SimpleW – Lightweight C# Web Server with optional addons

Hacker News

Hi HN, I’m the author of SimpleW, an MIT-licensed .NET web server for standalone or embedded applications. I shared it here last year ( https://news.ycombinator.com/item?id=44797834 ). Since then, I’ve rewritten it from scratch, removing the NetCoreServer dependency. The biggest change is the addon architecture: the core stays small, with no third-party dependencies, and you add what you need (JWT, OpenID Connect, automatic Let’s Encrypt certificates, Swagger, Razor, dependency injection, or logging integrations). Even the network engine is replaceable, with an optional io_uring addon for Linux, powered by ioxide. Benchmarks are available on https://http-arena.com . I wanted a server I could understand and modify easily, while keeping extra functionality in separate packages. You can try it by following the quickstart: https://simplew.net/guide/getting-started.html I’d love feedback on the addon design and what you’d build with it.

Visit product View on Hacker NewsSep 8, 2026

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6points

Did not reach leaderboard

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

78%78% 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: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, open · Missing: mac, agents, macos

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

21%21% 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.

Incorrect prediction on native model

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