Sparklevalidator.com (For Appcast.xml Files)

Hacker News

Fellow HN folks, Many of us who build macOS apps use Sparkle to keep our apps up to date, which uses a published appcast.xml file to track current releases & metadata. If you push out a bad file you could break your updates, but there wasn't a way to validate an appcast.xml before deploying it. So I built one. It's an NPM module, open source & MIT licensed. Also a web service that can take URLs, files, or pastes and validate as errors, warnings, or informational items. It's also a GitHub Action that's on the marketplace so you can integrate it into your CI/CD pipeline in about three lines. I also put together an XSD validator and plain .md docs as you (or your agent) are authoring. Feedback welcome!

Visit product View on Hacker NewsJan 29, 2026

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

84%84% 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.

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

44%44% 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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, pipe, io · Missing: https docs, excited, just released

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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