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Gha-outrunner – Ephemeral GitHub Actions runners without Kubernetes

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Gha-outrunner – Ephemeral GitHub Actions runners without Kubernetes

I needed isolated CI runners for my desktop CAD app (Qt/C++/Rust, three platforms) and didn't want to set up Kubernetes just for that. So I built gha-outrunner - which provisions a fresh Docker container, KVM/QEMU VM, or Tart macOS VM for each job, then destroys it when done. It uses GitHub's scaleset API so it works as an autoscaling runner group. Written in Go, MIT licensed. Happy to answer questions.

Visit productView on Hacker NewsMar 31, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, dock · Missing: agents, agent, cursor
76%76% 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 NewsStrong engagement from HN community · Strong signals: 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
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: platform · Missing: plus, intuitive, reviews
41%41% 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
33%33% predicted probability of success on Indie Hackers, 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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