Dittofeed v0.6.0 – Reverse ETL for open-source customer messaging

Hacker News

Hi HN, we just released Dittofeed v0.6.0. This release includes reverse ETL support, with the help of Polytomic. Reverse ETL support was highly requested by community members. Dittofeed is an open-source alternative to platforms like customer.io, Braze, and others. Reverse ETL is useful for allowing growth teams to track new events without blocking on the product engineering team. Polytomic is a data platform which supports both ETL and reverse ETL. A few other release highlights based on HN feedback: - Full overhaul of our “broadcasts” feature to improve the ease of sending one-off messages like newsletters and product updates. - You can now send test messages to yourself to ensure proper rendering in different email clients, validate ESP credentials, etc. Release blog post with all updates [here](https://dittofeed.com/blog/release-0-6-0). Hope to hear your thoughts! Github Repo - https://github.com/dittofeed/dittofeed Demo - https://demo.dittofeed.com/dashboard/journeys Docs - https://docs.dittofeed.com/getting-started Polytomic Docs - https://docs.polytomic.com/docs/getting-started Dittofeed Polytomic Docs - https://docs.dittofeed.com/integrations/sources/polytomic

Visit product View on Hacker NewsOct 30, 2023

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

85%85% 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 · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · 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.

Incorrect prediction on native model

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