Disregarding 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 years of work experience is worth a maximum of 25%, so any company using this idiotic system would pass on me immediately. Open source and personal projects are fine, but in no sane world are they worth 65% of a resume's score.
> 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 “screened” applicants in an hour. Were valid candidates screened out? Yes. Does you still have a candidate pool 35x larger than you need? Unfortunately, also yes.
The volume of applicants is SO HIGH such that your chances of getting moved to the next stage are actually markedly worse if AI isn’t involved. If you didn’t apply immediately (using an AI bot) there’s 50+ people ahead of you, and an exhausted technical leader if they ever make it to your resume.
An 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 not correct (and is briefly touched on later in the piece when he sets temperature to 0), temperature is not some kind of "deterministic" switch, but rather it affects the sampling distribution (which becomes more "spiky"—but is still very much a distribution).
The whole problem of text understanding is a problem of reasoning under uncertainty, that is, you can't really be sure which witch people are talking about all the time. A person you might hire might be successful or unsuccessful at the role, no matter what hiring process you use. Two people might look at the same resume and come to the same conclusions. Two patients with the same symptoms and clinical presentation might have different diseases, etc.
I don't buy the story that the old AI died primarily due to the cost of knowledge base maintenance [1], but rather the lack of a universal system of reasoning over uncertainty.
For me it's a running gag that Spock was always saying things like "Captain, we have a 21% probability of surviving this mission" when Bayes teaches us your probability distribution has a probability distribution, "we have a β(5,1) chance of surviving this mission" is more like it.
To that end it wouldn't be too crazy to run a resume through that machine 100 times and look at the probability distribution of the score.
[1] then again I am the kind of maniac who will sort images on a tablet lying in bed until my visual system malfunctions
Several of my claimed AI-expert colleagues repeat this as though it's gospel. I've heard "set the temperature to 0 so we get consistent results" more times that I can count.
My favorite recent example was submitting a resume for a job that was almost a word-for-word description of my current title and job at a similarly sized company. Within 24 hours, I got the rejection, and several days later, a recruiter reached out to let me know that my profile looked like a great match for the role and wanted to schedule an intro call.
I remember applying for one position that was around building care and management systems for pregnant mothers - EHR, practice management, claims benefits, etc., all of which I had over a decade of experience in. "Name something that might stand you out from the crowd." "In addition to all this I've also delivered 12 babies as a paramedic". Twenty minutes later "we are looking for candidates whose experiences and skill sets are more closely aligned with the role we are looking to fill".
This could be used as a good way to self-evaluate one's current position from the company's point of view. you would tweak prompts and guidelines that are expected from the company and see how you score
Corollary: If a computer makes a business decision, the person who delegated the decision to the computer must be held accountable.
Consequence: All business decisions will eventually be delegated to computers via sufficiently convoluted and untraceable processes such that no manager can ever be held accountable.
I wonder how is this even legal? The only useful job the HR departments are ever required to do - they decide to automate it? Aside from being a daycare for adults, what exactly does HR accomplish? It's clearly NOT on the side of employees, but this seems like they're clearly NOT on the side of employers, either.
While resume's are being filtered left and right, they just make TikTok's on company's dime [1]. What a sad state of affairs.
I'm a little confused, is this an ATS system that anyone actually uses? If not, I'm not sure how it's better than just asking ChatGPT to score your resume out of 100. Why would you want to optimize your resume for a system no one is using to score it?
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.
With such kind of ATS systems, is it still a thing to optimize for a one page resume that is easy for a human reviewer to scan, or just include enough buzzwords and external links to try and please the LLM?
I was inspired by this. I made a Claude skill to take my resume and compare it to any job description to point out viability and gaps. Pretty cool skill. I'll post it somewhere.
A better way to reformulate this problem is for the LLM to be tasked with making a _comparative_ judgement between two CVs. This should prove much more reliable, especially if you give it a third “too close to call” option. You can also ask for clear justifications of preference.
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[ 2.0 ms ] story [ 78.0 ms ] thread> 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 years of work experience is worth a maximum of 25%, so any company using this idiotic system would pass on me immediately. Open source and personal projects are fine, but in no sane world are they worth 65% of a resume's score.
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 “screened” applicants in an hour. Were valid candidates screened out? Yes. Does you still have a candidate pool 35x larger than you need? Unfortunately, also yes.
The volume of applicants is SO HIGH such that your chances of getting moved to the next stage are actually markedly worse if AI isn’t involved. If you didn’t apply immediately (using an AI bot) there’s 50+ people ahead of you, and an exhausted technical leader if they ever make it to your resume.
Referral bonuses exist for a reason.
> temperature 0.1 — low, supposedly nudging the model toward deterministic outputs
This is not correct (and is briefly touched on later in the piece when he sets temperature to 0), temperature is not some kind of "deterministic" switch, but rather it affects the sampling distribution (which becomes more "spiky"—but is still very much a distribution).
It's somewhat ironic that this "in depth" piece was written by an LLM as well.
I don't buy the story that the old AI died primarily due to the cost of knowledge base maintenance [1], but rather the lack of a universal system of reasoning over uncertainty.
For me it's a running gag that Spock was always saying things like "Captain, we have a 21% probability of surviving this mission" when Bayes teaches us your probability distribution has a probability distribution, "we have a β(5,1) chance of surviving this mission" is more like it.
To that end it wouldn't be too crazy to run a resume through that machine 100 times and look at the probability distribution of the score.
[1] then again I am the kind of maniac who will sort images on a tablet lying in bed until my visual system malfunctions
Several of my claimed AI-expert colleagues repeat this as though it's gospel. I've heard "set the temperature to 0 so we get consistent results" more times that I can count.
Not that I'm defending AI, but HR departments rarely knew how their ATS ranked and sorted applicants before they were AI powered.
Consequence: All business decisions will eventually be delegated to computers via sufficiently convoluted and untraceable processes such that no manager can ever be held accountable.
While resume's are being filtered left and right, they just make TikTok's on company's dime [1]. What a sad state of affairs.
[1] https://www.youtube.com/shorts/wSug80Vg5JU
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.
Well done you! It is difficult to avoid architectural complexity, but imho well worth it.