VoteFeature.com – Build what users want

Hacker News

Hi HN community, I'm excited to share a project I’ve been working on for quite a while: https://www.VoteFeature.com . VoteFeature is a place that aims to help users and builders come together to shape the future of products. It's a simple catalog of products where users can search by features, suggest enhancements and vote features to help prioritize the development. For Users: 1. Discover - Search products by specific features (e.g. AI capabilities, system integration etc.). 2. Vote - Impact products roadmaps by voting features. 3. Suggest - Share your ideas and suggest features to builders. For Builders: 1. Prioritize - You can prioritize the backlog based on user votes for specific features. 2. Simple Backlog - Very simple way to manage your product backlog. 3. Exposure - Get some exposure by adding your product to the platform. Try It: You can try the (very) beta version at https://www.VoteFeature.com Feedback: Any feedback is invaluable and much appreciated! What do you think? Do you find it useful? How can I make it better for both users and builders? Thank you!

Visit product View on Hacker NewsMay 24, 2024

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

69%69% predicted probability of success on Product Hunt, 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: platform, builder, users · Missing: plus, intuitive, reviews

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% 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

10%10% 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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