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Use Google Sheets to do realtime feature flagging

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Use Google Sheets to do realtime feature flagging

Feature flags are useful tools to manage rollouts, perform experimentation, and much more. There's lots of companies doing serious stuff in this space, eg LaunchDarkly, Split, Optimizely, etc; two of my previous employers rolled their own experimentation platforms with tons of staffing. However, I needed something way simpler and ideally free. So -- why not use Google Sheets as the backend? You can control who has access to read/edit flag values, there's audit history, it's incredibly simple to use. I intend to use this in production for a while, there's almost certainly a scale at which it won't make sense, but that'll be a problem for a different time.

Visit productView on Hacker NewsJun 30, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: google · 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.
TrustMRRFits verified-revenue profile · Strong signals: google, way · Missing: mobile apps, ios, personal
62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
30%30% 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.

Incorrect prediction on native model

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