Starthub – Deploy horizontal n8n to Digital Ocean with one command

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Hello, I’ve been experimenting with Starthub, an npm-like CLI tool that chains Docker and WASM units to perform arbitrary workflows. Deploying open-source stacks horizontally across multiple nodes is often tedious and error-prone, so this is an attempt to make that kind of task reproducible and composable. As an example, the command below deploys a horizontal n8n stack (postgres + redis + droplets + ssl) to DigitalOcean. npx @starthub/cli@latest run starthubhq/n8n-horizontal-do:0.0.1 It’s just a rough MVP (the CLI needs A LOT of work still), but I’d love feedback on: - The approach for composable, stateless deployment actions - Multi-cloud or multi-target deployment ideas Github repo: https://github.com/starthubhq/cli Thank you for your time!

Visit product View on Hacker NewsDec 29, 2025

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% 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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% 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

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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