ApplyAssists – AI resume builder using Google XYZ formula

Hacker News

Hey HN, My name is Daniel. About a year ago, while job hunting, I noticed that all the AI resume builders are focusing only on ATS scoring and templates, and of course I used them too. But no results, just rejections, and then I found videos from Farah Sharghi (ex-Google, Lyft, Uber, TikTok, and New York Times recruiter), and she mentioned the Google XYZ formula and the study that is on the Boston University website that used eye tracking software to see how recruiters look at a resume. Apparently, they spend only 6 seconds reviewing an individual resume. As a product owner with 20+ years of experience, naturally I started researching this resume vs. recruiters game. I have built a NotebookLM, and I gave it every winning resume I could find and talked to friends here in Romania, where I am located, to interview as many recruiters as I could find, plus all the studies I have found. I have asked a colleague of mine who is a software engineer to build me a system from all that knowledge so it will help me make my resume as perfect as possible. In 3 weeks, I found a job that I am currently working at. About 3 months ago, we decided to build a real product to help others, and this is how ApplyAssists was created. Now we are in the testing phase, and this is why I am here. We have 50 spots for a Free Private Beta test, and all we need from you is your feedback. You can use the system completely for free, and you have my step by step support. Looking forward to hearing from you!

Visit product View on Hacker NewsJul 27, 2026

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best fitHighest predicted score across all platforms for this description.

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

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

42%42% 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

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.

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29%29% 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

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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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