A webcomics app that works on RSS-like feeds

Hacker News

Hey, this is bam! its a comics app, specifically for Webtoons. Webtoons are vertically scrolling comics (built for mobile phones and popular with younger millennials and Gen Z) I built a podcast app before this, which 100,000 people use every month, https://podyssey.fm . No one tech company owns podcasts it is a medium that works through RSS feeds. See this image for a visual on how podcasts work [ https://ax0.taddy.org/blog/investment-pitch/podcast-ecosyste... ] See this image for a visual on how bam! works [ https://ax0.taddy.org/blog/investment-pitch/taddy-ecosystem.... ] (its the same!) If you are a webtoons creator and want to be on the bam! app, you can self-publish your Webtoon, you just have to output a file that conforms to this standard: https://3s-docs.org/comicseries (follow the instructions on how to notify us of your webtoons feed) If you aren't a developer and a webtoons creators, I also build taddy.org (where all you have to do is drag + drop your artwork and creates the feed for you) Let me know what you think!

Visit product View on Hacker NewsJul 21, 2023

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

80%80% 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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 000, 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.

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

50%50% 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

33%33% 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

20%20% 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.

Incorrect prediction on native model

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