Comment by ramesh31
7 days ago
>The scary thing is: I have seen professional human developers write worse code.
This is kind of the rub of it all. If the code works, passes all relevant tests, is reasonably maintainable, and can be fitted into the system correctly with a well defined interface, does it really matter? I mean at that point its kind of like looking at the output of a bytecode compiler and being like "wow what a mess". And it's not like they can't write code up to your stylistic standards, it's just literally a matter of prompting for that.
> If the code works, passes all relevant tests, is reasonably maintainable, and can be fitted into the system correctly with a well defined interface, does it really matter?
You're not wrong here, but there's a big difference in programming one-off tooling or prototype MVPs and programming things that need to be maintained for years and years.
We did this song and dance pretty recently with dynamic typing. Developers thought it was so much more productive to use dynamically typed languages, because it is in the initial phases. Then years went by, those small, quick-to-make dynamic codebases ended up becoming unmaintainable monstrosities, and those developers who hyped up dynamic typing invented Python/PHP type hinting and Flow for JavaScript, later moving to TypeScript entirely. Nowadays nobody seriously recommends building long-lived systems in untyped languages, but they are still very useful for one-off scripting and more interactive/exploratory work where correctness is less important, i.e. Jupyter notebooks.
I wouldn't be surprised to see the same pattern happen with low-supervision AI code; it's great for popping out the first MVP, but because it generates poor code, the gung-ho junior devs who think they're getting 10x productivity gains will wisen up and realize the value of spending an hour thinking about proper levels of abstraction instead of YOLO'ing the first thing the AI spits out when they want to build a system that's going to be worked on by multiple developers for multiple years.
I think the productivity gains of dynamic typed languages were real, and based on two things: dynamic typing (can) provide certain safety properties trivially, and dynamic typing neatly kills off the utterly inadequate type systems found in mainstream languages when they were launched (the 90s, mostly).
You'll notice the type systems being bolted onto dynamic languages or found in serious attempts at new languages are radically different than the type systems being rejected by the likes of javascript, python, ruby and perl.
> those small, quick-to-make dynamic codebases ended up becoming unmaintainable monstrosities
In my experience, type checking / type hinting already starts to pay off when more than one person is working on an even small-ish code base. Just because it helps you keep in mind what comes/goes to the other guy's code.
And in my experience "me 3 months later" counts as a whole second developer that needs accommodating. The only time I appreciate not having to think about types is on code that I know I will never, ever come back to—stuff like a one off bash script.
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Yep, I've seen type hinting even be helpful without a type checker in python. Just as a way for devs to tell each other what they intend on passing. Even when a small percent of the hints are incorrect, having those hints there can still pay off.
> You're not wrong here, but there's a big difference in programming one-off tooling or prototype MVPs and programming things that need to be maintained for years and years.
Humans also worry about their jobs, especially in PIP-happy companies; they are very well known for writing intentionally over-complicated code that only they understand so that they are irreplaceable
I'm not convinced this actually happens. Seems more like somthing people assume happens because they don't like whatever codebase is at the new job.
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hmm I have seen conda env with far too many packages and maybe a lot of current version bumping, and the dev says "who cares" and it naturally gets a bit more.. Intentionally complicated is more like an accusation of wrongdoing.
The ML world being nearly entirely in Python, much of it untyped (and that the Python type system is pretty weak) is really scary.
>The ML world being nearly entirely in Python, much of it untyped (and that the Python type system is pretty weak) is really scary
I think this has a ton to do with the mixed results from "vibe coding" we've seen as the codebase grows in scope and complexity. Agents seem to break down without a good type system. Same goes for JS.
I've just recently started on an Objective-C project using Cline, and it's like nirvana. I can code out an entire interface and have it implemented for me as I'm going. I see no reason it couldn't scale infinitely to massive LOC with good coding practices. The real killer feature is header files. Being able to have your entire projects headers in context at all time, along with a proper compiler for debugging, changes the game for how agents can reason on the whole codebase.
I'm certainly extremely happy for having an extensive type system in my daily driver languages especially when working with AI coding assistance — it's yet another very crucial guard rail that ensures that keeps the AI on track and makes a lot of fuckups downright impossible.
what are you going to do when something suddenly doesn't work and cursor endlessly spins without progress no matter how many "please don't make mistakes" you add? delete the whole thing and try to one-shot it again?
Why do you HAVE TO one-shot? No one says you have to code like those influencers. You are a software engineer, use AI like one, iteratively.
>No one says you have to code like those influencers. You are a software engineer, use AI like one, iteratively.
This is my issue with all the AI naysayers at this point. It seems to all boil down to "haha, stupid noob can't code so he uses AI" in their minds. It's like they are incapable of understanding that there could simultaneously be a bunch of junior devs pushing greenfield YouTube demos of vibe coding, while at the same time expert software engineers are legitimately seeing their productivity increase 10x on serious codebases through judicious use.
Go ahead and keep swinging that hammer, John Henry.
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The point is because it generally produces crap code you have to one shot or else iteration becomes hard. Similar to how a junior would try to refactor their mess and just make a bigger mess
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Good insight, and indeed quite exactly my state of mind while creating this particular solution.
Iin this case, I did put in the guard rails to ensure that I reach my goal in hopefully a straight line and a quickly as possible, but to be honest, I did not give much thought to long-term maintainability or ease of extending it with more and more features, because I needed a very specific solution for a use case that doesn't change much.
I'm definitely working differently in my brown-field projects where I'm intimately familiar with the tech stack and architecture — I do very thorough code reviews afterwards.
I think this code is at least twice the size than it needs to be compared to nicer, manually produced Python code: a lot of it is really superfluous.
People have different definitions of "reasonably maintainable", but if code has extra stuff that provides no value, it always perplexes the reader (what is the point of this? what am I missing?), and increases cognitive load significantly.
But if AI coding tools were advertised as "get 10x the output of your least capable teammate", would they really go anywhere?
I love doing code reviews as an opportunity to teach people. Doing this one would suck.
Right, and the reason why professional developers are writing worse code out there is most likely because they simply don't have the time/aren't paid to care more about it. The LLM is then mildly improving the output in this brand of common real world scenario