Comment by mgraczyk
6 days ago
Hmm how did you get that from what I listed?
Every codebase I listed was over 10 years old and had millions of lines of code. Instagram is probably the world's largest and most used python codebase, and the camera software I worked on was 13 years old and had millions of lines of c++ and Java. I haven't worked on many self contained things in my career.
LLMs can help with these things if you know how to use them.
Tbf, you're also the CTO of a startup selling AI tools and saying in a nonspecific way that you're sure LLMs would have been helpful on large code bases you worked on years ago. Maybe so, but not at all what they were asking for in the root comment
OK, great. All I'm saying is until we really have videos (or equivalent empirical analysis) of these use cases, it's hard to assess these claims.
Jobs comprise different tasks, some more amenable to LLMs than others. My view is that where scepticism exists amongst professional senior engineers, its probably well-founded and grounded in the kinds of tasks that they are engaged with.
I'd imagine everyone in the debate is using LLMs to some degree; and that it's mostly about what productivity factor we imagine exists.
Developers who are productive with these tools are not going to waste time on that.
They are busy doing their work and prefer their competitors (other developers) to not use these tools.
The article you’re replying to is just one of many examples of people who profess to be productive with these tools, but are spending significant time and energy attempting to convince skeptics to use them.
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