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Garnix, fast and easy CI for Nix

Hacker News

Garnix, fast and easy CI for Nix

Hello HN! For the past few months, I've been building a Nix-specific CI service, and quite a few people have been productively using it, so I thought I'd Show HN it. You might be wondering why build a Nix-specific CI. It turns out its quite hard to get a really good CI setup for Nix without spending a lot of time or money on it (or both), and even then solutions don't tend to be optimal. Garnix, on the other hand, handles everything, and simply. Just create an account, install the GitHub app on the repositories you want, and you're good to go! Each package gets its own GitHub check, and separate log output, which makes it really easy to figure out what went wrong (you can see an example here: https://garnix.io/build/X9knYZOB ). Builds are very fast compared to e.g Github Actions. The build artifacts are made available in a Nix cache so you never have to rebuild locally. And there are builders for x86-64 Linux as well as M1 Macs and aarch64 Linux. Try it out: https://garnix.io . It's free, though if you like it, consider donating! (Note that it only works with flakes.) Cheers, Julian

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
79%79% 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, using · Missing: agents, macos, agent
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
63%63% 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: builder · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, para · Missing: mobile apps, ios, personal
35%35% 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
22%22% 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.

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