I Built a Reddit/Wirecutter Hybrid for Product Reviews/Discovery

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Hey there HN ! I'm so excited to show you the platform I built! It's a product review website that's all about community and video reviews. I wanted to create a space where people could share their personal experiences and opinions on products, so others could make informed decisions based on real-life experiences. I noticed that there were a lot of fake reviews on other platforms, and I knew that people wanted a more reliable and trustworthy source for product reviews. That's why I decided to create a website that would prioritize genuine, honest curated reviews from real people. Our platform is super easy to use, and you can browse reviews across a variety of product categories. You will be able to filter reviews based on different criteria, such as popularity, rating, and category. And with our commenting system, you can engage in discussions around the products you review and get a better understanding of their strengths and weaknesses from a variety of perspectives. But what really sets us apart is our focus on video reviews. With video reviews, you get to see the product in action and hear directly from the reviewer about their experiences. We believe that video reviews provide a more authentic and personal touch that text-based reviews simply can't match. I'm really proud of what we've built, and I truly believe that our platform is the best place to find genuine, reliable reviews of products. (Note that it's in Beta right now and new features are being added all the time.) Edit (Temp Down, bringing it back online)

Visit product View on Hacker NewsMar 15, 2023

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

84%84% 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.

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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real people · 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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