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Comment by grugagag | Hacker News Reader

But why stop there? Why not test candidates with problems they have never seen before? Or problems similar to the problems of the organization hiring? Leetcode mostly relies on memorizing patterns with a shallow understanding but shows the candidates have a gaming ability. Does that imply quality in any way? Some people argue that willing to study for leetcode shows some virtue. I very much disagree with that.

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I think you have a misunderstanding. Most companies that do LC-style interviews usually show unknown problems.

Memorizing the Top 100 list from Leetcode only works for a few companies (notably and perplexingly, Meta) but doesn't for the vast majority.

Also, just solving the problem isn't enough to perform well on the interview. Getting the optimal solution is just the table stakes. There's communication, tradeoffs between alternative solutions, coding style, follow-up questions, opportunities to show off language trivia etc.

Memorizing problems is wholly not the point of Leetcode grinding at all.

In terms of memorizing "patterns", in mathematics and computer science all new discovery is just a recombination of what was already known. There's virtually no information coming from outside the system like in, say, biology or physics. The whole field is just memorized patterns being recombined in different ways to solve different problems.

It’s not about memorizing individual problems per se, but rather recognizing overall patterns and turning the process into a gameable endeavor. This can give candidates an edge, but it doesn’t necessarily demonstrate higher-level ability beyond surface familiarity with common patterns and the expectations around them. I’d understand the value if the job actually involved work similar to what's reflected in leetCode style problems, but in most cases, that couldn’t be further from reality. leetCode serves little purpose beyond measuring a candidate’s willingness to invest time and effort. That’s the only real virtue it rewards. But ultimately, I believe leetCode style interviews are measuring the wrong metric.

>a candidate’s willingness to invest time and effort

I guess it's a matter of opinion but my point is, this is probably the right metric. Arguably, the kind of people who shut up and play along with these stupid games because that's where the money is make better team players in large for-profit organizations than those who take a principled stance against ever touching Leetcode because their efforts wouldn't contribute anything to the art.

Maybe yes maybe not, I'm leaning not but it's just an opinion. But as a company be careful what you wish for, these same candidates are often skilled at gaming systems and may leave your team as soon as they've extracted the benefits. They’re likely more interested in playing the game than in seriously solving real-world problems.

Then what if the test was how well you play chess? That takes time to study to become good. But would it be a good metric for hiring programmers?

Because chess is more unrelated to the job? It is easy to see that LeetCode problems are closer to a programmers job than what chess is.

But yeah, people used to ask that level of unrelated questions to programmers, and they were happy with the results. "Why are manhole covers round" etc. LeetCode style questions do produce better results than those, so that is why they use them.

Then testing how they play Factorio will work better

To play the devils advocate, being able to memorize patterns and recognize which patterns apply to a given problem is extremely valuable. Tons of software dev is knowing the subset of algorithms, data structures, and architecture that apply to a similar problem and being able to adapt it.

It's funny you mention that.

That's literally what CS teaches you too. Which is what "leetcode" questions are: fundamental CS problems that you'd learn about in a computer science curriculum.

It's called "reducing" one problem to another. We had an entire semester's mandatory class spend a lot of time on reducing problems. Like figuring out how you can solve a new type of question/problem with an algorithm or two that you already know from before.

Like showing that "this is just bin packing". And there are algorithms for that, which "suck" in the CS kind of sense but there are real world algorithms that are "good enough" to be usable to get shit done.

Or showing that something "doesn't work, period" by showing that it can be reduced to the halting problem (assuming that nobody has solved that yet - oh and good luck btw. if you want to try ;) )

I did quite a bit of competitive programming in school, and pretty much all the world-class competitive problems are reduced to well-known algorithms. It's quite hard to come up with something new (not proven to be unsolvable for its constraints). I believe problem setters just try to disguise a known algorithm as much as possible.

Then comes the ability/memorization to actually code it, e.g. if I knew it needs coding red-black tree I wouldn't even start.

Solve one of my problems :)

https://www.acmicpc.net/problem/33797

Try with an LLM too :)

403 Forbidden :(

Algorithms and data structures are absolutely trivial for 99% of software dev work. 1% are inventing MapReduce.

Architecture is not part of leetcode.

> Or problems similar to the problems of the organization hiring?

People complain, rightly so in some cases, that their "interview" is really doing some (unpaid) work for the company

> Leetcode mostly relies on memorizing patterns

Math is like that as well though. It's about learning all the prior axioms, laws, knowing allowed simplifications, and so on.

In the same way that writing and performing a new song is "just memorizing prior patterns and law"

or that writing a new book is the same.

I.e. it's not about that. Like sure it helps to have a base set of shared language, knowledge, and symbols, but math is so much more than just that.

Programming competition problems are also much more than just memorizing patterns, that was the point of his post.

I think you've missed a big part of maths - yes knowing those things is necessary. But then you also need to be able to see how a difficult or complex problem could be restated or broken down in a different way which lets you use those techniques. Sometimes this is something as trivial as using the right notation or coordinates, sometimes it's much more involved.

In math, you usually need to prove said simplifications. So just memorizing is not enough. As you get more advanced, you then start swapping out axioms.

In programming the simplifications has to be correct even if you don't prove them, and being correct isn't that easy.

Pedantic: how do you know something is correct without proving it? How do you know you have covered all possible edge cases? /Pedantic

In all seriousness, the intersection between correctness and project delivery is where engineering sits. Solutions must be good enough, correct enough, and cheap enough to fit the use case, but ideally no more than that.

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