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A WebPDF Reader with AI assistance, for research and studying
Hacker NewsA WebPDF Reader with AI assistance, for research and studying
Hey, I've made a pdf reader with ai assistance, you can quickly ask questions about the text and the images, and it will have context about all of your reading material so you can ask questions freely, try it now, I'm in open beta to find bug and get feedback. upcoming features: - Highlighting - Highlighting with Notes - Bibliography - Automatic Reference file opening - Improved UX and robustness currenlty alot of bugs so please let me know if you found any. feedback appreciated
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Product HuntOn track for Day 1 leaderboard · Strong signals: context, notes, open · Missing: mac, agents, macos89%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, podcasting62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive35%35% 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 released33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto19%19% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
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