genekrapivin

We are building Esara – the math-based AI hiring platform for candidate sourcing and explainable evaluation.

We’ve just incorporated in Berlin together with my two great co-founders. Honestly never felt more excited.

Feel free to check it out: https://esara.ai

I am building Esara – the AI hiring platform for candidate sourcing and explainable evaluation.

We’ve just incorporated in Berlin together with two great co-founding colleagues. Honestly never felt more excited.

Though it is a B2B tool, check it out: https://esara.ai

I am running Hiring Method (https://hiring-method.com) – math-driven recruitment platform with AI.

It builds a scorecard from job description and mathematically matches candidates against it on every interview step, so that you know candidate fitness at all times.

I also do not understand and curious. Must be some undocumented heuristics.

Like the idea.

My honest opinion, take it with the pinch of salt:

The website is oversaturated with text and is lacking some spaces (air) and visuals.

Why not monetising it? Not from the job seekers but from employer side. You could check what the offering from big projects is. Besides, you do have pricing for the API, what about it?

With all major llms looking up the web and combining results with its enormous perceived-thru-training knowledge, as well as AI-assisted results in ggl I’m not sure there’s any use case left for perplexity.

I also remember there were big product doubts originally as it was stepping into the field dominated by hugely powerful players.

As a founder in the same space I am curious what problem are you solving?

And good luck on the journey.

I am building Hiring Method (https://hiring-method.com) – an AI-native, math-driven recruitment platform. It extracts structured scorecards from both CVs and job requirements and mathematically matches them, so you can actually review the exact reasoning behind why a candidate scored a, say, 85%. This fitness value is specified at every interview step – as applicant goes through an interview process their scorecard is updated at all steps.

I did the first Show HN a few days ago (https://news.ycombinator.com/item?id=49083987) but it was sadly left without attention.

Not, usually it is not. Generally speaking it is a combination of nuances.

While I do partially agree, I’m still seeing sparks every now and then.

Copying basically costs nothing. Successful marketing of a said copy is hard (and has always been).

Cannot leave unattained the fact ppl openly share metrics in various “those making X$/month” threads.

There are no answers here, I suspect, for at least two reasons (disclaimer: this is a very broad statement): 1. people who haven’t yet decided to resign don’t want to give their former colleagues and/or employer any unnecessary hints. 2. people rarely leave a company ‘with nowhere to go’, and naturally don’t want to ‘put off’ a potential new employer.

I'm working on Hiring Method (https://hiring-method.com) – an AI-native, deterministic, math-driven recruitment platform. It extracts structured scorecards from both CVs and job requirements and mathematically matches them, so you can actually review the exact reasoning behind why a candidate scored a, say, 85%. This fitness value is specified at every interview step – as applicant goes through an interview process their scorecard is updated at all steps.

I did the first Show HN a few days ago (https://news.ycombinator.com/item?id=49083987) but it was sadly left without attention.

I understand you perfectly. I’ve also worked on the employer side (as a hiring manager, working closely with recruiters) and have witnessed a huge number of instances of unfairness, manifesting in all sorts of ways. And, believe me, as the number of applicants grows, the situation doesn’t get any better. I’m trying to bring a little transparency to the process.

Let me disagree and explain myself.

When an HR is using Hiring Method, they are getting a fitness score for all applicants.

In case a backend engineer is seeking frontend roles – yes, the fitness will be low – but it will neither be zero nor will anyone be rejected anyhow automatically. HR will have an option to compare applicants visually and in detailed mode at all times.

I am building Hiring Method to augment people, not to remove them from decision making process.

I'm working on Hiring Method (https://hiring-method.com). After ~2 years of development and two exhausting pivots, v1 is finally live.

I see a lot of new (and, to be frank, a lot of mature ones) HR tools are just wrapping Chatgpt around resumes (almost like "OK, now match this resume against this job posting and tell me if applicant fits"), which introduces a massive bias/inference problem.

I decided to build the exact opposite – a deterministic, math-driven fitness engine. It extracts structured scorecards from both CVs and job requirements and mathematically matches them, so you can actually review the exact reasoning behind why a candidate scored a, say, 85%. This fitness value is specified at every interview step – as applicant goes through an interview process their scorecard is updated at all steps.

If anyone here builds in the HR space, I’d love your feedback.

Hiring Method helps recruiters take decisions backed by numbers and transparent analysis, we do not take human out of the loop.

fewer than 2–3% of the 300–500 applicants for a software engineer role progress to the next stage after CV screening (to put it very broadly). at this stage alone, Hiring Method saves between a few hours and several days per role, whilst maintaining impartiality and processing 100% of applicants

Hiring Method is an auxiliary tool that does not take any hiring decision. The vector it builds has few dozen parameters, with hard skills (eg plain old technology list) constituting a mere fraction. In this way you do not have to have all fancy buzzwords – the software also checks for neighboring stacks and transferrability level, among other fitness criteria.

We are GDPR compliant. We are striving for EU AI Act compliance as well (once the final version is accepted).

Recruiters aren’t afraid to give feedback, and there are often no legal grounds for withholding it in Europe (I’m not counting FAANG companies or certain sectors like fintech). Usually, they simply don’t have the time (and enough motivation) to provide individual feedback to every applicant. This leads to ghosting and transitively to the brand reputational damage. Hiring Method allows you to send feedback to almost everyone!

In our software, the candidate being assessed does not know all the assessment criteria. Furthermore, this assessment is merely a starting point for discussion during the technical round. I need to update the description of this feature.

Thanks for the valid points!

I'm working on Hiring Method (https://hiring-method.com).

After 1.5 years of development and two exhausting pivots, I’m incredibly happy to finally have our v1 live!

While most of the HR tech is rushing to use black-box AI, I built the exact opposite. It's a transparent, math-driven fitness engine. It extracts objective data from CVs and calculates how well applicants match requirements, letting you see the reasoning behind why someone scored an X%.

If anyone here builds in the HR space or regularly hires engineers, I would absolutely love your feedback or a roast of the landing page.

PS This is a project of immense importance for me, I've been working on for past ~2 years, I'd appreciate to know why this comment is flagged.