Blogloom – Create your blog quickly with Google Sheets

Hacker News

A year back, I had a dataset about eCommerce companies' performance in Google Sheets [1]. I wanted to publish a blog post for each e-commerce company based on this dataset, so that I can get them interested in my blog. But, it was painful to convert each row into blog post manually. So, I created a Google Sheets add-on to convert all the rows into blog posts in a one click [2]. Here is that add-on: https://www.blogloom.com [1] https://docs.google.com/spreadsheets/d/1ipHZUGvXkj2kavMyDU4t... [2] http://4be28a3b3c6f79a5c1b3f0053f09f9ec.of.blogloom.com

Visit product View on Hacker NewsAug 31, 2018

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Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

59%59% 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 · Missing: mac, agents, macos

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

TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal

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

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

36%36% 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

21%21% 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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