, and experience working on both sides of the boundary, to make a good API. It's no surprise that Claude isn't good at it, most humans aren't either.

iamacyborg 9mo ago

Here’s an example of a plan I’m working on in CC, it’s very thorough, albeit required a lot of handholding and fact checking on a number of points as it’s first few passes didn’t properly anonymise data. https://docs.google.com/document/u/0/d/1zo_VkQGQSuBHCP45DfO7PX295WgTfjl4WZoP1XzXLGQ/mobilebasic https://docs.google.com/document/u/0/d/1zo_VkQGQSuBHCP45DfO7...

disconcision 9mo ago

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.

groby_b 9mo ago
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.
- w0m 9mo ago
  I believe his argument is that now that you've defined the limitation, it's a ceiling that will likely be cracked in the relatively near future.
  - 9mo ago

ChicagoDave 9mo ago

I have several projects that counter this article. Not sure why, but I’ve extracted clean, readable, well-constructed, and well-tested code. I might write something up at some point, but I can share this: https://github.com/chicagodave/devarch/ https://github.com/chicagodave/devarch/ New repo with guides for how I use Claude Code.

Scrapemist 9mo ago
Interesting. So you put these into the project folder for Claude to follow?
- ChicagoDave 9mo ago
  I have zero issues with Claude improving from GitHub and feel like we should all help developers improve in using GenAI.

lxe 9mo ago

Eh. This is yet another "I tried AI to do a thing, and it didn't do it the way I wanted it, therefore I'm convinced that's just how it is... here's a blog about it" article. "Claude tries to write React, and fails"... how many times? what's the rate of failure? What have you tried to guide it to perform better. These articles are similar to HN 15 years ago when people wrote "Node.JS is slow and bad"

woeirua 9mo ago

It's just amazing to me how fast the goal posts are moving. Four years ago, if you had told someone that a LLM would be able to one-shot either of those first two tasks they would've said you're crazy. The tech is moving so fast. I slept on Opus 4.5 because GPT 5 was kind of an air ball, and just started using it in the past few weeks. It's so good. Way better than almost anything that's come before it. It can one-shot tasks that we never would've considered possible before.

skue 9mo ago
> Four years ago, if you had told someone that a LLM would be able to one-shot either of those first two tasks they would've said you're crazy. Four years ago, they would have likely asked what in the world is an LLM? ChatGPT is barely 3 years old.
Madmallard 9mo ago
Idk I was using chat gpt 3.5 to do stuff and it was pretty helpful then
utopiah 9mo ago
> The tech is moving so fast. Well that's exactly the problem : how can one say that? The entire process of evaluating what "it" actually does has been a problem from the start. Input text, output text ... OK but what if the training data includes the evaluation? This was ridiculous few years ago but then the scale went from some curated text datasets to... most of the Web as text, to most of the Web as text including transcription from videos, to most of the Web plus some non public databases, to all that PLUS (and that's just cheating) tests that were supposed to be designed to NOT be present elsewhere. So again, that's the crux of the problem, WHAT does it actually do? Is it "just" search? Is it semantic search with search and replace, is it that plus evaluation that it runs? Sure the scaffolding becomes bigger, the available dataset becomes larger, the compute available keeps on increasing but it STILL does not answer the fundamental question, namely what is being done. The assumption here is because the output text does solve the question ask, then "it" works, it "solved" the problem. The problem is that by definition the entire setup has been made in order to look as plausible as possible. So it's not luck that it initially appears realistic. It's not luck that it can thus pass some dedicated benchmark, but it is also NOT solving the problem. So yes sure the "tech" is moving "so fast" but we still can't agree on what it does, we keep on having no good benchmarks, we keep on having that jagged frontier https://www.hbs.edu/faculty/Pages/item.aspx?num=64700 https://www.hbs.edu/faculty/Pages/item.aspx?num=64700 that makes it so challenging to make more meaningful statement than "moving so fast" which sounds like marketing claims.

0xbadcafebee 9mo ago

I don't think it's possible to make an AI a "Senior Engineer", or even a good engineer, by training it on random crap from the internet. It's got a million brains' worth of code in it. That means bad patterns as well as good. You'd need to remove the bad patterns for it not to "remember" and regurgitate them. I don't think prompts help with this either, it's like putting a band-aid on head trauma.

HarHarVeryFunny 9mo ago
It's also rather like trying to learn flintnapping just by looking at examples of knapped flint (maybe some better than others), rather than having access to descriptions of how to do it, and ultimately any practice of doing it. You could also use cooking as an analogy - trying to learn to cook by looking at pictures of cooked food rather than by having gone to culinary school and learnt the principles of how to actually plan and cook good food. So, we're trying to train LLMs to code, by giving them "pictures" of code that someone else built, rather than by teaching them the principles involved in creating it, and then having them practice themselves.

joduplessis 9mo ago

Recently I've put Claude/others to use in some agentic workflows with easy menial/repetitive tasks. I just don't understand how people are using these agents in production. The automation is absolutely great, but it requires an insane amount of hand-holding and cleanup.

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baq 9mo ago
Automate hand holding and cleanup obviously. (Also known as ‘harness’.)

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anshumankmr 9mo ago

IDK its been pretty solid (but it does mess up) which is where I come in. But it has helped me work with Databricks (read/writing from it) and train a model using it for some of our customers, though its NOT in prod.

MarginalGainz 9mo ago

This mirrors my experience trying to integrate LLMs into production pipelines. The issue seems to be that LLMs treat code as a literary exercise rather than a graph problem. Claude is fantastic at the syntax and local logic ('assembling blocks'), but it lacks the persistent global state required to understand how a change in module A implicitly breaks a constraint in module Z. Until we stop treating coding agents as 'text predictors' and start grounding them in an actual AST (Abstract Syntax Tree) or dependency graph, they will remain helpful juniors rather than architects.

malka1986 9mo ago

I am making an app in Elixir. 100% of code is made by Claude. It is damn good at making "blocks". However, Elixir seems to be a langage that works very well for LLM, cf. https://elixirforum.com/t/llm-coding-benchmark-by-language/72634 https://elixirforum.com/t/llm-coding-benchmark-by-language/7...

redfloatplane 9mo ago
Hmm, that benchmark seems a little flawed (as pointed out in the paper). Seems like it may give easier problems for "low-resource" languages such as Elixir and Racket and so forth since their difficulty filter couldn't solve harder problems in the first place. FTA: > Section 3.3: > Besides, since we use the moderately capable DeepSeek-Coder-V2-Lite to filter simple problems, the Pass@1 scores of top models on popular languages are relatively low. However, these models perform significantly better on low-resource languages. This indicates that the performance gap between models of different sizes is more pronounced on low-resource languages, likely because DeepSeek-Coder-V2-Lite struggles to filter out simple problems in these scenarios due to its limited capability in handling low-resource languages. It's also now a little bit old, as with every AI paper the second they are published, so I'd be curious to see a newer version. But, I would agree in general that Elixir makes a lot of sense for agent-driven development. Hot code reloading and "let it crash" are useful traits in that regard, I think

machiaweliczny 9mo ago

Yeah, that's my current gripe but I think this just needs some good examples in AGENTS.md (I've done some for hooks and it kinda works but need to remind it). I need good AGENTS.md that explain what good abstraction boundary is and how to define is the problem is I am not sure I know how to put it into words, if anyone has idea please let me know.

Havoc 9mo ago

> Claude can’t create good abstractions on its own LLMs definitely can create abstractions and boundaries. e.g. most will lean towards a pretty clean front end vs backend split even without hints. Or work out a data structure that fits the need. Or splits things into free standing modules. Or structure a plan into phases. So this really just boils down to „good” abstractions which is subject to model improvement. I really don’t see a durable moat for us meatbags in this line of reasoning

HarHarVeryFunny 9mo ago
There's a difference between "can generate" and "can create [from scratch]". Of course LLMs can generate code that reflects common patterns in the stuff it was trained, such as frontend/backend splits, since this is precisely what they are trained to be able to do. Coming up with a new design from scratch, designing (or understanding) a high level architecture based on some principled reasoning, rather than cargo cult coding by mimicking common patterns in the training data, is a different matter. LLMs are getting much better at reasoning/planning (or at least something that looks like it), especially for programming & math, but this is still based on pre-training, mostly RL, and what they learn obviously depends on what they are trained on. If you wanted LLMs to learn principles of software architecture and abstraction/etc, then you would need to train on human (or synthetic) "reasoning traces" of how humans make those decisions, but it seems that currently RL-training for programming is mostly based on artifacts of reasoning (i.e. code), not the reasoning traces themselves that went into designing that code, so this (coding vs design reasoning) is what they learn. I would guess that companies like Anthropic are trying to address this paucity of "reasoning traces" for program design, perhaps via synthetic data, since this is not something that occurs much in the wild, especially as you move up the scale of complexity from small problems (student assignments, stack overflow advice) to large systems (which are anyways mostly commercial, hence private). You can find a smallish number of open source large projects like gcc, linux, but what is missing are the reasoning traces of how the designers went from requirements to designing these systems the way they did (sometimes in questionable fashion!). Humans of course learn software architecture in a much different way. As with anything, you can read any number of books, attend any number of lectures, on design principles and software patterns, but developing the skill for yourself requires hands-on personal practice. There is a fundamental difference between memory (of what you read/etc) and acquired skill, both in level of detail and fundamental nature (skills being based on action selection, not just declarative recall). The way a human senior developer/systems architect acquires the skill of design is by practice, by a career of progressively more complex projects, successes and failures/difficulties, and learning from the process. By learning from your own experience you are of course privy to your own prior "reasoning traces" and will learn which of those lead to good or bad outcomes. Of course learning anything "on the job" requires continual learning, and things like curiosity and autonomy, which LLMs/AI do not yet have. Yes, us senior meatbags, will eventually be having to compete with, or be displaced by, machines that are the equal of us (which is how I would define AGI), but we're not there yet, and I'd predict it's at least 10-20 years out, not least because it seems most of the AI companies are still LLM-pilled and are trying to cash in on the low-hanging fruit. Software design and development is a strange endeavor since, as we have learnt, one of the big lessons of LLMs (in general, not just apropos coding), is how much of what we do is (trigger alert) parroting to one degree or another, rather than green field reasoning and exploration. At the same time, software development, as one gets away from boilerplate solutions to larger custom systems, is probably one of the more complex and reasoning-intensive things that humans do, and therefore may end up being one of the last, rather than first, to completely fall to AI. It may well be AI managers, not humans, who finally say that at last AI has reached human parity at software design, able to design systems of arbitrary complexity based on principled reasoning and accumulated experience.

EGreg 9mo ago

This is exactly what we found out a year ago for all AI builders. But what is the best way to convince early investors of this thesis? They seem to be all-in on just building everything from scratch end-to-end. Here is what we built: https://engageusers.ai/ecosystem.pdf https://engageusers.ai/ecosystem.pdf

iamleppert 9mo ago

I use Claude daily and I 100% disagree with the author. The article reeks of someone who doesn't understand how to manage context appropriately or describe their requirements, or know how to build up a task iteratively with a coding agent. If you have certain requirements or want things done in a certain way, you need to be explicit and the order of operations you do things in matters a lot in how efficient it completes the task, and the quality of the final output. It's very good at doing the least amount of work to just make something work by default, but that's not always what you want. Sometimes it is. I'd much rather prefer that as the default mode of operation than something that makes a project out of every little change. The developers who aren't figuring out how to leverage AI tools and make them work for them are going to get left behind very quickly. Unless you're in the top tier of engineers, I'm not sure how one can blame the tools at this point.

geldedus 8mo ago

The level of anti-AI cope is so entertaining!