Private RubyGem Server

Hacker News

TL;DR - http://www.gemfury.com - pre-launch feedback appreciated Hello HN, In true hacker tradition, a tool built for ourselves often grows into a service of its own. In the past few months, we've been polishing our private RubyGem server into something that other Ruby developers can use. Today, I'd like to ask for your feedback on where we stand and whether we're "launch-ready". The service is called Gemfury and offers customers a private RubyGem repository in the cloud. The story is that many RubyGems are built as open source and solid infrastructure exists to develop and distribute those. However, if you want to encapsulate proprietary code into Gems, infrastructure becomes burdensome where all the pieces are disjoint and need to be self-hosted. Gemfury fills one piece of that story in the cloud. We will be officially launching soon, and I wanted to get your expert opinion on whether we're missing anything. Please take a look and reply what you think in the comments: http://www.gemfury.com

Visit product View on Hacker NewsFeb 9, 2012

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

78%78% 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.

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

68%68% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

37%37% 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

15%15% 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.

Correct prediction on native model

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