Looria – A product (re)search engine

Hacker News

About 1.5 years ago, I introduced my review aggregator BuyForLife on Hacker News, where it became the #8 most upvoted Show HN project of all time[1]. The idea of helping people to make better purchasing decisions continued to chase me over the last year. Here are some stats that illustrate how important online reviews are: • 90% check online reviews as part of their online buying journey • 43% visit 5-10 websites to research a product • 75% spend more than a day doing research before buying a product The top frustrations with the current process are: • Google full of SEO spam and Ads • Fake reviews • Fragmented trusted sources • Inconsistent information across sources Thanks to the recent advances in NLP (transformers, GPT-3, etc.) it became possible to solve these problems at scale, so I decided to team up with my co-founders Johnny and Tavis to build https://Looria.com . We aggregate and summarize the most trusted product reviews on the web like Reddit, Youtube, or Consumer Reports. Just like Rotten Tomatoes provides trustworthy ratings for movies, Looria offers ratings and reviews for all kinds of products. Our vision is to make Looria the go-to platform for making purchase decisions. Looria is still in beta and our data is far from perfect. We're working hard on improving the data quality, adding better filters, and scaling to many more categories. [1] https://bestofshowhn.com

Visit product View on Hacker NewsJul 26, 2022

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

96%96% 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.

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

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

nativeThis product was originally launched on this platform.

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

59%59% 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 · Missing: plus, intuitive, host

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

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

33%33% 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

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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