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I Built DevTools for Blazor (Like React DevTools but for .NET)

Hacker News

I Built DevTools for Blazor (Like React DevTools but for .NET)

Hi HN! I've been working on developer tools for Blazor that let you inspect Razor components in the browser, similar to React DevTools or Vue DevTools. The problem: Blazor is Microsoft's frontend framework that lets you write web UIs in C#. It's growing fast but lacks the debugging tools other frameworks have. When your component tree gets complex, you're stuck with Console.WriteLine debugging. What I built: A browser extension + NuGet package that: Shows the Razor component tree in your browser Maps DOM elements back to their source components Highlights components on hover Works with both Blazor Server and WASM How it works: The NuGet package creates shadow copies of your .razor files and injects invisible markers during compilation. These markers survive the Razor→HTML pipeline. The browser extension reads these markers to reconstruct the component tree. Current status: Beta - it works but has rough edges. Found some bugs when testing on larger production apps that I'm working through. All documented on GitHub. Technical challenges solved: Getting markers through the Razor compiler without breaking anything Working around CSS isolation that strips unknown attributes Making it work with both hosting models It's completely open source: https://github.com/joe-gregory/blazor-devtools Demo site where you can try it: https://blazordevelopertools.com Would love feedback, especially from anyone building production Blazor apps. What debugging pain points do you have that developer tools could solve?

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, models · Missing: mac, agents, macos
90%90% 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
76%76% 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, pipe, io · Missing: https docs, excited, just released
69%69% 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: host · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
40%40% 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
19%19% 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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