RankResume – A Tool to Tailor Your Resume for Any Job

Hacker News

I’m excited to share my second SaaS project, RankResume ( https://rankresume.io ), following my first launch, IdeaPulse ( https://ideapulse.io ). As a job seeker, I was frustrated by the constant need to tweak my resume for each application to get past ATS filters and to land the first phone screen. I built RankResume to tackle that problem by letting you upload your resume and provide a job description or URL which it then generates a tailored resume and cover letter that highlights the most relevant skills and keywords for that specific opportunity. I built this out of personal necessity and a belief that even small improvements in the job application process can make a big difference. I've been using it and landed a couple of phone screens next week. I’m offering 3 free credits (3 tailored resumes) on signup. I’d love to hear your feedback, or suggestions. Thanks for checking it out!

Visit product View on Hacker NewsMar 28, 2025

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

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best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

19%19% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

13%13% 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.

Incorrect prediction on native model

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