See your competitor's most popular blogs

Hacker News

Hi HN, Want to boost your site's traffic, but not sure what to write about? Our new tool allows you to compare your site to your top competitor, and find out their most popular blog articles, with monthly traffic for each, along with helpful information like number of words per article, and how many ranking keywords they have. Once you have this info, you can then write articles on the same topics that are longer or have more depth to grow your own site. Sample here: https://pagegenie.ai/blog/competitive-seo-analysis-massageto... All feedback and suggestions appreciated! We're small AI+human content agency. Please be kind :-)

Visit product View on Hacker NewsJun 19, 2024

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Launch Intel predictions

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

66%66% 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

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

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

22%22% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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