MailRender converts your emails to PDF, removes malicious scripts

Hacker News

Hello I'm Pierre, developper. I'd like to get early feedback about a side project I'm working on. MailRender has a simple concept which allows to convert any email with or without attachments to PDF. Just login to register, then send or forward an email to "pdf at mailrender.io", and you'll get an email back in your inbox with the body and attachments converted to PDFs. There are loads of possible applications / extensions, like redirecting output email destination to another mailbox or to s3, set other MailRender mailboxes which merge or watermark pdf attachments, provide APIs for automation, etc... The backend is a k8s cluster hosted on Hetzner. I'm basically using NATS for queing and actually 9 microservices for parallel processing. Access to the cluster have been Tailscale'd :) There are a bunch of licensed libraries I'm using which doesn't allow me to make this project opensource. No AI were harmed during the process. MailRender website : https://mailrender.io Discussion : https://github.com/mailrenderpdf/discussion/discussions

Visit product View on Hacker NewsAug 26, 2023

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

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% 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

11%11% 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.

Incorrect prediction on native model

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