Comment by necovek
16 days ago
> expert software engineers are legitimately seeing their productivity increase 10x
It's funny you would say this, because we are really commenting on an article where a self-proclaimed "expert" has done that and the "10x" output is terrible.
I have just checked my article — the word "expert" isn't in it, so not quite sure where you got this from.
I'm working in the field professionally since June 1998, and among other things, I was the tech lead on MyHammer.de, Germany's largest craftsman platform, and have built several other mid-scale online platforms over the decades.
How well I have done this, now that's for others to decide.
Quite objectively though, I do have some amount of experience — even a bad developer probably cannot help but pick up some learnings over so many years in relevant real-world projects.
However, and I think I stated this quite clearly, I am expressively not an expert in Python.
And yet, I could realize an actually working solution that solves an actual problem I had in a very real sense (and is nicely humming away for several weeks now).
And this is precisely where yes, I did experience a 10x productivity increase; it would have certainly taken me at least a week or two to realize the same solution myself.
Apologies for implying you are claiming to be an expert software engineer: I took the "senior" in the title and "25 years of experience" in the post to mean similar things as "expert".
I don't doubt this is doing something useful for you. It might even be mostly correct.
But it is not a positive advertisement for what AI can do: just like the code is objectively crap, you can't easily trust the output without a comprehensive review. And without doubting your expertise, I don't think you reviewed it, or you would have caught the same smells I did.
What this article tells me is that when the task is sufficiently non-critical that you can ignore being perfectly correct, you can steer AI coding assistants into producing some garbage code that very well might work or appear to work (when you are making stats, those are tricky even with utmost manual care).
Which is amazing, in my opinion!
But not what the premise seems to be (how a senior will make it do something very nice with decent quality code).
Out of curiosity why did you not build this tool in a language you generally use?
Because I wanted exactly this experience: can I get to the desired result — functionality-wise, if not code-wise! — even if I choose the stack that makes sense in terms of technology, not the one that I happen to be proficient in?
And if I cannot bring language-proficiency to the table — which of my capabilities as a seasoned software&systems guy can I put to use?
In the brown-field projects where my team and I have the AI implement whole features, the resulting code quality — under our sharp and experienced eyes — tends to end up just fine.
I think I need to make the differences between both examples more clear…
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