Op

Open-source low-code email editor

Hacker News

Open-source low-code email editor

Hey HN, Chandler from Dittofeed ( https://github.com/dittofeed/dittofeed ). We just added low-code functionality to our open-source email template editor. The editor features a Notion-inspired “/” command interface for inserting new blocks. You still have the option of using MJML or HTML, but we wanted to give everyone an easier option for creating primarily text-based emails. We have a live demo of the editor which you can access here (just hit the “Create Template” button): https://demo.dittofeed.com/dashboard/templates Since this is the first iteration of the feature, we’d love to hear your thoughts and feedback on how we can improve it. You can reach us anytime at: founders@dittofeed.com If you’d like to self-host Dittofeed and want help from us and the community, you can join our Discord: https://discord.gg/HajPkCG4Mm

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Actual performance

148points
23comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, using · Missing: mac, agents, macos
89%89% 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
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · 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.
AppSumoStrong fit for a featured deal · Strong signals: host, interface · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
55%55% 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
17%17% 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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