Add Stories-Like Widget to Any Website Based on RSS Feeds

Hacker News

Hey Hackers! I'm about to release a new plugin for websites and wanted to ask your feedback. This plugin is called "Automatic Stories" and it allows you to add a stories-like widget to any website based on existing RSS feeds, so you won't need to take care of creating stories manually. It will be updated based on the content of your website. If you're using Wordpress, you may find a quick demo here: https://www.commoninja.com/automatic-stories/wordpress Simply type your website url, and the plugin will generate the stories automatically :) If you're not using Wordpress, simply go directly to editor, add a category, and enter the feed URL to see the magic happen: https://www.commoninja.com/automatic-stories/editor Any feedback will be appreciated!

Visit product View on Hacker NewsDec 17, 2020

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

65%65% 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.

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

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% 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

12%12% 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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