Pu

Publish SEO-Optimized WP Posts(Semantic Internal Links) from G Docs

Hacker News

Publish SEO-Optimized WP Posts(Semantic Internal Links) from G Docs

I developed this Google Docs editor add-on to scratch my itch. It helps you write in Google Docs and helps you do the following: - With a click of a button, it finds the potential keywords to create internal links and add relevant internal links.(YouTube Demo: https://youtu.be/kdUXGiPLnuM ) - Allows you to set the Focus Keyword and the Meta description for the RankMath and Yoast. Checks your content and provides SEO suggestions similar to Yoast and RankMath. - Compress images before uploading them to the WordPress Media Library. - Automatic alt text generation for the images. - Push your posts to WordPress in a single click. More than 150 users have installed the add-on. But only a few have added sites and using it. Any suggestions for making the users add the sites and make them use the add-on? Thanks in advance.

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, single · Missing: mac, agents, macos
81%81% 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
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: google, users · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, 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 · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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