never understood how can people think an .md file can actually evaluate a human being.

Are many people using HackerRank ATS ?

I’ve always assumed any LLM output that was some type of rating or score was bullshit. Unless the LLM writes a Python script to calculate the score (and even then…) then the score it outputs is just the next most likely token, taking into account temperature and what not.

You see a lot of frameworks for things like spec-driven development make use of scoring how good the spec/design/plan is and it’s like, uhhh…

They forgot to add "masterpiece" /s https://www.youtube.com/watch?v=mcYl70vq_Ns

https://github.com/interviewstreet/hiring-agent/blob/main/pr...

The most concerning thing here is the temperature problem. If your harness isn't providing deterministic output at a temperature setting of 0.0, it is broken.

I'm from genx. This has been a serious issue in the tech industry for decades even before AI somehow made it worse. The real problem is resumes themselves. It's an outdated format that was originally designed for completely different industries that just doesn't work with ours. And this is a great example of what I'm talking about:

The scoring is out of 100, with up to 20 bonus points on top:

35 points for open source contributions

30 for personal projects

25 for work experience

10 for technical skills

Up to 20 bonus points for startup experience, a portfolio site, a technical blog, etc.

All the AI is doing is trying to sus out the candidates portfolio which is really what we should be submitting when we apply for a position instead of being forced to somehow condense it to a set of BS business-speak bullet points. Especially when employers are now deploying AI systems just to figure out what's in a candidate's portfolio to begin with.

When all you have is a hammer every problem is a nail. The process itself is broken. We need to kill the outdated concept of resumes before it kills the industry.

"If your company’s cutoff sits at 85, I fail 65% of the time. Same exact resume, different luck."

Sounds like they have replicated the existing recruitment process

so if a scoring system automatically ranks candidates lower for lacking a public GitHub or open-source contributions, that might indirectly disadvantage certain groups. ie ppl in defense, security, or other restricted industries who cannot legally share work publicly, people under strict employer IP/confidentiality rules, some demographics or nationalities with different access to open-source engagement (something like non-citizens not allowed to use xyz ie mythos), this can be construed as disparate impact which is protected under the equal employment opportunities commission (EEOC) and employers can be held liable for this as it can be seen as a biased discriminatory practice.

It's a good analysis but the AI slop writing makes me not trust you've reviewed this and I'm unable to finish or subscribe. I'm sure you're a great blogger but this is holding it back!