reimagined digital magazine, using Backbone and GSAP

Hacker News

My team was granted a couple of months to explore creating a new magazine-like medium for our home decor website, Lonny. A major goal of the project was to preserve ideas that make magazines what they are (rich layouts, big imagery, editorial driven pacing) and to accent them with technology. Here is our v1: http://www.lonny.com/mag/osWdLZXQXc7/May+2014 We would mostly classify ourselves as backend engineers, so for us the majority of the technical difficulty came from developing the front end. We used Backbone as our MVC framework and GSAP as our animation platform. It was our first large project with either library and we really enjoyed both of them. All in all, it was a great project to work on and we thought you guys might enjoy seeing it, even if you don’t have any interest in home decor. We’d love to hear any feedback you have to offer!

Visit product View on Hacker NewsMay 6, 2014

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2points

Did not reach leaderboard

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

88%88% 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: new, using · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

57%57% predicted probability of success on AppSumo, 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.

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

40%40% 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

13%13% 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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