Top HN articles in human-read podcast format

Hacker News

Hi HN, Like many of you, I found myself discovering dozens of great posts on HN and never actually finding the time read them. I used to save every cool link to Pocket, but… well… same story — I rarely found the time to read them. So I thought it'd be great to listen to the top articles, the same way I listen to podcasts. Initially, I tried out narrating articles with latest text-to-speech synthesis from Amazon and Google, but it was still pretty bad. Especially with long form content. So I thought I'll do this with real humans, real voice actors. So I made ReadByHumans. [1] We are starting out by narrating top articles from HN and some longer cryptocurrency white-papers. Giving away 3 top articles from last week and we’ll be sending one more audio article weekly. In future and at scale, we’d love to narrate any article or a document, on demand and with only a few hours turnaround time. Would love your feedback! What type of content would you like us to narrate? Is it easy to access the podcast feed? Thanks! [1] https://readbyhumans.com

Visit product View on Hacker NewsJun 13, 2017

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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.

best fitHighest predicted score across all platforms for this description.

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

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

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

45%45% 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

42%42% 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: google, way · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto, cryptocurrency, audio · Missing: web3, chat, make money

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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