> 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).
I've been studying AI for 20 years. What really needs to be added to this statement is:
"An alarming number of people don't understand that LLMs work via purely stochastic processes - and so does human thinking. People do NOT arrive at the same conclusion if merely the weather's different. Worse: with human thinking not only do most people not think this is real, a subset of people will actively fight the idea. Of course, depending on the weather"
It is a common misconception, but it is not true even in principle. If I have 2 or more logits which are equal to the maximum of my logits, I will sample uniformly random from them with any temperature, even zero. Sampling from softmax([1, 0, 1]) is still stochastic at temperature 0, because the limit is to sample uniformly from the first or the last element.
Anyway: "GPUs don't do deterministic matrix multiplications" is the biggest source of randomness in LLMs. GPUs put the associativity of the sums in matrix multiplications in arbitrary order, and this has a huge impact on the logits coming out of the neural network.
I don't know for sure, but I would be surprised if it was illegal in my particular US state. You might be able to argue the AI has inherent biases that introduce illegal discrimination in the hiring process, but my understanding is winning I case like that would be very difficult, especially since most employers are very cagey about their hiring process and why they mades a decision.
Show someone a list of resumes with an "applicant score*" and they'll naturally ignore the ones with a low ranking
*scores are generated with AI, mistakes may be made, use only as a guide and verify results
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.
You read my mind. If the answer is “no”, then we can ignore this.
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.
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.
The volume is infeasible to review everyone for quality, even at an hour scale. The conclusion and solution is inevitable, though I wish it were different. 35% is actually really good if you’re not coming in through a referral.
The current reality is <1% and the person reviewing you is exhausted.
Corpo bullshittery at its finest.
*According to our proprietary, undisclosed, non-deterministic metric, which may or may not be Math.random
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.
> 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.
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.
I guess there sadly are many nobodies who do this to hope to become somebody.
Now all my "non-work" time is spent on startup work. And none of that is visible via GitHub.