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Cargo-pup – ArchUnit-style linting for rust

Hacker News

Cargo-pup – ArchUnit-style linting for rust

Hey HN! I’ve been working on cargo-pup, a Rust tool for defining and enforcing architectural rules in your codebase - like ArchUnit for Java, or Clippy, but for project-specific architecture constraints rather than linting. It lets you write architectural assertions in Rust using a builder-style API. For example: Enforce layering: "REST modules shouldn’t use SQL clients" Enforce consistency: "Types implementing MyTrait must be named MyTrait and live in Impl* modules" Pup emits regular rustc lints (just like Clippy or cargo check) and runs either as a test or via a `cargo pup` CLI, the latter of which you can drop into your CI. This is very early days! I work at Datadog, but this isn’t an official project — just something I built to explore what architectural linting might look like in Rust, and to scratch my own itch :) Would love any feedback or questions! Scott

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code · Missing: mac, agents, macos
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
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
49%49% 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
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
46%46% 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
11%11% 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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