Listen to PDFs in Any Language

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Hey, I built this tool last year to listen to machine learning books and papers. The first version was very simple, but I saw some interest from users and found myself using it regularly whenever a new machine learning paper was published. Now, after a year and over 3,000 signups and some paying users, I've invested more time into it, with the help of AI programming :) During that year I've switched to the newest models, which made the app smarter over time. I’m curious what you’d use it for. Do you like the AI explantions? This is my favorite feature, but most users prefer listening to the original text. Sometimes the AI explanations are a bit repetitive, and I'm working on improving that. Currently, I’m creating RSS podcasts so I can listen to the episodes on my phone using AntennaPod. But I think the next step is to make an app. Google Gemini now offers Audio Overviews, which are quite good. However, my app is different because it breaks the document into semantic sections and you can listen to each section independently. A whole paper will be a multi-hour audio, like an audio book. The audio experience is the product. I would love your feedback (I even made a form for that on the website). https://www.pdftomp3.com/

Visit product View on Hacker NewsMar 19, 2025

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

94%94% 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: mac, model, google · Missing: agents, macos, agent

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: google, users · Missing: mobile apps, ios, personal

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% 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

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, smart · 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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