Sendune – open-source HTML email designer

Hacker News

Demo: https://designer.sendune.com/ Code: https://github.com/SendWithSES/Drag-and-Drop-Email-Designer HTML for email is probably the hardest code to write. Even a teeny-tiny deviation from the rules will break the email in untold combination of os/desktop/mobile clients. It's mid 2024. Almost 50 years since email was invented and 35 years since HTML was born. A 'basic-open-source-HTML-email-designer' must be a solved problem, right? We thought so too. Sadly, that's not the case. There are a few decent open source email designers but they carry dependencies that make them cumbersome to embed within your app. That's why we decided to open source our HTML Email Designer. The SENDUNE email designer focuses on simplicity and ease of use. It is light-weight. It does pure HTML - no intermediate code wranglers like mjml. There is no lock-in of any kind. Save HTML output as a template and use with ANY email service provider. Feel free to fork the repository, make improvements, and submit pull requests. AMA: hello at sendune dot com

Visit product View on Hacker NewsJul 19, 2024

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429points

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

80%80% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, 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.

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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