Dittofeed v0.10.0 – Transactional Messaging

Hacker News

Hi HN, we just released Dittofeed v0.10.0. Dittofeed is an open-source (MIT-licensed) omnichannel customer engagement platform similar to Customer.io, Braze, and Iterable. This is a big release for us which includes: - Support for transactional messaging via Event Entry journeys. This was a highly requested feature among community members. Read more here: https://docs.dittofeed.com/resources/journey-nodes/entry#event-entry - Support for new email service providers, Amazon SES, and Postmark. Amazon SES, in particular, has been a long-requested feature, given SES’s reputation for great deliverability and being low cost. This release also includes several other additions, like event search and optimizations to our segmentation engine. If you’re interested in reading more, we’ve got a release post: https://dittofeed.com/blog/release-0-10-0 We’d love to hear your thoughts on the tools you currently use for transactional or marketing messaging. If you’d like to make suggestions, or ask questions, we’re available on our discord. https://discord.gg/HajPkCG4Mm Thanks!

Visit product View on Hacker NewsFeb 29, 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 · Strong signals: new, email, open · Missing: mac, agents, macos

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

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

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

55%55% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

45%45% predicted probability of success on AppSumo, 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.

Correct prediction on native model

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