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Pattern recognition and code quality

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The discussion highlights a shift in software development where code generation has become a "cheap" commodity, moving the primary focus of human developers toward rigorous validation and high-level judgment. While some contributors argue that AI can master complex, niche tasks like building functional parsers, others warn that only experienced engineers possess the intuition necessary to prevent "slop" and avoid the pitfalls of "vibecoding" without a total system understanding. There is also significant debate surrounding pattern recognition, specifically how the predictable linguistic style of LLMs can signal a superficial regurgitation of buzzwords even when the underlying logic remains functional. Ultimately, the consensus suggests that while AI may soon handle the majority of code production, the human role remains essential for maintaining quality standards and navigating the boundary between efficient automation and meaningful innovation.

12 comments tagged with this topic

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It's not gibberish. More than that, LLMs frequently write comments (some are fluff but some explain the reasoning quite well), variables are frequently named better than cdx, hgv, ti, stuff like that, plus looking at the reasoning while it's happening provides more clues. Also, it's actually fun watching LLMs debug. Since they're reasonably similar to devs while investigating, but they have a data bank the size of the internet so they can pull hints that sometimes surprise even experienced devs. I think hard earned knowledge coming from actual coding is still useful to stay sharp but it might turn out the balance is something like 25% handmade - 75% LLM made.
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‘Why were they long term?’ is what you need to ask. Code has become essentially free in relative terms, both in time and money domains. What stands out now is validation - LLMs aren’t oracles for better or worse, complex code still needs to be tested and this takes time and money, too. In projects where validation was a significant percentage of effort (which is every project developed by more than two teams) the speed up from LLM usage will be much less pronounced… until they figure out validation, too; and they just might with formal methods.
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My expectations don’t change whether or not I’m using AI, and neither do my standards. Whether or not you use my software is up to you.
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> That sounds reasonable to me. AI is best at generating super basic and common code I'm currently using AI (Claude Code) to write a new Lojban parser in Haskell from scratch, which is hardly something "super basic and common". It works pretty well in practice, so I don't think that assertion is valid anymore. There are certainly differences between different tasks in terms of what works better with coding agents, but it's not as simple as "super basic".
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I'm sure there is plenty of language parsers written in Haskell in the training data. Regardless, the question isn't if LLMs can generate code (they clearly can), it's if agentic workflows are superior to writing code by hand.
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Don't worry, it's an LLM that wrote it based on the patterns in the text, e.g. "Starting a new project once felt insurmountable. Now, it feels realistic again."
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That is a normal, run of the mill sentence.
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Yes, for an LLM. The good thing about LLMs is that they can infer patterns. The bad thing about LLMs is that they infer patterns. The patterns change a bit over time, but the overuse of certain language patterns remains a constant. One could argue that some humans write that way, but ultimately it does not matter if the text was generated by an LLM, reworded by a human in a semi-closed loop or organically produced by human. The patterns indicate that the text is just a regurgitation of buzzwords and it's even worse if an LLM-like text was produced organically.
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Claiming that use of more complicated words and sentences is evidence of LLM use is just paranoia. Plenty of folk write like OP does, myself included.
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This author simultaneously admits, he cannot hold the system in his head, but then also claims he’s not vibecoding, and I assert that these are two conflicting positions and you cannot simultaneously hold both positions I am also doing my pattern recognition. It seems that a common pattern is people claiming it sped me up by x! (and then there’s no AB test, n=1)
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The OP is not talking about making slop, he's talking about using AI to write good code.
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Agree with this. Like the author, I've been keeping ajour with web development for multiple decades now. If you have deep software knowledge pre-LLM, you are equipped with the intuition and knowledge to judge the output. You can tell the difference between good and bad, if it looks and works the way you want, and you can ask the relevant questions to push the solution to the actual thing that you envisioned in your mind. Without prior software dev experience people may take what the LLM gives them at face value, and that's where the slop comes from imho.