Contribution to OS projects would allow us to bypass this screen altogether, but great point.
Haha we might have to switch to Rust just to ease our interviewing woes here - we're primarily a PHP/Laravel shop, and have tried to be as charitable as possible to candidates by allowing them to program in the language they're strongest in. Perhaps we need to change that.
The screen we're doing is meant to filter out the frauds/imposters and so far has a 100% success rate, but unfortunately we've caught three out of the last three people who have made it to that point. It's become a huge waste of time.
Maybe we just need to curate candidates from OS contributors to prevent this, or something of that nature.
Having the GPT answer beforehand would allow us to confront them about it sooner, but at that point we're definitely not hiring them since they thought it a good idea to use an LLM to cheat on questions they should be able to do in their sleep.
Interesting that you're asking the candidate to have their hands visible. We haven't wanted to have to go that way, but we might.
I disagree with the carpenter-hammer analogy here pretty strongly.
We're basically trying to figure out if they can code generally, or if somehow they've skated by in their last positions without the fundamental skills of programming.
I'm not sure how, but we've come across a number of programmers from F1000 companies that can't seem to hit some of the basics in their chosen language.
LLMs have their place as a tool, but before we empower them with the latest and greatest programming assistance, we want to make sure that they have the skills to do things like critically interpret the output of Copilot, etc.
We want to make sure we're that the people we hire possess the skills they claim, and that they won't serve as a very-slow wrapper for the LLM tools we already pay for.
Great point on the Sieve - knowing it off of the top of his head wasn't itself a red-handed indicator of cheating, but claiming that he'd used it in a B2B SaaS app was. Especially given that he wasn't able to explain how he'd allegedly used it.
The candidates we're bringing into this screen typically have 1-3 prior positions on their resume, so the point here is to throw them some softballs that they should be able to crank through with some ease to demonstrate that their basic programming skills are there.
We've had experiences where people who have held legitimate programming jobs at F1000 companies struggle greatly with some of the basic questions that I've listed above. I'm not sure why, but it's the case.
We try as best as we can to adjust for anxiousness, I know that programming in front of others can suck, but all the same we're just trying to establish, "before we go forward, can you do some elementary tasks that anyone with your claimed experience should be able to do"
All the questions I come up with that are both narrow enough in scope to reasonably do in an interview and get unhelpful answers from an LLM end up being trick questions for humans too, which is not really fair or productive.
What's particularly frustrating is that I've got some actual unsolved algorithms problems I want a solution to. One might think that since the LLMs are smart enough to re-solve problems from their training data in interviews, they could help - alas, they have been of no help so far.
The screen we are performing here is a basic "can you program" type of evaluation.
We've run into a number of people with seemingly-decent resumes (several positions as engineers at reputable albeit non-FAANG companies like insurers or e-commerce firms) who have struggled to complete basic tasks like the Pascal's Triangle question mentioned above.
The intent here is to toss them a couple of softballs that they should be able to knock out of the park, almost like if they were helping a younger sibling with CS 1XX or 2XX level work.
We're not against the use of Copilot, etc. once onboarded. We just want to make sure that these candidates possess basic skills that their resumes would suggest they mastered years ago.
Your questions are pointless. I have no interest in CS puzzles at all, I don't memorize or practice them, and that's what you're testing for. I write code to build stuff.
Why not have a conversation with them about things they have built instead? Not form questions that say "tell us about a time you encountered a problem you had to solve" but an actual conversation. Like... an interview.
This screen is after the "walk us through your resume, explain what you've built" conversation, where we ask them about specific challenges, decisions they made, etc.
Everyone who has made it to this screen has already spoken to us about their previous work and at least seemed to have some skill in their general area of programming.
What we're testing for doesn't need to be memorized or practiced. Anyone who can program for money should be able to do things like FizzBuzz, repeat the pattern in Pascal's Triangle with a for loop, or come up with a basic strategy to eliminate non-primes.
Even if you blow the part of the interview where you need to identify prime/not-prime, as long as you show us that you have a process you're fine.
Maybe we just suck at or are too charitable when it comes to processing what candidates have claimed to have done - I'm unsure.
Would you hire someone who can't solve fizz buzz without a LLM? Are we being for real here? Fizz Buzz tests the absolute basics of understanding control flow.
Here is the thing though. Solving "FizzBuzz" is trivial if you happen to know how to do it. Same with "Pascal's Triangle" or many LeetCode style interview questions.
Solving either, particularly the latter when you don't "know" the test and are under pressure is not trivial. I have been in interviews for Ph.D. candidates who when asked simple questions like "Why do you want a Ph.D?" suddenly can't even remember how to speak English let alone explain why they want the advanced degree.
FizzBuzz is kind of a trap. If you don't know it, it seems like it would be easy to try nested if statements as candidates want to write efficient code and can fall into the trap of not wanting to repeat a test for 3 or they try a test for 3 then 5 then 15 before realizing that the easiest solution is three properly ordered if statements and that there is no "elegant" way to solve this trivial problem.
In fact, i work every day with data scientists who write perfect classification code and generate complex statistical models who would likely struggle to write FizzBuzz on the whiteboard in front of the entire team.
I hate interview questions like these and struggle to connect them to performance. If one must test then give a small project (something that might take me 30 mins to an hour) and ask the candidate to complete it at their own pace and publish their work in progress to a git repo. Can they solve the problem (even if they used google and/or an LLM)? Are their commits in meaningful units of work? Can I read their code in a review? Did they ask for proper clarification when uncertain? Do they have tests?
This is generally what we do and what raised our suspicions in the first place - they both could "walk us through" their code, but had trouble explaining why they did certain things, how they could improve things, etc.
We thought that the approach you've outlined would generally be good enough, and has led us to catch instances where people are leaning heavily on LLMs, but our issue now is that everyone appears to be using these things. Admittedly, our sample size here is low (n=3). But it's still frustrating nonetheless.
You could try to give a challenge that has a few hidden gotchas, and discard candidates that do not spot them. How to do this depends on the role you are hiring for.
For example, in our data scientist interviews we also candidates to analyze datasets with imbalanced classes, outliers, correlated samples, etc. Correctly dealing with these issues requires particular techniques, and most importantly the candidate has to explicitly check whether these issues are present or not. Those who use LLMs mindlessly will not even realize this is the case.
We're definitely not doing any "Leetcode style" interviews over here - these are basic "can you perform basic programming tasks as you've stated" type questions.
Not really, since failing FizzBuzz basically ends the interview.
That said, Pascal's and Non-Primes allows us to do a few minutes of basic, "that looks great, what are some ideas for optimizing it" work together once they have established that they have the basics of programming.
I am incredibly open to suggestion on better ways to evaluate people for both (a) their basic ability to code, and (b) their ability to think about code/optimization.
How do you reconcile recent extreme weather and weather events with your apparent view that most climate reporting is fake? Or that recent years have all been the hottest in modern human history?
Your comment isn't very useful, and not in the spirit of HN with regard to helpful/thoughtful discussion. It's just complaining without evidence.
Haha we might have to switch to Rust just to ease our interviewing woes here - we're primarily a PHP/Laravel shop, and have tried to be as charitable as possible to candidates by allowing them to program in the language they're strongest in. Perhaps we need to change that.
The screen we're doing is meant to filter out the frauds/imposters and so far has a 100% success rate, but unfortunately we've caught three out of the last three people who have made it to that point. It's become a huge waste of time.
Maybe we just need to curate candidates from OS contributors to prevent this, or something of that nature.