Automatisch – Open source workflow automation, an alternative to Zapier

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Hey, HN community, We're so excited to share Automatisch with HN finally. Automatisch is an open-source workflow automation tool, an alternative to Zapier. Together with my co-founder (@barinali), we have been working on it for about 15 months and have started getting early adopters. Automatisch is a workflow automation tool that lets you connect different web services like Slack, Github, Twitter, and more to automate your business processes. For example, you can build automation that gets all new tweets, including the "open source" phrase, and post them to the Slack channel you specified. You can adjust the services and steps depending on what you actually need to automate in your business. Even though some existing cloud solutions do the job well enough, we still wanted to build an open-source and self-hosted alternative to those. Because it allows you to store your data on your own servers, which is essential for businesses that handle sensitive user information and cannot risk sharing it with external cloud services. This is especially relevant for industries such as healthcare and finance, as well as for European companies that must comply with the General Data Protection Regulation (GDPR). You can see the available integrations here ( https://automatisch.io/docs/guide/available-apps ). We currently have limited integrations but are constantly working on adding more and enhancing the existing ones. You can also request a new integration by using GitHub discussions: ( https://github.com/automatisch/automatisch/discussions/categ... ). You can use the following links to check it out: Website: https://automatisch.io Docs: https://automatisch.io/docs GitHub: https://github.com/automatisch/automatisch Please give it a try and let us know if you have any feedback, and if you like what we are doing with Automatisch, please give us a star on GitHub. Cheers!

Visit product View on Hacker NewsJan 25, 2023

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

91%91% 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.

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

78%78% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: slack, apps, user · Missing: mac, agents, macos

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

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · 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 · Missing: arr, mrr, revenue

14%14% 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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