Oration (iOS) turns pdfs into audiobooks

Hacker News

Hello HN community! I'm excited to introduce a project I've recently launched: Oration, an iOS app designed to convert PDFs into audiobooks. This idea was inspired by my experiences as an engineering student with ADHD, struggling to engage with dense academic papers. Relying on Text-to-Speech tools, despite their robotic quality, was a workaround for me and others with similar learning preferences or challenges, such as Dyslexia. Recognizing the limitations of existing tools—difficulty with complex formats, inability to skip over citations or footnotes, and inadequate handling of tables, graphs, and figures—I developed Oration. Our goal is to refine these areas continuously, offering both summarized and full versions of PDFs for a more accessible learning experience. Oration aims to serve as a high-quality, user-friendly platform for auditory learners and those who find traditional reading methods challenging, with features akin to popular audiobook apps like Audible or Spotify. How Oration Works: 1. Download the app and sign up using either a username and password or through Google, with a 2-week free trial that doesn't require a payment method. 2. Upload a PDF document. 3. Within about 5-10 minutes, you'll receive a notification that your Audiobook is ready. 4. Listen to your Audiobook directly in the app or through a browser-based web player, which also facilitates easy sharing with friends and family. Also, to emphasize - all audio generated by the user is yours to own! We're working on some updates to easily export .MP3 files of Oration Audiobooks you create For an example of how the web player looks and functions, check out this link: https://player.oration.app/75e079c1-bd7e-4a16-8e02-23636837a... I believe Oration can significantly benefit those who prefer or require alternative learning formats. We're committed to enhancing the app's functionality and user experience, so feedback and constructive criticism are always welcome. Thank you for considering Oration, and I hope it proves to be a valuable tool for you or someone you know.

Visit product View on Hacker NewsFeb 9, 2024

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

85%85% 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, apps, user · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

50%50% 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, exist, lua · Missing: https docs, just released, open source

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

9%9% 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, introduce · 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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