FaaStRuby 0.2 released – feedback appreciated

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Hey all, I just put out a new release. Would you spare a couple minutes to try and give some feedback? If you already have the gem installed, you will have to update it: gem update faastruby Tutorial => https://faastruby.io/tutorial.html Top 3 Highlights - No more fiddling with git. - Functions are packaged, deployed and managed individually. - TESTS! Functions can have tests and they can be configured to run before each deploy, aborting if it fails. Thanks in advance!

Visit product View on Hacker NewsOct 13, 2018

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

72%72% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

39%39% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

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

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

31%31% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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