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Comment by dionidium | Hacker News Reader

The notion that somebody could simply "memorize algorithms" and then apply that information on-the-fly to questions they won't know in advance -- demonstrating adaptiveness, creativity, and the application of general problem-solving knowledge to novel problems -- and that this doesn't measure intelligence is not credible.

I'd bet my net worth that the ability to perform well on these interviews positively correlates with IQ.

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I don't think it is as you describe it though, the leetcode questions in interviews are usually just slightly rehashed. What would be interesting is giving someone a puzzle they hadn't seen before. My wife can't remember formulas or algorithms for anything - she defends that by saying she can just look it up if she needs it. Saying that, we did Advent of Code last December and she came up with quite a few optimal solutions by first principle (albeit slowly). She'd suck getting through a leetcode interview. I'm quite lucky in that I remember these things really really easily but really struggle with truly novel problems. I just reach for my toolbox. I don't believe these interviews correlate with genuine intelligence, just a particular type of memory and question recognition.

There is a bit of reasoning backwards from conclusions here.

For example, you will likely find the applicants rejected numerous times from FAANG have higher age-adjusted bodyfat levels and more dental cavities compared to successful candidates. And of course these measures negatively correlate with IQ too.

So what? Correlation doesn't necessarily make for reasonable selection criteria. Variance is too high.

Algorithm design is a skill you can learn. It probably correlates with IQ, but you have to control for experience to see it. Experience is certainly more important than intelligence.

In my experience, algorithm design is primarily pattern matching. You have a toolkit with a set of abstractions for modeling the problem and a set of algorithmic techniques for solving it. If you have the right tools in the toolkit, potential solutions will jump out. You then pick a promising solution and figure out the details. If you don't have the right tools, you have to go back to reading or start building new tools from basic principles. That can take hours, days, weeks, months, or years, if you succeed at all.

I guess this is similar to proving mathematical theorems, but I haven't done much of that after grad school.

> primarily pattern matching. You have a toolkit with a set of abstractions for modeling the problem and a set of algorithmic techniques for solving it. If you have the right tools in the toolkit, potential solutions will jump out. You then pick a promising solution and figure out the details. If you don't have the right tools, you have to go back to reading or start building new tools from basic principles. That can take hours, days, weeks, months, or years, if you succeed at all.

But you're claiming this is somehow not "intelligence"?

Intelligence is usually understood as a relatively fixed factor that cannot be trained effectively. Algorithm design is mostly about the patterns you already know.

IME how many patterns you were able to rote-memorize is a very small factor in how good you are at algorithm design; the limiting factor is more likely to be your ability to filter out irrelevant details and abstract out the essence of the problem and see how that relates your library of patterns, and that part is mostly innate rather than trainable.

and then apply that information on-the-fly to questions they won't know in advance

But that's the whole point of LC grinding: to claw your way through enough of these problems until, lo and behold... there ends up being an 80 percent chance that any given "medium" problem thrown at you -- is one that you have in fact seen and worked on, already.

I'm just guessing at the 80 percent figure. But it seems pretty clear from the cult of LC grinding that one of the goals of this strategy is not just to learn general techniques for solving these problems -- but to get a significantly high percentage of these problems under your belt already, or nearly so.

While also learning to deploy the just the right grunts and moans and pauses to make your interviewer think you're seeing the problem for the first time, of course.

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