I guess this is in response to Google (and I think even Amazon) changing up their hiring to now include some AI component. I don't think Google is ditching leetcode questions altogether though.
I still think that much the same way Google Search had the goal of answering our questions even before we asked them, I think a long term goal for interviewing platforms should be to assess/consult someone’s suitability for a job without an interview at all.
> answering our questions even before we asked them
That's an interesting angle, and would perfectly explain how bad they became, even before AI.
Going that route requires to make a lot of assumptions and rely on heuristics, bin users in large buckets that behave predictively.
People speaking different languages than the country they're flagged on, asking different questions than what the zeitgeist is about, searching for things outside of the consensus or switching interests too quickly would all have definitely worse experiences and become the collateral damage of that vision.
Will they nerf the AI so it doesn’t help you too much? Is it going to keep making mistakes that you have to correct? I guess that would be a good skill to check.
>Chakra runs on HackerRank’s existing, audited production infrastructure and [...] Candidate data is never used to train AI models, and all hosting is done securely with each customer’s data isolated.
Homepage
>Developers don’t write code anymore. They plan, build and review with AI.
I wonder which LLM provider is actually running the inference, getting all that juicyness. Because "runs on.." doesn't really mean "stays inside". Also the name is weird, "let's check your chakras!" "how did your chakra test go?".
It should be said that there were others - like https://interviewing.io/https://karat.com/ et al - that figured out the importance of moving beyond "grind LeetCode" years hackerrank did, but it's good to see them acknoweldge this, too.
As someone who is top x% on Leetcode (I think I also held the longest daily streak), I would say Leetcode is a test of perseverance. I became a worse developer because I spent time on Leetcode instead of my open source projects, but it taught me marathon long grind. I truly believe its unintended positives are somewhat worthwhile.
> I truly believe its unintended positives are somewhat worthwhile.
If the point you're making is that solving complicated, synthetic programming challenges helped built your stamina and mental endurance, is there another way you could have built the same but in a much more productive and pleasurable manner?
Perhaps in a manner that would also have resulted in the improvement of your community or humanity as a whole instead of being the millionth person to "solve" the same exact problem?
I flat out rejected all interviews that required any type of LeetCode or similar tests, in the last 5 years. I have 10+ years experience though so I guess I do have an advantage here. It's often enough to ask for an alternative.
My first impression when I see that LeetCode or similar tools are used is what is the level of their technical team? Am I joining a company that has no person capable of evaluating my skills? Do I still want to join such an incapable organization? Not only during the interviewing process, mind you. My annual performance review, as bad as it is in good companies, will most likely be some kind of LeetCode again.
I think it should be the norm that any company using LeetCode for hiring must post their own staff's scores.
Well, I am happy that people are noticing that LC interviews were not the best idea, but I am sad to see that they are noticing it for the wrong reasons (LLMs will replace the need for algorithms).
> Tasks: Algorithmic questions → real work in code repositories
> Evaluation: Code correctness → AI fluency and judgment
I am never trading algorithmic knowledge for "real work", unless I am hiring for something that does not really require any kind of intellect, which would make me question why I am hiring in the first place. Same goes for code correctness. I wouldn't really like working with anyone who can't read and assess the code they are trying to ship (whether they wrote it or generated it).
That said, I really hate(d?) LeetCode. I recently passed two long interview loops at two FAANG companies, and the number of LC questions was frustrating. Some of them were problems I practically had to memorize because their solutions relied on very specific tricks that rarely come up in real engineering work (take, for example, the in-place index manipulation in First Missing Positive, the binary search trick in Median of Two Sorted Arrays, or the pivot trick in Next Permutation). There were so many of them, and yes, some came up. I did well and got offers, but part of me felt sad for those who might not have had the same opportunity simply because they weren't lucky enough to have practiced and memorized the same ad hoc problems I did.
Replacing this process with engineers prompting a CLI is not evaluating much at all. In fact, it might be even less reliable because agent behavior is not deterministic. It's possible that one candidate will simply get luckier with their agent than another.
What I have always proposed is to keep algorithmic knowledge as part of the process. At least IMO, it is a crucial part of engineering intellect, but it must be combined with other assessments: communication, problem clarification, code correctness, etc. Otherwise, HackerRank is just inventing another cult, using tokens rather than problems.
This is Hackerrank quickly realizing that their platform was becoming obsolete due to developers using AI agents and had to quickly adapt to that.
They only waited for AI to get this good to then post this fake letter with new assessments which tests for outcomes written by a machine but not raw understanding.
I wouldn't go that far. My last job had someone apply that blatantly lied on their resume. Had we just hired on vibes or a trial basis, it would have been awful for what was then a tiny business with a very narrow runway.
That happens. As a hiring manager, you'll probably hire one once. You gotta manage your own workload to leave yourself enough time to circle back through their work, because inevitably, that's where the talkers are separated from the walkers.
>it would have been awful for what was then a tiny business with a very narrow runway.
I 100% understand that. Sometimes though, you just gotta hit the button. You can't be everywhere, so go with your gut, take small measured chances on people, keep the ones that help things, be prepared to part with the ones who don't, but always keep in mind that it's a human on the other side of the table, either way.
Business ain't worth losing your humanity for. But there's damn well people out there more than happy to do just that. I can't give you the secret to picking those types out. Experience, sadly, is the incomparable teacher in that department. Be prepared to land on your face a few times getting it.
As much as I detested the idea of Leetcoding the reason they became the de-facto for hiring was two fold:
- Software development for the most part did not require any particular specialization but it paid really well because of the explosive growth of the internet so there was an oversupply of talent.
- Most companies had no idea what they wanted from hires and how to test them so started copycat-ing puzzle based and algorithm and data science questions from the days when programming was mostly numerical computing
It devolved into a race to the bottom as more and more people lured by the 300k packages lined up and companies well aware of "No Silver Bullet" threw these people at increasingly menial tasks.
Companies at Leetcode and Hackerrank have no relevance now that you can ask an AI agent to do a hyper specialised candidate selection based on the role, with most roles now being redundant every six months.
Sorry we increased the AI/word rate to 20% in order to summon agentic investment and sales harnesses as if reciting a satanic Super Intelligent invocation.
They just see the writing on the wall, establishing a new business model.
I find it awkward that they try to sell such an emotional angle.
Is that too much of an ask?
That's an interesting angle, and would perfectly explain how bad they became, even before AI.
Going that route requires to make a lot of assumptions and rely on heuristics, bin users in large buckets that behave predictively.
People speaking different languages than the country they're flagged on, asking different questions than what the zeitgeist is about, searching for things outside of the consensus or switching interests too quickly would all have definitely worse experiences and become the collateral damage of that vision.
Would that be what we want for job interviews ?
i dont think anyone would ever want a role once given _that_ level of precision of what you'll be asked to do :(
If the point you're making is that solving complicated, synthetic programming challenges helped built your stamina and mental endurance, is there another way you could have built the same but in a much more productive and pleasurable manner?
Perhaps in a manner that would also have resulted in the improvement of your community or humanity as a whole instead of being the millionth person to "solve" the same exact problem?
Technically all of us are top x%, for some x!
My first impression when I see that LeetCode or similar tools are used is what is the level of their technical team? Am I joining a company that has no person capable of evaluating my skills? Do I still want to join such an incapable organization? Not only during the interviewing process, mind you. My annual performance review, as bad as it is in good companies, will most likely be some kind of LeetCode again.
I think it should be the norm that any company using LeetCode for hiring must post their own staff's scores.
Adding insult to injury, instead of grinding leet code, now you're expected to perform in front of a fucking machine to get a job.
> Tasks: Algorithmic questions → real work in code repositories
> Evaluation: Code correctness → AI fluency and judgment
I am never trading algorithmic knowledge for "real work", unless I am hiring for something that does not really require any kind of intellect, which would make me question why I am hiring in the first place. Same goes for code correctness. I wouldn't really like working with anyone who can't read and assess the code they are trying to ship (whether they wrote it or generated it).
That said, I really hate(d?) LeetCode. I recently passed two long interview loops at two FAANG companies, and the number of LC questions was frustrating. Some of them were problems I practically had to memorize because their solutions relied on very specific tricks that rarely come up in real engineering work (take, for example, the in-place index manipulation in First Missing Positive, the binary search trick in Median of Two Sorted Arrays, or the pivot trick in Next Permutation). There were so many of them, and yes, some came up. I did well and got offers, but part of me felt sad for those who might not have had the same opportunity simply because they weren't lucky enough to have practiced and memorized the same ad hoc problems I did.
Replacing this process with engineers prompting a CLI is not evaluating much at all. In fact, it might be even less reliable because agent behavior is not deterministic. It's possible that one candidate will simply get luckier with their agent than another.
What I have always proposed is to keep algorithmic knowledge as part of the process. At least IMO, it is a crucial part of engineering intellect, but it must be combined with other assessments: communication, problem clarification, code correctness, etc. Otherwise, HackerRank is just inventing another cult, using tokens rather than problems.
This is Hackerrank quickly realizing that their platform was becoming obsolete due to developers using AI agents and had to quickly adapt to that.
They only waited for AI to get this good to then post this fake letter with new assessments which tests for outcomes written by a machine but not raw understanding.
>it would have been awful for what was then a tiny business with a very narrow runway.
I 100% understand that. Sometimes though, you just gotta hit the button. You can't be everywhere, so go with your gut, take small measured chances on people, keep the ones that help things, be prepared to part with the ones who don't, but always keep in mind that it's a human on the other side of the table, either way.
Business ain't worth losing your humanity for. But there's damn well people out there more than happy to do just that. I can't give you the secret to picking those types out. Experience, sadly, is the incomparable teacher in that department. Be prepared to land on your face a few times getting it.
Here's a slop bot to talk to instead.
- Software development for the most part did not require any particular specialization but it paid really well because of the explosive growth of the internet so there was an oversupply of talent.
- Most companies had no idea what they wanted from hires and how to test them so started copycat-ing puzzle based and algorithm and data science questions from the days when programming was mostly numerical computing
It devolved into a race to the bottom as more and more people lured by the 300k packages lined up and companies well aware of "No Silver Bullet" threw these people at increasingly menial tasks.
Companies at Leetcode and Hackerrank have no relevance now that you can ask an AI agent to do a hyper specialised candidate selection based on the role, with most roles now being redundant every six months.
The first question doesn’t make sense given the last, you should ask the ai the first one.
Algorithmic interviews have ZERO value in a world where Opus 5.5 is better than any coder on earth at writing algorithms.