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Aha

People resent commute. The Aha! app will change that by turning wasted time into a social learning and teaching opportunity. 



Description

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General
Title Aha
CreatorEllen Katsnelson
List in galleryYes
Splashsnow flakes
Investment info
Other
ModifiedJul-28, 2014 16:40
Last publishedJul 28, 2014 4:40 PM
Version1
ActivatedYes
Gen menu on home pageYes
Home page
DashboardAha
People resent commute. 
The Aha! app will change that by turning wasted time into a social learning and teaching opportunity. 

Commuters who are reading news, blogs or viewing any kind of web media will be able to click 'Aha!' bookmarklet to indicate that they understand the content and can explain it to others. Another commuter may choose a 'Huh?' option to ask for help. 

Those who said Aha make a commitment to help others using comments and text chat. The Aha button is simple to use, similar to the Pinterest's Pin It button or a Facebook Like button. However, the Aha button is more specific, as the goal is to help or get helped; therefore, it is more personal.

The app will present a stream of all the Aha and Huh posts. There will also be streams of posts filtered by 'My Routes'. This means users will be able to specify their commute routes, which will be used to tag their Aha and Huh posts and to filter the stream. Existing routes are loaded from the MTA data.

Commuters that have overlapping routes will be able to set up meetups at common stops, nearby cafes (we plan to add deals from local vendors), and maybe even share a ride; all by mutual consent and with privacy controls in place.

The Aha! app will allow commuters exchange small parcels of expertise during commute, while automatically building up their knowledge profiles in a simpler, more dynamic, yet more content rich way than what LinkedIn, Skillshare, and job seeking sites are allowing today. Every user's profile shows their Aha posts and their comments on the Huh posts, so that others can assess their expertise and helpfulness to others.

Users also accumulate the reputation points, called Mojo points. As Mojo grows, users level up. Others can see your Mojo level and thus quickly assess your level of expertise and trustworthiness. 

Privacy controls

  • Location privacy. Users cannot see the exact commute route of others, they can only indirectly derive that they have some intersecting routes.
  • Identity privacy. Users can register with the Aha! app using their Twitter, Facebook, Google Plus, Microsoft Live, or LinkedIn account. Those users who want to stay completely anonymous can create a new Twitter account and use it to register with Aha!. Later on, if desired, such a user can connect their Aha! account with their real life identity, using Facebook and all other social nets supported by the Aha! app.
  • Huh posts are not shown on other user's profiles, only on your own.

Designed for offline use

Apps usually have a hard time working with an intermittent internet connection, which is typical for commute. The Aha! app was designed to overcome these challenges with an open source Urbini framework http://github.com/urbien/ urbini.

Roadmap

  • Load all MTA routes, stops, etc. (currently only several MTA lines are loaded).
  • Add tagging system to classify posts. Extract tagging meta info from the original websites and allow user to edit these tags.
  • Create maps that show which MTA lines engage in what type of knowledge exchange.
  • Load NYC cafes and show them around MTA stops.
  • Use deals from the NYC cafes to entice users to exchange knowledge.
  • Use real-time MTA data to create a real-time map that shows where the knowledge exchange is taking place (maybe with small blinking lights)
  • Add badges similar to those on Huffington Post
  • Add the reputation-based access control, like on Stackoverflow. With a higher reputation the user gains rights to edit other users' posts. Thus the system becomes self-moderated.
  • Allow multimedia responses to the Huh posts such as videos, images, photos.

This app uses open data provided by NYC MTA, and we provide a browser for these data.



Ellen Katsnelson
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