We Built FlyCI – Cost-Effective Mac Runners for GitHub Actions

Hacker News

Hi, I'm Kiril Gantchev, one of the co-founders of FlyCI. FlyCI offers substantial cost savings for M1 and exclusive access to M2 runners for GitHub Actions, boosting mobile CI/CD workflows. Beta users get exclusive access to our M1 and M2 runners for free until the program ends (January 31, 2024). The key benefits are: - Up to 2x Cost Savings: Compared to traditional options, FlyCI's Mac runners offer significant price reductions, optimizing development budgets. - Exclusive M2 Access: Stay ahead of the curve with FlyCI's exclusive access to M2 runners, ensuring your development environment has the latest hardware technology. - Easy Configuration: Simple one-line change integrates FlyCI seamlessly into existing GitHub workflows. Getting Started: 1. Enter your email in the FlyCI website to receive the GitHub App install link. 2. Update your workflow configuration with `runs-on: flyci-macos-latest-xlarge` for M1 or `flyci-macos-large-latest-m2` for M2 runners. Simple as that and you get to try it for free until January 31, 2024! I hope you all enjoy it and please provide us with as much feedback as possible.

Visit product View on Hacker NewsDec 11, 2023

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

87%87% 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, user · Missing: agents, agent, cursor

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: exclusive, users · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

54%54% 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 · Strong signals: users · Missing: mobile apps, ios, personal

25%25% 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.

Correct prediction on native model

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