I made a gamified no-code portfolio builder

Hacker News

Hi HN Community! I’m thrilled to introduce portfolio2me to you all! As a creator and professional, I know how challenging it can be to showcase your work in a way that stands out. That’s why I built portfolio2me, a gamified portfolio builder designed to make creating stunning portfolios fun and easy. Here's what makes it special: Intuitive Design: With a clean UI/UX, crafting your portfolio is a breeze. Customization: Personalize your portfolio with themes, fonts, and custom domains. Comprehensive Sections: Add your name, biography, social links, references, projects, skills, contacts, and more. Visitor Insights: Track who's visiting your portfolio and gain valuable insights. I created portfolio2me to help you focus on showcasing your work, not on figuring out how to do it. Whether you're a freelancer, a student, or a professional, my tool is here to help you shine. Thank you so much for your support! I'm excited to see the amazing portfolios you create with portfolio2me. Please share your feedback, questions, and comments. I’m here and eager to engage with you all.

Visit product View on Hacker NewsJun 24, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

83%83% 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.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, builder · Missing: plus, platform, reviews

67%67% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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