Valmi.io Open Source Reverse-ETL Engine

Hacker News

Hi HN Community, We have built the valmi.io open-source reverse-ETL platform. You can quickly check out a live demo sync by following the link on the GitHub page or at the bottom. Please try it out. You can also easily deploy it locally or schedule a run on the demo website. We are seeking feedback on the product. We have outlined a variety of use cases valmi.io currently supports on the blog. Currently, the engine supports 13+ connectors. The connectors are built using the airbyte protocol, and dagster is used as the orchestration engine. We will soon provide a detailed architecture diagram. We are currently working on essential features such as injecting state on connector failures. More upcoming features are listed on the website: https://www.valmi.io . Please give it a try, and valuable feedback from early adopters will shape the product. Live Sync - https://demo.valmi.io/spaces/a9195c50-60ca-4692-8f03-5a486ee... GitHub - https://github.com/valmi-io/valmi-activation Blog - https://blog.valmi.io Docs - https://docs.valmi.io

Visit product View on Hacker NewsJun 21, 2023

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

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

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

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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

16%16% 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.

Incorrect prediction on native model

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