One of the reasons to avoid rewriting has long been that the time spent rewriting code that works is time not spent writing code to meet new business needs. In other words, it's usually a huge opportunity cost, rarely balanced out by sufficient gains to be net positive.

I do think AI does challenge this prior, as the time needed to do a rewrite shrinks dramatically. It's not zero, of course, but it now becomes worth asking if it's worth doing, rather than defaulting to avoiding rewrites in nearly all cases.

Comments URL: https://news.ycombinator.com/item?id=50233208

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The declaration here, as I understand it, is taking the premise that the point of mathematics _is_ the domain knowledge, and that using "solves math problems" as a benchmark is exactly the kind of shallow, domain-eroding approach to AI that in software we label "slop."

So much, of course, depends on the execution -- a crappy city store won't be a help to anyone -- but the concept isn't inherently bad on its face.

Comments URL: https://news.ycombinator.com/item?id=49169011

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Comments URL: https://news.ycombinator.com/item?id=48939742

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Comments URL: https://news.ycombinator.com/item?id=48902184

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[0]https://bookshop.org/p/books/the-barbarous-years-the-peoplin...

Comments URL: https://news.ycombinator.com/item?id=48260436

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Comments URL: https://news.ycombinator.com/item?id=47909815

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Isn't this kind of true, though? Housing construction, for instance, isn't bottlenecked by the technical difficulties of building, but by political and regulatory hurdles. Or look at large, capital-intensive projects such as the always-proposed, never built new Hudson river train tubes. Actually building these will take billions of dollars and many years, but even they would be long built by now were it not for their constantly being blocked by political jockeying.

Building stuff _does_ often involve difficult technical challenges, but I still think that as a general aphorism the observation that this isn't the _hardest_ part holds true.

So long as this is all true, then the code needs to be human readable, even if it's not human-written.

Maybe we'll get to the point that LLMS really are equivalent to compilers in terms of reliability -- but at that point, why would be have them write in Java or other human-readable languages? LLMs would _be_ a compiler at that point, with a natural-language UI, outputing some kind of machine code. Until then, we do need readable code.

My prompts end to be in the pattern of "I am looking to implement . . Review the code base to find similar examples of how this is currently done, and propose a plan for how to implement this."

These days I'm on Claude Code, and I do that first part in Plan mode, though even a few months ago on earlier, not-as-performant models and tools, I was still finding value with this approach. It's just getting better, as the company is investing in shared skills/tools/plugins/whatever the current terminology is that is specific to various use cases within the code base.

I haven't been writing so much code directly, but I do still very much feel that this is my code. My sessions are very interactive -- I ask the agent to explain decisions, question its plans, review the produced code and often revise it. I find it frees me up to spend more time thinking through and having higher level architecture applied instead of spending frustrating hours hunting down more basic "how does this work" information.

I think it might have been an article by Simon Willison that made the case for there being a way to use AI tooling to make you smarter, or to make you dumber. Point and shoot and blindly accept output makes you dumber -- it places more distance between you and your code base. Using AI tools to automate away a lot of the toil give you energy and time to dive deeper into your code base and develop a stronger mental model of how it works -- it makes you smarter. I keep in mind that at the end of the day, it's my name on the PR, regardless of how much Claude directly created or edited the files.