I'm building ProductHunt for Actual Products: Would love feedback

Hacker News

The product discovery process is totally broken, and this is where Styrate comes in. With so many products available online, it's becoming increasingly difficult for consumers to differentiate between genuine and fake reviews. Amazon and other e-commerce platforms are full of fake reviews that can mislead consumers into buying substandard products. This means that consumers can end up wasting their time and money on products that don't meet their needs, and this problem is only getting worse. Styrate solves this problem by connecting consumers with trusted influencers who provide honest reviews and recommendations. With video reviews, upvoting, and sorting/search by type of product, users can easily discover the most highly rated reviews for their needs. Fake reviews cost Americans 0.12 cents on every dollar they spend online, making it crucial to have a platform like Styrate for informed purchasing decisions. The MVP is a little rough around the edges and is missing a few pages(top influencers, Product Categories,etc) and the ability to search by product. Any feedback about the idea or website is highly appreciated. styrate.vercel.app is the correct page, can't edit the url.

Visit product View on Hacker NewsFeb 21, 2023

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79%79% 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

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, reviews, users · Missing: plus, intuitive, host

37%37% 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

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.

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

27%27% 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

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

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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