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Prediction: faangs will come up with something clever or random or just fly everyone onsite, they are so rich and popular, they can filter by any arbitrary criteria.

Second-rate companies will keep some superficial coding, but will start to emphasize more of the verbal parts like system design and retrospective. Which sucks, because those are totally subjective and mostly filters for whoever can BS better on the spot and/or cater to the interviewer's mood and biases better.

My favorite still: in-person pair programming for a realistic problem (could be made-up or shortened, but similar to the real ones on the job). Use whatever tools you want, but get the correct requirements and then explain what you just did, and why.

A shorter/easier task is to code review/critique a chunk of code, could even just print it out if in person.

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nprateem 2 days ago | parent [-]

It's not that hard. Just ask them to explain the code. Then ask them how they'd change it for several different scenarios.

	▲	Gormo a day ago \| parent [-]
		I've taken this approach, and found that it's trivially easy to distinguish people relying on LLMs from people who have thought the problem through and can explain their own decision-making process. I had a couple of people who, when asked to explain specific approaches reflected in their code, very obviously typed my question right back into ChatGPT and then recited its output verbatim. Those interviews came to an end rather quickly. One of my favorite ones was when I asked a candidate to estimate the complexity of their solution, and ChatGPT got it wrong, giving O(log(n)) for an O(n) algorithm. When I asked leading questions to see if the candidate could see where the error came in, they starting verbatim reciting a dictionary definition of computational complexity, and could not address the specifics of the problem at all.