Globset for Nix Source Filtering

Hacker News

Hi there! I am Edgar and I wanted to share with you “globset”, a Nix library to help specify what sources to include when building a Nix package using familiar glob patterns like “*/*.go”. Nix is package manager and meta build system, in that you glue together compilers and other build systems in repeatable sandboxes like “docker build”. When specifying the source inputs to a Nix package, you typically end up with irrelevant files that when changed causes a rebuild unnecessarily. “globset” provides a concise and simple way of specifying what sources should be included. It is built on top of the new “fileset” library introduced in Nixpkgs 23.11, and is composable with any libraries using it. This is our second open source project after releasing nix-snapshotter last year. (See: https://news.ycombinator.com/item?id=37407758 )

Visit product View on Hacker NewsOct 25, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock, new, using · Missing: mac, agents, macos

87%87% 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.

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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

47%47% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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