WarpBuild – x86-64 and arm GitHub Action runners for 30% faster builds

Hacker News

Hey HN, I’m Surya and I’m excited to show you WarpBuild! WarpBuild provides fast, secure `x86-64` and `arm64` Github actions runners. This speeds up your workloads by 30%, at half the cost, and takes ~2mins to get started. We’ve been seeing pretty good results since we opened up signups a week ago and I’ve shared some numbers publicly here [1]. Currently, we support linux runners for Github organizations (not personal accounts) and MacOS support is coming soon (~Jan). The way the runners work is deceptively simple: Runners are assigned to hardware that is ideal for build workloads with fast NVMe disks and high single-core performance. The runners are allocated on VMs, not containers. This provides faster performance and enables use cases requiring (1) nested virtualization for running firecracker and other hypervisors, (2) k8s without relying on kind, and (3) Android emulators on `arm64` instances in test workflows. We also have released a Github Action called `Action-Debugger` that allows you to SSH into a running workflow for simplifying pesky debugging[2]. The same set of packages that you’d get on Github hosted runners are pre-configured (on x86-64 runners) so everything works out of the box with no modifications needed. A very minor detail that I’m rather proud of, and I’d love your thoughts on improving it further, is the onboarding flow for the ease of moving workflows to WarpBuild. We’ve also put in a lot of effort into making the workflow start up time where we are as fast or faster than Github. [1] https://x.com/suryaoruganti/status/1732932591001735419 [2] https://github.com/WarpBuilds/action-debugger , h/t to tmate Making builds faster by providing optimal hardware and configurations across CI providers is the first step in our mission to make build engineering better. I’d love your feedback on the product and thoughts on other CI pain points we could solve to enable better collaboration and developer experience.

Visit product View on Hacker NewsDec 8, 2023

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

95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, single · Missing: agents, agent, cursor

83%83% 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

71%71% 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: host, soon · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

18%18% 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

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