Shareholdr – Listen to Startup Essays

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Hi everyone! A little background – I'm dyslexic and have always wanted to listen to PG's essays. A month ago, I was testing ElevenLabs and realized TTS had gotten really good. So, over the past month, I spent a bunch of time cleaning essays (removing things like: — _ % $ = +) to make the audio translations sound good and working with my cofounder to build an app. We currently have essays by Paul Graham, Jessica Livingston, Sam Altman, and Marc Andreessen, and will work to add more quickly. We made this for founders and it's free to use (also no sign-up). Let me know if you have any thoughts or suggestions!

Visit product View on Hacker NewsDec 6, 2023

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Did not reach leaderboard

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

89%89% 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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% 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: month, way · Missing: mobile apps, ios, personal

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

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

21%21% 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

20%20% 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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