Pippy – Pipelines for GitHub Actions

Hacker News

I am excited to share pippy, which allows users to create configurable pipelines using Github Actions. If you have used Azure pipelines, in summary, this would be Azure Pipelines meets Github Actions. Cloud version: https://app.pippy.dev/login (closed beta) I am also open sourcing command line version: https://github.com/nixmade/pippy . Key features: - Automatic Rollback - Datadog Monitoring - Pause/Resume The product is built using open source orchestrator. Orchestrator: https://github.com/nixmade/orchestrator Orchestrator allows you to orchestrate rollout to specific set of targets and provide monitoring capabilities to optionally rollback. I hope to provide Cloud API so platforms can orchestrate updates using the framework. Tech stack: Database: Postgres Backend: Golang Frontend: React + Shadcn UI Cloud: Azure container apps Please leave your feedback in comments. Hope you can give the command line version a try, and sign up for beta of the cloud version.

Visit product View on Hacker NewsJul 17, 2024

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

89%89% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, open source, ide · Missing: https docs, just released, exist

43%43% 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, users · Missing: plus, intuitive, reviews

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

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

33%33% 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

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

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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