The Good NET Guide

Posts Tagged ‘facebook’

Mobile social network MocoSpace now has a count of 11 million members, with 500,000 members forming new friendships every day on MocoSpace. The startup’s mobile only social network targets users who have non-smartphones that have simpler interfaces.

MocoSpace, which launched in 2006, makes money with its virtual currency and through advertising and mainly reaches the 18 to 34 age demographic. The site claims to generate 3 billion pages per month, with users mobile users accessing the site over 5 times per day on average. The site is also generating interest from musicians using the site to share their music, with over 200 artists submitting music on MocoSpace every day. Though not nearly as popular as Facebook or MySpace. MocoSpace is now one of the largest mobile-only social networks.

The startup prides itself on its users mainly being non-techies who don’t own an iPhone, Android or BlackBerry device. MocoSpace also claims to have a diverse user base; 1/3 of their user base is Hispanic and 1/3 is African-American. We recently published some surprising stats about the breakup habits of MocoSpace users.

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Yelp has been hit with another lawsuit, the third in a matter of a few weeks. Similar to the previous complaints, this lawsuit filed by Boris Levitt, the owner of Renaissance Furniture Restoration in San Francisco, claims that Yelp’s “unfair and unethical conduct in promoting, marketing and advertising its website as maintaining unbiased reviews” is unlawful and hurt his business. Levitt’s suit is similar to the previous claims that Yelp is extorting businesses for advertising. We’ve embedded the complaint below.

The business claims that after declining a request to purchase advertising on Yelp, a number of positive reviews from his business’ listing on the reviews site mysteriously disappeared, downgrading the company’s rating on the site. Levitt claims that ten out of eleven five star reviews were removed from his company’s page following his decision not to purchase advertising on Yelp.

Two weeks ago, the company got slapped by a lawsuit from from the D’ames Day Spa of San Diego County, accusing Yelp of removing many positive reviews because the spa declined to run ads on the site. And the previous week, two law firms, Beck & Lee from Miami and The Weston Firm in San Diego, filed a class action lawsuit in Los Angeles federal court alleging unfair business practices by local business review and rating website operator Yelp.

The lawsuit alleged that the heavily funded startup runs an “extortion scheme” and has “unscrupulous sales practices” in place to generate revenue, in which the company’s employees call businesses demanding monthly payments in the guise of advertising contracts, in exchange for removing or modifying negative reviews.

Additionally, today, nine small businesses from across the United States have joined the Beck & Lee and Weston suit, including The Bleeding Heart Bakery in Chicago; Bleeding Heart Bakery of Chicago, Illinois; Scion Restaurant of Washington, D.C.; J.L. Ferri Entertainment, Inc. of New York, New York; Sofa Outlet of San Mateo, California; Celibré, Inc. of Torrance, California; Astro Appliance Service of San Carlos, California; Wag My Tail, Inc., of Tujunga, California; Le Petite Retreat, of Los Angeles, California and Mermaids Cruise of San Francisco, California

Last year, the East Bay Express ran an explosive story, basically accusing Yelp of being in the ‘Business of Extortion 2.0′, which covered similar ground. Shortly after reporter Kathleen Richards published the article, Yelp vehemently denied everything and called her piece inaccurate.

Yelp CEO and co-founder Jeremy Stoppelman has explicitly denied that they ever offered preferential treatment in exchange for money.

Boris Levitt Vs. Yelp

It’s hard to imagine a Web sans URL shortening services nowadays but you can rest assured that they’re here to stay – for better or worse. Question is: how do the likes of bit.ly, TinyURL and Goo.gl score in terms of speed and availability?

That’s exactly what Dutch startup WatchMouse sought to find out, by monitoring the performance and uptime for 14 popular URL shorteners for a whole month.

Turns out most really don’t perform all that well, and that URL shorteners actually increase the load time of pages significantly. As you can tell from the graph embedded above, a lot of URL shortening services add half to nearly a full second to page load times.

To measure this, WatchMouse checked each URL shortener every five minutes from one of its monitoring stations, which are located across the globe. For each short URL, only the redirection was measured, not the actual loading of the target page.

Pingdom did similar research on the speed and reliability of URL shortening services in August 2009, although they only looked at independent URL shorteners and not the ones from Microsoft, Facebook and Google.

Google does a pretty good job in terms of performance with Goo.gl and YouTu.be, but it still takes those about 1/3 of a second to resolve pages, which makes a world of difference if you think about how many website addresses get shortened on a daily basis.

According to WatchMouse’s findings, Facebook’s FB.me is by far the slowest of the pack, adding over two seconds on average to the page load time after the click on a link.

Another interesting thing the company noticed is that only a few of the URL shorteners optimized their name servers for international use – i.e. it takes half a second for some of the URL shorteners just to look up the IP address that is needed for a browser to retrieve a Web page.

As for the availability of the URL shortening services: most do reasonably well in this regard, with snurl.com performing worst of the bunch with south of 98% uptime. Facebook’s fb.me registered the third worst uptime out of the 14 services that were tracked, although that still means about 99.5% availability – which isn’t terrible.

Now all they have to do is speed it up a little.

Editor’s note: Big Data has been around for a long time between credit card transactions, phone call records and financial markets. Companies like AT&T, Visa, Bank of America, Ebay, Google, Amazon and more have massive databases they mine for competitive advantage. But lately, Big Data is finding its way to the smallest startups. The Web and cloud computing brings Big Data everywhere. But what exactly is pushing Big Data forward?

To answer that we brought in an expert, Bradford Cross. Bradford is the Co-Founder and Head of Research at FlightCaster. FlightCaster is backed by Y Combinator, Tandem Entrepreneurs and Sherpalo Ventures. The company analyzes large data sets to predict flight delays. Bradford is chair of the Dealing with Big Data track at Cloud Connect this week.

We are in a Renaissance for computer science, engineering, and learning from data right now. The scale of data and computations is an important issue, but the data age is less about the raw size of your data, and more about the cool stuff you can do with it. Now that there is so much data, it is time to unlock its value. Really neat things are happening already—like the way the people of the world can educate themselves on all manner of issues and topics, or the way data and computing serves as leverage in other scientific and technical endeavors. There will be lots of amazing stuff on the web, but innovation will come in other domains as well.

The recent big data trend is about the democratization of large data more than its growth. In articles like the Economist’s recent piece on the data deluge, we hear about big data everywhere. We hear about what big data and the cloud mean for the enterprise, but they have had big data for a long time. eBay manages petabytes in its Teradata and Greenplum data warehouses. Sophisticated startups extracting value from big data is also nothing new—it has been happening at least since the days of Yahoo! and Google, and they have done it without the data warehousing folks.

Now focused early stage startups can get up and running faster than ever. Less technical analysts at companies like Facebook and Twitter can access massive amounts of data easily. Even individuals can undertake cool projects with big data, such as Pete Skomoroch of Data Wrangling did with trending topics for Wikipedia.

Why Now?

We do not have to build all our own hardware and software infrastructure anymore.

Pioneers such as Amazon have given us the cloud, where we have the capability to run very large server clusters at a low startup cost. Pioneers like Google have paved the way for open source projects like Hadoop and HBase, that are backed by big company contributors like Facebook.

The combination has paved the way for a new class of data driven startup like Aardvark (just acquired by Google) and Factual, it has reduced both cost and time to market for these startups, as we showed with Flightcaster. And, it has allowed startups that were not necessarily data driven to become more analytical as they evolved, such as Facebook, LinkedIn, Twitter, and many others.

So we have big data, the cloud, and open source facilitating new data-driven startups. I like to break this trend down from the technical perspective into three chunks; storing data, processing data, and learning from data. I define “learning from data” to mean data mining, AI, machine learning, statistics, and so on.

Supersize my data. Oh wait, I’ll just have a Medium.

The first time I heard the “Medium Data” idea was from Christophe Bisciglia and Todd Lipcon at Cloudera. I think the concept is great. Companies do not have to be at Google scale to have data issues. Scalability issues occur with less than a terabyte of data. If a company works with relational databases and SQL, they can drown in complex data transformations and calculations that do not fit naturally into sequences of set operations. In that sense, the “big data” mantra is misguided at times. For instance, a GigaOm article about big data in the cloud states:

What is becoming increasingly clear is that Big Data is the future of IT. To that end, tackling Big Data will determine the winners and losers in the next wave of cloud computing innovation.

The big issue is not that everyone will suddenly operate at petabyte scale; a lot of folks do not have that much data.

The more important topics are the specifics of the storage and processing infrastructure and what approaches best suit each problem. How much data do you have and what are you trying to do with it? Do you need to do offline batch processing of huge amounts of data to compute statistics? Do you need all your data available online to back queries from a web application or a service API?

Once your data and its processing are large enough to require distributing the data and the work among machines across network boundaries, things get a lot harder. You have to deal with distributed computing and make tradeoffs like a real computer scientist.

Big Data & The Cloud: Viral Buzzwords 4.0!

The cloud, and hosted services, present very interesting opportunities. One of the greatest is that people can leverage the a la carte economics of elastic computing to do things that were prohibitively expensive due to the requirements of building and maintaining their own hardware infrastructure. The interesting parts about the current cloud are its lack of entrance friction and elastic cost efficiency, the speed with which new entrants can set up, and the elastic capability to run 100 machine clusters for 1 hour if that is what is needed.

We started Flightcaster almost a year ago, and it is a good example of how startups can leverage cloud compute and storage resources, mix some open source like Hadoop with some data mining, and create interesting new technologies with relatively low capital upfront.

The cloud is not cheaper in general. Once people scale to a certain point, they move off the cloud onto dedicated hardware—not the other way around. That may change, and better hosted services may play a role in the transition, but that will take a while. In the meantime, the interesting part of the cloud is the use of elastic resources and the ability to get up and going quickly. The interesting part is the freedom it gives startups to try things they would never otherwise do.

Another notable thing about the cloud is the new architectures emerging as a result of economic and resource tradeoffs.

Storage of large amounts of data in the cloud is much cheaper with blobstores like Amazon S3 than it is to maintain an always-up cluster for a distributed datastore. If you do mostly offline batch processing and you do not need bulk storage to be online, then it is an attractive setup.

Storage and NoSQL

Taking another glimpse from the future of big data in the cloud.

A Big Data stack…will also need to emerge before cloud computing will be broadly embraced by the enterprise. In many ways, this cloud stack has already been implemented, albeit in primitive form, at large-scale Internet data centers, which quickly encountered the scaling limitations of traditional SQL databases as the volume of data exploded. Instead, high-performance, scalable/distributed, object-orientated data stores are being developed internally and implemented at scale…large web properties have been building their own so-called “NoSQL” databases, also known as distributed, non-relational database systems (DNRDBMS).

There are several misguided points here. First, there is not going to be a big data or cloud stack. Distributed systems are about making trade offs and a move toward problem-specific solutions rather than one-size-fits-all stacks. Second, enterprises already have their solution—expensive data warehousing and consulting support. Will open source projects like Hadoop supported by people like Cloudera take a chunk of the business? Sure. But as I mentioned earlier, the most interesting part about big data and the cloud is not cheaper alternatives for the enterprise, it is the opportunities it facilitates for data-driven startups.

There is a lot of talk about the NoSQL movement. The big idea here is that distributed systems are hard, require tradeoffs, and sometimes we are better off with data storage and processing that are specific to what we are doing with the data. Sometimes even with a small amount of data on a single node, there are better alternatives to SQL queries and relational databases—time series data has long been a good example.

Processing and Hadoop: The Elephant In The Room

There is a broad range of needs for processing large amounts of data. These range from simple needs like calculations for log analysis that just need to occur at scale, to middle of the road needs like BI, to complex needs like scalable modern machine learning and retrieval systems.

There are a different approaches one can use to service specific needs. Again, we see the pattern of moving away from one-size-fits-all stacks, and toward building for your needs. That said, there are very generic abstractions like Map-Reduce that work well for a lot of use cases. Distributed systems are hard to get right, so when something like Hadoop gets a lot of momentum, it retains that momentum until alternatives have the time to mature enough to solve the hard problems with fault tolerance, performance, and so forth. Not everyone is Leonardo da Vinci, so people should not attempt to create these systems on their own unless they really know what they are doing. In that sense, the cloud and big data are facilitators of open source.

An important aspect of processing at scale is abstraction. Writing complex or even simple computations in raw Map-Reduce is verbose for programmers and intimidating for others who might want to play with the data. Abstractions over Map-Reduce like Pig and Hive make simple things easy, and abstractions like Cascading make hard things possible. The Map-Reduce paradigm, and Hadoop in particular, have been a big success. That said, Map-Reduce is not the only important piece of compute infrastructure. Message queues serve as the backbone of a lot of compute architectures – implementations of AMQP, such as rabbitmq, are a prime example. You can accomplish a lot with producers, consumers, and a messaging system. Distributed storage and processing systems can also be very tricky to configure and deploy, requiring a pretty deep understanding of the system – hence the business case for folks like Cloudera.

Learning from Big Data

Hal Varian, Google’s Chief Economist, recently said,

The sexy job in the next ten years will be statisticians… The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it

Unfortunately for those of us working on these problems in real life, it is not so simple. The archetypal data-renaissance man is mathematician, statistician, computer scientist, machine learner, and engineer all rolled into one. There are opportunities where you can lack some of these skills and work with a team that supplements your weak points—a startup is not one of those.

Now that we can store so much data, it is attractive to do previously unimaginable things with it. We are sure to see cool applications in fields from the internet to biotechnology to nanotechnology and fundamental materials science research. Almost all advances in every field of science and technology are now heavily dependent upon data and computing. Machine learning is serving a fantastic role as a bridge between mathematical and statistical models and the worlds of AI, computer science, and software engineering. We are exploring applications in learning from text, social networks, data from scientific experiments, and any other data sources we can get our hands on.

The data renaissance does present some difficult issues. There are not many places one can recieve a good education on working on these problems at large scale. Scaling our modeling and optimization algorithms is hard. We need to figure out how to partition and parallelize, or sometimes trade speed and scale for approximately correct calculations. Another issue is that we are often using simplistic models, albeit with pretty good results in many cases. We would like to move toward a deeper approximation of real intelligence.

But the data renaissance is here. Be a part of it.

Argentinian social gaming company MetroGames, has just raised $5 million in series A funding from game developer Playdom. According to a release, the investment will be used to expand MetroGames’ development of games and its social gaming platform. Playdom’s CEO, John Pleasants will join MetroGames’ board.

MetroGames has over 30 games on both Facebook and its own standalone social gaming site. In the release, Pleasants said that he believes that the company will become a “big player in the social gaming market.”

It’s no secret that Playdom is eying the Facebook gaming market that Zynga dominates. The social gaming company just bought Facebook game developer Offbeat Creations. In November, Playdom raised a massive $43 million at a $260 million valuation. As we reported at that time, Playdom’s presence on MySpace was strong. Their Mob Wars game has 14 million or so users there, and the company was likely pulling in $60 million or more in revenue at that time. According to our stats from November, Playdom has 28 million monthly game users with 60% of traffic is from MySpace v. 40% from Facebook. They have twelve MySpace apps and 6 Facebook Apps.

It took almost a year, but Xobni finally released its email app for the Blackberry. It works as a standalone app integrated with the email on your Blackberry, but similar to Xobni’s Outlook plugin, it ranks your contacts by importance and pulls in social data from Facebook, LinkedIn and other places.

Along with the Blackberry app, Xobni is introducing another product which may turn out to be more important in the long run. It is called Xobni One, and it syncs your Xobni contacts in Outlook with your contacts on your Blackberry, all in the cloud. As Xobni rolls out more apps in the future, Xobni One should be able to sync contacts across those as well (very Mesh-like).

Xobni One is a way to sync your desktop and mobile contacts. If you use Outlook on your desktop at work, but Gmail on your Blackberry, Xobni One reconciles the two. And when you leave your job, your contacts stay with you. Xobni One isn’t free. It costs $4 a month or $40 a year, bundled with the Blackberry app. Keeping your contacts in sync is expensive. Doesn’t it seem that Google or Microsoft will eventually just do this for free?

TechCrunch50 2008 DemoPit company Shorthand Mobile (formerly Smart Touch) aims to help consumers who don’t use mobile data plans to access SMS and web content. Upon launch in 2008, the startup had developed a suite of basic widgets for mobile phones that visualize SMS services, allowing users to navigate through an intuitive menu. Today, Shorthand Mobile is launching TextApps, a new category of apps that deliver content from websites via SMS in a rich, interactive interface, aiming to expand the capabilities of non-smartphones and provide access to web content for mobile users without data plans.

Launching in beta today on select Motorola and Nokia handsets on AT&T and on Windows Mobile phones, Shorthand TextApps use SMS to expand access to top brands and mobile content including social networks, local search, sports scores, weather forecasts, movie times, news and entertainment. TextApps is an app you download which then creates a more intuitive UI for text-based apps.

Once you download Shorthand, it uses your SMS text messaging plan to connect you to the web content you want. Apps in the TextApps library include CitySearch, Netflix, Facebook Mobile, Twitter, The New York Times and Yelp. Of course, Facebook, Twitter and others have independently integrate with SMS for their sites but Shorthand claims to add more functionality by almost recreating a basic smartphone app. Shorthand is also now available in India on all major carriers and will launch in Brazil this spring. The starup will offer localized TextApps for these countries. Shorthand is free to download, but you will be charged for SMS messages via your SMS plan with your carrier.

As we wrote in our initial review, year, the technology behind is very basic so users shouldn’t expect to see a iPhone like Facebook-app on their phone with TextApps. That being said, the fact that Shorthand has struck deals with Nokia and Motorola to include its offering on their phones and could become a useful way to incorporate extra functionality into basic mobile phones.

PowerReviews, a company that provides customer review technology for retailers and e-commerce sites, has raised $6.1 million in funding led by current investors Menlo Ventures and Tenaya Capital. This brings the company’s total funding to over $30 million.

The additional funding will be used fuel customer acquisition and for new product development. The company’s original products let retailers include Amazon-like product review features into their websites, for free. Last year, PowerReviews launched two more social technologies for retailers to integrate: BrandConnect and Social Megaphone. BrandConnect tracks what consumers are saying about a company and/or brand on the social web and Social Megaphone allows customers to post their reviews to Facebook, Twitter and blogs. Last fall the company also brought on a new CEO, Pehr Luedtke.

PowerReviews, which launched in 2007, also powers a consumer-facing site, Buzzillions.com, that aggregates reviews from its partners retailers. The site includes over ten million product reviews. Customers include Staples, Drugstore.com, Walgreens, Diapers.com, Callaway and Jockey.

I can’t see this on my own Facebook profile yet, but we’ve gotten a number of tips in our inbox in the past 10 minutes so it’s safe to assume it’s not a hoax or anything: Facebook appears to have started enabling users to generate custom two-dimensional QR codes.

From the looks of the screenshot embedded above, there are two types of QR codes: a personal barcode or a “status QR barcode”. This also seems to appear on Facebook Fan Pages.

Judging from the tweets about this, the QR codes don’t seem to actually function yet, and the links on pages appear to come and go.

We’re trying to get more information and will update accordingly.

Update – Facebook gave us the boilerplate response:

“We’re always testing various features on the site, but we have nothing more to share at this time.”

In case you’re not familiar with QR codes: QR is short for Quick Response (because they can be read quickly by a mobile phone through its camera). They are used to take a piece of information from a transitory media and put it in to your cell phone – this can be links, videos, text, photos and more.

QR codes are generally more useful than a standard barcode is that they can store much more data. The other key feature of QR Codes is that instead of requiring a scanner to ‘read’ them, most modern mobile phones can convert them and there are applications out there for smartphone that don’t support native QR code scanning.

Imagine wearing the link to your Facebook profile on your t-shirt – this is what that would look like when converted to a QR code:

(Thanks to Marco Schierhorn for the tip and screenshot – bottom image via Webometric Thoughts)

During his keynote at SXSW this afternoon (live blog here), Twitter CEO Evan Williams just announced a new “At Anywhere” platform, which allows websites to more deeply integrate the service into their sites. The idea is to offer a more seamless experience to Twitter users navigating third party sites like the Huffington Post and the New York Times, giving them Twitter content without forcing them to jump off the page they’re currently viewing. The details on the new platform are still scant, but this is Twitter’s answer to Facebook Connect, which we reported on back in January.

Among the features:

• When you browse a site that uses @anywhere, people and brands that have Twitter accounts will be highlighted with a hyperlink. Mousing over that hyperlink will show a small box (a “hovercard”) containing their Twitter information, including their most recent tweet (in effect it means you don’t have to click over to Twitter’s homepage to see their Twitter profile)
• Publishers will be able to more deeply integrate their own Twitter profiles, making them easier for their readers to ‘follow’ them
• Sites will be able to implement @anywhere with a few lines of Javascript.
• The new platform is launching with a number of major sites and services, including the New York Times, Huffington Post, Meebo, Amazon, Yahoo, Bing, and eBay.

It looks like the platform may eventually be hosted at Twitter.com/anywhere, which currently features a placeholder Twitter account that tweeted “Stay Tuned”. Update This may actually be a Twitter account related to the platform — it just tweeted “If you’re a javascript guru and want to help us build @anywhere and work with publishers @jointheflock”.

From the Twitter blog:

We’ve developed a new set of frameworks for adding this Twitter experience anywhere on the web. Soon, sites many of us visit every day will be able to recreate these open, engaging interactions providing a new layer of value for visitors without sending them to Twitter.com. Our open technology platform is well known and Twitter APIs are already widely implemented but this is a different approach because we’ve created something incredibly simple. Rather than implementing APIs, site owners need only drop in a few lines of javascript. This new set of frameworks is called @anywhere.

When we’re ready to launch, initial participating sites will include Amazon, AdAge, Bing, Citysearch, Digg, eBay, The Huffington Post, Meebo, MSNBC.com, The New York Times, Salesforce.com, Yahoo!, and YouTube. Imagine being able to follow a New York Times journalist directly from her byline, tweet about a video without leaving YouTube, and discover new Twitter accounts while visiting the Yahoo! home page—and that’s just the beginning. Twitter has proven to be compelling in a variety of ways. With @anywhere, web site owners and operators will be able to offer visitors more value with less heavy lifting.