Performance GitHub Actions runners powered by Gaming CPUs

Hacker News

Hey HN, BuildJet makes GitHub Actions faster, by running the workflows on our Gaming CPU cloud. You still get to keep everything GitHub Actions, we simply replace the underlying GitHub Actions runner with our Gaming CPUs. All you do is change 1 line, and get 2x faster and cheaper builds. GitHub Action is very slow - Developers are wasting their time – Waiting for GitHub Actions to give you a green checkbox on PR, is very wasteful and extremely tedious. - GitHub Actions runner hardware is old and slow – GitHub Actions use old and slow machines from Azure. Some of your workflows run on servers from 2015. - GitHub Actions use the wrong type of hardware – CI workflows are usually computing intense, and specifically require high single-core performance. GitHub Actions uses server CPUs, which generally have low single-core performance. What BuildJet for GitHub Actions does: GitHub Actions rely on server CPUs from Azure. Server CPUs mostly have low single-core performance. This is where BuildJet differentiates itself, with its fundamentally different approach to computing, using dedicated high-performance gaming CPUs that all have a very high-single core performance. This provides an inherent speed advantage for BuildJet, even without software optimizations. High single-core performance is the key to a fast CI process, and BuildJet's gaming CPU cloud is optimized to deliver just that. Last year, we ran a benchmark where a MacBook Pro 2015 hardware beating a GitHub Actions runner in 2022. It’s yet another instance where you can see the limitation of using server CPUs in tasks that need high single-core performance. Website: https://buildjet.com/for-github-actions

Visit product View on Hacker NewsFeb 28, 2023

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

85%85% 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, single, tasks · Missing: agents, macos, agent

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% 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 · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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

15%15% 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.

Correct prediction on native model

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