I made an app to make podcasts out of your reading list

Hacker News

hey folks - i made an ios app to make summarized audio from articles that i don't have time to read (or i'm not sure is worth my time quite yet). to try it out: 1. go to the website 2. click early access button 3. submit the form (only 4 questions) on the confirmation page after you submit the form, you'll see a link to download the beta app via testflight. i haven't spent much time on the landing page, marketing materials, etc. the app is still in beta, but i appreciate all feedback including: the app functionality, design, how it solves people's problems, use cases, feature requests, bugs, etc! the stack is relatively simple - a native ios front end supported by a node.js server. i'm using firebase for easy auth/database and google cloud for storage. this is the first app i built in native ios (swiftui)!

Visit product View on Hacker NewsMay 16, 2024

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Actual performance

3points

6comments

Did not reach leaderboard

Launch Intel predictions

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

96%96% 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: google, using · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, google · Missing: mobile apps, personal, entrepreneurs

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

47%47% 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

18%18% 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.

Incorrect prediction on native model

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