Voxcreo – Turn written text into podcasts

Hacker News

Hey everyone, I was recently doing the dishes and I got notified that a new PG essay had been released. In that moment I wished I could listen to it so that I could read without needing to context switch. That was the inspiration for this project. It's pretty simple to use--you provide the app some text via a webpage, pdf, or directly inputting and we run that through a text to speech model that outputs a high quality narration. You can either use one of our default voices, or upload a voice sample and use a voice clone. Once you've created your first dictation in the app, you can then connect Voxcreo to your podcast app of choice via RSS (we teach you how to do this in the app) to have all of your future dictations automatically synced to it as if they were episodes of your favorite podcast. So going forward from there you can just link Voxcreo to any content you want turned into audio and it will automatically handle the rest. If you're using Chrome and want to make it easier we also have an open source Chrome extension--if you install it, all you'll need to do is click a single button on any web page and we'll automatically process, convert it into a dictation, and send it to your podcast queue. Thanks for checking us out! If you have any questions or feature resquests I'd love to hear them in the comments.

Visit product View on Hacker NewsAug 28, 2024

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

98%98% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

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

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

16%16% 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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