FeatureFlags.app – Feature Management for .NET

Hacker News

Feature flags are common these days, and the .NET feature management library provides a lot of the plumbing to make them easy to get started with. The missing piece is a UI for managing flags - unless you are using Azure App Configuration. Relying on `appSettings.json` gets awkward when non-technical users need to change flags, deploys are slow, or the application is running in a distributed environment. I built FeatureFlags.app to fill that gap - a simple UI for managing .NET feature flags that doesn't require Azure App Configuration. It's still early, so I'd love feedback from anyone who has dealt with feature flags in .NET.

Visit product View on Hacker NewsAug 28, 2026

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4points

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

64%64% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, using · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, users · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% 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: users · Missing: plus, platform, intuitive

40%40% 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

20%20% 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.

Correct prediction on native model

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