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Papiers.ai, a new workspace for reading and research

Hacker News

Papiers.ai, a new workspace for reading and research

Hey HN, I built http://papiers.ai , a new workspace for everything you read. As AI gets better at writing, there will be far more worth reading, even in technical domains like scientific research. Our time and attention won’t grow at the same rate, so software should help us make sense of more information while learning what matters to us from what we save, highlight, and share, and the questions we ask. The Papiers desktop app and Chrome extension are a first step in that direction. Download today for Mac, Windows, or Ubuntu at http://papiers.ai !

Visit product View on Hacker NewsSep 10, 2026

Actual performance

1points

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

94%94% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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