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Clarisu – Create educational podcasts

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Clarisu – Create educational podcasts

Hey HN, I’ve built Clarisu, an app that generates short educational podcasts from any topic you give it: https://clarisu.com It started from https://pdftomp3.com , a tool I made to turn PDFs into audio. I mostly used it to listen to AI papers, but I still had to download the MP3s to my phone. So I made an app instead! Now you just enter a topic in a textbox, and it builds a “podcast episode” with paragraphs and chapters. It has a “Clarify” button which goes deeper into a topic. This is where the name comes from :) I think I’ve learned a lot of new things by listening to my own episodes! Next up: adding PDF uploads directly into the app. I would love your feedback! Android: https://play.google.com/store/apps/details?id=com.jbuild.und... iOS: https://apps.apple.com/us/app/clarisu-understand-app/id67511...

Visit productView on Hacker NewsOct 27, 2025
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3points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para, ios · Missing: supports, reddit linkedin, podcasting
81%81% 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: apple, google, apps · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, google · Missing: mobile apps, personal, entrepreneurs
59%59% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% 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 · 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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