Hey, Community

Hacker News

Hey, Community

I’m Emily, part of the team at Habox. I just wanted to take a quick moment to introduce us. At Habox, we’re all about making file sharing as simple as it should be. We know how frustrating it can get when it takes too many steps to send a file or collaborate smoothly with others. That’s why we’ve worked hard to create a tool that streamlines everything for you—whether you're managing documents or collaborating with your team, it’s all straightforward and efficient. I’m not here to make big claims or say we’ve got it all figured out. We’re learning and growing every day, based on real feedback from users like you. What we can promise is that we’re always listening and committed to making the experience better. I’m purposely keeping this post light, without any flashy links or promotions. I just wanted to introduce Habox, share a little about what we’re aiming for, and connect with anyone who’s curious. If you have thoughts or ideas, feel free to share—I’d love to hear them! Thanks for your time!

Visit product View on Hacker NewsSep 25, 2024

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Actual performance

4points

3comments

Did not reach leaderboard

Launch Intel predictions

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

85%85% 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.

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

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: efficient, users · Missing: plus, platform, intuitive

67%67% 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: users, way · Missing: mobile apps, ios, personal

37%37% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: collaborate, 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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