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Ask HN: What is interviewing like now with everyone using AI?

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Re: Ask HN: What is interviewing like now with everyone using AI?

#501

Post by sanarothe »

feoren wrote: post #348
codeyperson wrote: post #296

Earlier quoted context omitted.

High level - having a discussion with the LLM about different approaches and the tradeoffs between each Low level - I'll write up the structure of what I want in the form of a set functions with defined inputs and outputs but without the implementation detail. If I care about any specifics with the functions I'll throw some comments in there. And sometimes I'll define the data structures in advance as well. Once all…

> High level - having a discussion with the LLM about different approaches and the tradeoffs between each I honestly can't imagine this. If the AI says "However, a downside of approach B is that it takes O(n^2) time instead of the optimal O(n log(n))", what do you think the odds are that it literally made up both of those facts? Because I'd be surprised if they were any lower than 30%. It's an extremely confident bul…

I don't code for a living, and I'm probably worse than a fresh grad would be but I use:

"Please don't generate or rewrite code, I just want to discuss the general approach."

Bc I don't know any design patterns or idiomatic approach, being able to discuss is amazing.

Though quality and consistency of responses is another thing... :)

Re: Ask HN: What is interviewing like now with everyone using AI?

#502

Post by joshvm »

fhd2 wrote: post #416

The last time I've used a leet code style interview was in 2012, and it resulted in a bad hire (who just happened to have trained on the questions we used). I've hired something like 150 developers so far, and what I ended up with after a few years of trial and error: 1. Use recruiters and network: Wading through the sheer volume of applications was even nasty before COVID, I don't even want to imagine what it's like…

I’ve done the “at home” test for ML recently for a small AI consulting firm. It's a nice approach and got me to the next round, but the way the company evaluated it was to go through the questions and ask "fundamental ML bingo" questions. I don't think I had a single discussion about the company in the entire interview process. I was told up front "we probably won't get to the third question because it will take time to discuss theory for the first two".

If you're a company that does this, please dog food your problems and make sure the interview makes the effort feel valued. It also smells weird if you claim it's representative of a typical engineering discussion. We all know that consultancy is wrangling data, bad data and really bad data. If you're arguing over what optimiser we're choosing I'd say there's better ways to waste your customer's money.

On the other hand I like leetcode interviews. They're a nice equalizer and I do think getting good at them improves your coding skill. The point is to not ask ludicrously obscure hard problems that need tricks. I like the screen share + remote IDE. We used Code which was nice and they even had tests integrated so there wasn't the whiteboard pressure to get everything right in your head. You also know instantly if your solution works and it's a nice confidence if you get it first try, plus you can see how candidates would actually debug, etc.

Re: Ask HN: What is interviewing like now with everyone using AI?

#503

Post by shaneoh »

Aeolun wrote: post #154
shaneoh wrote: post #68

Earlier quoted context omitted.

I recently interviewed for my team and tried this same approach. I thought it made sense because I want to see how people can actually work and problem solve given all the tools at their disposal, just like on the job. It proved to be awkward and clumsy very quickly. Some candidates resisted it since they clearly thought it would make them judged harsher. Some candidates were on the other extreme and basically tried…

What are you supposed to ask chatGPT if you can’t just ask it the answer? That’d confuse me too.

One example would be looking up syntax and common functions. In a high-pressure situation it's much tougher to bumble around Google and Stack Overflow, so this would be a way for solving for "I totally know how to do this thing but it's just not coming to mind at this moment" which is fair. Usually we the interviews can obviously just tell them ourselves though, but that's what I was going for.

But yeah, the point is that once I applied it in practice it did quickly become confusing, so now I know from experience not to use it.

I think the other suggestions in this thread about how to use it are good ones, but they would present their own meta challenges for an interview too. Just about finding whatever balance works for you I guess.

Re: Ask HN: What is interviewing like now with everyone using AI?

#504

Post by joshvm »

lewisleclerc wrote: post #442
pllbnk wrote: post #437

Earlier quoted context omitted.

I think you (your company) and many other commenters here are just trying too hard. I had just recently lead through several interview rounds for software engineering role and we have not had any issue with LLM use. What we do for the technical interview part is very simple - live whiteboarding design task where we try to identify what the candidate's focus is and might pivot at any time or dig deeper into particular…

Unfortunately, we've noticed that candidates are on another call and their screen is fed by someone else using chatGPT and pasting the responses, as they can hear both the interviewer and the candidate

I saw a pretty impressive cheat tool that could apparently grab the screen from the live share, process text on the screen in response to an obscure keybind and then run it through OCR to solve (or just look up a LC solution).

At that point it seems like trying too hard, but be aware there are theoretical approaches which are extremely hard to detect (the inevitable evolution of sticky notes on the desk, or wall behind the monitor).

Re: Ask HN: What is interviewing like now with everyone using AI?

#505

Post by anonzzzies »

swiftcoder wrote: post #483
anonzzzies wrote: post #404

Earlier quoted context omitted.

Hmm, that is interesting; reading is harder? You have to read a lot of code anyway right? From team members, examples, 3rd party code/libraries? Through the decades of programming at least I became very proficient and rapidly spotting 'fishy' code and generally understanding code written by others. AI coding is nice because it is, for me, the opposite of what you have; reading the code it generates is much faster tha…

> Hmm, that is interesting; reading is harder? Much, much harder. Sure, you can skim large volumes of code very quickly. But the type of close reading that is required to spot logic bugs in the small is quite taxing - which is the reason that we generally don't expect code review processes to catch trivial errors, and instead invest in testing.

But we are not talking about large volumes of code here; we are talking about; LLM generates something, you check it and close read it to spot logic bugs and either fix yourself, ask the LLM or approve. It is very puzzling to me how this is more work/taxing than writing it yourself unless for very specific examples;

Examples from every day reality in my company; writing 1000s of lines of react frontend code is all LLM (in very little time) and reviews catch all the issues while the database implementation we are working on we spend sometimes one hour on a few lines and the LLM suggest things but they never help. Reviewing such a little bit of code has no use as it's the result of testing a LOT of scenarios to get the most performance out in the real world (across different environments/settings). However, almost everyone in the world is working on (similar issues like) the former, not the latter, so...

Re: Ask HN: What is interviewing like now with everyone using AI?

#506

Post by adastra22 »

lmz wrote: post #441
adastra22 wrote: post #436

Earlier quoted context omitted.

Why not? Plenty of large organizations hire this way. My first employer is bigger than any FAANG company by head count, and they hired this way. Why is big tech different?

The desire for a scalable, standardized scoring mechanism so they can avoid lawsuits.

A lawsuit on what basis?

Re: Ask HN: What is interviewing like now with everyone using AI?

#507

Post by abenga »

anonzzzies wrote: post #480
abenga wrote: post #446

Earlier quoted context omitted.

"In the small", it's easy to read code. This code computes this value, and writes it there, etc. The harder part is answering why it does what it does, which is harder for code someone else wrote. I think it is worthwhile expending this effort for code review, design review, or understanding a library. Not for code that I allegedly wrote. Especially weeks removed, loading code I wrote into "working memory" to fix iss…

> The harder part is answering why it does what it does, which is harder for code someone else wrote. That's a vital part of writing software though.

True. I will save effort by only expending it when needed (when I need to review my coworkers' code, legacy code, or libraries).

Re: Ask HN: What is interviewing like now with everyone using AI?

#508

Post by rockyj »

Xmd5a wrote: post #84

Shouldn't a portfolio of personal projects be enough ? In the past couple years I: - adapted Java's Regex engine to work on streams of characters - wrote a basic parametric geometry engine - wrote a debugger for an async framework - did innovative work with respect to whole-codebase transformation using macros Among other things. As for ChatGPT in the context of an interview, I'd only use it if I were asked to do cha…

This is what I do not get. I just do not understand the technical interview process these days.

I have 20 years of experience in software development, I have hundreds of LinkedIn contacts you can check on, a dozen recommendations on LinkedIn and a dozen projects on Github, not to mention a blog and let's say a other indicators (e.g. Stackoverflow creds).

Now what exactly are people checking on? My picture is on LinkedIn and Github. Clearly I can code and have done dozens of projects. What is the point of asking me - "Do you know Kafka?", "Have you used AWS S3 and how?", "How would you build / scale a Node.js project" - these are the real questions I was asked. Yes, had you cared to look at my Github / blogs you would have seen I have done this multiple times, what are we verifying now?

I tried my best but at one point stopped caring about interviews.

Re: Ask HN: What is interviewing like now with everyone using AI?

#509

Post by swiftcoder »

anonzzzies wrote: post #505
swiftcoder wrote: post #483

Earlier quoted context omitted.

> Hmm, that is interesting; reading is harder? Much, much harder. Sure, you can skim large volumes of code very quickly. But the type of close reading that is required to spot logic bugs in the small is quite taxing - which is the reason that we generally don't expect code review processes to catch trivial errors, and instead invest in testing.

But we are not talking about large volumes of code here; we are talking about; LLM generates something, you check it and close read it to spot logic bugs and either fix yourself, ask the LLM or approve. It is very puzzling to me how this is more work/taxing than writing it yourself unless for very specific examples; Examples from every day reality in my company; writing 1000s of lines of react frontend code is all LL…

> writing 1000s of lines of react frontend code

Maybe we just located the actual problem in this scenario.

Re: Ask HN: What is interviewing like now with everyone using AI?

#510

Post by dalmo3 »

what wrote: post #139
danielbln wrote: post #135

Earlier quoted context omitted.

It's not negative that the candidate can solve it without an LLM, but it is positive if the candidate can use the LLM to speed up the solution. The code challenge is timeboxed. We are providing an API key for LLM inference, as implementing the challenge requires this as well. And I haven't heard a good answer yet for not using one, ideally the candidate knows how to mitigate the drawbacks of LLMs while benefiting fro…

>I haven’t heard a good answer for not using one Again, what would be a good answer? Or are you just saying there isn’t one?

A good answer in this situation would focus on demonstrating that you made a conscious decision based on the problem requirements and the approach that best suited the task. Here’s an example of a thoughtful response:

"I considered various approaches for solving this problem. Initially, I thought about using an LLM, as it's great for natural language processing and generating text-based solutions. However, for this particular challenge, I felt that a more algorithmic or structured approach was more appropriate, given the problem's nature (e.g., the need for performance optimization, a specific coding pattern, or better control over the output). While LLMs are powerful tools, they may not always provide the precision and control required for highly specific, performance-critical tasks, so I chose to solve the problem through a more traditional method. That said, if the problem had been more open-ended or involved unstructured data like text generation, I would definitely consider leveraging an LLM."

This answer reflects the candidate's ability to critically assess the problem and use the right tools for the job, showing maturity and sound judgment.

- GP, probably

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