A-

A-to-P – Convert articles into podcasts

Hacker News

Hi HN – I’m excited to share my open source project a-to-p (article to podcast) available at https://github.com/collinc777/a-to-p. A to P is a simple web app that converts any article to a conversational podcast experience. You can try it out here: https://a-to-p.vercel.app/ I’ve explored text-to-speech (TTS) for articles using tools like Speechify. However, I noticed a gap: TTS lacked the engagement I found in podcasts. I think there’s two reasons why: 1. Articles are visually oriented, while podcasts are audio-centric. 2. The personal connection we develop with our favorite podcasters. Addressing this, a-to-p leverages AI to bridge the gap. The idea is to morph articles into a format resembling podcast transcripts before applying TTS. This approach, in my experience, significantly enhances engagement compared to standard TTS. The roadmap for a-to-p includes: - Tackling the content and length alterations in the transformation process. - Implementing open-source LLMs for generating transcripts. - Integrating high-quality, affordable open-source TTS models. - Adding Intro and Outro segments for a complete podcast experience. I’m eager for any feedback or contributions to the project. Your insights can greatly shape a-to-p’s future!

Visit product View on Hacker NewsJan 22, 2024

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

82%82% 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: model, models, visual · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, open source, ide · Missing: https docs, just released, exist

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

37%37% 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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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