DocSend, but Made 10x Cheaper

Hacker News

Hey HN, founder of Peony here. Most founders use DocSend to share investment or sales docs and have to pay $65 to $300 a month to get the security and analytics features. It pains me to see startups getting ripped off when the availability of technologies such as Clickhouse makes real time analytics almost a trivial feature to implement in 2024. We think it's time to end the carnage and democratize the technology. We set ourselves a high bar: we don't want to build another DocSend - we want it to be a far more superior product and also 10x cheaper. After a couple of months of work, I'm proud to share with you Peony - a first preview into what we believe will be the future of file sharing. In addition to the analytics and security features you'd expect coming from DocSend, we built Peony to be flexible and adaptable across all your file sharing needs and give you the ability to deeply personalize your file share per recipient. I'd love to hear what you think? We are the Prometheus stealing fire from the guarded Olympian mountains. Help us win the rebellion.

Visit product View on Hacker NewsNov 11, 2024

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

87%87% 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 · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% 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 · Missing: plus, platform, intuitive

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

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

22%22% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · 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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