I fixed what LinkedIn couldn't, no more unqualified applicants

Hacker News

Hi HN, After getting encouraging feedback from early users, I wanted to share a side project I’ve been slowly building. The idea is simple: Candidates can only apply if their skills and location actually match the role. Recruiters see only relevant applicants, no more noise in the inbox. Profiles are free for candidates. Job posts are free for companies (for now). If budget’s an issue, just reach out, I’d rather see real teams using it. This project started as a small experiment while working full-time and studying. It’s been a long journey through backend rewrites, self-hosting headaches, UI refactors, and a few AI ideas that didn’t pan out. Learned a lot along the way. It’s still early, plenty left to improve. I’d love feedback, good, bad, or brutal. You can use the feedback link in the footer or drop me a note. Thanks!

Visit product View on Hacker NewsOct 26, 2025

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

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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: host, users · Missing: plus, platform, intuitive

36%36% 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

15%15% 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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