Papermark – the open-source DocSend alternative with custom domains

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Hi HN! We’re Marc and Iuliia from Papermark ( https://papermark.io ). We're building an open-source, modern document sharing platform with real-time engagement analytics and 100% customization. It all started as a tweet [1] and led to our launch on Product Hunt [2] last month. We crossed over 1000 stars and over 10 contributors on GitHub ( https://github.com/mfts/papermark ). Incumbents, like DocSend, founded in the early 2010s have been acquired already and just don't innovate anymore. Their main priority is enterprise clients. As founders and developers ourselves we always felt that our needs are not being served. We struggle to get actionable insights when sending pitch decks to investors or sales proposals to customers. We are missing the flexibility and integrations with our existing applications and tech stack. That’s why we are building Papermark. Our bigger vision A developer-friendly document sharing platform doesn’t exist yet! We believe that document analytics should be available to anyone that is creating and sharing documents. - Rich observability: Don’t get left in the dark when sharing a document. Papermark will provide full transparency by exposing all document-related events through webhooks. - Fast performance: We are built on top of Vercel to serve your documents fast wherever your recipients are located and watching the engagement data arrive in real-time with Clickhouse under the hood. - Developer-friendly: We will be expanding the platform with a clean REST API, so you can create, share, connect and expand documents that you are sending both externally and internally. Let's stop sharing documents like it's 2013. I would love to hear your ideas, experiences, and feedback on any and all of the above. What issues have you had with document sharing? [1] https://x.com/mfts0/status/1660980644065730561?s=20 [2] https://x.com/mfts0/status/1698596179578147142?s=20

Visit product View on Hacker NewsOct 23, 2023

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

93%93% 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: open · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, friendly · Missing: plus, intuitive, reviews

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

TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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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