Comment by vidarh
5 hours ago
I saw that part and I disagreed with the very notion, hence why I wrote what I did.
> Because they didn't understand the architecture or the domain models otherwise.
My point is that requiring or expecting an in-depth understanding of all the algorithms you rely on is not a productive use of developer time, because outside narrow niches it is not what we're being paid for.
It is also not something the vast majority of us do now, or have done for several decades. I started with assembler, but most developers have never-ever worked less than a couple of abstractions up, often more, and leaned heavily on heaps of code they do not understand because it is not necessary.
Sometimes it is. But for the vast majority of us pretending it is necessary all the time or even much of the time is a folly.
> I do wonder, however, how much of your actual job also entails ensuring that whoever is doing the implementation is also growing in their understanding of the domain models. Are you developing the people under you? Is that part of your job?
Growing the people under me involves teaching them to solve problems, and already long before AI that typically involved teaching developers to stop obsessing over details with low ROI for the work they were actually doing in favour of understanding and solving the problems of the business. Often that meant making them draw a line between what actually served the needs they were paid to solve rather than the ones that were personally fun to them (I've been guilty of diving into complex low-level problems I find fun rather than what solves the highest ROI problems too - ask me about my compilers, my editor, my terminal - I'm excellent at yak shaving, but I work hard to keep that away from my work)
> If it is an AI that is reporting to you, how are you doing this? Are you writing "skills" files? How are you verifying that it is following them? How are you verifying that it understands them the same way that you intended it to?
For AI use: Tests. Tests. More tests. And, yes, skills and agents. Not primarily even to verify that it understands the specs, but to create harnesses to run them in agent loops without having to babysit them every step of the way. If you use AI and spend your time babysitting them, you've become a glorified assistant to the machine.
Most of the tests are BS too.
And nobody is talking about verifying if the AI bubble sort is correct or not - but recognizing that if the AI is implementing it’s own bubble sort, you’re waaaay out in left field.
Especially if it’s doing it inline somewhere.
The underlying issue with AI slop, is that it’s harder to recognize unless you look closely, and then you realize the whole thing is bullshit.
> Most of the tests are BS too.
Why are you creating BS tests?
> And nobody is talking about verifying if the AI bubble sort is correct or not - but recognizing that if the AI is implementing it’s own bubble sort, you’re waaaay out in left field.
Verifying time and space complexity is part of what your tests should cover.
But this is also a funny example - I'm willing to bet the average AI model today can write a far better sort than the vast majority of software developers, and is far more capable of analyzing time and space complexity than the average developer.
In fact, I just did a quick test with Claude, and asked for a simple sort that took into account time and space complexity, and "of course" it knows that it's well established that pure quicksort is suboptimal for a general-purpose sort, and gave me a simple hybrid sort based on insertion sort for small arrays, heapsort fallback to stop pathological recursion, and a decently optimized quicksort - this won't beat e.g. timsort on typical data, but it's a good tradeoff between "simple" (quicksort can be written in 2-20 lines of code or so depending on language and how much performance you're willing to sacrifice for simplicity) and addressing the time/space complexity constraints. It's also close to a variant that incidentally was covered in an article in DDJ ca. 30 years ago because most developers didn't know how to, and were still writing stupidly bad sorts manually instead of relying on an optimized library. Fewer developers knows how to write good sorts today. And that's not bad - it's a result of not needing to think at that level of abstraction most of the time any more.
And this is also a great illustration of the problem: Even great developers often have big blind spots, where AI will draw onresults they aren't even aware of. Truly great developers will be aware of their blind spots and know when to research, but most developers are not great.
But a human developer, even a not so great one, might know something about the characteristics of the actual data a particular program is expected to encounter that is more efficient than this AI-coded hybrid sort for this particular application. This is assuming the AI can't deduce the characteristics of the expected data from the specs, even if a particular time and space complexity is mandated.
I encountered something like this recently. I had to replace an exact data comparison operation (using a simple memcmp) with a function that would compare data and allow differences within a specified tolerance. The AI generated beautiful code using chunking and all kinds of bit twiddling that I don't understand.
But what it couldn't know was that most of the time the two data ranges would match exactly, thus taking the slowest path through the comparison by comparing every chunk in the two ranges. I had to stick a memcmp early in the function to exit early for the most common case, because it only occurred to me during profiling that most of the time the data doesn't change. There was no way I could have figured this out early enough to put it in a spec for an AI.
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> Most of the tests are BS too.
Only if you don't constrain the tests. If you use agents adversarially in generating test cases, tests and review of results, you can get robust and tight test cases.
Unless you're in research, most of what we do in our day jobs is boilerplate. Using these tools is not yet foolproof, but with some experience and experimentation you can get excellent results.
I don’t have to do boilerplate, generally.
And with all the stacking LLMs against each other, that just sounds like more work than just… writing the damn tests.
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