travel.meteor.com: clone of mywanderlust in Meteor

Hacker News

The basis of this project is a discussion started earlier today about a wonderful, open source project: http://mywanderlust.co/ The discussion thread: https://news.ycombinator.com/item?id=6653827 My claim was that a project that took weeks or months to implement in RoR and Angular could be completely implemented in Meteor in 1 hour or so. My statements were not meant to attack the original author because he did a great job thinking out the UI, user flows and structure of the project, which I simply copied. Many users got angry at my claims and downvoted me, challenging me to prove my assertion. Meteor is perfect for this type of rapid prototyping and I wanted to show the community what could be completed in 1 hour (56 minutes to be precise) on a Friday evening after work. As promised, I cloned the functionality of the showcased project in Meteor in 1 hour. Finished product: https://travel.meteor.com Source code: https://github.com/andreioprisan/meteor-trave I'd love to get some feedback or answer any questions about the project or Meteor.

Visit product View on Hacker NewsNov 2, 2013

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

75%75% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month, users · Missing: mobile apps, ios, personal

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

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% 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

16%16% 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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