Featurevisor – Git-based feature flags and experiments management

Hacker News

Why? - Decouple application deployment from releases How it works: - Make changes to your features and segments (YAMLs) via Pull Requests - Generate datafile (JSON file) in CI/CD workflow and upload to your CDN - Fetch datafile in your application runtime and consume with SDKs Supports: - Feature flags: boolean flags - Experimentation: a/b tests - Segments: targeting traffic with conditions - Variables: namespaced under each feature and conditional - Gradual rollouts: avoid big bang releases, go from 0% to 100% - Consistent bucketing: same user sees same variation - Multiple environments: prod, staging, test, and more - Force bucketing: allow testers to force enable/disable for themselves only - Tagging: resulting in smaller datafiles for your application(s) - Tracking: integration with any analytics tool - Status site generator: for human friendly status reporting - SDKs: for Node.js and browser environments, Kotlin/Swift planned for future

Visit product View on Hacker NewsApr 24, 2023

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48points

9comments

Made the leaderboard

Launch Intel predictions

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

81%81% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created

71%71% 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: io · Missing: https docs, excited, just released

48%48% 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: friendly · Missing: plus, platform, intuitive

40%40% 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

33%33% 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 · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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