Gw – lightweight GitOps tool that runs anywhere

Hacker News

Hey HN, I built `gw`, a minimal, open-source GitOps tool written entirely in Rust. I'm a big fan of pull-based continuous deployments — just push code and let the deployment update automatically (like ArgoCD or Netlify). However these tools often lock you into heavy cloud or Kubernetes-based solutions. I wanted to make a simple binary that you can download and run on anything: from a cheap VM or Raspberry Pi to servers behind NAT or VPN. It regularly pulls from a repository, runs custom scripts or restarts a process whenever changes are detected. I'd love your feedback and suggestions. Thanks for checking it out! GitHub link: https://github.com/daniel7grant/gw Docs: https://gw.danielgrants.com/

Visit product View on Hacker NewsOct 24, 2024

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

66%66% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

65%65% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% 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

39%39% 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

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.

Incorrect prediction on native model

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