FeatureFlare – safer feature rollouts with targeting, release workflows

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I’m building FeatureFlare, a feature flag platform focused on safe production releases for small engineering teams. The newest work adds a tighter “release safety loop”: stronger rollout controls, better targeting workflow, and production-hardening around automated rollback behavior. The goal is to make it easier to ship gradually, detect bad rollouts quickly, and recover without manual scramble. I’ve also been building integration foundations so flag changes can connect to the tools teams already use across CI/CD, observability, and incident response. What I’d love feedback on: Does the rollback + targeting flow match how your team actually ships? Which integration would make this immediately useful for you? What would block you from trusting this in prod? Happy to share implementation details, tradeoffs, and roadmap if helpful.

Visit product View on Hacker NewsFeb 26, 2026

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

86%86% 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

67%67% predicted probability of success on Indie Hackers, 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

47%47% 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: platform · Missing: plus, intuitive, reviews

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% 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

18%18% 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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