> The AI has suggested a solution, but the added code is arguably useless or wrong. There is a huge decision space to consider, but the AI tool has picked one set of decisions, without any rationale for this decision.
> [...]
> Programming is about lots of decisions, large and small. Architecture decisions. Data validation decisions. Button color decisions.
> Some decisions are inconsequential and can be safely outsourced. There is indeed a ton of boilerplate involved in software development, and writing boilerplate-heavy code involves near zero decisions.
> But other decisions do matter.
(from https://lukasatkinson.de/2025/net-negative-cursor/)
Proponents of AI coding often talk about boilerplate as if that's what we spend most of our time on, but boilerplate is a cinch. You copy/paste, change a few fields, and maybe run a macro on it. Or you abstract it away entirely. As for the "agent" thing, typing git fetch, git commit, git rebase takes up even less of my time than boilerplate.
Most of what we write is not highly creative, but it is load-bearing, and it's full of choices. Most of our time is spent making those choices, not typing out the words. The problem isn't hallucination, it's the plain bad code that I'm going to have to rewrite. Why not just write it right myself the first time? People say "it's like a junior developer," but do they have any idea how much time I've spent trying to coax junior developers into doing things the right way rather than just doing them myself? I don't want to waste time mentoring my tools.
Coming at this from a computer-science or PLT perspective, this idea of an "abstract, repeatable meta-boilerplate" is exactly the payoff we expect from language features like strong type systems. Part of the point of rigorous languages is to create these kinds of patterns. You had total expressiveness back in assembly language! Repeatable rigor is most of the point of modern languages.
That's what libraries and frameworks are here for. And that's why no experienced engineers consider those an issue. What's truly important is the business logic, then you find a set of libraries that solves the common use cases and you write the rest. Sometimes you're in some novel space that doesn't have libraries (new programming language), but you still have specs and reference implementation that helps you out.
The actual boilerplate is when you have to write code twice because the language ecosystem don't have good macros à la lisp so you can invent some metastuff for the problem at end. (think writing routers for express.js)
That's not what I see the parent comment saying. They're not saying that LLMs can't use frameworks, they're saying that if you have rote solutions that you are being forced to write over and over and over again, you shouldn't be using an LLM to automate it, you should use a framework and get that code out of your project.
And at that point, you won't have a ton of boilerplate to write.
The two sides to this I see online are between the people who think we need a way to automate boilerplate and setup code, and the people who want to eliminate boilerplate (not just the copy-paste kind, but also the "ugh, I've got to do this thing again that I've done 20 times" kind).
Ideally:
> a common set of rote solutions to isomorphic problems
Should not be a thing you have to write very often (or if it is, you should have tools that make it as quick to implement as it would be to type a prompt into an LLM). If that kind of rote repetitive problem solving is a huge part of your job, then to borrow your phrasing: the language or the tools you're using have let you down.