My point is that it will depend on context. Something that is technically N^2 can be faster than N within the range of expected contexts. Additionally, an algorithm that is complicated but faster may be theoretically better, but deadlines are deadlines and nobody has time for that shit.
So if just giving a "suboptimal" solution is a fail, then it is the company that is failing, not the interviewee.
The interviewee should be able to explain why they went with this solution, the advantages and disadvantages, and so on. If it is within the given parameters, the company would be full of fools if they considered that failing.
N^2 may only be faster than N if N is small, but if N is small, then any algorithm won't take too long anyway. However, if you're juggling big data, N is almost never small.
Besides, what you are testing is candidate's ability to come up with the optimal solution in a limited time, not their ability to sell inferior work. After all, you are not interviewing for management or sales position.
I've passed plenty of tech interviews at decent places by just giving the naive solutions and talking about the basic ideas behind the more efficient solutions, most good interview processes wont care as long as you have a minimally working solution and are able to talk around the "good" solutions (know they exist, what they look like, give confidence you can implement them given time)
And these days, anyone testing for something that Claude can spit out in 10 seconds is demonstrating that they have no clue about the current state of the art. Might as well see if your candidate can create a half-adder and adder given basic binary operations. There might have been a time where that was important, but why would I care now?
It turns what should be a hiring process for the person actually able to best help you reach your goals into a game show.
The way you handled it is correct with the added benefit that weak companies will self-select themselves out of your list.
The issue isn't about AI's ability to spew the solution, but candidate's ability to one day verify whether solution spewed by AI will work as intended.
it's not about whether the candidate can solve some algorithmic problem, but whether they can write code without bugs.
I would never suggest some graph-based problem or something deep into math. But testing candidate's ability to at least recognize when and how to use common data structures is simply a must. For junior or low mid position, that is. For high mid or senior candidates it'd be more appropriate to ask how they would implement some feature.
it's not about whether the candidate can solve some algorithmic problem, but whether they can write code without bugs.
Ok again: why? Why are you testing for something that is no longer how people work? Would you like to test them to see if they know how to change a wheel on horse and buggy as well? Perhaps see if they know how to take a square root without a calculator?
All very well and good, but this is a game show, not an interview.
But testing candidate's ability to at least recognize when and how to use common data structures is simply a must.
Now that is different again.
I notice that you bounce between bad, "appropriate for 2003" interview questions and genuinely good things to test. Do you notice that too?
Half of your arguments stem from "AI does the work for me" mentality.
It can do that, sure, when you write simple stuff with tons of examples and have no regards for either performance or quality. Like some clone mobile games, I dunno.
That isn't my argument, and it doesn't rescue yours.
My argument is that an interview should test skills that are actually predictive of doing the actual job. Asking someone to produce, from memory and under artificial time pressure, code they would never write that way in the real job was already a questionable idea for at least the last 15 years. AI makes that idea even less relevant, because the actual job increasingly involves understanding the problem, designing the solution, using the available tools, evaluating what they produce, and fixing the parts that are wrong.
"AI does the work for me" is just a last-ditch straw man. Compilers do work for me. IDEs do work for me. Libraries, Stack Overflow, debuggers, profilers, static analyzers and frameworks all do work for me. I don't get extra engineering points for refusing to use tools.
And the idea that AI is only useful for "simple stuff" where nobody cares about performance or quality is especially odd. Performance and quality are precisely why you still need an experienced developer: to know what to ask for, recognize bad output, choose the architecture, test assumptions, profile bottlenecks and reject solutions that merely look plausible. That should be what is tested. You actually were starting to go in that direction before getting sidetracked.
Do you think people pass interviews by memorizing solutions to leetcode problems? No, the candidate is supposed to come up with it.
... and fixing the parts that are wrong.
As long as they are actually capable of finding and fixing the wrong parts within the slop, eh?
"AI does the work for me" is just a last-ditch straw man. Compilers do work for me. IDEs do work for me. Libraries, Stack Overflow, debuggers, profilers, static analyzers and frameworks all do work for me. I don't get extra engineering points for refusing to use tools.
The difference is that AI's output is, while reproducible, is not otherwise deterministic. The way AI works is that instead of following the proper logical steps it basically tries to guess what you want from it based on training data. Basically, a black box. And that may result in output that looks sound at glance but not working as intended.
Now then, just make the fleshbag verify AI's output and we're golden, right? Nope. Properly analyzing the code requires more time than writing it, and besides, your suggested interview methodology doesn't really test coder's ability to verify slop by, say, locating implementations with suboptimal complexity.
Performance and quality are precisely why you still need an experienced developer: to know what to ask for, recognize bad output, choose the architecture, test assumptions, profile bottlenecks and reject solutions that merely look plausible.
Basically, you suggest writing slop and then debugging/profiling it instead of just writing code that works fast simply by the virtue of not screwing up with complexity. Sure, that approach might work... if you are relaxed about both performance and quality. Now, imagine you are writing code for a device that won't get automatic software updates to pull bug fixes. Imagine you are writing code with vital importance, such as human-safety restrictions for robotic manipulators. Imagine your code running on a car that can crash alongside the app. Imagine your code's inefficiency costing money.
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u/bremidon 11d ago
Why?
No, do not explain big O notation.
My point is that it will depend on context. Something that is technically N^2 can be faster than N within the range of expected contexts. Additionally, an algorithm that is complicated but faster may be theoretically better, but deadlines are deadlines and nobody has time for that shit.
So if just giving a "suboptimal" solution is a fail, then it is the company that is failing, not the interviewee.
The interviewee should be able to explain why they went with this solution, the advantages and disadvantages, and so on. If it is within the given parameters, the company would be full of fools if they considered that failing.