Actionate – GitHub Actions for JetBrains IDEs

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I’m excited to share Actionate, a passion project my team and I have been building to reimagine GitHub Actions within JetBrains IDEs. We’ve spent over a decade working in innovation labs at major tech companies, but our true passion lies in crafting tools that we genuinely want to use every day. With Actionate, we’re not just integrating CI/CD into JetBrains; we’re leveraging the powerful building blocks provided by JetBrains and GitHub Actions to create new, transformative functionality. Our MVP (Minimum Viable Product) focuses on the most essential features we find critical for a smoother workflow, but the goal is to push beyond typical CI/CD boundaries and empower developers in ways that haven’t been possible before. If this vision resonates with you, we’d love for you to check out Actionate and let us know what you think—good or bad. We thrive on community input, and your feedback will shape our roadmap as we continue expanding on what’s possible inside the IDE. Thanks for reading, and I hope Actionate helps you take your GitHub Actions workflow to the next level!

Visit product View on Hacker NewsJan 25, 2025

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best fitHighest predicted score across all platforms for this description.

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

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

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

nativeThis product was originally launched on this platform.

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39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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13%13% 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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