Social Browsing on your iPhone with Safari Browser Extensions

Plug-ins, add-ons, extensions – every desktop browser supports them: Firefox, Chrome, Internet Explorer, Opera and Safari. Third party developers can easily add features to these web browsers to enhance our web browsing pleasure.

But what about the mobile browser on your phone?

Mobile Firefox is the only major mobile browser to officially support extensions, and that is currently only for Maemo and Windows Mobile.

I’ve decided that’s not enough!

According to this man below, the mobile browser that accounts for most of our browsing is the iPhone’s Mobile Safari, so let’s start with that.


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Fresh From @hitching and the Interwebs

getting sucked into The Mongoliad, Neal Stephenson’s latest creation http://bit.ly/b0koTu is this the future of storytelling?

the bit.ly Sidebar Bookmarklet is bloody marvelous http://bit.ly/cy5JJO because it shows you who else is sharing the page

here’s hoping the learning algorithm in my Gmail Priority Inbox will normalize the weighting of SMTP Importance headers

infobesity; the result of poor information nutrition, see http://bit.ly/cN0MqS on the topic of infovore consumption habits /via @timyoung

polling sucks, battery and bandwidth and generally http://code.google.com/p/pubsubhubbub/wiki/WhyPollingSucks

in transit

Samsung Omnia (i900) Unboxing

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GeoMeme adds Google Buzz to detect real-time geo-located trends

If you’ve been using Google Buzz on a mobile phone recently, you would know that you can choose between two filters to the real-time stream of content:

  • Social – choose ‘Following’ to filter the stream based on your social graph, or social ‘circle’ as Google prefers to call it. You will see posts from your friends, and also some public posts from friends-of-friends if the Buzz filtration algorithm thinks you want to flex your social circle.
  • Geo – choose ‘Nearby’ to filter the stream based on your location, as detected by your mobile phone. You will see public posts from nearby Buzz users, as a chronological list, or located on a map. Most of the value here comes from the stream being updated in real-time.

Now, with the release of the new Google Buzz API from Google Labs, I have added the real-time stream of geo-located Google Buzz content to GeoMeme, my pet project.

GeoMeme detects real-time geo-located trends, now based on millions of daily posts from various Google Buzz and Twitter and MySpace mobile apps.

GeoMeme can detect, for example, that Justin Bieber beats Lady Gaga in New York City.

If you’re curious about Justin Bieber, or about the amount and contents of geo-located Buzz posts, compared to geo-located Twitter and MySpace posts, check it out and let me know what you think.

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Social Recommendations For Every Site On The Web

Today Facebook announced that over 50,000 websites have implemented Social Plugins in the first week since their launch.

My favorite Social Plugin is ‘Recommendations’ which lists the pages on a site which have enjoyed the most Sharing activity by Facebook users lately. It’s a good crowdsourced measure of quality.

But I couldn’t find the plugin on any of my favorite sites.

So here’s a handy bookmarklet that allows you to see Social Recommendations for any website, not just those sites which have implemented the plugin. You might call it a Facebookmarklet.

  1. Drag this link up to the bookmarks bar of your web browser: FB-Recommended

  2. Navigate to your favorite site, and click the ‘FB-Recommended’ button to see the pages on that site which are most recommended.

If you are worried about privacy, don’t be. The plugin does not require you to be logged in to Facebook. Here’s the anonymous recommendations on news.bbc.co.uk today. On the site homepage itself, there’s no mention of Gordon Brown’s ‘bigoted woman’ gaff. But that story tops the list of recommended pages:

If you are logged-in to Facebook, the plugin gives preference to and highlights pages that your friends have shared:

This filtering-by-social-graph is hugely significant and valuable, for users needing to filter the signal from the noise, and for Facebook who can apply the same social filtering algorithms to improve their ad targeting.

Enjoy!

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How to display approximately geo-located Tweets on a map

Most geo mashups such as GeoMeme display Tweets and other geo-located content as points on a map, based on exact latitude/longitude coordinates. Easy.

At the inaugural Chirp Conference this week, Twitter released its Places feature which instead allows Tweets to be approximately geo-located, within a ‘Place’ of chosen granularity; a city, or a neighborhood, perhaps a restaurant or a park.

This is a great option for users who have ‘geo-privacy’ concerns about revealing an exact latitude/longitude.

However, this approach presents a challenge to developers on the Twitter platform: how can approximately-located Tweets be displayed on a map?

Moreover, users need app developers to adopt a standard way of showing these approximately-located Tweets on a map. A consistent approach by developers will help users form a consistent understanding of this Twitter feature, in a similar way that @anywhere Hovercards provide a consistent approach to showing data about a particular Twitter user.

polytweet is a javascript library which displays approximately-located Tweets on a Google Map.

I hacked it together at Chirp, because I will need something like this for GeoMeme, and also to share it with other developers and encourage a standard approach.

Exactly-located tweets are represented by a profile image atop a blue pin.

Approximately-located tweets are represented by a semi-transparent profile image, placed along one of the edges of the Place polygon, at a consistent position so that zooming in and out does not shuffle the tweets.

Here’s an example, with thanks to the Twitter API team for sharing their geo-location. The tweet on the left hand side from @raffi is approximately located:

Hovering over a marker will trigger the display of any corresponding Place as a semi-transparent polygon. Hence the user can understand the area from which an approximately-located tweet was posted:

You can see the working demo at http://bit.ly/polytweetdemo which includes an added bonus of Hovercards.

See the source code for usage instructions and details of how to tweak the style of the markers and polygons.

Enjoy!

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GeoMeme Wins MySpace Developer Challenge

How exciting! My pet project GeoMeme has been awarded ‘Most innovative use of the Real-Time Stream API’ in the MySpace Developer Challenge.

GeoMeme Wins MySpace Developer Challenge

The awards were judged by Mike Jones, MySpace’s new Co-President, and Ron Conway, renowned angel investor, and David Glazer, Director of Engineering at Google, and Robert Scoble, tech blogger and uber-geek.

GeoMeme uses the new MySpace Real-Time Stream API to tap into the flood of geo-located updates being posted by MySpace users all around the world.

Activity Streams from MySpace are mashed up with tweets from a number of mobile Twitter apps, and located onto a Google Map. Local trends are identified using semantic analysis services from Yahoo.

For example, GeoMeme knows that Rihanna beats Lady Gaga in New York and that Avatar beats Hurt Locker in Los Angeles.

Beyond the discovery and measurement of real-time local trends, GeoMeme also provides a unique view into local activity streams, as a way to discover new like-minded and nearby friends. You can then buy the t-shirt (really, you can!) to share your trends with your friends.

GeoMeme is a lightning fast web app, and is also available on iPhone as a mobile web app, optimized for mobile using Google Maps v3 API. GeoMeme is built on Google App Engine for massive scalability.

And congratulations to the other winners of the Challenge:

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GeoMeme adds MySpace real-time local trends

In other news, GeoMeme now measures real-time local trends based on both MySpace and Twitter content.

GeoMeme uses the new Real-Time Stream API from MySpace to tap into the flood of geo-located updates being posted by MySpace users all around the world.

MySpace content is mashed up with tweets from a number of mobile Twitter apps, and located onto a Google Map. Local trends are identified using semantic analysis services from Yahoo.

A couple of example GeoMemes generated by all this real-time geo-located content: Rihanna beats Lady Gaga in New York, and Avatar beats Hurt Locker in Los Angeles.

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Mobile awesomeness, innovation and disruption

The good people at MitchelLake recently asked me to write an article about mobile technology.

So I created a list of awesomeness, innovation and disruption, including topics such as ‘Mobile is big’, ‘Phones are getting better’, and ‘People pay for stuff on their phones’.

Here’s the full article; 10 awesome, innovative and disruptive things about mobile.

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Murdoch should worry less about the Googlebot and more about social media

I remember in January 2000, old media mogul Rupert Murdoch said he was not going to waste his money buying any ‘dotcom’ upstarts. The very next day, AOL bought Time Warner. Not the other way around!

Murdoch had apparently failed to grasp the significance of the interwebs.

However, ten years later Time Warner has regained its mojo and is now trying to offload a spent and jaded AOL. Did Murdoch get it wrong ten years ago, or did it simply take a whole decade for him to be proven right?

In 2009, the mob is rushing once again to the conclusion that Murdoch is losing his marbles, planning to charge for his online content and blocking the Googlebot from stealing it.

Personally I believe that Murdoch should worry less about the Googlebot, and more about how social media is turning his industry on its head.

The problem is that all of those dotcom upstarts have brought us information overload. There has been an exponential increase in the amount of information and content available to us, way beyond the capacity of the human brain to process.

The solution is social media, which empowers us to easily share the content we care about with our friends and contacts, and adds valuable metadata to that shared content, such as Likes or Retweet counts. This metadata helps us filter the signal from the noise, so that we can focus on just the best quality content from our trusted circle of friends.

This works great for movie reviews. People have always listened to the advice of friends when it comes to choosing what movie to watch. Social media simply provides an efficient and scalable way to do this.

The best example of this social filter is currently FriendFeed, although we can expect to soon see something equally impressive on Facebook. Twitter Search could do this even better if only it were possible to search the entire tweet history of just your friends, or a chosen social distance into your social graph, rather then merely search 7 days of the public timeline. I am hoping that the Google Social Search Experiment will enable this sort of social filter when Google completes its Twitter integration.

Back to Mr. Murdoch… Social media also works for the filtering of news content, however it’s more tricky than movie reviews because there is a need for trustworthy fact rather than mere opinion. This is why Eric Schmidt believes that figuring out how to rank real-time social content, perhaps based on a reliable measure of reputation and authority, is “the great challenge of the age“. It also explains why Twitter’s Retweet feature does not allow the original tweet to be modified, because this makes the Retweet count a more reliable indicator of authority.

So my advice to Rupert Murdoch and other media companies struggling with this; worry less about the Googlebot and more about social media. Focus on improving the quality of your content, so that people share it with their friends.

And if your own social media strategy is not delivering any tangible benefits, try moving it from your Marketing department to your Customer Service department. Use social media to listen more carefully to the needs of your customers, so you can improve the quality of your content to the point where a paid online content model becomes viable.

If Marketing and Customer Service argue about who owns the customer relationship, remind them both that thanks to social media it’s actually the customer who owns and controls the relationship with your business. Not the other way around!

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OpenAustralia Hackfest: ‘Mobile + Geo + Social’ slides

I popped into the OpenAustralia Hackfest at the weekend to learn and talk about some of the latest developments in the Gov2.0 revolution.

There are now some quite interesting public datasets available, and the developer community is hard at work turning this data into useful APIs, and building innovative applications to consume the data.

Some of the notable apps to emerge from OpenAustralia include:

  • It’s Buggered, Mate – from the Canberra Hackfest, a geo app to crowdsource the reporting of broken public infrastructure.
  • Suburb Matchmaker – the winner of the Sydney Hackfest, a tool to help you find your ideal suburb to live in.
  • FridgeMate – currently winning the MashupAustralia contest and only a couple of days away from the $10,000 prize. FridgeMate lets you assemble a map of local public amenities to stick on your fridge door. My advice to the Creative Possums behind FridgeMate would be to look at using the Zazzle API so people could buy the actual fridge magnet.

My own presentation focussed on some mobile, geo and social technologies to create location-aware mobile mashups to share OpenGov content with friends on Twitter, friends on Facebook, and *real* friends on a t-shirt. Here’s the deck:

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Location-aware mobile web apps using Google Maps v3 + geolocation

When hiring Engineers, I always look for evidence of pet projects, so recently I thought it was fair to create one of my own: GeoMeme, the fun way to measure and share real-time local twitter trends.

Visitors to GeoMeme choose a location on the map, and two search terms to compare. GeoMeme then measures and compares the number of matching tweets within the bounds of the map, based on public data from a number of mobile twitter apps.

As an example, GeoMeme can work out that ‘love’ beats ‘hate’ in Manhattan:

GeoMeme is a desktop web application and also a location-aware mobile web app for iPhone and Android phones.

Implementing the mobile version of GeoMeme as a web app has some advantages and disadvantages, compared to building native iPhone &/or Android applications.

Native apps are great because they currently offer the deepest integration to the full capability of the phone, for example using device APIs to access Contacts, the Camera Roll, an Accelerometer, or the GPS chip. For some applications, this deep device integration is essential and so a native application is beneficial.

On the other hand, emerging HTML5-based mobile browsers are aiming to standardise integration to such device APIs, starting with Geolocation APIs; meaning that location-aware mobile web apps are now becoming viable. Aligned with this development is the new version of the Google Maps API. v3 has been greatly simplified since v2, and is now optimized for use on mobile phones. Less is more.

The deciding factor for me choosing to build a mobile web app for GeoMeme rather than a native app was development speed. A mobile web app enjoys far greater code re-use from the desktop web version, and it is possible to push regular updates and improvements to users, without having to wait for appstore approval or for users to upgrade.

I believe this need for development speed is common among a good proportion of mobile apps that are still in ‘rapid iteration’ or ‘release early, release often’ mode, so this post is intended to share some of the techniques used in GeoMeme with developers wanting to build their own location-aware mobile web apps.

Let’s build an example location-aware mobile web app called ‘Here I Am!’, for the photographically challenged. The app will present some local photographs (from Panoramio) which can be shared with friends on Twitter or Facebook.

Where on earth is that mobile phone..?

The first job of a location-aware mobile app is to work out where on earth the mobile phone currently is. Unfortunately, at the time of writing, there is still no universally reliable and accurate solution for a mobile web app to detect the location of the mobile phone it is running on. However the following partial solutions can be combined to good effect:

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Scalable, fast, accurate geo apps using Google App Engine + geohash + faultline correction

GeoMeme is a web app (and also a mobile web app for iPhone and Android) that I recently developed as a pet project. It measures real-time local twitter trends.

Visitors to GeoMeme choose a location on the map, and two search terms to compare. GeoMeme then measures and compares the number of matching tweets within the bounds of the map, based on public data from a number of mobile twitter apps.

As an example, GeoMeme can work out that :) beats :( in San Francisco:

A large amount of geo-data is generated by GeoMeme, and so arises a need shared by many geo apps: scalable, fast, and accurate spatial queries, used to select a subset of geo-data for display as markers on a map, or on Google Earth.

:)Google App Engine

Google App Engine is an obvious choice for hosting your geo app. The App Engine datastore is built on top of Google’s BigTable technology which scales very well, and is optimized for fast data retrieval. And it doesn’t cost the earth like some traditional GIS database solutions.

:( Inequality constraint

If you are coming from a background of relational databases, you might think the solution here would be to store the latitude and longitude of all your markers in a database table, and do a simple query to retrieve only those contained within the bounds of the map.

However, the flipside of being optimized for fast data retrieval is that BigTable only allows inequality filters on a single dimension, to avoid the burden of full table scans. For example, the following form of spatial query is not supported because it specifies inequality filters on both latitude and longitude dimensions:

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