Bookmark an article, listen to it later on your own podcast feed

Hacker News

Hey guys, I drive to work a lot, spending on average of 2 hours each day. So I have plenty of time to listen to Podcast and audiobook on my ride. And I think it would be nice to be able to do the same with my reading list, but I couldn't find any decent app on the market that doing this. So I set out to do it myself. Sooo this is the product: http://sendtopod.com/ You may ask why? The reasons are simple: - It's more convenient to listen to articles while you driving, running, or even before going to bed. - Listen take less effort than reading. - Your eyes need to rest after staring at screens all day. - Podcast app is a great tools for listening to audio content with support for: - CarPlay, Android Auto support built-in - Smart queuing - Offline listening I see that https://hq.getmatter.app/ did a great job of read later app, it also have an OK text to speech audio, but does not have a podcast feed I can subscribe to. I also see AudioRead.com do this but text to speech is IMHO not pleasant enough to listen to for a long time. I'm running beta for this, would love to hear your feedback on ... anything really. Thanks you all!

Visit product View on Hacker NewsJul 6, 2022

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6points

Did not reach leaderboard

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

85%85% 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 · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% 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

42%42% 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

42%42% 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 · Strong signals: audio, smart · 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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