Martlet – Have Q&A with your team and clients in the margins of PDFs

Hacker News

Hi HN, I'm Marcos, founder of Martlet, and I made a website that allows teams and their clients to ask and answer questions in the margins of PDFs. It's like an interactive FAQ in your PDFs so that your colleagues and clients can look up answers to their questions instead of emailing and waiting for a response, or having to respond to the same question again and again. It also allows your team to save experience based knowledge for others to learn! I'd love to hear y'alls feedback! Thanks for checking it out!

Visit product View on Hacker NewsApr 18, 2023

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

82%82% 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.

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

69%69% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

21%21% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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