BlazorPathHelper – Type-safe URL builder library for Blazor

Hacker News

Hello HN, I Introduce a library of type-safe URL builder that can be used in the Blazor project. Blazor is a cool technology that makes it easy for anyone to write modern web in C#, but I found it lacking in features around URLs. Similar projects already exist, but this library is * Compatible with razor's source generator (the previous one had to explicitly turn this off with the advent of .NET6) * Automatic generation of @page, [Parameter], and [CounterWithQuery] attributes (handwriting is very tedious, right?) * Automatic generation of highly customizable menu structures (icons, descriptions, L10N, all possible!) Happy hacking!

Visit product View on Hacker NewsDec 16, 2024

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

90%90% 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.

Hacker NewsStrong engagement from HN community · Strong signals: exist, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: builder · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% 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

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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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