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CandiScreen – AI-powered candidate pre-screening tool

Hacker News

CandiScreen – AI-powered candidate pre-screening tool

Hi HN! Today I'm making CandiScreen publicly available. I've spent years leading tech teams and experienced firsthand the frustration of manually screening piles of resumes. Current solutions primarily rely on comparing resumes to job descriptions, which often has limitations, or extensive testing that's not respectful of candidates' time. That's why I built CandiScreen, an AI-powered platform designed to automate candidate pre-screening, providing quick, insightful, and fair assessments to instantly identify the most suitable candidates. With CandiScreen, candidates experience short, engaging assessments rather than tedious forms or lengthy tests, while recruiters and hiring managers gain clear, data-driven insights into each candidate's strengths. I'd love your feedback, thoughts, or questions about the approach, tech stack, or any aspect of the product!

Visit product View on Hacker NewsMar 19, 2025

Actual performance

2points

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

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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% 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

11%11% 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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