I made an app to transform your Newsletters into Podcasts

Hacker News

Hi everyone! I built Lettercast to solve a personal frustration: too many great newsletters and not enough time to read them all. It generates a daily podcast from your newsletters, you can add your newsletters to your account by forwarding from your inbox to a personalized email account. Key features: -Uses GPT4o to summarize content and create a conversational script -Generates audio with two hosts talking to each other using natural-sounding voices -Delivers a daily podcast episode with your curated newsletter content I'd love to get feedback from the HN community on the concept and implementation. What newsletters would you want to listen to? Any concerns about email/privacy? The app is live on the App Store if you want to try it out. Looking forward to your thoughts!

Visit product View on Hacker NewsAug 24, 2024

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Actual performance

4points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, inbox, email · Missing: mac, agents, macos

84%84% 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.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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

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.

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

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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