this will get patched, as in I'll optimize my resume for this and so will many other people that any edge disintegrates
HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
31–40 of 463 posts
Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
#32Disregarding the fact that this thing is completely broken, its grading rubric is ridiculous to begin with (as was mentioned in the article itself, but I must reiterate how completely stupid this is): > 35 points for open source contributions > 30 for personal projects I don't contribute to open source or have personal projects because I don't spend my free time doing what I do 40 hours a week to make a living. My 15…
They are selecting for people who are fine working in their free time. If you contribute to open source you are more likely to contribute to the company on weekends. If instead you have other hobbies or a family that takes up non-work hours you are more likely to drop your pen after forty hours.
I'm not sure that follows. I stopped making open source contributions when I switched from mature companies to startups.
Now all my "non-work" time is spent on startup work. And none of that is visible via GitHub.
Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
#33Hiring and job search has been so hard and AI has amplified the existing problems instead of solving any.
Wdym, cant you just litter your applications with buzzwords and other bs to automatically get a high score in these systems?
Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
#34Disregarding the fact that this thing is completely broken, its grading rubric is ridiculous to begin with (as was mentioned in the article itself, but I must reiterate how completely stupid this is): > 35 points for open source contributions > 30 for personal projects I don't contribute to open source or have personal projects because I don't spend my free time doing what I do 40 hours a week to make a living. My 15…
They are selecting for people who are fine working in their free time. If you contribute to open source you are more likely to contribute to the company on weekends. If instead you have other hobbies or a family that takes up non-work hours you are more likely to drop your pen after forty hours.
Maybe they're selecting for intrinsic motivation. People who enjoy programming to the point they do it for fun, not just because it pays.
Free software work doesn't imply we work for free. We work on our projects, the stuff that we actually enjoy working on. Nobody is going to work on corporate products without adequate compensation.
Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
#35An alarming number of people don't understand that LLMs work via purely stochastic processes, so I'm happy to see in-depth pieces like this. I'm looking for a job and maybe this is why it's so hard to get a callback these days: resumes are just dumped in some LLM black hole and no one really knows how it works. The author says: > temperature 0.1 — low, supposedly nudging the model toward deterministic outputs This is…
A distribution with all probability mass on one outcome is deterministic, so in principle, setting temperature to 0 _should_ result in deterministic outputs. There are a few reasons it might not, but I don't think any of these apply when running a local model like the author did.
Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
#36> I fail 65% of the time. Same exact resume, different luck. As someone who’s run hiring pipelines for technical roles in the past few years, that’s actually a fantastic number. I objectively hate saying that, but it’s true. 35% chance of elevating a technical individual to the next stage with no effort? I’ve seen as many as 100+ applicants an hour even when including a domain specific screener question. That’s 35 “s…
Is it? Or is it a 65% chance of a resume getting ignored before a single human sees it, reducing your pipeline's likelihood of catching qualified candidates by the same? Gates that reduce resume flow-through are only useful if their reduction is correlated with quality. Otherwise they're just dragging out your hiring process or unnecessarily causing you to ultimately lower your hiring bars.
So the question is: is the score given by this system correlated with candidate quality? I don't think this post gives enough data to know.
Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
#37> I fail 65% of the time. Same exact resume, different luck. As someone who’s run hiring pipelines for technical roles in the past few years, that’s actually a fantastic number. I objectively hate saying that, but it’s true. 35% chance of elevating a technical individual to the next stage with no effort? I’ve seen as many as 100+ applicants an hour even when including a domain specific screener question. That’s 35 “s…
Except the bit about ranking a decades long S3 engineer lower than an intern with GitHub repo.
Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
#38An alarming number of people don't understand that LLMs work via purely stochastic processes, so I'm happy to see in-depth pieces like this. I'm looking for a job and maybe this is why it's so hard to get a callback these days: resumes are just dumped in some LLM black hole and no one really knows how it works. The author says: > temperature 0.1 — low, supposedly nudging the model toward deterministic outputs This is…
A distribution with all probability mass on one outcome is deterministic, so in principle, setting temperature to 0 _should_ result in deterministic outputs. There are a few reasons it might not, but I don't think any of these apply when running a local model like the author did.
I mean the easiest explanation would be that the model harness doesn't always take the most likely token but does top-k sampling or similar. temperatur just means that probabilities get more and more equalized, boosting the chance that an unlikely token gets picked. but even with temp 0 you could have 0.8 T1, 0.19 T2, ... and sometimes sample T2
Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
#39> The default model is gemma3:4b
That’s a tiny model. No LLM is going to be a perfect and repeatable judge, but a tiny 4B model is like plugging an RNG into this system.
This whole exercise feels like someone vibe coded an ATS and got it to the point where the tests were passing because they decided they should have an open source ATS project.
Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88
#40Earlier quoted context omitted.
A distribution with all probability mass on one outcome is deterministic, so in principle, setting temperature to 0 _should_ result in deterministic outputs. There are a few reasons it might not, but I don't think any of these apply when running a local model like the author did.
I mean the easiest explanation would be that the model harness doesn't always take the most likely token but does top-k sampling or similar. temperatur just means that probabilities get more and more equalized, boosting the chance that an unlikely token gets picked. but even with temp 0 you could have 0.8 T1, 0.19 T2, ... and sometimes sample T2
No, this can't happen at temperature 0. The formula defining temperature-adjusted softmax isn't strictly defined at 0, but taking the limit (in the case where all logits are distinct) results in probability 1 being placed on the largest logit. Samplers will typically special case temperature 0 and pick the most likely token at each step.