Laika – Open Source Feature flag/toggle system w. slack integration

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Hey everyone, Here is a link to the first version of an on-premise feature flag/toggle system, made with GO, that I developed at the company I'm working at (MEDIGO). It allows you to isolate features within your projects, and enable/disable them in multiple environments with a simple click in a dashboard. For instance if you just developed a new feature and you want to enable it on the testing environment, all you have to do is go to Laika's web page and activate the flag for the feature on this environment. Or if a bug is spotted on a feature already in production, disabling is just as simple. Part of this first release is a "dashboard" (pages.js & polymer) to create and toggle flags, two clients (Go and PHP) that check flag status, and, at the core of it, a restful service that stores flag status, enabled/disabled events in MySQL, and optionally notifies a slack channel. The whole thing runs in docker, if you want to try it out (https://github.com/MEDIGO/laika). Our objective is, by using Laika in our projects, to make our continuous delivery, built around the trunk-model, smoother. Obviously there can be many future paths for this tool but on the short term we might add statistics, auto-registration of new flags from code, SSL support :) and some lazy-man's AB-testing features. This was my first bigger project and I realise it is quite simplistic in itself but maybe it helps others who work on the trunk model and with other "deploy first, enable later" approaches.

Visit product View on Hacker NewsJul 20, 2016

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

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

89%89% 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: open source, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% 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

19%19% 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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