Po

PodBear – A Podcast Search Engine

Hacker News

PodBear – A Podcast Search Engine

Hi all, a few weeks ago I found myself in a situation without anything interesting to listen to. Been through all podcast recommendations from friends and the usual recos from itunes, etc were not cutting it for me. So I build myself a podcast search engine. Based on the data from podcastindex.org and some other available infos. It doesnt (yet) have all available podcasts, only the ones I could find some rating/engagement infos on. But its still around 60k podcasts to discover :) One can search / filter / sort by category, rating, popularity, episodes, language, update date, etc. There is also a very simple full text search that searches through title, description, author, etc. Would love to hear what everybody is thinking! All feedback is welcome! Tech: Postgres + PostgREST backend, Sveltekit frontend. Disclaimer: its still a bit hack-ish currently, so I fully expect things to break somehow :) just post me a note about it Here you go: https://podbear.netlify.app/

Visit product View on Hacker NewsApr 22, 2024

Actual performance

6points

6comments

Did not reach leaderboard

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best fitHighest predicted score across all platforms for this description.

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44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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23%23% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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