A GPT-3 mobile-friendly resume builder

Hacker News

Hey HN, When I graduated as a software engineer in 2020, I was really struggling to get job interviews. I spent so much time iterating on my CV, trying to find the perfect way to present my experiences and skills. It was really hard to sell myself, and I found it really stressful trying to come up with the perfect bullet points for my resume. It was during this time that the idea for Resume Studio came to me. I realized that so many people must be going through the same struggles I was, and I wanted to help them. So, I decided to create a service that would help people create powerful, effective resumes that would help them land the job they wanted. And then I discovered GPT-3 and all the mind blowing stuff that can come out of It, and I figured It could be a great help in that matter. Resume Studio is now on Its first iteration and there's a lot of fun and exciting functionalities/ideas that are soon coming. And I'm happy to get your feedbacks and suggestions

Visit product View on Hacker NewsJan 5, 2023

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Actual performance

2points

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Launch Intel predictions

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

94%94% 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.

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

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.

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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly, soon, builder · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on TrustMRR, 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.

Incorrect prediction on native model

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