Brisk a complete CI system is now Open Source

Hacker News

Brisk is a complete CI system that runs your test suite in seconds. The main speedup over standard CI systems (think CircleCI or Github Actions) is that we maintain your test environments and don't rebuild them on each test run. This allows you to scale up your workers without just scaling up the build time on each test run. Brisk can be run from your local dev environment (the command line or VS Code) allowing your to access the speed of many servers from your local machine or it can be run as part of a traditional CI/CD pipeline (in Github actions etc). I've been working on Brisk for the last few years and I've decided to open source the project. You can now run your own super fast CI service on your own hardware or in your own cloud. https://github.com/brisktest/brisk

Visit product View on Hacker NewsMay 29, 2024

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

88%88% 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.

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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released

65%65% 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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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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