A hiring tool that ranks candidates by job-specific criteria

Hacker News

Hi HN, We’ve been building a hiring tool that ranks candidates using multiple job-specific criteria rather than keyword matching (traditional ATS-style filtering). For each job listing, the system generates hiring criteria and sub-criteria, then analyzes and ranks candidate CVs based on how well they meet those criteria. The original target users are hiring agencies, HR teams, and recruiters. Our current challenge is early adoption. Agencies are hard to onboard without seeing real results, and real results usually require usage at some scale. So we’re running an experiment: We added a candidate-facing tool where job seekers can paste a job link, upload their CV, and get an analysis showing which hiring criteria they meet and which they’re missing. The hypothesis is that candidate usage and data can act as a bottom-up wedge to eventually attract agencies and recruiters. We’re early and unsure whether this approach will work but let's see.

Visit product View on Hacker NewsJan 29, 2026

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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

20%20% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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