My first app, Taxonomy: your taxi traveling companion. Feedback wanted

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This is my first app, it's a taxi "assistant" for want of a better term- gives you street intersections for bars and restaurants, tracks your journey and lets you share it with your friends, splits the cost between passengers and lets you bill each other using Venmo. The web site is: http://www.taxono.my and I have an introductory blog post about it's transition from hackathon entry to app at: http://blog.untogether.co.uk/post/17301824816/introducing-taxonomy It's NYC-only for now because it's entered into an NYC app competition (more on that below), and had to be done to a deadline so I haven't had a huge opportunity to get opinions- any and all feedback is welcomed! I intend to follow up with more blog posts about the tech backend of the app very soon, but it's 95% HTML5 in the app, and node.js on the remote server. Shameless plug alert: It's entered into a City-run competition called NYC BigApps, and there is a public choice award. I'm way behind on votes, so if you like the app, I'd really appreciate a vote: http://bit.ly/yURqU8 Thanks!

Visit product View on Hacker NewsFeb 10, 2012

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

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% 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

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

34%34% 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

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.

Incorrect prediction on native model

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