Tailor your resume in seconds with AI

Hacker News

Hi Everyone, My name is Tosin Kuye, a computer science student from Canada. After looking online for various ways to tailor your resume/write a cover letter and being disappointed in the results, I built CoverQuick. CoverQuick is a site that uses the job description to tailor your resume and write a personalized cover letter for that position. It uses a number of different NLP techniques along with GPT-3 to find the right keywords and add the ideal suggestions to the given resume. Try it out below and let me know what you think! https://app.coverquick.co/playground

Visit product View on Hacker NewsJan 3, 2023

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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

65%65% 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.

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

43%43% predicted probability of success on AppSumo, 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

42%42% 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 · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

14%14% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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