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Free app for podcasts to share episode resources/links with listeners

Hacker News

Free app for podcasts to share episode resources/links with listeners

For full transparency, I built hero as a one-man team. I've been working with this wonderfully responsive podcast, and they consistently create a Hero Space (which is a list of links) per episode and share them with their listeners. My favorite feature in Hero is definitely the pretty URLs! For example, if a podcast called "HackerNews" had its 19th episode, titled "The Bees Knees", it would be reached at https://hero.page/hackernews/ep-19-the-bees-knees Like I said, Hero is free and we're (as in, I) am always working on making it better, listening to feedback, and doing what's right for the users! My next goal is to make it simple for visitors to discover podcasts with hero pages, since now it's only source of traffic is podcasts sharing a hero link. I'm happy to answer any questions, and would love to speak to any podcast host(s) who would like to try it out! Let me know what you think! Sam, from Hero (@HeroMeers on Twitter)

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
89%89% 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
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: host, users · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
47%47% 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
17%17% 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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