Hiring Method – a deterministic, math-driven recruitment platform

Hacker News

Hi hackers! My partner and I got tired of nontransparent hiring processes and "AI-native" HR software that just wrap an LLM around CVs (or even simply claims doing so) so we built Hiring Method to do the opposite. It converts CVs and job requirements into structured data, then uses a transparent math engine to score the match. Instead of trusting a black box you get a clear, audit-ready scorecard that shows exactly why a candidate is a good fit. We are currently building a sourcing feature to help find those candidates in the first place. We have been working on this for 1.5 years, and after a couple of idea pivots we finally pushed it to prod a month ago. A quick note: signups are currently put behind an onboarding call as it's a B2B tool. I know Show HN prefers immediate open access so I hope it's still okay to share our project here! Would love your feedback on the technical approach.

Visit product View on Hacker NewsJul 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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