We Built and Open Sourced a Feature Flagging Platform

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Hi. We just open sourced Bullet Train, a Feature Flag and Remote Config platform. It's a collection of tools: server API, admin front end and clients for a bunch of different platforms. Together they allow you to control feature flags and remote config across projects, environments and users. We realised that we were building ad-hoc feature flag stuff into individual projects over and over, so we had a look around to see if there was a way of doing this in a more formal manner. There are a few commercial solutions around but they were quite expensive and seemed to be aimed more at the enterprise. We wanted something that was targeting start-ups and dev agencies, because that's what we do. The platform is 100% Open Source, so you can self host all of it if you want. We're also hosting an API and admin front end for people for free right now. If we can get enough interest we will start offering paid hosting plans that are priced for startups and small businesses, but right now we are just trying to build some interest in the open source projects. Ideally we'd love some feedback on the product and some help writing clients for languages like Go and .Net where we don't have the expertise to do so. You can check it out at https://bullet-train.io/ Thanks!

Visit product View on Hacker NewsJun 11, 2018

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

92%92% 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: user, open · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, users · Missing: plus, intuitive, reviews

45%45% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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